chore: import local project into Gitea

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/accelerometer_handler.cpp

@@ -0,0 +1,36 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "accelerometer_handler.h"
+
+int begin_index = 0;
+
+TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter) {
+  return kTfLiteOk;
+}
+
+bool ReadAccelerometer(tflite::ErrorReporter* error_reporter, float* input,
+                       int length, bool reset_buffer) {
+  begin_index += 3;
+  // Reset begin_index to simulate behavior of loop buffer
+  if (begin_index >= 600) begin_index = 0;
+  // Only return true after the function was called 100 times, simulating the
+  // desired behavior of a real implementation (which does not return data until
+  // a sufficient amount is available)
+  if (begin_index > 300) {
+    for (int i = 0; i < length; ++i) input[i] = 0;
+    return true;
+  } else { return false; }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/accelerometer_handler.h

@@ -0,0 +1,29 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+
+#define kChannelNumber 3
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+extern int begin_index;
+extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
+extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
+                              float* input, int length, bool reset_buffer);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/arduino_main.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "main_functions.h"
+
+// Arduino automatically calls the setup() and loop() functions in a sketch, so
+// where other systems need their own main routine in this file, it can be left
+// empty.

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/constants.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "constants.h"
+
+// The number of expected consecutive inferences for each gesture type.
+// These defaults were established with the SparkFun Edge board.
+const int kConsecutiveInferenceThresholds[3] = {15, 12, 10};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/constants.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+
+// The expected accelerometer data sample frequency
+const float kTargetHz = 25;
+
+// The number of expected consecutive inferences for each gesture type
+extern const int kConsecutiveInferenceThresholds[3];
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/gesture_predictor.cpp

@@ -0,0 +1,55 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "gesture_predictor.h"
+
+#include "constants.h"
+
+// How many times the most recent gesture has been matched in a row
+int continuous_count = 0;
+// The result of the last prediction
+int last_predict = -1;
+
+// Return the result of the last prediction
+// 0: wing("W"), 1: ring("O"), 2: slope("angle"), 3: unknown
+int PredictGesture(float* output) {
+  // Find whichever output has a probability > 0.8 (they sum to 1)
+  int this_predict = -1;
+  for (int i = 0; i < 3; i++) {
+    if (output[i] > 0.8) this_predict = i;
+  }
+  // No gesture was detected above the threshold
+  if (this_predict == -1) {
+    continuous_count = 0;
+    last_predict = 3;
+    return 3;
+  }
+  if (last_predict == this_predict) {
+    continuous_count += 1;
+  } else {
+    continuous_count = 0;
+  }
+  last_predict = this_predict;
+  // If we haven't yet had enough consecutive matches for this gesture,
+  // report a negative result
+  if (continuous_count < kConsecutiveInferenceThresholds[this_predict]) {
+    return 3;
+  }
+  // Otherwise, we've seen a positive result, so clear all our variables
+  // and report it
+  continuous_count = 0;
+  last_predict = -1;
+  return this_predict;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/gesture_predictor.h

@@ -0,0 +1,21 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+
+extern int PredictGesture(float* output);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/magic_wand.ino

@@ -0,0 +1,134 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "accelerometer_handler.h"
+#include "gesture_predictor.h"
+#include "magic_wand_model_data.h"
+#include "output_handler.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+int input_length;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 60 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+
+// Whether we should clear the buffer next time we fetch data
+bool should_clear_buffer = false;
+}  // namespace
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  static tflite::MicroErrorReporter micro_error_reporter;  // NOLINT
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_magic_wand_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+        "Model provided is schema version %d not equal "
+        "to supported version %d.",
+        model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;  // NOLINT
+  micro_mutable_op_resolver.AddBuiltin(
+      tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+      tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+      tflite::BuiltinOperator_MAX_POOL_2D,
+      tflite::ops::micro::Register_MAX_POOL_2D());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_CONV_2D,
+                                       tflite::ops::micro::Register_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+      tflite::BuiltinOperator_FULLY_CONNECTED,
+      tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with
+  static tflite::MicroInterpreter static_interpreter(
+      model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+      error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors
+  interpreter->AllocateTensors();
+
+  // Obtain pointer to the model's input tensor
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != 128) ||
+      (model_input->dims->data[2] != kChannelNumber) ||
+      (model_input->type != kTfLiteFloat32)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  input_length = model_input->bytes / sizeof(float);
+
+  TfLiteStatus setup_status = SetupAccelerometer(error_reporter);
+  if (setup_status != kTfLiteOk) {
+    error_reporter->Report("Set up failed\n");
+  }
+}
+
+void loop() {
+  // Attempt to read new data from the accelerometer
+  bool got_data = ReadAccelerometer(error_reporter, model_input->data.f,
+                                    input_length, should_clear_buffer);
+  // Don't try to clear the buffer again
+  should_clear_buffer = false;
+  // If there was no new data, wait until next time
+  if (!got_data) return;
+  // Run inference, and report any error
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed on index: %d\n", begin_index);
+    return;
+  }
+  // Analyze the results to obtain a prediction
+  int gesture_index = PredictGesture(interpreter->output(0)->data.f);
+  // Clear the buffer next time we read data
+  should_clear_buffer = gesture_index < 3;
+  // Produce an output
+  HandleOutput(error_reporter, gesture_index);
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/magic_wand_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i magic_wand_model.tflite > magic_wand_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+
+extern const unsigned char g_magic_wand_model_data[];
+extern const int g_magic_wand_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/output_handler.cpp

@@ -0,0 +1,35 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "output_handler.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind) {
+  // light (red: wing, blue: ring, green: slope)
+  if (kind == 0) {
+    error_reporter->Report(
+        "WING:\n\r*         *         *\n\r *       * *       "
+        "*\n\r  *     *   *     *\n\r   *   *     *   *\n\r    * *       "
+        "* *\n\r     *         *\n\r");
+  } else if (kind == 1) {
+    error_reporter->Report(
+        "RING:\n\r          *\n\r       *     *\n\r     *         *\n\r "
+        "   *           *\n\r     *         *\n\r       *     *\n\r      "
+        "    *\n\r");
+  } else if (kind == 2) {
+    error_reporter->Report(
+        "SLOPE:\n\r        *\n\r       *\n\r      *\n\r     *\n\r    "
+        "*\n\r   *\n\r  *\n\r * * * * * * * *\n\r");
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand/output_handler.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/accelerometer_handler.cpp

@@ -0,0 +1,94 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "accelerometer_handler.h"
+
+#define M5STACK_MPU6886
+// #define M5STACK_MPU9250
+// #define M5STACK_MPU6050
+// #define M5STACK_200Q
+#include <M5Stack.h>
+
+int begin_index = 0;
+
+float save_data[600] = {0.0};
+bool pending_initial_data = true;
+long last_sample_millis = 0;
+
+TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter) {
+  M5.IMU.Init();
+  error_reporter->Report("IMU Init");
+
+  return kTfLiteOk;
+}
+
+static bool UpdateData() {
+  bool new_data = false;
+  if ((millis() - last_sample_millis) < 40) {
+    return false;
+  }
+  last_sample_millis = millis();
+
+  float accX = 0.0F;
+  float accY = 0.0F;
+  float accZ = 0.0F;
+  M5.IMU.getAccelData(&accX, &accY, &accZ);
+
+  /* this is a little annoying to figure out, as a tip - when
+     holding the board straight, output should be (0, 0, 1)
+     tiling the board 90* left, output should be (0, 1, 0)
+     tilting the board 90* forward, output should be (1, 0, 0);
+  */
+  save_data[begin_index++] = 1000 * accZ;
+  save_data[begin_index++] = -1000 * accX;
+  save_data[begin_index++] = 1000 * accY;
+
+  if (begin_index >= 600) {
+    begin_index = 0;
+  }
+  new_data = true;
+
+  return new_data;
+}
+
+bool ReadAccelerometer(tflite::ErrorReporter* error_reporter, float* input,
+                       int length, bool reset_buffer) {
+  if (reset_buffer) {
+    memset(save_data, 0, 600 * sizeof(float));
+    begin_index = 0;
+    pending_initial_data = true;
+  }
+
+  if (!UpdateData()) {
+    return false;
+  }
+
+  if (pending_initial_data && begin_index >= 200) {
+    pending_initial_data = false;
+  }
+
+  if (pending_initial_data) {
+    return false;
+  }
+
+  for (int i = 0; i < length; ++i) {
+    int ring_array_index = begin_index + i - length;
+    if (ring_array_index < 0) {
+      ring_array_index += 600;
+    }
+    input[i] = save_data[ring_array_index];
+  }
+  return true;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/accelerometer_handler.h

@@ -0,0 +1,29 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+
+#define kChannelNumber 3
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+extern int begin_index;
+extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
+extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
+                              float* input, int length, bool reset_buffer);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/arduino_main.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "main_functions.h"
+
+// Arduino automatically calls the setup() and loop() functions in a sketch, so
+// where other systems need their own main routine in this file, it can be left
+// empty.

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/constants.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "constants.h"
+
+// The number of expected consecutive inferences for each gesture type.
+// These defaults were established with the SparkFun Edge board.
+const int kConsecutiveInferenceThresholds[3] = {5, 5, 5};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/constants.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+
+// The expected accelerometer data sample frequency
+const float kTargetHz = 25;
+
+// The number of expected consecutive inferences for each gesture type
+extern const int kConsecutiveInferenceThresholds[3];
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/gesture_predictor.cpp

@@ -0,0 +1,55 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "gesture_predictor.h"
+
+#include "constants.h"
+
+// How many times the most recent gesture has been matched in a row
+int continuous_count = 0;
+// The result of the last prediction
+int last_predict = -1;
+
+// Return the result of the last prediction
+// 0: wing("W"), 1: ring("O"), 2: slope("angle"), 3: unknown
+int PredictGesture(float* output) {
+  // Find whichever output has a probability > 0.8 (they sum to 1)
+  int this_predict = -1;
+  for (int i = 0; i < 3; i++) {
+    if (output[i] > 0.8) this_predict = i;
+  }
+  // No gesture was detected above the threshold
+  if (this_predict == -1) {
+    continuous_count = 0;
+    last_predict = 3;
+    return 3;
+  }
+  if (last_predict == this_predict) {
+    continuous_count += 1;
+  } else {
+    continuous_count = 0;
+  }
+  last_predict = this_predict;
+  // If we haven't yet had enough consecutive matches for this gesture,
+  // report a negative result
+  if (continuous_count < kConsecutiveInferenceThresholds[this_predict]) {
+    return 3;
+  }
+  // Otherwise, we've seen a positive result, so clear all our variables
+  // and report it
+  continuous_count = 0;
+  last_predict = -1;
+  return this_predict;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/gesture_predictor.h

@@ -0,0 +1,21 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+
+extern int PredictGesture(float* output);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/magic_wand_M5StackFire.ino

@@ -0,0 +1,193 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "accelerometer_handler.h"
+#include "gesture_predictor.h"
+#include "magic_wand_model_data.h"
+#include "output_handler.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+#define M5STACK_MPU6886 
+// #define M5STACK_MPU9250 
+// #define M5STACK_MPU6050
+// #define M5STACK_200Q
+#include <M5Stack.h>
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+int input_length;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 60 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+
+// Whether we should clear the buffer next time we fetch data
+bool should_clear_buffer = false;
+}  // namespace
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  M5.begin();
+  M5.Power.begin();
+  M5.Lcd.fillScreen(BLACK);
+  M5.Lcd.setCursor(0, 0);
+  M5.Lcd.setTextFont(2);
+  M5.Lcd.setTextColor(YELLOW);
+  M5.Lcd.printf("Magic Wand\n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.setTextFont(1);
+  M5.Lcd.setTextSize(2);
+
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  static tflite::MicroErrorReporter micro_error_reporter;  // NOLINT
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_magic_wand_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+      "Model provided is schema version %d not equal "
+      "to supported version %d.",
+      model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;  // NOLINT
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+    tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_MAX_POOL_2D,
+    tflite::ops::micro::Register_MAX_POOL_2D());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_CONV_2D,
+                                       tflite::ops::micro::Register_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_FULLY_CONNECTED,
+    tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with
+  static tflite::MicroInterpreter static_interpreter(
+    model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+    error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors
+  interpreter->AllocateTensors();
+
+  // Obtain pointer to the model's input tensor
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != 128) ||
+      (model_input->dims->data[2] != kChannelNumber) ||
+      (model_input->type != kTfLiteFloat32)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  input_length = model_input->bytes / sizeof(float);
+
+  TfLiteStatus setup_status = SetupAccelerometer(error_reporter);
+  if (setup_status != kTfLiteOk) {
+    error_reporter->Report("Set up failed\n");
+  }
+}
+
+void loop() {
+  // Attempt to read new data from the accelerometer
+  bool got_data = ReadAccelerometer(error_reporter, model_input->data.f,
+                                    input_length, should_clear_buffer);
+  // Don't try to clear the buffer again
+  should_clear_buffer = false;
+  // If there was no new data, wait until next time
+  if (!got_data) return;
+  // Run inference, and report any error
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed on index: %d\n", begin_index);
+    return;
+  }
+
+  char s[64];
+  float *f = model_input->data.f;
+  float *p = interpreter->output(0)->data.f;
+  sprintf(s, "%+6.0f : %+6.0f : %+6.0f || W %3.2f : R %3.2f : S %3.2f", \
+          f[381], f[382], f[383], p[0], p[1], p[2]);
+  error_reporter->Report(s);
+
+  M5.Lcd.setCursor(0, 32);
+  M5.Lcd.setTextColor(ORANGE, BLACK);
+  M5.Lcd.printf("INPUT \n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.printf("X%5.0f\n", f[381]);
+  M5.Lcd.printf("Y%5.0f\n", f[382]);
+  M5.Lcd.printf("Z%5.0f\n", f[383]);
+  M5.Lcd.setTextColor(ORANGE, BLACK);
+  M5.Lcd.printf("OUTPUT\n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.printf("W%5.2f\n", p[0]);
+  M5.Lcd.printf("R%5.2f\n", p[1]);
+  M5.Lcd.printf("S%5.2f\n", p[2]);
+
+
+  // Analyze the results to obtain a prediction
+  int gesture_index = PredictGesture(interpreter->output(0)->data.f);
+  // Clear the buffer next time we read data
+  should_clear_buffer = gesture_index < 3;
+  // Produce an output
+  HandleOutput(error_reporter, gesture_index);
+
+  if (gesture_index < 3) {
+    M5.Lcd.setCursor(20, 60);
+    M5.Lcd.setTextFont(1);
+    M5.Lcd.setTextSize(7);
+    if (gesture_index == 0) {
+      M5.Lcd.setTextColor(RED, BLACK);
+      M5.Lcd.print("W");
+    } else if (gesture_index == 1) {
+      M5.Lcd.setTextColor(BLUE, BLACK);
+      M5.Lcd.print("R");
+    } else if (gesture_index == 2) {
+      M5.Lcd.setTextColor(GREEN, BLACK);
+      M5.Lcd.print("S");
+    }
+    M5.Lcd.setTextSize(2);
+    M5.Lcd.setTextFont(1);
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/magic_wand_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i magic_wand_model.tflite > magic_wand_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+
+extern const unsigned char g_magic_wand_model_data[];
+extern const int g_magic_wand_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/output_handler.cpp

@@ -0,0 +1,35 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "output_handler.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind) {
+  // light (red: wing, blue: ring, green: slope)
+  if (kind == 0) {
+    error_reporter->Report(
+        "WING:\n\r*         *         *\n\r *       * *       "
+        "*\n\r  *     *   *     *\n\r   *   *     *   *\n\r    * *       "
+        "* *\n\r     *         *\n\r");
+  } else if (kind == 1) {
+    error_reporter->Report(
+        "RING:\n\r          *\n\r       *     *\n\r     *         *\n\r "
+        "   *           *\n\r     *         *\n\r       *     *\n\r      "
+        "    *\n\r");
+  } else if (kind == 2) {
+    error_reporter->Report(
+        "SLOPE:\n\r        *\n\r       *\n\r      *\n\r     *\n\r    "
+        "*\n\r   *\n\r  *\n\r * * * * * * * *\n\r");
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StackFire/output_handler.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/accelerometer_handler.cpp

@@ -0,0 +1,84 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "accelerometer_handler.h"
+#include <M5StickC.h>
+
+int begin_index = 0;
+
+float save_data[600] = {0.0};
+bool pending_initial_data = true;
+long last_sample_millis = 0;
+
+TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter) {
+  M5.IMU.Init();
+  error_reporter->Report("IMU Init");
+
+  return kTfLiteOk;
+}
+
+static bool UpdateData() {
+  bool new_data = false;
+  if ((millis() - last_sample_millis) < 40) {
+    return false;
+  }
+  last_sample_millis = millis();
+
+  float accX = 0.0F;
+  float accY = 0.0F;
+  float accZ = 0.0F;
+  M5.IMU.getAccelData(&accX, &accY, &accZ);
+
+  save_data[begin_index++] = 1000 * accZ;
+  save_data[begin_index++] = 1000 * accX;
+  save_data[begin_index++] = 1000 * accY;
+
+  if (begin_index >= 600) {
+    begin_index = 0;
+  }
+  new_data = true;
+
+  return new_data;
+}
+
+bool ReadAccelerometer(tflite::ErrorReporter* error_reporter, float* input,
+                       int length, bool reset_buffer) {
+  if (reset_buffer) {
+    memset(save_data, 0, 600 * sizeof(float));
+    begin_index = 0;
+    pending_initial_data = true;
+  }
+
+  if (!UpdateData()) {
+    return false;
+  }
+
+  if (pending_initial_data && begin_index >= 200) {
+    pending_initial_data = false;
+  }
+
+  if (pending_initial_data) {
+    return false;
+  }
+
+  for (int i = 0; i < length; ++i) {
+    int ring_array_index = begin_index + i - length;
+    if (ring_array_index < 0) {
+      ring_array_index += 600;
+    }
+    input[i] = save_data[ring_array_index];
+  }
+  return true;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/accelerometer_handler.h

@@ -0,0 +1,29 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_
+
+#define kChannelNumber 3
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+extern int begin_index;
+extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
+extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
+                              float* input, int length, bool reset_buffer);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/arduino_main.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "main_functions.h"
+
+// Arduino automatically calls the setup() and loop() functions in a sketch, so
+// where other systems need their own main routine in this file, it can be left
+// empty.

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/constants.cpp

@@ -0,0 +1,20 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "constants.h"
+
+// The number of expected consecutive inferences for each gesture type.
+// These defaults were established with the SparkFun Edge board.
+const int kConsecutiveInferenceThresholds[3] = {5, 5, 5};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/constants.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_
+
+// The expected accelerometer data sample frequency
+const float kTargetHz = 25;
+
+// The number of expected consecutive inferences for each gesture type
+extern const int kConsecutiveInferenceThresholds[3];
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_CONSTANTS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/gesture_predictor.cpp

@@ -0,0 +1,55 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "gesture_predictor.h"
+
+#include "constants.h"
+
+// How many times the most recent gesture has been matched in a row
+int continuous_count = 0;
+// The result of the last prediction
+int last_predict = -1;
+
+// Return the result of the last prediction
+// 0: wing("W"), 1: ring("O"), 2: slope("angle"), 3: unknown
+int PredictGesture(float* output) {
+  // Find whichever output has a probability > 0.8 (they sum to 1)
+  int this_predict = -1;
+  for (int i = 0; i < 3; i++) {
+    if (output[i] > 0.8) this_predict = i;
+  }
+  // No gesture was detected above the threshold
+  if (this_predict == -1) {
+    continuous_count = 0;
+    last_predict = 3;
+    return 3;
+  }
+  if (last_predict == this_predict) {
+    continuous_count += 1;
+  } else {
+    continuous_count = 0;
+  }
+  last_predict = this_predict;
+  // If we haven't yet had enough consecutive matches for this gesture,
+  // report a negative result
+  if (continuous_count < kConsecutiveInferenceThresholds[this_predict]) {
+    return 3;
+  }
+  // Otherwise, we've seen a positive result, so clear all our variables
+  // and report it
+  continuous_count = 0;
+  last_predict = -1;
+  return this_predict;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/gesture_predictor.h

@@ -0,0 +1,21 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_
+
+extern int PredictGesture(float* output);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_GESTURE_PREDICTOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/magic_wand_M5StickC.ino

@@ -0,0 +1,188 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "accelerometer_handler.h"
+#include "gesture_predictor.h"
+#include "magic_wand_model_data.h"
+#include "output_handler.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+#include <M5StickC.h>
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+int input_length;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 60 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+
+// Whether we should clear the buffer next time we fetch data
+bool should_clear_buffer = false;
+}  // namespace
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  M5.begin();
+  M5.Lcd.fillScreen(BLACK);
+  M5.Lcd.setCursor(0, 0);
+  M5.Lcd.setTextFont(2);
+  M5.Lcd.setTextColor(YELLOW);
+  M5.Lcd.printf("Magic Wand\n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.setTextFont(1);
+  M5.Lcd.setTextSize(2);
+
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  static tflite::MicroErrorReporter micro_error_reporter;  // NOLINT
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_magic_wand_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+      "Model provided is schema version %d not equal "
+      "to supported version %d.",
+      model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;  // NOLINT
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+    tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_MAX_POOL_2D,
+    tflite::ops::micro::Register_MAX_POOL_2D());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_CONV_2D,
+                                       tflite::ops::micro::Register_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_FULLY_CONNECTED,
+    tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with
+  static tflite::MicroInterpreter static_interpreter(
+    model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+    error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors
+  interpreter->AllocateTensors();
+
+  // Obtain pointer to the model's input tensor
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != 128) ||
+      (model_input->dims->data[2] != kChannelNumber) ||
+      (model_input->type != kTfLiteFloat32)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  input_length = model_input->bytes / sizeof(float);
+
+  TfLiteStatus setup_status = SetupAccelerometer(error_reporter);
+  if (setup_status != kTfLiteOk) {
+    error_reporter->Report("Set up failed\n");
+  }
+}
+
+void loop() {
+  // Attempt to read new data from the accelerometer
+  bool got_data = ReadAccelerometer(error_reporter, model_input->data.f,
+                                    input_length, should_clear_buffer);
+  // Don't try to clear the buffer again
+  should_clear_buffer = false;
+  // If there was no new data, wait until next time
+  if (!got_data) return;
+  // Run inference, and report any error
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed on index: %d\n", begin_index);
+    return;
+  }
+
+  char s[64];
+  float *f = model_input->data.f;
+  float *p = interpreter->output(0)->data.f;
+  sprintf(s, "%+6.0f : %+6.0f : %+6.0f || W %3.2f : R %3.2f : S %3.2f", \
+          f[381], f[382], f[383], p[0], p[1], p[2]);
+  error_reporter->Report(s);
+
+  M5.Lcd.setCursor(0, 32);
+  M5.Lcd.setTextColor(ORANGE, BLACK);
+  M5.Lcd.printf("INPUT \n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.printf("X%5.0f\n", f[381]);
+  M5.Lcd.printf("Y%5.0f\n", f[382]);
+  M5.Lcd.printf("Z%5.0f\n", f[383]);
+  M5.Lcd.setTextColor(ORANGE, BLACK);
+  M5.Lcd.printf("OUTPUT\n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+  M5.Lcd.printf("W%5.2f\n", p[0]);
+  M5.Lcd.printf("R%5.2f\n", p[1]);
+  M5.Lcd.printf("S%5.2f\n", p[2]);
+
+
+  // Analyze the results to obtain a prediction
+  int gesture_index = PredictGesture(interpreter->output(0)->data.f);
+  // Clear the buffer next time we read data
+  should_clear_buffer = gesture_index < 3;
+  // Produce an output
+  HandleOutput(error_reporter, gesture_index);
+
+  if (gesture_index < 3) {
+    M5.Lcd.setCursor(20, 60);
+    M5.Lcd.setTextFont(1);
+    M5.Lcd.setTextSize(7);
+    if (gesture_index == 0) {
+      M5.Lcd.setTextColor(RED, BLACK);
+      M5.Lcd.print("W");
+    } else if (gesture_index == 1) {
+      M5.Lcd.setTextColor(BLUE, BLACK);
+      M5.Lcd.print("R");
+    } else if (gesture_index == 2) {
+      M5.Lcd.setTextColor(GREEN, BLACK);
+      M5.Lcd.print("S");
+    }
+    M5.Lcd.setTextSize(2);
+    M5.Lcd.setTextFont(1);
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/magic_wand_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i magic_wand_model.tflite > magic_wand_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_
+
+extern const unsigned char g_magic_wand_model_data[];
+extern const int g_magic_wand_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAGIC_WAND_MODEL_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/output_handler.cpp

@@ -0,0 +1,35 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "output_handler.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind) {
+  // light (red: wing, blue: ring, green: slope)
+  if (kind == 0) {
+    error_reporter->Report(
+        "WING:\n\r*         *         *\n\r *       * *       "
+        "*\n\r  *     *   *     *\n\r   *   *     *   *\n\r    * *       "
+        "* *\n\r     *         *\n\r");
+  } else if (kind == 1) {
+    error_reporter->Report(
+        "RING:\n\r          *\n\r       *     *\n\r     *         *\n\r "
+        "   *           *\n\r     *         *\n\r       *     *\n\r      "
+        "    *\n\r");
+  } else if (kind == 2) {
+    error_reporter->Report(
+        "SLOPE:\n\r        *\n\r       *\n\r      *\n\r     *\n\r    "
+        "*\n\r   *\n\r  *\n\r * * * * * * * *\n\r");
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/magic_wand_M5StickC/output_handler.h

@@ -0,0 +1,24 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+void HandleOutput(tflite::ErrorReporter* error_reporter, int kind);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/audio_provider.cpp

@@ -0,0 +1,176 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "audio_provider.h"
+#include "micro_model_settings.h"
+#include <Arduino.h>
+#include <M5Atom.h>
+#include <driver/i2s.h>
+
+#define I2S_NUM           I2S_NUM_0           // 0 or 1
+#define I2S_SAMPLE_RATE   16000
+
+#define I2S_PIN_CLK       I2S_PIN_NO_CHANGE
+#define I2S_PIN_WS        33
+#define I2S_PIN_DOUT      I2S_PIN_NO_CHANGE
+#define I2S_PIN_DIN       23
+
+#define BUFFER_SIZE       512
+
+void CaptureSamples();
+extern QueueHandle_t xQueueAudioWave;
+
+namespace {
+bool g_is_audio_initialized = false;
+// An internal buffer able to fit 16x our sample size
+constexpr int kAudioCaptureBufferSize = BUFFER_SIZE * 16;
+int16_t g_audio_capture_buffer[kAudioCaptureBufferSize];
+// A buffer that holds our output
+int16_t g_audio_output_buffer[kMaxAudioSampleSize];
+// Mark as volatile so we can check in a while loop to see if
+// any samples have arrived yet.
+volatile int32_t g_latest_audio_timestamp = 0;
+// Our callback buffer for collecting a chunk of data
+volatile int16_t recording_buffer[BUFFER_SIZE];
+}  // namespace
+
+void InitI2S()
+{
+  i2s_config_t i2s_config = {
+    .mode                 = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_RX | I2S_MODE_PDM),
+    .sample_rate          = I2S_SAMPLE_RATE,
+    .bits_per_sample      = I2S_BITS_PER_SAMPLE_16BIT,
+    .channel_format       = I2S_CHANNEL_FMT_ALL_LEFT,
+    .communication_format = I2S_COMM_FORMAT_I2S,
+    .intr_alloc_flags     = ESP_INTR_FLAG_LEVEL1,
+    .dma_buf_count        = 4,
+    .dma_buf_len          = 256,
+    .use_apll             = false,
+    .tx_desc_auto_clear   = false,
+    .fixed_mclk           = 0
+  };
+  i2s_pin_config_t pin_config = {
+    .bck_io_num           = I2S_PIN_CLK,
+    .ws_io_num            = I2S_PIN_WS,
+    .data_out_num         = I2S_PIN_DOUT,
+    .data_in_num          = I2S_PIN_DIN,
+  };
+
+  i2s_driver_install(I2S_NUM, &i2s_config, 0, NULL);
+  i2s_set_pin(I2S_NUM, &pin_config);
+}
+
+void AudioRecordingTask(void *pvParameters) {
+  static uint16_t audio_idx = 0;
+  size_t bytes_read;
+  int16_t i2s_data[2];
+  int16_t sample;
+
+  while (1) {
+
+    if (audio_idx >= BUFFER_SIZE) {
+      xQueueSend(xQueueAudioWave, &sample, 0);
+      CaptureSamples();
+      audio_idx = 0;
+    }
+
+    i2s_read(I2S_NUM_0, &i2s_data, 4, &bytes_read, portMAX_DELAY );
+
+    if (bytes_read > 0) {
+      sample = i2s_data[0];
+      recording_buffer[audio_idx] = sample;
+      audio_idx++;
+    }
+  }
+}
+
+void CaptureSamples() {
+  // This is how many bytes of new data we have each time this is called
+  const int number_of_samples = BUFFER_SIZE;
+  // Calculate what timestamp the last audio sample represents
+  const int32_t time_in_ms =
+    g_latest_audio_timestamp +
+    (number_of_samples / (kAudioSampleFrequency / 1000));
+  // Determine the index, in the history of all samples, of the last sample
+  const int32_t start_sample_offset =
+    g_latest_audio_timestamp * (kAudioSampleFrequency / 1000);
+  // Determine the index of this sample in our ring buffer
+  const int capture_index = start_sample_offset % kAudioCaptureBufferSize;
+  // Read the data to the correct place in our buffer, note 2 bytes per buffer entry
+  memcpy(g_audio_capture_buffer + capture_index, (void *)recording_buffer, BUFFER_SIZE * 2);
+  // This is how we let the outside world know that new audio data has arrived.
+  g_latest_audio_timestamp = time_in_ms;
+
+  //int peak = (max_audio - min_audio);
+  //Serial.printf("peak-to-peak:  %6d\n", peak);
+}
+
+TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) {
+  delay(10);
+
+  InitI2S();
+
+  xTaskCreatePinnedToCore(AudioRecordingTask, "AudioRecordingTask", 2048, NULL, 10, NULL, 0);
+
+  // Block until we have our first audio sample
+  while (!g_latest_audio_timestamp) {
+    delay(1);
+  }
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples) {
+  // Set everything up to start receiving audio
+  if (!g_is_audio_initialized) {
+    TfLiteStatus init_status = InitAudioRecording(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    g_is_audio_initialized = true;
+  }
+  // This next part should only be called when the main thread notices that the
+  // latest audio sample data timestamp has changed, so that there's new data
+  // in the capture ring buffer. The ring buffer will eventually wrap around and
+  // overwrite the data, but the assumption is that the main thread is checking
+  // often enough and the buffer is large enough that this call will be made
+  // before that happens.
+
+  // Determine the index, in the history of all samples, of the first
+  // sample we want
+  const int start_offset = start_ms * (kAudioSampleFrequency / 1000);
+  // Determine how many samples we want in total
+  const int duration_sample_count =
+    duration_ms * (kAudioSampleFrequency / 1000);
+  for (int i = 0; i < duration_sample_count; ++i) {
+    // For each sample, transform its index in the history of all samples into
+    // its index in g_audio_capture_buffer
+    const int capture_index = (start_offset + i) % kAudioCaptureBufferSize;
+    // Write the sample to the output buffer
+    g_audio_output_buffer[i] = g_audio_capture_buffer[capture_index];
+  }
+
+  // Set pointers to provide access to the audio
+  *audio_samples_size = kMaxAudioSampleSize;
+  *audio_samples = g_audio_output_buffer;
+
+  return kTfLiteOk;
+}
+
+int32_t LatestAudioTimestamp() {
+  return g_latest_audio_timestamp;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/audio_provider.h

@@ -0,0 +1,46 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// This is an abstraction around an audio source like a microphone, and is
+// expected to return 16-bit PCM sample data for a given point in time. The
+// sample data itself should be used as quickly as possible by the caller, since
+// to allow memory optimizations there are no guarantees that the samples won't
+// be overwritten by new data in the future. In practice, implementations should
+// ensure that there's a reasonable time allowed for clients to access the data
+// before any reuse.
+// The reference implementation can have no platform-specific dependencies, so
+// it just returns an array filled with zeros. For real applications, you should
+// ensure there's a specialized implementation that accesses hardware APIs.
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples);
+
+// Returns the time that audio data was last captured in milliseconds. There's
+// no contract about what time zero represents, the accuracy, or the granularity
+// of the result. Subsequent calls will generally not return a lower value, but
+// even that's not guaranteed if there's an overflow wraparound.
+// The reference implementation of this function just returns a constantly
+// incrementing value for each call, since it would need a non-portable platform
+// call to access time information. For real applications, you'll need to write
+// your own platform-specific implementation.
+int32_t LatestAudioTimestamp();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/command_responder.cpp

@@ -0,0 +1,89 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "command_responder.h"
+
+#include <M5Atom.h>
+
+int dispMode = 0;
+
+void InitResponder() {
+  M5.begin(true, false, true);
+}
+
+namespace {
+enum {
+  COMMAND_SILENCE,
+  COMMAND_UNKNOWN,
+  COMMAND_YES,
+  COMMAND_NO,
+
+  COMMAND_MAX
+};
+uint8_t scoreList[COMMAND_MAX];
+uint8_t lastCommand;
+int8_t lastCommandTime;
+}
+
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command) {
+  static int32_t last_timestamp = 0;
+
+  // Score List Update
+  uint8_t command = COMMAND_SILENCE;
+  memset(scoreList, 0, sizeof(scoreList));
+  if (strcmp(found_command, "silence") == 0) {
+    command = COMMAND_SILENCE;
+  } else   if (strcmp(found_command, "unknown") == 0) {
+    command = COMMAND_UNKNOWN;
+  } else   if (strcmp(found_command, "yes") == 0) {
+    command = COMMAND_YES;
+  } else   if (strcmp(found_command, "no") == 0) {
+    command = COMMAND_NO;
+  }
+  scoreList[command] = score;
+
+  // New Command
+  if (is_new_command) {
+    lastCommand = command;
+    lastCommandTime = 10;
+  }
+
+  if (lastCommand == COMMAND_UNKNOWN && 0 < lastCommandTime) {
+    M5.dis.drawpix(0, 0xf00000);
+  } else if (lastCommand == COMMAND_YES && 0 < lastCommandTime) {
+    M5.dis.drawpix(0, 0x0000f0);
+  } else if (lastCommand == COMMAND_NO && 0 < lastCommandTime) {
+    M5.dis.drawpix(0, 0x00f000);
+  } else {
+    M5.dis.drawpix(0, 0x707070);
+  }
+
+  if (0 < lastCommandTime) {
+    lastCommandTime--;
+  }
+
+  Serial.printf("current_time(%d) found_command(%s) score(%d) is_new_command(%d)\n", current_time, found_command, score, is_new_command);
+}
+
+void drawWave(int16_t value) {
+}
+
+void drawInput(uint8_t *uint8) {
+}
+
+void updateM5() {
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/command_responder.h

@@ -0,0 +1,37 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Provides an interface to take an action based on an audio command.
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Called every time the results of an audio recognition run are available. The
+// human-readable name of any recognized command is in the `found_command`
+// argument, `score` has the numerical confidence, and `is_new_command` is set
+// if the previous command was different to this one.
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command);
+
+void InitResponder();
+void drawWave(int16_t value);
+void drawInput(uint8_t *uint8);
+void updateM5();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/feature_provider.cpp

@@ -0,0 +1,118 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "feature_provider.h"
+
+#include "audio_provider.h"
+#include "micro_features_generator.h"
+#include "micro_model_settings.h"
+
+FeatureProvider::FeatureProvider(int feature_size, uint8_t* feature_data)
+    : feature_size_(feature_size),
+      feature_data_(feature_data),
+      is_first_run_(true) {
+  // Initialize the feature data to default values.
+  for (int n = 0; n < feature_size_; ++n) {
+    feature_data_[n] = 0;
+  }
+}
+
+FeatureProvider::~FeatureProvider() {}
+
+TfLiteStatus FeatureProvider::PopulateFeatureData(
+    tflite::ErrorReporter* error_reporter, int32_t last_time_in_ms,
+    int32_t time_in_ms, int* how_many_new_slices) {
+  if (feature_size_ != kFeatureElementCount) {
+    error_reporter->Report("Requested feature_data_ size %d doesn't match %d",
+                           feature_size_, kFeatureElementCount);
+    return kTfLiteError;
+  }
+
+  // Quantize the time into steps as long as each window stride, so we can
+  // figure out which audio data we need to fetch.
+  const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
+  const int current_step = (time_in_ms / kFeatureSliceStrideMs);
+
+  int slices_needed = current_step - last_step;
+  // If this is the first call, make sure we don't use any cached information.
+  if (is_first_run_) {
+    TfLiteStatus init_status = InitializeMicroFeatures(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    is_first_run_ = false;
+    slices_needed = kFeatureSliceCount;
+  }
+  if (slices_needed > kFeatureSliceCount) {
+    slices_needed = kFeatureSliceCount;
+  }
+  *how_many_new_slices = slices_needed;
+
+  const int slices_to_keep = kFeatureSliceCount - slices_needed;
+  const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
+  // If we can avoid recalculating some slices, just move the existing data
+  // up in the spectrogram, to perform something like this:
+  // last time = 80ms          current time = 120ms
+  // +-----------+             +-----------+
+  // | data@20ms |         --> | data@60ms |
+  // +-----------+       --    +-----------+
+  // | data@40ms |     --  --> | data@80ms |
+  // +-----------+   --  --    +-----------+
+  // | data@60ms | --  --      |  <empty>  |
+  // +-----------+   --        +-----------+
+  // | data@80ms | --          |  <empty>  |
+  // +-----------+             +-----------+
+  if (slices_to_keep > 0) {
+    for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
+      uint8_t* dest_slice_data =
+          feature_data_ + (dest_slice * kFeatureSliceSize);
+      const int src_slice = dest_slice + slices_to_drop;
+      const uint8_t* src_slice_data =
+          feature_data_ + (src_slice * kFeatureSliceSize);
+      for (int i = 0; i < kFeatureSliceSize; ++i) {
+        dest_slice_data[i] = src_slice_data[i];
+      }
+    }
+  }
+  // Any slices that need to be filled in with feature data have their
+  // appropriate audio data pulled, and features calculated for that slice.
+  if (slices_needed > 0) {
+    for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount;
+         ++new_slice) {
+      const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
+      const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
+      int16_t* audio_samples = nullptr;
+      int audio_samples_size = 0;
+      // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
+      GetAudioSamples(error_reporter, (slice_start_ms > 0 ? slice_start_ms : 0),
+                      kFeatureSliceDurationMs, &audio_samples_size,
+                      &audio_samples);
+      if (audio_samples_size < kMaxAudioSampleSize) {
+        error_reporter->Report("Audio data size %d too small, want %d",
+                               audio_samples_size, kMaxAudioSampleSize);
+        return kTfLiteError;
+      }
+      uint8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
+      size_t num_samples_read;
+      TfLiteStatus generate_status = GenerateMicroFeatures(
+          error_reporter, audio_samples, audio_samples_size, kFeatureSliceSize,
+          new_slice_data, &num_samples_read);
+      if (generate_status != kTfLiteOk) {
+        return generate_status;
+      }
+    }
+  }
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/feature_provider.h

@@ -0,0 +1,52 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Binds itself to an area of memory intended to hold the input features for an
+// audio-recognition neural network model, and fills that data area with the
+// features representing the current audio input, for example from a microphone.
+// The audio features themselves are a two-dimensional array, made up of
+// horizontal slices representing the frequencies at one point in time, stacked
+// on top of each other to form a spectrogram showing how those frequencies
+// changed over time.
+class FeatureProvider {
+ public:
+  // Create the provider, and bind it to an area of memory. This memory should
+  // remain accessible for the lifetime of the provider object, since subsequent
+  // calls will fill it with feature data. The provider does no memory
+  // management of this data.
+  FeatureProvider(int feature_size, uint8_t* feature_data);
+  ~FeatureProvider();
+
+  // Fills the feature data with information from audio inputs, and returns how
+  // many feature slices were updated.
+  TfLiteStatus PopulateFeatureData(tflite::ErrorReporter* error_reporter,
+                                   int32_t last_time_in_ms, int32_t time_in_ms,
+                                   int* how_many_new_slices);
+
+ private:
+  int feature_size_;
+  uint8_t* feature_data_;
+  // Make sure we don't try to use cached information if this is the first call
+  // into the provider.
+  bool is_first_run_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/micro_features_generator.cpp

@@ -0,0 +1,102 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_features_generator.h"
+
+#include <cmath>
+#include <cstring>
+
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"
+
+// Configure FFT to output 16 bit fixed point.
+#define FIXED_POINT 16
+
+namespace {
+
+FrontendState g_micro_features_state;
+bool g_is_first_time = true;
+
+}  // namespace
+
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter) {
+  FrontendConfig config;
+  config.window.size_ms = kFeatureSliceDurationMs;
+  config.window.step_size_ms = kFeatureSliceStrideMs;
+  config.noise_reduction.smoothing_bits = 10;
+  config.filterbank.num_channels = kFeatureSliceSize;
+  config.filterbank.lower_band_limit = 125.0;
+  config.filterbank.upper_band_limit = 7500.0;
+  config.noise_reduction.smoothing_bits = 10;
+  config.noise_reduction.even_smoothing = 0.025;
+  config.noise_reduction.odd_smoothing = 0.06;
+  config.noise_reduction.min_signal_remaining = 0.05;
+  config.pcan_gain_control.enable_pcan = 1;
+  config.pcan_gain_control.strength = 0.95;
+  config.pcan_gain_control.offset = 80.0;
+  config.pcan_gain_control.gain_bits = 21;
+  config.log_scale.enable_log = 1;
+  config.log_scale.scale_shift = 6;
+  if (!FrontendPopulateState(&config, &g_micro_features_state,
+                             kAudioSampleFrequency)) {
+    error_reporter->Report("FrontendPopulateState() failed");
+    return kTfLiteError;
+  }
+  g_is_first_time = true;
+  return kTfLiteOk;
+}
+
+// This is not exposed in any header, and is only used for testing, to ensure
+// that the state is correctly set up before generating results.
+void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) {
+  for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
+    g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
+  }
+}
+
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read) {
+  const int16_t* frontend_input;
+  if (g_is_first_time) {
+    frontend_input = input;
+    g_is_first_time = false;
+  } else {
+    frontend_input = input + 160;
+  }
+  FrontendOutput frontend_output = FrontendProcessSamples(
+      &g_micro_features_state, frontend_input, input_size, num_samples_read);
+
+  for (int i = 0; i < frontend_output.size; ++i) {
+    // These scaling values are derived from those used in input_data.py in the
+    // training pipeline.
+    constexpr int32_t value_scale = (10 * 255);
+    constexpr int32_t value_div = (256 * 26);
+    int32_t value =
+        ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
+        value_div;
+    if (value < 0) {
+      value = 0;
+    }
+    if (value > 255) {
+      value = 255;
+    }
+    output[i] = value;
+  }
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/micro_features_generator.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Sets up any resources needed for the feature generation pipeline.
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter);
+
+// Converts audio sample data into a more compact form that's appropriate for
+// feeding into a neural network.
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/micro_model_settings.cpp

@@ -0,0 +1,23 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_model_settings.h"
+
+const char* kCategoryLabels[kCategoryCount] = {
+    "silence",
+    "unknown",
+    "yes",
+    "no",
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/micro_model_settings.h

@@ -0,0 +1,41 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+
+// Keeping these as constant expressions allow us to allocate fixed-sized arrays
+// on the stack for our working memory.
+
+// The size of the input time series data we pass to the FFT to produce the
+// frequency information. This has to be a power of two, and since we're dealing
+// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
+constexpr int kMaxAudioSampleSize = 512;
+constexpr int kAudioSampleFrequency = 16000;
+
+// All of these values are derived from the values used during model training,
+// if you change your model you'll need to update these constants.
+constexpr int kFeatureSliceSize = 40;
+constexpr int kFeatureSliceCount = 49;
+constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
+constexpr int kFeatureSliceStrideMs = 20;
+constexpr int kFeatureSliceDurationMs = 30;
+
+constexpr int kCategoryCount = 4;
+constexpr int kSilenceIndex = 0;
+constexpr int kUnknownIndex = 1;
+extern const char* kCategoryLabels[kCategoryCount];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/micro_speech_ATOM-Echo.ino

@@ -0,0 +1,197 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "audio_provider.h"
+#include "command_responder.h"
+#include "feature_provider.h"
+#include "micro_model_settings.h"
+#include "tiny_conv_micro_features_model_data.h"
+#include "recognize_commands.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+FeatureProvider* feature_provider = nullptr;
+RecognizeCommands* recognizer = nullptr;
+int32_t previous_time = 0;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 10 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+}  // namespace
+
+QueueHandle_t xQueueAudioWave;
+#define QueueAudioWaveSize 32
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  xQueueAudioWave = xQueueCreate(QueueAudioWaveSize, sizeof(int16_t));
+  
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_tiny_conv_micro_features_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+      "Model provided is schema version %d not equal "
+      "to supported version %d.",
+      model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  //
+  // tflite::ops::micro::AllOpsResolver resolver;
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+    tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_FULLY_CONNECTED,
+    tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with.
+  static tflite::MicroInterpreter static_interpreter(
+    model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+    error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    error_reporter->Report("AllocateTensors() failed");
+    return;
+  }
+
+  // Get information about the memory area to use for the model's input.
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != kFeatureSliceCount) ||
+      (model_input->dims->data[2] != kFeatureSliceSize) ||
+      (model_input->type != kTfLiteUInt8)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  // Prepare to access the audio spectrograms from a microphone or other source
+  // that will provide the inputs to the neural network.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static FeatureProvider static_feature_provider(kFeatureElementCount,
+      model_input->data.uint8);
+  feature_provider = &static_feature_provider;
+
+  static RecognizeCommands static_recognizer(error_reporter);
+  recognizer = &static_recognizer;
+
+  previous_time = 0;
+
+  InitResponder();
+
+  Serial.printf("model_input->name          : %s\n", model_input->name);
+  Serial.printf("model_input->type          : %d\n", model_input->type);
+  Serial.printf("model_input->bytes         : %d\n", model_input->bytes);
+  Serial.printf("model_input->dims->size    : %d\n", model_input->dims->size);
+  Serial.printf("model_input->dims->data[0] : %d\n", model_input->dims->data[0]); // 1
+  Serial.printf("model_input->dims->data[1] : %d\n", model_input->dims->data[1]); // kFeatureSliceCount
+  Serial.printf("model_input->dims->data[2] : %d\n", model_input->dims->data[2]); // kFeatureSliceSize
+}
+
+// The name of this function is important for Arduino compatibility.
+void loop() {
+  updateM5();
+
+  int16_t wave = 0;
+  for (int i = 0; i < QueueAudioWaveSize; i++) {
+    if (xQueueReceive(xQueueAudioWave, &wave, 0) == pdTRUE) {
+      drawWave(wave);
+    }
+  }  
+
+  // Fetch the spectrogram for the current time.
+  const int32_t current_time = LatestAudioTimestamp();
+  int how_many_new_slices = 0;
+  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
+                                  error_reporter, previous_time, current_time, &how_many_new_slices);
+  if (feature_status != kTfLiteOk) {
+    error_reporter->Report("Feature generation failed");
+    delay(1);
+    return;
+  }
+  previous_time = current_time;
+  // If no new audio samples have been received since last time, don't bother
+  // running the network model.
+  if (how_many_new_slices == 0) {
+    delay(1);
+    return;
+  }
+
+  // Run the model on the spectrogram input and make sure it succeeds.
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed");
+    delay(1);
+    return;
+  }
+
+  // Obtain a pointer to the output tensor
+  TfLiteTensor* output = interpreter->output(0);
+  // Determine whether a command was recognized based on the output of inference
+  const char* found_command = nullptr;
+  uint8_t score = 0;
+  bool is_new_command = false;
+  TfLiteStatus process_status = recognizer->ProcessLatestResults(
+                                  output, current_time, &found_command, &score, &is_new_command);
+  if (process_status != kTfLiteOk) {
+    error_reporter->Report("RecognizeCommands::ProcessLatestResults() failed");
+    delay(1);
+    return;
+  }
+  // Do something based on the recognized command. The default implementation
+  // just prints to the error console, but you should replace this with your
+  // own function for a real application.
+  RespondToCommand(error_reporter, current_time, found_command, score,
+                   is_new_command);
+
+  drawInput(model_input->data.uint8);
+
+  delay(1);
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/no_micro_features_data.cpp

@@ -0,0 +1,165 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "no_micro_features_data.h"
+
+/* File automatically created by
+ * tensorflow/examples/speech_commands/wav_to_features.py \
+ * --sample_rate=16000 \
+ * --clip_duration_ms=1000 \
+ * --window_size_ms=30 \
+ * --window_stride_ms=20 \
+ * --feature_bin_count=40 \
+ * --quantize=1 \
+ * --preprocess="micro" \
+ * --input_wav="speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav" \
+ * --output_c_file="/tmp/no_micro_features_data.cc" \
+ */
+
+const int g_no_micro_f9643d42_nohash_4_width = 40;
+const int g_no_micro_f9643d42_nohash_4_height = 49;
+const unsigned char g_no_micro_f9643d42_nohash_4_data[] = {
+    230, 205, 191, 203, 202, 181, 180, 194, 205, 187, 183, 197, 203, 198, 196,
+    186, 202, 159, 151, 126, 110, 138, 141, 142, 137, 148, 133, 120, 110, 126,
+    117, 110, 117, 116, 137, 134, 95,  116, 123, 110, 184, 144, 183, 189, 197,
+    172, 188, 164, 194, 179, 175, 174, 182, 173, 184, 174, 200, 145, 154, 148,
+    147, 135, 143, 122, 127, 138, 116, 99,  122, 105, 110, 125, 127, 133, 131,
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+    152, 112, 134, 144, 117, 116, 105, 85,  105, 105, 99,  90,  123, 112, 112,
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+    119, 0,   114, 0,   95,  110, 116, 51,  112, 0,   90,  0,   116, 51,  68,
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+    144, 130, 129, 138, 134, 132, 120, 134, 0,   51,  78,  147, 51,  51,  0,
+    51,  0,   78,  0,   68,  68,  95,  78,  90,  0,   0,   0,   68,  0,   90,
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+    68,  0,   90,  0,   85,  0,   107, 0,   78,  0,   85,  51,  102, 0,   68,
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+    173, 85,  116, 141, 164, 68,  150, 123, 133, 51,  114, 0,   117, 68,  150,
+    51,  116, 68,  78,  0,   68,  0,   68,  0,   85,  0,   78,  0,   51,  78,
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+    0,   162, 143, 85,  0,   107, 78,  125, 90,  90,  51,  51,  0,   85,  0,
+    0,   0,   132, 102, 102, 154, 128, 0,   99,  68,  162, 102, 151, 0,   99,
+    51,  147, 141, 156, 0,   112, 120, 158, 127, 145, 139, 187, 171, 135, 138,
+    146, 0,   95,  68,  127, 0,   85,  0,   105, 0,   0,   0,   187, 170, 162,
+    188, 165, 51,  51,  78,  243, 215, 225, 196, 205, 181, 205, 168, 176, 134,
+    157, 110, 126, 114, 133, 139, 193, 163, 159, 116, 160, 126, 122, 127, 171,
+    99,  114, 68,  123, 85,  90,  0,   157, 146, 166, 179, 136, 0,   116, 90,
+    242, 219, 240, 204, 216, 164, 188, 171, 176, 164, 154, 158, 190, 157, 190,
+    141, 182, 177, 169, 128, 172, 145, 105, 129, 157, 90,  78,  51,  119, 68,
+    137, 68,  116, 78,  141, 132, 151, 122, 156, 140, 234, 206, 229, 201, 216,
+    174, 191, 144, 162, 85,  122, 157, 194, 167, 204, 149, 180, 166, 166, 139,
+    122, 133, 156, 126, 145, 85,  128, 0,   99,  51,  145, 0,   126, 51,  166,
+    162, 166, 162, 177, 157, 228, 198, 221, 197, 214, 177, 173, 166, 173, 139,
+    185, 191, 202, 163, 205, 172, 206, 189, 135, 68,  166, 134, 149, 134, 135,
+    90,  127, 107, 175, 90,  136, 117, 135, 140, 172, 167, 166, 149, 177, 152,
+    221, 191, 215, 194, 211, 0,   156, 147, 182, 178, 208, 163, 190, 157, 208,
+    200, 195, 164, 179, 154, 181, 150, 143, 99,  132, 137, 185, 143, 163, 85,
+    51,  107, 132, 134, 164, 127, 167, 159, 175, 141, 216, 195, 223, 211, 238,
+    223, 243, 215, 226, 204, 232, 211, 232, 213, 240, 218, 235, 214, 238, 205,
+    207, 173, 149, 201, 215, 200, 230, 213, 208, 195, 175, 151, 195, 175, 182,
+    163, 235, 217, 218, 190, 211, 191, 215, 191, 217, 220, 241, 215, 229, 206,
+    236, 210, 227, 216, 236, 188, 183, 149, 202, 189, 208, 172, 191, 201, 220,
+    193, 221, 207, 216, 208, 201, 131, 170, 187, 229, 197, 211, 194, 226, 201,
+    205, 184, 206, 177, 221, 210, 226, 184, 204, 197, 218, 198, 212, 209, 213,
+    141, 172, 110, 175, 167, 180, 156, 213, 188, 192, 179, 213, 205, 204, 174,
+    200, 147, 162, 181, 203, 167, 198, 187, 210, 164, 196, 169, 189, 168, 224,
+    198, 213, 204, 198, 195, 230, 211, 221, 197, 208, 0,   0,   0,   85,  90,
+    167, 130, 175, 173, 203, 164, 193, 144, 170, 145, 185, 148, 154, 139, 198,
+    159, 180, 171, 216, 174, 178, 161, 166, 136, 216, 184, 215, 197, 199, 190,
+    228, 195, 208, 51,  117, 0,   0,   0,   0,   0,   140, 51,  135, 154, 188,
+    155, 168, 0,   90,  0,   156, 85,  110, 0,   174, 90,  172, 154, 179, 99,
+    142, 166, 179, 157, 177, 95,  192, 142, 204, 198, 217, 147, 173, 0,   112,
+    0,   0,   0,   0,   0,   0,   0,   110, 0,   107, 0,   160, 0,   148, 95,
+    172, 0,   0,   0,   116, 0,   122, 114, 170, 0,   0,   0,   0,   0,   179,
+    110, 196, 85,  205, 183, 169, 0,   99,  0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   141, 0,   112, 0,   0,   0,   134, 0,   0,   0,   0,
+    0,   0,   0,   139, 0,   0,   0,   0,   112, 186, 78,  163, 0,   169, 128,
+    174, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   95,
+    0,   105, 0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   95,  0,
+    0,   0,   0,   0,   0,   0,   119, 0,   164, 78,  0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   90,  0,   0,   68,
+    117, 0,   0,   0,   0,   0,   0,   0,   148, 0,   0,   0,   0,   0,   0,
+    0,   0,   0,   116, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   78,  0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/no_micro_features_data.h

@@ -0,0 +1,23 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+
+extern const int g_no_micro_f9643d42_nohash_4_width;
+extern const int g_no_micro_f9643d42_nohash_4_height;
+extern const unsigned char g_no_micro_f9643d42_nohash_4_data[];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/recognize_commands.cpp

@@ -0,0 +1,139 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "recognize_commands.h"
+
+#include <limits>
+
+RecognizeCommands::RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                                     int32_t average_window_duration_ms,
+                                     uint8_t detection_threshold,
+                                     int32_t suppression_ms,
+                                     int32_t minimum_count)
+    : error_reporter_(error_reporter),
+      average_window_duration_ms_(average_window_duration_ms),
+      detection_threshold_(detection_threshold),
+      suppression_ms_(suppression_ms),
+      minimum_count_(minimum_count),
+      previous_results_(error_reporter) {
+  previous_top_label_ = "silence";
+  previous_top_label_time_ = std::numeric_limits<int32_t>::min();
+}
+
+TfLiteStatus RecognizeCommands::ProcessLatestResults(
+    const TfLiteTensor* latest_results, const int32_t current_time_ms,
+    const char** found_command, uint8_t* score, bool* is_new_command) {
+  if ((latest_results->dims->size != 2) ||
+      (latest_results->dims->data[0] != 1) ||
+      (latest_results->dims->data[1] != kCategoryCount)) {
+    error_reporter_->Report(
+        "The results for recognition should contain %d elements, but there are "
+        "%d in an %d-dimensional shape",
+        kCategoryCount, latest_results->dims->data[1],
+        latest_results->dims->size);
+    return kTfLiteError;
+  }
+
+  if (latest_results->type != kTfLiteUInt8) {
+    error_reporter_->Report(
+        "The results for recognition should be uint8 elements, but are %d",
+        latest_results->type);
+    return kTfLiteError;
+  }
+
+  if ((!previous_results_.empty()) &&
+      (current_time_ms < previous_results_.front().time_)) {
+    error_reporter_->Report(
+        "Results must be fed in increasing time order, but received a "
+        "timestamp of %d that was earlier than the previous one of %d",
+        current_time_ms, previous_results_.front().time_);
+    return kTfLiteError;
+  }
+
+  // Add the latest results to the head of the queue.
+  previous_results_.push_back({current_time_ms, latest_results->data.uint8});
+
+  // Prune any earlier results that are too old for the averaging window.
+  const int64_t time_limit = current_time_ms - average_window_duration_ms_;
+  while ((!previous_results_.empty()) &&
+         previous_results_.front().time_ < time_limit) {
+    previous_results_.pop_front();
+  }
+
+  // If there are too few results, assume the result will be unreliable and
+  // bail.
+  const int64_t how_many_results = previous_results_.size();
+  const int64_t earliest_time = previous_results_.front().time_;
+  const int64_t samples_duration = current_time_ms - earliest_time;
+  if ((how_many_results < minimum_count_) ||
+      (samples_duration < (average_window_duration_ms_ / 4))) {
+    *found_command = previous_top_label_;
+    *score = 0;
+    *is_new_command = false;
+    return kTfLiteOk;
+  }
+
+  // Calculate the average score across all the results in the window.
+  int32_t average_scores[kCategoryCount];
+  for (int offset = 0; offset < previous_results_.size(); ++offset) {
+    PreviousResultsQueue::Result previous_result =
+        previous_results_.from_front(offset);
+    const uint8_t* scores = previous_result.scores_;
+    for (int i = 0; i < kCategoryCount; ++i) {
+      if (offset == 0) {
+        average_scores[i] = scores[i];
+      } else {
+        average_scores[i] += scores[i];
+      }
+    }
+  }
+  for (int i = 0; i < kCategoryCount; ++i) {
+    average_scores[i] /= how_many_results;
+  }
+
+  // Find the current highest scoring category.
+  int current_top_index = 0;
+  int32_t current_top_score = 0;
+  for (int i = 0; i < kCategoryCount; ++i) {
+    if (average_scores[i] > current_top_score) {
+      current_top_score = average_scores[i];
+      current_top_index = i;
+    }
+  }
+  const char* current_top_label = kCategoryLabels[current_top_index];
+
+  // If we've recently had another label trigger, assume one that occurs too
+  // soon afterwards is a bad result.
+  int64_t time_since_last_top;
+  if ((previous_top_label_ == kCategoryLabels[0]) ||
+      (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
+    time_since_last_top = std::numeric_limits<int32_t>::max();
+  } else {
+    time_since_last_top = current_time_ms - previous_top_label_time_;
+  }
+  if ((current_top_score > detection_threshold_) &&
+      ((current_top_label != previous_top_label_) ||
+       (time_since_last_top > suppression_ms_))) {
+    previous_top_label_ = current_top_label;
+    previous_top_label_time_ = current_time_ms;
+    *is_new_command = true;
+  } else {
+    *is_new_command = false;
+  }
+  *found_command = current_top_label;
+  *score = current_top_score;
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/recognize_commands.h

@@ -0,0 +1,156 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+
+#include <cstdint>
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Partial implementation of std::dequeue, just providing the functionality
+// that's needed to keep a record of previous neural network results over a
+// short time period, so they can be averaged together to produce a more
+// accurate overall prediction. This doesn't use any dynamic memory allocation
+// so it's a better fit for microcontroller applications, but this does mean
+// there are hard limits on the number of results it can store.
+class PreviousResultsQueue {
+ public:
+  PreviousResultsQueue(tflite::ErrorReporter* error_reporter)
+      : error_reporter_(error_reporter), front_index_(0), size_(0) {}
+
+  // Data structure that holds an inference result, and the time when it
+  // was recorded.
+  struct Result {
+    Result() : time_(0), scores_() {}
+    Result(int32_t time, uint8_t* scores) : time_(time) {
+      for (int i = 0; i < kCategoryCount; ++i) {
+        scores_[i] = scores[i];
+      }
+    }
+    int32_t time_;
+    uint8_t scores_[kCategoryCount];
+  };
+
+  int size() { return size_; }
+  bool empty() { return size_ == 0; }
+  Result& front() { return results_[front_index_]; }
+  Result& back() {
+    int back_index = front_index_ + (size_ - 1);
+    if (back_index >= kMaxResults) {
+      back_index -= kMaxResults;
+    }
+    return results_[back_index];
+  }
+
+  void push_back(const Result& entry) {
+    if (size() >= kMaxResults) {
+      error_reporter_->Report(
+          "Couldn't push_back latest result, too many already!");
+      return;
+    }
+    size_ += 1;
+    back() = entry;
+  }
+
+  Result pop_front() {
+    if (size() <= 0) {
+      error_reporter_->Report("Couldn't pop_front result, none present!");
+      return Result();
+    }
+    Result result = front();
+    front_index_ += 1;
+    if (front_index_ >= kMaxResults) {
+      front_index_ = 0;
+    }
+    size_ -= 1;
+    return result;
+  }
+
+  // Most of the functions are duplicates of dequeue containers, but this
+  // is a helper that makes it easy to iterate through the contents of the
+  // queue.
+  Result& from_front(int offset) {
+    if ((offset < 0) || (offset >= size_)) {
+      error_reporter_->Report("Attempt to read beyond the end of the queue!");
+      offset = size_ - 1;
+    }
+    int index = front_index_ + offset;
+    if (index >= kMaxResults) {
+      index -= kMaxResults;
+    }
+    return results_[index];
+  }
+
+ private:
+  tflite::ErrorReporter* error_reporter_;
+  static constexpr int kMaxResults = 50;
+  Result results_[kMaxResults];
+
+  int front_index_;
+  int size_;
+};
+
+// This class is designed to apply a very primitive decoding model on top of the
+// instantaneous results from running an audio recognition model on a single
+// window of samples. It applies smoothing over time so that noisy individual
+// label scores are averaged, increasing the confidence that apparent matches
+// are real.
+// To use it, you should create a class object with the configuration you
+// want, and then feed results from running a TensorFlow model into the
+// processing method. The timestamp for each subsequent call should be
+// increasing from the previous, since the class is designed to process a stream
+// of data over time.
+class RecognizeCommands {
+ public:
+  // labels should be a list of the strings associated with each one-hot score.
+  // The window duration controls the smoothing. Longer durations will give a
+  // higher confidence that the results are correct, but may miss some commands.
+  // The detection threshold has a similar effect, with high values increasing
+  // the precision at the cost of recall. The minimum count controls how many
+  // results need to be in the averaging window before it's seen as a reliable
+  // average. This prevents erroneous results when the averaging window is
+  // initially being populated for example. The suppression argument disables
+  // further recognitions for a set time after one has been triggered, which can
+  // help reduce spurious recognitions.
+  explicit RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                             int32_t average_window_duration_ms = 1000,
+                             uint8_t detection_threshold = 200,
+                             int32_t suppression_ms = 1500,
+                             int32_t minimum_count = 3);
+
+  // Call this with the results of running a model on sample data.
+  TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results,
+                                    const int32_t current_time_ms,
+                                    const char** found_command, uint8_t* score,
+                                    bool* is_new_command);
+
+ private:
+  // Configuration
+  tflite::ErrorReporter* error_reporter_;
+  int32_t average_window_duration_ms_;
+  uint8_t detection_threshold_;
+  int32_t suppression_ms_;
+  int32_t minimum_count_;
+
+  // Working variables
+  PreviousResultsQueue previous_results_;
+  const char* previous_top_label_;
+  int32_t previous_top_label_time_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/static_alloc.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+
+// Checks to ensure that the C-style array passed in has a compile-time size of
+// at least the number of bytes requested. This doesn't work with raw pointers
+// since sizeof() doesn't know their actual length, so only use this to check
+// statically-allocated arrays with known sizes.
+#define STATIC_ALLOC_ENSURE_ARRAY_SIZE(A, N)                                 \
+  do {                                                                       \
+    if (sizeof(A) < (N)) {                                                   \
+      error_reporter->Report(#A " too small (%d bytes, wanted %d) at %s:%d", \
+                             sizeof(A), (N), __FILE__, __LINE__);            \
+      return 0;                                                              \
+    }                                                                        \
+  } while (0)
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/tiny_conv_micro_features_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i tiny_conv.tflite > tiny_conv_simple_features_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+
+extern const unsigned char g_tiny_conv_micro_features_model_data[];
+extern const int g_tiny_conv_micro_features_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/yes_micro_features_data.cpp

@@ -0,0 +1,165 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "yes_micro_features_data.h"
+
+/* File automatically created by
+ * tensorflow/examples/speech_commands/wav_to_features.py \
+ * --sample_rate=16000 \
+ * --clip_duration_ms=1000 \
+ * --window_size_ms=30 \
+ * --window_stride_ms=20 \
+ * --feature_bin_count=40 \
+ * --quantize=1 \
+ * --preprocess="micro" \
+ * --input_wav="speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav" \
+ * --output_c_file="yes_micro_features_data.cc" \
+ */
+
+const int g_yes_micro_f2e59fea_nohash_1_width = 40;
+const int g_yes_micro_f2e59fea_nohash_1_height = 49;
+const unsigned char g_yes_micro_f2e59fea_nohash_1_data[] = {
+    244, 226, 245, 223, 234, 213, 228, 208, 194, 110, 95,  116, 102, 0,   137,
+    161, 183, 173, 137, 116, 133, 157, 151, 156, 128, 110, 128, 0,   68,  78,
+    78,  90,  68,  68,  78,  102, 95,  78,  95,  78,  210, 188, 209, 183, 204,
+    188, 201, 191, 166, 119, 90,  107, 110, 107, 175, 157, 179, 168, 182, 145,
+    152, 164, 171, 165, 136, 143, 122, 68,  0,   78,  90,  90,  110, 90,  102,
+    99,  90,  68,  78,  68,  223, 186, 179, 123, 182, 110, 196, 171, 159, 110,
+    102, 95,  90,  99,  160, 134, 125, 136, 153, 152, 164, 134, 164, 151, 141,
+    136, 99,  90,  90,  90,  78,  78,  102, 119, 102, 90,  110, 90,  68,  51,
+    177, 175, 211, 172, 183, 0,   95,  68,  129, 102, 68,  85,  114, 105, 110,
+    85,  102, 95,  140, 51,  85,  51,  95,  90,  143, 116, 90,  78,  78,  51,
+    107, 85,  68,  0,   68,  51,  90,  51,  68,  0,   164, 117, 193, 120, 156,
+    0,   138, 51,  90,  0,   51,  0,   51,  85,  0,   0,   51,  0,   0,   0,
+    0,   0,   114, 0,   85,  78,  90,  51,  0,   0,   51,  85,  99,  85,  107,
+    68,  90,  85,  78,  0,   51,  0,   110, 0,   68,  0,   0,   0,   51,  0,
+    51,  0,   0,   0,   68,  90,  107, 0,   68,  0,   0,   0,   68,  0,   51,
+    68,  0,   78,  68,  0,   51,  0,   78,  68,  90,  68,  78,  51,  51,  0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   90,  0,   0,   0,   0,
+    0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  68,
+    0,   0,   78,  0,   78,  0,   78,  0,   51,  0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   51,  0,   51,  0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  0,   51,
+    0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,
+    0,   0,   0,   0,   51,  78,  0,   0,   51,  51,  0,   0,   0,   78,  0,
+    213, 170, 192, 180, 196, 188, 173, 131, 173, 116, 137, 105, 159, 127, 0,
+    0,   0,   0,   127, 164, 165, 161, 170, 164, 185, 197, 195, 167, 134, 138,
+    159, 134, 136, 105, 51,  0,   99,  0,   51,  0,   228, 215, 229, 218, 237,
+    215, 228, 210, 237, 222, 239, 211, 208, 211, 234, 218, 220, 209, 225, 219,
+    235, 222, 245, 225, 245, 224, 243, 223, 241, 218, 237, 224, 234, 213, 221,
+    193, 197, 164, 157, 128, 227, 188, 232, 196, 220, 220, 240, 219, 234, 213,
+    234, 211, 231, 218, 233, 213, 239, 215, 228, 207, 229, 206, 224, 208, 226,
+    207, 232, 210, 225, 208, 230, 199, 227, 206, 210, 205, 218, 174, 178, 141,
+    235, 208, 220, 206, 225, 203, 233, 203, 225, 167, 205, 199, 208, 190, 221,
+    204, 223, 207, 225, 188, 225, 197, 215, 188, 199, 183, 225, 195, 224, 200,
+    216, 178, 208, 188, 215, 202, 214, 183, 176, 140, 198, 150, 211, 194, 203,
+    120, 175, 188, 204, 189, 219, 192, 223, 202, 216, 186, 203, 185, 210, 182,
+    214, 183, 204, 170, 204, 125, 184, 187, 206, 185, 198, 182, 210, 161, 202,
+    198, 218, 173, 145, 120, 188, 183, 205, 168, 200, 170, 210, 177, 187, 190,
+    209, 193, 193, 166, 210, 162, 175, 119, 174, 147, 182, 161, 181, 134, 176,
+    143, 187, 165, 186, 149, 185, 141, 192, 181, 202, 123, 170, 143, 144, 78,
+    149, 0,   208, 182, 170, 78,  170, 0,   117, 51,  156, 99,  195, 170, 200,
+    130, 152, 68,  175, 141, 173, 134, 194, 132, 189, 164, 198, 134, 173, 117,
+    171, 149, 183, 181, 185, 99,  153, 117, 125, 0,   166, 0,   173, 117, 144,
+    0,   117, 102, 188, 120, 193, 166, 197, 68,  163, 119, 169, 99,  134, 0,
+    162, 0,   164, 68,  171, 116, 126, 0,   120, 68,  68,  0,   105, 0,   159,
+    95,  150, 51,  90,  85,  0,   0,   131, 0,   105, 0,   145, 51,  170, 51,
+    120, 0,   107, 0,   145, 85,  160, 0,   85,  0,   0,   51,  149, 0,   78,
+    0,   0,   0,   0,   0,   0,   0,   90,  0,   112, 0,   78,  102, 122, 0,
+    0,   0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   0,   0,   112,
+    0,   164, 120, 143, 0,   0,   0,   0,   0,   51,  0,   90,  0,   78,  0,
+    0,   0,   0,   0,   110, 0,   139, 0,   112, 51,  0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   102, 0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   107,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   78,  0,   51,  0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   127, 110, 133, 0,   167, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   132, 0,   190,
+    194, 202, 0,   197, 187, 161, 0,   0,   0,   0,   0,   0,   0,   0,   0,
+    214, 213, 223, 203, 218, 189, 200, 122, 78,  0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   191, 210, 231, 197, 226, 217, 238, 216, 236, 207,
+    199, 0,   0,   0,   0,   0,   107, 122, 155, 160, 214, 215, 236, 202, 235,
+    203, 225, 191, 203, 188, 199, 194, 212, 127, 51,  0,   174, 188, 219, 196,
+    228, 221, 240, 207, 235, 220, 241, 219, 237, 207, 212, 142, 95,  0,   139,
+    78,  162, 177, 197, 183, 211, 199, 235, 208, 238, 215, 227, 207, 211, 201,
+    224, 213, 226, 192, 213, 170, 223, 205, 234, 221, 245, 225, 242, 220, 245,
+    221, 239, 221, 238, 213, 226, 180, 159, 112, 176, 159, 208, 202, 213, 191,
+    205, 191, 225, 197, 238, 219, 224, 201, 227, 200, 221, 201, 225, 203, 212,
+    195, 229, 210, 228, 210, 239, 216, 226, 212, 233, 205, 225, 200, 229, 207,
+    222, 151, 147, 119, 179, 185, 230, 218, 223, 192, 202, 136, 205, 177, 223,
+    204, 228, 215, 232, 209, 221, 189, 221, 205, 209, 200, 226, 209, 229, 205,
+    235, 192, 209, 198, 228, 190, 206, 185, 207, 187, 214, 175, 177, 184, 220,
+    195, 214, 207, 230, 184, 205, 159, 208, 184, 189, 169, 224, 213, 219, 199,
+    229, 203, 216, 205, 222, 204, 224, 206, 231, 208, 231, 176, 197, 184, 216,
+    193, 211, 139, 212, 195, 231, 164, 166, 195, 217, 182, 208, 190, 217, 179,
+    205, 68,  182, 119, 195, 168, 182, 136, 204, 179, 193, 158, 182, 140, 188,
+    154, 197, 169, 190, 99,  184, 0,   125, 0,   131, 0,   99,  68,  179, 85,
+    190, 184, 213, 203, 223, 202, 212, 190, 209, 138, 178, 0,   159, 51,  128,
+    51,  105, 0,   139, 51,  179, 125, 185, 114, 171, 128, 175, 132, 181, 174,
+    155, 0,   0,   0,   90,  0,   125, 0,   176, 188, 227, 217, 244, 215, 234,
+    221, 239, 192, 224, 210, 0,   0,   134, 0,   51,  0,   105, 0,   105, 0,
+    143, 90,  192, 119, 175, 147, 141, 51,  184, 110, 85,  0,   0,   0,   0,
+    0,   0,   0,   151, 139, 201, 203, 232, 203, 226, 208, 236, 206, 230, 212,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   169, 0,   119,
+    0,   78,  0,   0,   0,   0,   0,   0,   0,   0,   0,   68,  0,   0,   133,
+    200, 180, 220, 197, 228, 201, 221, 184, 213, 193, 110, 0,   0,   0,   0,
+    0,   0,   0,   0,   0,   78,  0,   164, 0,   0,   0,   0,   0,   107, 0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   150, 164, 202, 182, 224,
+    197, 211, 179, 212, 193, 134, 0,   0,   0,   0,   0,   0,   0,   0,   0,
+    85,  0,   150, 0,   85,  0,   95,  0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   102, 90,  193, 160, 203, 164, 200, 178, 205, 174,
+    116, 0,   0,   0,   0,   0,   0,   0,   0,   0,   120, 114, 123, 0,   114,
+    0,   145, 68,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    102, 68,  199, 170, 195, 180, 208, 176, 200, 164, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   110, 0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   142, 102, 172, 110, 186,
+    167, 185, 147, 189, 154, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   177, 0,   158, 136, 197, 155, 189, 166,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    85,  0,   155, 90,  175, 117, 175, 138, 202, 165, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  0,   139,
+    0,   120, 68,  51,  123, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   119, 0,   78,  0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ATOM-Echo/yes_micro_features_data.h

@@ -0,0 +1,23 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
+
+extern const int g_yes_micro_f2e59fea_nohash_1_width;
+extern const int g_yes_micro_f2e59fea_nohash_1_height;
+extern const unsigned char g_yes_micro_f2e59fea_nohash_1_data[];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/audio_provider.cpp

@@ -0,0 +1,177 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "audio_provider.h"
+#include "micro_model_settings.h"
+#include <Arduino.h>
+#include <driver/i2s.h>
+
+#define I2S_NUM           I2S_NUM_0           // 0 or 1
+#define I2S_SAMPLE_RATE   16000
+
+#define I2S_PIN_CLK       26
+#define I2S_PIN_WS        32
+#define I2S_PIN_DOUT      I2S_PIN_NO_CHANGE
+#define I2S_PIN_DIN       33
+
+#define BUFFER_SIZE       512
+
+void CaptureSamples();
+extern QueueHandle_t xQueueAudioWave;
+
+namespace {
+bool g_is_audio_initialized = false;
+// An internal buffer able to fit 16x our sample size
+constexpr int kAudioCaptureBufferSize = BUFFER_SIZE * 16;
+int16_t g_audio_capture_buffer[kAudioCaptureBufferSize];
+// A buffer that holds our output
+int16_t g_audio_output_buffer[kMaxAudioSampleSize];
+// Mark as volatile so we can check in a while loop to see if
+// any samples have arrived yet.
+volatile int32_t g_latest_audio_timestamp = 0;
+// Our callback buffer for collecting a chunk of data
+volatile int16_t recording_buffer[BUFFER_SIZE];
+}  // namespace
+
+void InitI2S()
+{
+  i2s_config_t i2s_config = {
+    .mode                 = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_RX),
+    .sample_rate          = I2S_SAMPLE_RATE,
+    .bits_per_sample      = I2S_BITS_PER_SAMPLE_16BIT,
+    .channel_format       = I2S_CHANNEL_FMT_ONLY_LEFT,
+    .communication_format = I2S_COMM_FORMAT_I2S,
+    .intr_alloc_flags     = ESP_INTR_FLAG_LEVEL1,
+    .dma_buf_count        = 4,
+    .dma_buf_len          = 256,
+    .use_apll             = false,
+    .tx_desc_auto_clear   = false,
+    .fixed_mclk           = 0
+  };
+  i2s_pin_config_t pin_config = {
+    .bck_io_num           = I2S_PIN_CLK,
+    .ws_io_num            = I2S_PIN_WS,
+    .data_out_num         = I2S_PIN_DOUT,
+    .data_in_num          = I2S_PIN_DIN,
+  };
+
+  i2s_driver_install(I2S_NUM, &i2s_config, 0, NULL);
+  i2s_set_pin(I2S_NUM, &pin_config);
+  i2s_set_clk(I2S_NUM, I2S_SAMPLE_RATE, I2S_BITS_PER_SAMPLE_16BIT, I2S_CHANNEL_MONO);
+}
+
+void AudioRecordingTask(void *pvParameters) {
+  static uint16_t audio_idx = 0;
+  size_t bytes_read;
+  int16_t i2s_data;
+  int16_t sample;
+
+  while (1) {
+
+    if (audio_idx >= BUFFER_SIZE) {
+      xQueueSend(xQueueAudioWave, &sample, 0);
+      CaptureSamples();
+      audio_idx = 0;
+    }
+
+    i2s_read(I2S_NUM_0, &i2s_data, 2, &bytes_read, portMAX_DELAY );
+
+    if (bytes_read > 0) {
+      //sample = (0xfff - (i2s_data & 0xfff)) - 0x800;
+      sample = i2s_data;
+      recording_buffer[audio_idx] = sample;
+      audio_idx++;
+    }
+  }
+}
+
+void CaptureSamples() {
+  // This is how many bytes of new data we have each time this is called
+  const int number_of_samples = BUFFER_SIZE;
+  // Calculate what timestamp the last audio sample represents
+  const int32_t time_in_ms =
+    g_latest_audio_timestamp +
+    (number_of_samples / (kAudioSampleFrequency / 1000));
+  // Determine the index, in the history of all samples, of the last sample
+  const int32_t start_sample_offset =
+    g_latest_audio_timestamp * (kAudioSampleFrequency / 1000);
+  // Determine the index of this sample in our ring buffer
+  const int capture_index = start_sample_offset % kAudioCaptureBufferSize;
+  // Read the data to the correct place in our buffer, note 2 bytes per buffer entry
+  memcpy(g_audio_capture_buffer + capture_index, (void *)recording_buffer, BUFFER_SIZE * 2);
+  // This is how we let the outside world know that new audio data has arrived.
+  g_latest_audio_timestamp = time_in_ms;
+
+  //int peak = (max_audio - min_audio);
+  //Serial.printf("peak-to-peak:  %6d\n", peak);
+}
+
+TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) {
+  delay(10);
+
+  InitI2S();
+
+  xTaskCreatePinnedToCore(AudioRecordingTask, "AudioRecordingTask", 2048, NULL, 10, NULL, 0);
+
+  // Block until we have our first audio sample
+  while (!g_latest_audio_timestamp) {
+    delay(1);
+  }
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples) {
+  // Set everything up to start receiving audio
+  if (!g_is_audio_initialized) {
+    TfLiteStatus init_status = InitAudioRecording(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    g_is_audio_initialized = true;
+  }
+  // This next part should only be called when the main thread notices that the
+  // latest audio sample data timestamp has changed, so that there's new data
+  // in the capture ring buffer. The ring buffer will eventually wrap around and
+  // overwrite the data, but the assumption is that the main thread is checking
+  // often enough and the buffer is large enough that this call will be made
+  // before that happens.
+
+  // Determine the index, in the history of all samples, of the first
+  // sample we want
+  const int start_offset = start_ms * (kAudioSampleFrequency / 1000);
+  // Determine how many samples we want in total
+  const int duration_sample_count =
+    duration_ms * (kAudioSampleFrequency / 1000);
+  for (int i = 0; i < duration_sample_count; ++i) {
+    // For each sample, transform its index in the history of all samples into
+    // its index in g_audio_capture_buffer
+    const int capture_index = (start_offset + i) % kAudioCaptureBufferSize;
+    // Write the sample to the output buffer
+    g_audio_output_buffer[i] = g_audio_capture_buffer[capture_index];
+  }
+
+  // Set pointers to provide access to the audio
+  *audio_samples_size = kMaxAudioSampleSize;
+  *audio_samples = g_audio_output_buffer;
+
+  return kTfLiteOk;
+}
+
+int32_t LatestAudioTimestamp() {
+  return g_latest_audio_timestamp;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/audio_provider.h

@@ -0,0 +1,46 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// This is an abstraction around an audio source like a microphone, and is
+// expected to return 16-bit PCM sample data for a given point in time. The
+// sample data itself should be used as quickly as possible by the caller, since
+// to allow memory optimizations there are no guarantees that the samples won't
+// be overwritten by new data in the future. In practice, implementations should
+// ensure that there's a reasonable time allowed for clients to access the data
+// before any reuse.
+// The reference implementation can have no platform-specific dependencies, so
+// it just returns an array filled with zeros. For real applications, you should
+// ensure there's a specialized implementation that accesses hardware APIs.
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples);
+
+// Returns the time that audio data was last captured in milliseconds. There's
+// no contract about what time zero represents, the accuracy, or the granularity
+// of the result. Subsequent calls will generally not return a lower value, but
+// even that's not guaranteed if there's an overflow wraparound.
+// The reference implementation of this function just returns a constantly
+// incrementing value for each call, since it would need a non-portable platform
+// call to access time information. For real applications, you'll need to write
+// your own platform-specific implementation.
+int32_t LatestAudioTimestamp();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/command_responder.cpp

@@ -0,0 +1,75 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "command_responder.h"
+
+#include <Arduino.h>
+
+int dispMode = 0;
+
+void InitResponder() {
+  Serial.begin(115200);
+}
+
+namespace {
+enum {
+  COMMAND_SILENCE,
+  COMMAND_UNKNOWN,
+  COMMAND_YES,
+  COMMAND_NO,
+
+  COMMAND_MAX
+};
+uint8_t scoreList[COMMAND_MAX];
+uint8_t lastCommand;
+int8_t lastCommandTime;
+}
+
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command) {
+  static int32_t last_timestamp = 0;
+
+  // Score List Update
+  uint8_t command = COMMAND_SILENCE;
+  memset(scoreList, 0, sizeof(scoreList));
+  if (strcmp(found_command, "silence") == 0) {
+    command = COMMAND_SILENCE;
+  } else   if (strcmp(found_command, "unknown") == 0) {
+    command = COMMAND_UNKNOWN;
+  } else   if (strcmp(found_command, "yes") == 0) {
+    command = COMMAND_YES;
+  } else   if (strcmp(found_command, "no") == 0) {
+    command = COMMAND_NO;
+  }
+  scoreList[command] = score;
+
+  // New Command
+  if (is_new_command) {
+    lastCommand = command;
+    lastCommandTime = 3;
+  }
+
+  Serial.printf("current_time(%d) found_command(%s) score(%d) is_new_command(%d)\n", current_time, found_command, score, is_new_command);
+}
+
+int drawWaveX = 160;
+int drawWaveMin = 1000;
+int drawWaveMax = -1000;
+void drawWave(int16_t value) {
+}
+
+void drawInput(uint8_t *uint8) {
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/command_responder.h

@@ -0,0 +1,36 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Provides an interface to take an action based on an audio command.
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Called every time the results of an audio recognition run are available. The
+// human-readable name of any recognized command is in the `found_command`
+// argument, `score` has the numerical confidence, and `is_new_command` is set
+// if the previous command was different to this one.
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command);
+
+void InitResponder();
+void drawWave(int16_t value);
+void drawInput(uint8_t *uint8);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/feature_provider.cpp

@@ -0,0 +1,118 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "feature_provider.h"
+
+#include "audio_provider.h"
+#include "micro_features_generator.h"
+#include "micro_model_settings.h"
+
+FeatureProvider::FeatureProvider(int feature_size, uint8_t* feature_data)
+    : feature_size_(feature_size),
+      feature_data_(feature_data),
+      is_first_run_(true) {
+  // Initialize the feature data to default values.
+  for (int n = 0; n < feature_size_; ++n) {
+    feature_data_[n] = 0;
+  }
+}
+
+FeatureProvider::~FeatureProvider() {}
+
+TfLiteStatus FeatureProvider::PopulateFeatureData(
+    tflite::ErrorReporter* error_reporter, int32_t last_time_in_ms,
+    int32_t time_in_ms, int* how_many_new_slices) {
+  if (feature_size_ != kFeatureElementCount) {
+    error_reporter->Report("Requested feature_data_ size %d doesn't match %d",
+                           feature_size_, kFeatureElementCount);
+    return kTfLiteError;
+  }
+
+  // Quantize the time into steps as long as each window stride, so we can
+  // figure out which audio data we need to fetch.
+  const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
+  const int current_step = (time_in_ms / kFeatureSliceStrideMs);
+
+  int slices_needed = current_step - last_step;
+  // If this is the first call, make sure we don't use any cached information.
+  if (is_first_run_) {
+    TfLiteStatus init_status = InitializeMicroFeatures(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    is_first_run_ = false;
+    slices_needed = kFeatureSliceCount;
+  }
+  if (slices_needed > kFeatureSliceCount) {
+    slices_needed = kFeatureSliceCount;
+  }
+  *how_many_new_slices = slices_needed;
+
+  const int slices_to_keep = kFeatureSliceCount - slices_needed;
+  const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
+  // If we can avoid recalculating some slices, just move the existing data
+  // up in the spectrogram, to perform something like this:
+  // last time = 80ms          current time = 120ms
+  // +-----------+             +-----------+
+  // | data@20ms |         --> | data@60ms |
+  // +-----------+       --    +-----------+
+  // | data@40ms |     --  --> | data@80ms |
+  // +-----------+   --  --    +-----------+
+  // | data@60ms | --  --      |  <empty>  |
+  // +-----------+   --        +-----------+
+  // | data@80ms | --          |  <empty>  |
+  // +-----------+             +-----------+
+  if (slices_to_keep > 0) {
+    for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
+      uint8_t* dest_slice_data =
+          feature_data_ + (dest_slice * kFeatureSliceSize);
+      const int src_slice = dest_slice + slices_to_drop;
+      const uint8_t* src_slice_data =
+          feature_data_ + (src_slice * kFeatureSliceSize);
+      for (int i = 0; i < kFeatureSliceSize; ++i) {
+        dest_slice_data[i] = src_slice_data[i];
+      }
+    }
+  }
+  // Any slices that need to be filled in with feature data have their
+  // appropriate audio data pulled, and features calculated for that slice.
+  if (slices_needed > 0) {
+    for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount;
+         ++new_slice) {
+      const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
+      const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
+      int16_t* audio_samples = nullptr;
+      int audio_samples_size = 0;
+      // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
+      GetAudioSamples(error_reporter, (slice_start_ms > 0 ? slice_start_ms : 0),
+                      kFeatureSliceDurationMs, &audio_samples_size,
+                      &audio_samples);
+      if (audio_samples_size < kMaxAudioSampleSize) {
+        error_reporter->Report("Audio data size %d too small, want %d",
+                               audio_samples_size, kMaxAudioSampleSize);
+        return kTfLiteError;
+      }
+      uint8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
+      size_t num_samples_read;
+      TfLiteStatus generate_status = GenerateMicroFeatures(
+          error_reporter, audio_samples, audio_samples_size, kFeatureSliceSize,
+          new_slice_data, &num_samples_read);
+      if (generate_status != kTfLiteOk) {
+        return generate_status;
+      }
+    }
+  }
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/feature_provider.h

@@ -0,0 +1,52 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Binds itself to an area of memory intended to hold the input features for an
+// audio-recognition neural network model, and fills that data area with the
+// features representing the current audio input, for example from a microphone.
+// The audio features themselves are a two-dimensional array, made up of
+// horizontal slices representing the frequencies at one point in time, stacked
+// on top of each other to form a spectrogram showing how those frequencies
+// changed over time.
+class FeatureProvider {
+ public:
+  // Create the provider, and bind it to an area of memory. This memory should
+  // remain accessible for the lifetime of the provider object, since subsequent
+  // calls will fill it with feature data. The provider does no memory
+  // management of this data.
+  FeatureProvider(int feature_size, uint8_t* feature_data);
+  ~FeatureProvider();
+
+  // Fills the feature data with information from audio inputs, and returns how
+  // many feature slices were updated.
+  TfLiteStatus PopulateFeatureData(tflite::ErrorReporter* error_reporter,
+                                   int32_t last_time_in_ms, int32_t time_in_ms,
+                                   int* how_many_new_slices);
+
+ private:
+  int feature_size_;
+  uint8_t* feature_data_;
+  // Make sure we don't try to use cached information if this is the first call
+  // into the provider.
+  bool is_first_run_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/micro_features_generator.cpp

@@ -0,0 +1,102 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_features_generator.h"
+
+#include <cmath>
+#include <cstring>
+
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"
+
+// Configure FFT to output 16 bit fixed point.
+#define FIXED_POINT 16
+
+namespace {
+
+FrontendState g_micro_features_state;
+bool g_is_first_time = true;
+
+}  // namespace
+
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter) {
+  FrontendConfig config;
+  config.window.size_ms = kFeatureSliceDurationMs;
+  config.window.step_size_ms = kFeatureSliceStrideMs;
+  config.noise_reduction.smoothing_bits = 10;
+  config.filterbank.num_channels = kFeatureSliceSize;
+  config.filterbank.lower_band_limit = 125.0;
+  config.filterbank.upper_band_limit = 7500.0;
+  config.noise_reduction.smoothing_bits = 10;
+  config.noise_reduction.even_smoothing = 0.025;
+  config.noise_reduction.odd_smoothing = 0.06;
+  config.noise_reduction.min_signal_remaining = 0.05;
+  config.pcan_gain_control.enable_pcan = 1;
+  config.pcan_gain_control.strength = 0.95;
+  config.pcan_gain_control.offset = 80.0;
+  config.pcan_gain_control.gain_bits = 21;
+  config.log_scale.enable_log = 1;
+  config.log_scale.scale_shift = 6;
+  if (!FrontendPopulateState(&config, &g_micro_features_state,
+                             kAudioSampleFrequency)) {
+    error_reporter->Report("FrontendPopulateState() failed");
+    return kTfLiteError;
+  }
+  g_is_first_time = true;
+  return kTfLiteOk;
+}
+
+// This is not exposed in any header, and is only used for testing, to ensure
+// that the state is correctly set up before generating results.
+void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) {
+  for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
+    g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
+  }
+}
+
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read) {
+  const int16_t* frontend_input;
+  if (g_is_first_time) {
+    frontend_input = input;
+    g_is_first_time = false;
+  } else {
+    frontend_input = input + 160;
+  }
+  FrontendOutput frontend_output = FrontendProcessSamples(
+      &g_micro_features_state, frontend_input, input_size, num_samples_read);
+
+  for (int i = 0; i < frontend_output.size; ++i) {
+    // These scaling values are derived from those used in input_data.py in the
+    // training pipeline.
+    constexpr int32_t value_scale = (10 * 255);
+    constexpr int32_t value_div = (256 * 26);
+    int32_t value =
+        ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
+        value_div;
+    if (value < 0) {
+      value = 0;
+    }
+    if (value > 255) {
+      value = 255;
+    }
+    output[i] = value;
+  }
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/micro_features_generator.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Sets up any resources needed for the feature generation pipeline.
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter);
+
+// Converts audio sample data into a more compact form that's appropriate for
+// feeding into a neural network.
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/micro_model_settings.cpp

@@ -0,0 +1,23 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_model_settings.h"
+
+const char* kCategoryLabels[kCategoryCount] = {
+    "silence",
+    "unknown",
+    "yes",
+    "no",
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/micro_model_settings.h

@@ -0,0 +1,41 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+
+// Keeping these as constant expressions allow us to allocate fixed-sized arrays
+// on the stack for our working memory.
+
+// The size of the input time series data we pass to the FFT to produce the
+// frequency information. This has to be a power of two, and since we're dealing
+// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
+constexpr int kMaxAudioSampleSize = 512;
+constexpr int kAudioSampleFrequency = 16000;
+
+// All of these values are derived from the values used during model training,
+// if you change your model you'll need to update these constants.
+constexpr int kFeatureSliceSize = 40;
+constexpr int kFeatureSliceCount = 49;
+constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
+constexpr int kFeatureSliceStrideMs = 20;
+constexpr int kFeatureSliceDurationMs = 30;
+
+constexpr int kCategoryCount = 4;
+constexpr int kSilenceIndex = 0;
+constexpr int kUnknownIndex = 1;
+extern const char* kCategoryLabels[kCategoryCount];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/micro_speech_ESP-EYE.ino

@@ -0,0 +1,195 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "audio_provider.h"
+#include "command_responder.h"
+#include "feature_provider.h"
+#include "micro_model_settings.h"
+#include "tiny_conv_micro_features_model_data.h"
+#include "recognize_commands.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+FeatureProvider* feature_provider = nullptr;
+RecognizeCommands* recognizer = nullptr;
+int32_t previous_time = 0;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 10 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+}  // namespace
+
+QueueHandle_t xQueueAudioWave;
+#define QueueAudioWaveSize 32
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  xQueueAudioWave = xQueueCreate(QueueAudioWaveSize, sizeof(int16_t));
+  
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_tiny_conv_micro_features_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+      "Model provided is schema version %d not equal "
+      "to supported version %d.",
+      model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  //
+  // tflite::ops::micro::AllOpsResolver resolver;
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+    tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_FULLY_CONNECTED,
+    tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with.
+  static tflite::MicroInterpreter static_interpreter(
+    model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+    error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    error_reporter->Report("AllocateTensors() failed");
+    return;
+  }
+
+  // Get information about the memory area to use for the model's input.
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != kFeatureSliceCount) ||
+      (model_input->dims->data[2] != kFeatureSliceSize) ||
+      (model_input->type != kTfLiteUInt8)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  // Prepare to access the audio spectrograms from a microphone or other source
+  // that will provide the inputs to the neural network.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static FeatureProvider static_feature_provider(kFeatureElementCount,
+      model_input->data.uint8);
+  feature_provider = &static_feature_provider;
+
+  static RecognizeCommands static_recognizer(error_reporter);
+  recognizer = &static_recognizer;
+
+  previous_time = 0;
+
+  InitResponder();
+
+  Serial.printf("model_input->name          : %s\n", model_input->name);
+  Serial.printf("model_input->type          : %d\n", model_input->type);
+  Serial.printf("model_input->bytes         : %d\n", model_input->bytes);
+  Serial.printf("model_input->dims->size    : %d\n", model_input->dims->size);
+  Serial.printf("model_input->dims->data[0] : %d\n", model_input->dims->data[0]); // 1
+  Serial.printf("model_input->dims->data[1] : %d\n", model_input->dims->data[1]); // kFeatureSliceCount
+  Serial.printf("model_input->dims->data[2] : %d\n", model_input->dims->data[2]); // kFeatureSliceSize
+}
+
+// The name of this function is important for Arduino compatibility.
+void loop() {
+  int16_t wave = 0;
+  for (int i = 0; i < QueueAudioWaveSize; i++) {
+    if (xQueueReceive(xQueueAudioWave, &wave, 0) == pdTRUE) {
+      drawWave(wave);
+    }
+  }  
+
+  // Fetch the spectrogram for the current time.
+  const int32_t current_time = LatestAudioTimestamp();
+  int how_many_new_slices = 0;
+  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
+                                  error_reporter, previous_time, current_time, &how_many_new_slices);
+  if (feature_status != kTfLiteOk) {
+    error_reporter->Report("Feature generation failed");
+    delay(1);
+    return;
+  }
+  previous_time = current_time;
+  // If no new audio samples have been received since last time, don't bother
+  // running the network model.
+  if (how_many_new_slices == 0) {
+    delay(1);
+    return;
+  }
+
+  // Run the model on the spectrogram input and make sure it succeeds.
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed");
+    delay(1);
+    return;
+  }
+
+  // Obtain a pointer to the output tensor
+  TfLiteTensor* output = interpreter->output(0);
+  // Determine whether a command was recognized based on the output of inference
+  const char* found_command = nullptr;
+  uint8_t score = 0;
+  bool is_new_command = false;
+  TfLiteStatus process_status = recognizer->ProcessLatestResults(
+                                  output, current_time, &found_command, &score, &is_new_command);
+  if (process_status != kTfLiteOk) {
+    error_reporter->Report("RecognizeCommands::ProcessLatestResults() failed");
+    delay(1);
+    return;
+  }
+  // Do something based on the recognized command. The default implementation
+  // just prints to the error console, but you should replace this with your
+  // own function for a real application.
+  RespondToCommand(error_reporter, current_time, found_command, score,
+                   is_new_command);
+
+  drawInput(model_input->data.uint8);
+
+  delay(1);
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/no_micro_features_data.cpp

@@ -0,0 +1,165 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "no_micro_features_data.h"
+
+/* File automatically created by
+ * tensorflow/examples/speech_commands/wav_to_features.py \
+ * --sample_rate=16000 \
+ * --clip_duration_ms=1000 \
+ * --window_size_ms=30 \
+ * --window_stride_ms=20 \
+ * --feature_bin_count=40 \
+ * --quantize=1 \
+ * --preprocess="micro" \
+ * --input_wav="speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav" \
+ * --output_c_file="/tmp/no_micro_features_data.cc" \
+ */
+
+const int g_no_micro_f9643d42_nohash_4_width = 40;
+const int g_no_micro_f9643d42_nohash_4_height = 49;
+const unsigned char g_no_micro_f9643d42_nohash_4_data[] = {
+    230, 205, 191, 203, 202, 181, 180, 194, 205, 187, 183, 197, 203, 198, 196,
+    186, 202, 159, 151, 126, 110, 138, 141, 142, 137, 148, 133, 120, 110, 126,
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+    205, 184, 206, 177, 221, 210, 226, 184, 204, 197, 218, 198, 212, 209, 213,
+    141, 172, 110, 175, 167, 180, 156, 213, 188, 192, 179, 213, 205, 204, 174,
+    200, 147, 162, 181, 203, 167, 198, 187, 210, 164, 196, 169, 189, 168, 224,
+    198, 213, 204, 198, 195, 230, 211, 221, 197, 208, 0,   0,   0,   85,  90,
+    167, 130, 175, 173, 203, 164, 193, 144, 170, 145, 185, 148, 154, 139, 198,
+    159, 180, 171, 216, 174, 178, 161, 166, 136, 216, 184, 215, 197, 199, 190,
+    228, 195, 208, 51,  117, 0,   0,   0,   0,   0,   140, 51,  135, 154, 188,
+    155, 168, 0,   90,  0,   156, 85,  110, 0,   174, 90,  172, 154, 179, 99,
+    142, 166, 179, 157, 177, 95,  192, 142, 204, 198, 217, 147, 173, 0,   112,
+    0,   0,   0,   0,   0,   0,   0,   110, 0,   107, 0,   160, 0,   148, 95,
+    172, 0,   0,   0,   116, 0,   122, 114, 170, 0,   0,   0,   0,   0,   179,
+    110, 196, 85,  205, 183, 169, 0,   99,  0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   141, 0,   112, 0,   0,   0,   134, 0,   0,   0,   0,
+    0,   0,   0,   139, 0,   0,   0,   0,   112, 186, 78,  163, 0,   169, 128,
+    174, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   95,
+    0,   105, 0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   95,  0,
+    0,   0,   0,   0,   0,   0,   119, 0,   164, 78,  0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   90,  0,   0,   68,
+    117, 0,   0,   0,   0,   0,   0,   0,   148, 0,   0,   0,   0,   0,   0,
+    0,   0,   0,   116, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   78,  0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/no_micro_features_data.h

@@ -0,0 +1,23 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+
+extern const int g_no_micro_f9643d42_nohash_4_width;
+extern const int g_no_micro_f9643d42_nohash_4_height;
+extern const unsigned char g_no_micro_f9643d42_nohash_4_data[];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/recognize_commands.cpp

@@ -0,0 +1,139 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "recognize_commands.h"
+
+#include <limits>
+
+RecognizeCommands::RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                                     int32_t average_window_duration_ms,
+                                     uint8_t detection_threshold,
+                                     int32_t suppression_ms,
+                                     int32_t minimum_count)
+    : error_reporter_(error_reporter),
+      average_window_duration_ms_(average_window_duration_ms),
+      detection_threshold_(detection_threshold),
+      suppression_ms_(suppression_ms),
+      minimum_count_(minimum_count),
+      previous_results_(error_reporter) {
+  previous_top_label_ = "silence";
+  previous_top_label_time_ = std::numeric_limits<int32_t>::min();
+}
+
+TfLiteStatus RecognizeCommands::ProcessLatestResults(
+    const TfLiteTensor* latest_results, const int32_t current_time_ms,
+    const char** found_command, uint8_t* score, bool* is_new_command) {
+  if ((latest_results->dims->size != 2) ||
+      (latest_results->dims->data[0] != 1) ||
+      (latest_results->dims->data[1] != kCategoryCount)) {
+    error_reporter_->Report(
+        "The results for recognition should contain %d elements, but there are "
+        "%d in an %d-dimensional shape",
+        kCategoryCount, latest_results->dims->data[1],
+        latest_results->dims->size);
+    return kTfLiteError;
+  }
+
+  if (latest_results->type != kTfLiteUInt8) {
+    error_reporter_->Report(
+        "The results for recognition should be uint8 elements, but are %d",
+        latest_results->type);
+    return kTfLiteError;
+  }
+
+  if ((!previous_results_.empty()) &&
+      (current_time_ms < previous_results_.front().time_)) {
+    error_reporter_->Report(
+        "Results must be fed in increasing time order, but received a "
+        "timestamp of %d that was earlier than the previous one of %d",
+        current_time_ms, previous_results_.front().time_);
+    return kTfLiteError;
+  }
+
+  // Add the latest results to the head of the queue.
+  previous_results_.push_back({current_time_ms, latest_results->data.uint8});
+
+  // Prune any earlier results that are too old for the averaging window.
+  const int64_t time_limit = current_time_ms - average_window_duration_ms_;
+  while ((!previous_results_.empty()) &&
+         previous_results_.front().time_ < time_limit) {
+    previous_results_.pop_front();
+  }
+
+  // If there are too few results, assume the result will be unreliable and
+  // bail.
+  const int64_t how_many_results = previous_results_.size();
+  const int64_t earliest_time = previous_results_.front().time_;
+  const int64_t samples_duration = current_time_ms - earliest_time;
+  if ((how_many_results < minimum_count_) ||
+      (samples_duration < (average_window_duration_ms_ / 4))) {
+    *found_command = previous_top_label_;
+    *score = 0;
+    *is_new_command = false;
+    return kTfLiteOk;
+  }
+
+  // Calculate the average score across all the results in the window.
+  int32_t average_scores[kCategoryCount];
+  for (int offset = 0; offset < previous_results_.size(); ++offset) {
+    PreviousResultsQueue::Result previous_result =
+        previous_results_.from_front(offset);
+    const uint8_t* scores = previous_result.scores_;
+    for (int i = 0; i < kCategoryCount; ++i) {
+      if (offset == 0) {
+        average_scores[i] = scores[i];
+      } else {
+        average_scores[i] += scores[i];
+      }
+    }
+  }
+  for (int i = 0; i < kCategoryCount; ++i) {
+    average_scores[i] /= how_many_results;
+  }
+
+  // Find the current highest scoring category.
+  int current_top_index = 0;
+  int32_t current_top_score = 0;
+  for (int i = 0; i < kCategoryCount; ++i) {
+    if (average_scores[i] > current_top_score) {
+      current_top_score = average_scores[i];
+      current_top_index = i;
+    }
+  }
+  const char* current_top_label = kCategoryLabels[current_top_index];
+
+  // If we've recently had another label trigger, assume one that occurs too
+  // soon afterwards is a bad result.
+  int64_t time_since_last_top;
+  if ((previous_top_label_ == kCategoryLabels[0]) ||
+      (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
+    time_since_last_top = std::numeric_limits<int32_t>::max();
+  } else {
+    time_since_last_top = current_time_ms - previous_top_label_time_;
+  }
+  if ((current_top_score > detection_threshold_) &&
+      ((current_top_label != previous_top_label_) ||
+       (time_since_last_top > suppression_ms_))) {
+    previous_top_label_ = current_top_label;
+    previous_top_label_time_ = current_time_ms;
+    *is_new_command = true;
+  } else {
+    *is_new_command = false;
+  }
+  *found_command = current_top_label;
+  *score = current_top_score;
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/recognize_commands.h

@@ -0,0 +1,156 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+
+#include <cstdint>
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Partial implementation of std::dequeue, just providing the functionality
+// that's needed to keep a record of previous neural network results over a
+// short time period, so they can be averaged together to produce a more
+// accurate overall prediction. This doesn't use any dynamic memory allocation
+// so it's a better fit for microcontroller applications, but this does mean
+// there are hard limits on the number of results it can store.
+class PreviousResultsQueue {
+ public:
+  PreviousResultsQueue(tflite::ErrorReporter* error_reporter)
+      : error_reporter_(error_reporter), front_index_(0), size_(0) {}
+
+  // Data structure that holds an inference result, and the time when it
+  // was recorded.
+  struct Result {
+    Result() : time_(0), scores_() {}
+    Result(int32_t time, uint8_t* scores) : time_(time) {
+      for (int i = 0; i < kCategoryCount; ++i) {
+        scores_[i] = scores[i];
+      }
+    }
+    int32_t time_;
+    uint8_t scores_[kCategoryCount];
+  };
+
+  int size() { return size_; }
+  bool empty() { return size_ == 0; }
+  Result& front() { return results_[front_index_]; }
+  Result& back() {
+    int back_index = front_index_ + (size_ - 1);
+    if (back_index >= kMaxResults) {
+      back_index -= kMaxResults;
+    }
+    return results_[back_index];
+  }
+
+  void push_back(const Result& entry) {
+    if (size() >= kMaxResults) {
+      error_reporter_->Report(
+          "Couldn't push_back latest result, too many already!");
+      return;
+    }
+    size_ += 1;
+    back() = entry;
+  }
+
+  Result pop_front() {
+    if (size() <= 0) {
+      error_reporter_->Report("Couldn't pop_front result, none present!");
+      return Result();
+    }
+    Result result = front();
+    front_index_ += 1;
+    if (front_index_ >= kMaxResults) {
+      front_index_ = 0;
+    }
+    size_ -= 1;
+    return result;
+  }
+
+  // Most of the functions are duplicates of dequeue containers, but this
+  // is a helper that makes it easy to iterate through the contents of the
+  // queue.
+  Result& from_front(int offset) {
+    if ((offset < 0) || (offset >= size_)) {
+      error_reporter_->Report("Attempt to read beyond the end of the queue!");
+      offset = size_ - 1;
+    }
+    int index = front_index_ + offset;
+    if (index >= kMaxResults) {
+      index -= kMaxResults;
+    }
+    return results_[index];
+  }
+
+ private:
+  tflite::ErrorReporter* error_reporter_;
+  static constexpr int kMaxResults = 50;
+  Result results_[kMaxResults];
+
+  int front_index_;
+  int size_;
+};
+
+// This class is designed to apply a very primitive decoding model on top of the
+// instantaneous results from running an audio recognition model on a single
+// window of samples. It applies smoothing over time so that noisy individual
+// label scores are averaged, increasing the confidence that apparent matches
+// are real.
+// To use it, you should create a class object with the configuration you
+// want, and then feed results from running a TensorFlow model into the
+// processing method. The timestamp for each subsequent call should be
+// increasing from the previous, since the class is designed to process a stream
+// of data over time.
+class RecognizeCommands {
+ public:
+  // labels should be a list of the strings associated with each one-hot score.
+  // The window duration controls the smoothing. Longer durations will give a
+  // higher confidence that the results are correct, but may miss some commands.
+  // The detection threshold has a similar effect, with high values increasing
+  // the precision at the cost of recall. The minimum count controls how many
+  // results need to be in the averaging window before it's seen as a reliable
+  // average. This prevents erroneous results when the averaging window is
+  // initially being populated for example. The suppression argument disables
+  // further recognitions for a set time after one has been triggered, which can
+  // help reduce spurious recognitions.
+  explicit RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                             int32_t average_window_duration_ms = 1000,
+                             uint8_t detection_threshold = 200,
+                             int32_t suppression_ms = 1500,
+                             int32_t minimum_count = 3);
+
+  // Call this with the results of running a model on sample data.
+  TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results,
+                                    const int32_t current_time_ms,
+                                    const char** found_command, uint8_t* score,
+                                    bool* is_new_command);
+
+ private:
+  // Configuration
+  tflite::ErrorReporter* error_reporter_;
+  int32_t average_window_duration_ms_;
+  uint8_t detection_threshold_;
+  int32_t suppression_ms_;
+  int32_t minimum_count_;
+
+  // Working variables
+  PreviousResultsQueue previous_results_;
+  const char* previous_top_label_;
+  int32_t previous_top_label_time_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/static_alloc.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+
+// Checks to ensure that the C-style array passed in has a compile-time size of
+// at least the number of bytes requested. This doesn't work with raw pointers
+// since sizeof() doesn't know their actual length, so only use this to check
+// statically-allocated arrays with known sizes.
+#define STATIC_ALLOC_ENSURE_ARRAY_SIZE(A, N)                                 \
+  do {                                                                       \
+    if (sizeof(A) < (N)) {                                                   \
+      error_reporter->Report(#A " too small (%d bytes, wanted %d) at %s:%d", \
+                             sizeof(A), (N), __FILE__, __LINE__);            \
+      return 0;                                                              \
+    }                                                                        \
+  } while (0)
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/tiny_conv_micro_features_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i tiny_conv.tflite > tiny_conv_simple_features_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+
+extern const unsigned char g_tiny_conv_micro_features_model_data[];
+extern const int g_tiny_conv_micro_features_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/yes_micro_features_data.cpp

@@ -0,0 +1,165 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "yes_micro_features_data.h"
+
+/* File automatically created by
+ * tensorflow/examples/speech_commands/wav_to_features.py \
+ * --sample_rate=16000 \
+ * --clip_duration_ms=1000 \
+ * --window_size_ms=30 \
+ * --window_stride_ms=20 \
+ * --feature_bin_count=40 \
+ * --quantize=1 \
+ * --preprocess="micro" \
+ * --input_wav="speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav" \
+ * --output_c_file="yes_micro_features_data.cc" \
+ */
+
+const int g_yes_micro_f2e59fea_nohash_1_width = 40;
+const int g_yes_micro_f2e59fea_nohash_1_height = 49;
+const unsigned char g_yes_micro_f2e59fea_nohash_1_data[] = {
+    244, 226, 245, 223, 234, 213, 228, 208, 194, 110, 95,  116, 102, 0,   137,
+    161, 183, 173, 137, 116, 133, 157, 151, 156, 128, 110, 128, 0,   68,  78,
+    78,  90,  68,  68,  78,  102, 95,  78,  95,  78,  210, 188, 209, 183, 204,
+    188, 201, 191, 166, 119, 90,  107, 110, 107, 175, 157, 179, 168, 182, 145,
+    152, 164, 171, 165, 136, 143, 122, 68,  0,   78,  90,  90,  110, 90,  102,
+    99,  90,  68,  78,  68,  223, 186, 179, 123, 182, 110, 196, 171, 159, 110,
+    102, 95,  90,  99,  160, 134, 125, 136, 153, 152, 164, 134, 164, 151, 141,
+    136, 99,  90,  90,  90,  78,  78,  102, 119, 102, 90,  110, 90,  68,  51,
+    177, 175, 211, 172, 183, 0,   95,  68,  129, 102, 68,  85,  114, 105, 110,
+    85,  102, 95,  140, 51,  85,  51,  95,  90,  143, 116, 90,  78,  78,  51,
+    107, 85,  68,  0,   68,  51,  90,  51,  68,  0,   164, 117, 193, 120, 156,
+    0,   138, 51,  90,  0,   51,  0,   51,  85,  0,   0,   51,  0,   0,   0,
+    0,   0,   114, 0,   85,  78,  90,  51,  0,   0,   51,  85,  99,  85,  107,
+    68,  90,  85,  78,  0,   51,  0,   110, 0,   68,  0,   0,   0,   51,  0,
+    51,  0,   0,   0,   68,  90,  107, 0,   68,  0,   0,   0,   68,  0,   51,
+    68,  0,   78,  68,  0,   51,  0,   78,  68,  90,  68,  78,  51,  51,  0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   90,  0,   0,   0,   0,
+    0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  68,
+    0,   0,   78,  0,   78,  0,   78,  0,   51,  0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   51,  0,   51,  0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  0,   51,
+    0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,
+    0,   0,   0,   0,   51,  78,  0,   0,   51,  51,  0,   0,   0,   78,  0,
+    213, 170, 192, 180, 196, 188, 173, 131, 173, 116, 137, 105, 159, 127, 0,
+    0,   0,   0,   127, 164, 165, 161, 170, 164, 185, 197, 195, 167, 134, 138,
+    159, 134, 136, 105, 51,  0,   99,  0,   51,  0,   228, 215, 229, 218, 237,
+    215, 228, 210, 237, 222, 239, 211, 208, 211, 234, 218, 220, 209, 225, 219,
+    235, 222, 245, 225, 245, 224, 243, 223, 241, 218, 237, 224, 234, 213, 221,
+    193, 197, 164, 157, 128, 227, 188, 232, 196, 220, 220, 240, 219, 234, 213,
+    234, 211, 231, 218, 233, 213, 239, 215, 228, 207, 229, 206, 224, 208, 226,
+    207, 232, 210, 225, 208, 230, 199, 227, 206, 210, 205, 218, 174, 178, 141,
+    235, 208, 220, 206, 225, 203, 233, 203, 225, 167, 205, 199, 208, 190, 221,
+    204, 223, 207, 225, 188, 225, 197, 215, 188, 199, 183, 225, 195, 224, 200,
+    216, 178, 208, 188, 215, 202, 214, 183, 176, 140, 198, 150, 211, 194, 203,
+    120, 175, 188, 204, 189, 219, 192, 223, 202, 216, 186, 203, 185, 210, 182,
+    214, 183, 204, 170, 204, 125, 184, 187, 206, 185, 198, 182, 210, 161, 202,
+    198, 218, 173, 145, 120, 188, 183, 205, 168, 200, 170, 210, 177, 187, 190,
+    209, 193, 193, 166, 210, 162, 175, 119, 174, 147, 182, 161, 181, 134, 176,
+    143, 187, 165, 186, 149, 185, 141, 192, 181, 202, 123, 170, 143, 144, 78,
+    149, 0,   208, 182, 170, 78,  170, 0,   117, 51,  156, 99,  195, 170, 200,
+    130, 152, 68,  175, 141, 173, 134, 194, 132, 189, 164, 198, 134, 173, 117,
+    171, 149, 183, 181, 185, 99,  153, 117, 125, 0,   166, 0,   173, 117, 144,
+    0,   117, 102, 188, 120, 193, 166, 197, 68,  163, 119, 169, 99,  134, 0,
+    162, 0,   164, 68,  171, 116, 126, 0,   120, 68,  68,  0,   105, 0,   159,
+    95,  150, 51,  90,  85,  0,   0,   131, 0,   105, 0,   145, 51,  170, 51,
+    120, 0,   107, 0,   145, 85,  160, 0,   85,  0,   0,   51,  149, 0,   78,
+    0,   0,   0,   0,   0,   0,   0,   90,  0,   112, 0,   78,  102, 122, 0,
+    0,   0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   0,   0,   112,
+    0,   164, 120, 143, 0,   0,   0,   0,   0,   51,  0,   90,  0,   78,  0,
+    0,   0,   0,   0,   110, 0,   139, 0,   112, 51,  0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   102, 0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   107,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   78,  0,   51,  0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   51,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   127, 110, 133, 0,   167, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   132, 0,   190,
+    194, 202, 0,   197, 187, 161, 0,   0,   0,   0,   0,   0,   0,   0,   0,
+    214, 213, 223, 203, 218, 189, 200, 122, 78,  0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   191, 210, 231, 197, 226, 217, 238, 216, 236, 207,
+    199, 0,   0,   0,   0,   0,   107, 122, 155, 160, 214, 215, 236, 202, 235,
+    203, 225, 191, 203, 188, 199, 194, 212, 127, 51,  0,   174, 188, 219, 196,
+    228, 221, 240, 207, 235, 220, 241, 219, 237, 207, 212, 142, 95,  0,   139,
+    78,  162, 177, 197, 183, 211, 199, 235, 208, 238, 215, 227, 207, 211, 201,
+    224, 213, 226, 192, 213, 170, 223, 205, 234, 221, 245, 225, 242, 220, 245,
+    221, 239, 221, 238, 213, 226, 180, 159, 112, 176, 159, 208, 202, 213, 191,
+    205, 191, 225, 197, 238, 219, 224, 201, 227, 200, 221, 201, 225, 203, 212,
+    195, 229, 210, 228, 210, 239, 216, 226, 212, 233, 205, 225, 200, 229, 207,
+    222, 151, 147, 119, 179, 185, 230, 218, 223, 192, 202, 136, 205, 177, 223,
+    204, 228, 215, 232, 209, 221, 189, 221, 205, 209, 200, 226, 209, 229, 205,
+    235, 192, 209, 198, 228, 190, 206, 185, 207, 187, 214, 175, 177, 184, 220,
+    195, 214, 207, 230, 184, 205, 159, 208, 184, 189, 169, 224, 213, 219, 199,
+    229, 203, 216, 205, 222, 204, 224, 206, 231, 208, 231, 176, 197, 184, 216,
+    193, 211, 139, 212, 195, 231, 164, 166, 195, 217, 182, 208, 190, 217, 179,
+    205, 68,  182, 119, 195, 168, 182, 136, 204, 179, 193, 158, 182, 140, 188,
+    154, 197, 169, 190, 99,  184, 0,   125, 0,   131, 0,   99,  68,  179, 85,
+    190, 184, 213, 203, 223, 202, 212, 190, 209, 138, 178, 0,   159, 51,  128,
+    51,  105, 0,   139, 51,  179, 125, 185, 114, 171, 128, 175, 132, 181, 174,
+    155, 0,   0,   0,   90,  0,   125, 0,   176, 188, 227, 217, 244, 215, 234,
+    221, 239, 192, 224, 210, 0,   0,   134, 0,   51,  0,   105, 0,   105, 0,
+    143, 90,  192, 119, 175, 147, 141, 51,  184, 110, 85,  0,   0,   0,   0,
+    0,   0,   0,   151, 139, 201, 203, 232, 203, 226, 208, 236, 206, 230, 212,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   169, 0,   119,
+    0,   78,  0,   0,   0,   0,   0,   0,   0,   0,   0,   68,  0,   0,   133,
+    200, 180, 220, 197, 228, 201, 221, 184, 213, 193, 110, 0,   0,   0,   0,
+    0,   0,   0,   0,   0,   78,  0,   164, 0,   0,   0,   0,   0,   107, 0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   150, 164, 202, 182, 224,
+    197, 211, 179, 212, 193, 134, 0,   0,   0,   0,   0,   0,   0,   0,   0,
+    85,  0,   150, 0,   85,  0,   95,  0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   102, 90,  193, 160, 203, 164, 200, 178, 205, 174,
+    116, 0,   0,   0,   0,   0,   0,   0,   0,   0,   120, 114, 123, 0,   114,
+    0,   145, 68,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    102, 68,  199, 170, 195, 180, 208, 176, 200, 164, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   110, 0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   142, 102, 172, 110, 186,
+    167, 185, 147, 189, 154, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   177, 0,   158, 136, 197, 155, 189, 166,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    85,  0,   155, 90,  175, 117, 175, 138, 202, 165, 0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,  0,   139,
+    0,   120, 68,  51,  123, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   119, 0,   78,  0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_ESP-EYE/yes_micro_features_data.h

@@ -0,0 +1,23 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
+
+extern const int g_yes_micro_f2e59fea_nohash_1_width;
+extern const int g_yes_micro_f2e59fea_nohash_1_height;
+extern const unsigned char g_yes_micro_f2e59fea_nohash_1_data[];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/audio_provider.cpp

@@ -0,0 +1,175 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "audio_provider.h"
+#include "micro_model_settings.h"
+#include <Arduino.h>
+#include <M5Stack.h>
+#include <driver/i2s.h>
+
+#define BACKLIGHT         32
+
+#define I2S_NUM           I2S_NUM_0           // 0 or 1
+#define I2S_SAMPLE_RATE   16000
+#define ADC_INPUT         ADC1_GPIO34_CHANNEL // ADC CHANNEL
+#define ADC_UNIT          ADC_UNIT_1          // ADC1 or ADC2
+
+#define BUFFER_SIZE       512
+
+void CaptureSamples();
+extern QueueHandle_t xQueueAudioWave;
+
+namespace {
+bool g_is_audio_initialized = false;
+// An internal buffer able to fit 16x our sample size
+constexpr int kAudioCaptureBufferSize = BUFFER_SIZE * 16;
+int16_t g_audio_capture_buffer[kAudioCaptureBufferSize];
+// A buffer that holds our output
+int16_t g_audio_output_buffer[kMaxAudioSampleSize];
+// Mark as volatile so we can check in a while loop to see if
+// any samples have arrived yet.
+volatile int32_t g_latest_audio_timestamp = 0;
+// Our callback buffer for collecting a chunk of data
+volatile int16_t recording_buffer[BUFFER_SIZE];
+}  // namespace
+
+void InitI2S()
+{
+  i2s_config_t i2s_config = {
+    .mode                 = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_RX | I2S_MODE_ADC_BUILT_IN),
+    .sample_rate          = I2S_SAMPLE_RATE,
+    .bits_per_sample      = I2S_BITS_PER_SAMPLE_16BIT,
+    .channel_format       = I2S_CHANNEL_FMT_ALL_LEFT,
+    .communication_format = I2S_COMM_FORMAT_I2S_MSB,
+    .intr_alloc_flags     = ESP_INTR_FLAG_LEVEL1,
+    .dma_buf_count        = 4,
+    .dma_buf_len          = 512,
+    .use_apll             = false,
+    .tx_desc_auto_clear   = false,
+    .fixed_mclk = 0
+  };
+
+  i2s_driver_install(I2S_NUM, &i2s_config, 0, NULL);
+  i2s_set_adc_mode(ADC_UNIT_1, ADC_INPUT);
+  i2s_set_clk(I2S_NUM, I2S_SAMPLE_RATE, I2S_BITS_PER_SAMPLE_16BIT, I2S_CHANNEL_MONO);
+  i2s_adc_enable(I2S_NUM);
+}
+
+void AudioRecordingTask(void *pvParameters) {
+  static uint16_t audio_idx = 0;
+  size_t bytes_read;
+  uint16_t i2s_data;
+  int16_t sample;
+
+  while (1) {
+
+    if (audio_idx >= BUFFER_SIZE) {
+      xQueueSend(xQueueAudioWave, &sample, 0);
+      CaptureSamples();
+      audio_idx = 0;
+    }
+
+    i2s_read(I2S_NUM_0, &i2s_data, 2, &bytes_read, portMAX_DELAY );
+
+    if (bytes_read > 0) {
+      sample = (0xfff - (i2s_data & 0xfff)) - 0x800;
+      recording_buffer[audio_idx] = sample;
+      audio_idx++;
+    }
+  }
+}
+
+void CaptureSamples() {
+  // This is how many bytes of new data we have each time this is called
+  const int number_of_samples = BUFFER_SIZE;
+  // Calculate what timestamp the last audio sample represents
+  const int32_t time_in_ms =
+    g_latest_audio_timestamp +
+    (number_of_samples / (kAudioSampleFrequency / 1000));
+  // Determine the index, in the history of all samples, of the last sample
+  const int32_t start_sample_offset =
+    g_latest_audio_timestamp * (kAudioSampleFrequency / 1000);
+  // Determine the index of this sample in our ring buffer
+  const int capture_index = start_sample_offset % kAudioCaptureBufferSize;
+  // Read the data to the correct place in our buffer, note 2 bytes per buffer entry
+  memcpy(g_audio_capture_buffer + capture_index, (void *)recording_buffer, BUFFER_SIZE * 2);
+  // This is how we let the outside world know that new audio data has arrived.
+  g_latest_audio_timestamp = time_in_ms;
+
+  //int peak = (max_audio - min_audio);
+  //Serial.printf("peak-to-peak:  %6d\n", peak);
+}
+
+TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) {
+  delay(10);
+
+  pinMode( BACKLIGHT, OUTPUT );
+  digitalWrite( BACKLIGHT, HIGH ); // This gives the least noise
+  ledcDetachPin(25);
+
+  InitI2S();
+
+  xTaskCreatePinnedToCore(AudioRecordingTask, "AudioRecordingTask", 2048, NULL, 10, NULL, 0);
+
+  // Block until we have our first audio sample
+  while (!g_latest_audio_timestamp) {
+    delay(1);
+  }
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples) {
+  // Set everything up to start receiving audio
+  if (!g_is_audio_initialized) {
+    TfLiteStatus init_status = InitAudioRecording(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    g_is_audio_initialized = true;
+  }
+  // This next part should only be called when the main thread notices that the
+  // latest audio sample data timestamp has changed, so that there's new data
+  // in the capture ring buffer. The ring buffer will eventually wrap around and
+  // overwrite the data, but the assumption is that the main thread is checking
+  // often enough and the buffer is large enough that this call will be made
+  // before that happens.
+
+  // Determine the index, in the history of all samples, of the first
+  // sample we want
+  const int start_offset = start_ms * (kAudioSampleFrequency / 1000);
+  // Determine how many samples we want in total
+  const int duration_sample_count =
+    duration_ms * (kAudioSampleFrequency / 1000);
+  for (int i = 0; i < duration_sample_count; ++i) {
+    // For each sample, transform its index in the history of all samples into
+    // its index in g_audio_capture_buffer
+    const int capture_index = (start_offset + i) % kAudioCaptureBufferSize;
+    // Write the sample to the output buffer
+    g_audio_output_buffer[i] = g_audio_capture_buffer[capture_index];
+  }
+
+  // Set pointers to provide access to the audio
+  *audio_samples_size = kMaxAudioSampleSize;
+  *audio_samples = g_audio_output_buffer;
+
+  return kTfLiteOk;
+}
+
+int32_t LatestAudioTimestamp() {
+  return g_latest_audio_timestamp;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/audio_provider.h

@@ -0,0 +1,46 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// This is an abstraction around an audio source like a microphone, and is
+// expected to return 16-bit PCM sample data for a given point in time. The
+// sample data itself should be used as quickly as possible by the caller, since
+// to allow memory optimizations there are no guarantees that the samples won't
+// be overwritten by new data in the future. In practice, implementations should
+// ensure that there's a reasonable time allowed for clients to access the data
+// before any reuse.
+// The reference implementation can have no platform-specific dependencies, so
+// it just returns an array filled with zeros. For real applications, you should
+// ensure there's a specialized implementation that accesses hardware APIs.
+TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
+                             int start_ms, int duration_ms,
+                             int* audio_samples_size, int16_t** audio_samples);
+
+// Returns the time that audio data was last captured in milliseconds. There's
+// no contract about what time zero represents, the accuracy, or the granularity
+// of the result. Subsequent calls will generally not return a lower value, but
+// even that's not guaranteed if there's an overflow wraparound.
+// The reference implementation of this function just returns a constantly
+// incrementing value for each call, since it would need a non-portable platform
+// call to access time information. For real applications, you'll need to write
+// your own platform-specific implementation.
+int32_t LatestAudioTimestamp();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/command_responder.cpp

@@ -0,0 +1,137 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include "command_responder.h"
+
+#include <M5Stack.h>
+
+void InitResponder() {
+  M5.begin();
+  M5.Lcd.fillScreen(BLACK);
+  M5.Lcd.setTextSize(2);
+  M5.Lcd.setCursor(0, 0);
+  M5.Lcd.setTextColor(YELLOW);
+  M5.Lcd.printf("Micro Speech\n");
+  M5.Lcd.setTextColor(WHITE, BLACK);
+}
+
+namespace {
+enum {
+  COMMAND_SILENCE,
+  COMMAND_UNKNOWN,
+  COMMAND_YES,
+  COMMAND_NO,
+
+  COMMAND_MAX
+};
+uint8_t scoreList[COMMAND_MAX];
+uint8_t lastCommand;
+int8_t lastCommandTime;
+}
+
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command) {
+  static int32_t last_timestamp = 0;
+
+  // Score List Update
+  uint8_t command = COMMAND_SILENCE;
+  memset(scoreList, 0, sizeof(scoreList));
+  if (strcmp(found_command, "silence") == 0) {
+    command = COMMAND_SILENCE;
+  } else   if (strcmp(found_command, "unknown") == 0) {
+    command = COMMAND_UNKNOWN;
+  } else   if (strcmp(found_command, "yes") == 0) {
+    command = COMMAND_YES;
+  } else   if (strcmp(found_command, "no") == 0) {
+    command = COMMAND_NO;
+  }
+  scoreList[command] = score;
+
+  // New Command
+  if (is_new_command) {
+    lastCommand = command;
+    lastCommandTime = 3;
+  }
+
+  Serial.printf("current_time(%d) found_command(%s) score(%d) is_new_command(%d)\n", current_time, found_command, score, is_new_command);
+  M5.Lcd.setCursor(0, 16);
+
+  if (lastCommand == COMMAND_SILENCE && 0 < lastCommandTime) {
+    M5.Lcd.setTextColor(RED, BLACK);
+  } else {
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+  M5.Lcd.printf("Silence : %3d\n", scoreList[COMMAND_SILENCE]);
+
+  if (lastCommand == COMMAND_UNKNOWN && 0 < lastCommandTime) {
+    M5.Lcd.setTextColor(RED, BLACK);
+  } else {
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+  M5.Lcd.printf("Unknown : %3d\n", scoreList[COMMAND_UNKNOWN]);
+
+  if (lastCommand == COMMAND_YES && 0 < lastCommandTime) {
+    M5.Lcd.setTextColor(RED, BLACK);
+  } else {
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+  M5.Lcd.printf("Yes     : %3d\n", scoreList[COMMAND_YES]);
+
+  if (lastCommand == COMMAND_NO && 0 < lastCommandTime) {
+    M5.Lcd.setTextColor(RED, BLACK);
+  } else {
+    M5.Lcd.setTextColor(WHITE, BLACK);
+  }
+  M5.Lcd.printf("No      : %3d\n", scoreList[COMMAND_NO]);
+  if (0 < lastCommandTime) {
+    lastCommandTime--;
+  }
+}
+
+void drawWave(int16_t value) {
+  static int drawX = 320;
+
+  static int min = -1000;
+  static int max = 1000;
+
+  if (value < min) {
+    value = min;
+  }
+  if (max < value) {
+    value = max;
+  }
+
+  int drawY = map(value, min, max, 84, 240);
+
+  M5.Lcd.drawPixel(drawX, drawY, WHITE);
+  drawX++;
+  if (320 <= drawX) {
+    drawX = 0;
+    M5.Lcd.fillRect(0, 84, 320, 240-84, BLUE);
+  }
+}
+
+void drawInput(uint8_t *uint8) {
+  for (int y = 0; y < 49; y++) {
+    for (int x = 0; x < 40; x++) {
+      int pos = y * 40 + x;
+      int drawX = 160 + y * 3;
+      int drawY = 80 - x * 2;
+      int color = (uint8[pos]>>2) << 5;
+      M5.Lcd.fillRect(drawX, drawY, 3, 2, color);
+    }
+  }
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/command_responder.h

@@ -0,0 +1,36 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Provides an interface to take an action based on an audio command.
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Called every time the results of an audio recognition run are available. The
+// human-readable name of any recognized command is in the `found_command`
+// argument, `score` has the numerical confidence, and `is_new_command` is set
+// if the previous command was different to this one.
+void RespondToCommand(tflite::ErrorReporter* error_reporter,
+                      int32_t current_time, const char* found_command,
+                      uint8_t score, bool is_new_command);
+
+void InitResponder();
+void drawWave(int16_t value);
+void drawInput(uint8_t *uint8);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/feature_provider.cpp

@@ -0,0 +1,118 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "feature_provider.h"
+
+#include "audio_provider.h"
+#include "micro_features_generator.h"
+#include "micro_model_settings.h"
+
+FeatureProvider::FeatureProvider(int feature_size, uint8_t* feature_data)
+    : feature_size_(feature_size),
+      feature_data_(feature_data),
+      is_first_run_(true) {
+  // Initialize the feature data to default values.
+  for (int n = 0; n < feature_size_; ++n) {
+    feature_data_[n] = 0;
+  }
+}
+
+FeatureProvider::~FeatureProvider() {}
+
+TfLiteStatus FeatureProvider::PopulateFeatureData(
+    tflite::ErrorReporter* error_reporter, int32_t last_time_in_ms,
+    int32_t time_in_ms, int* how_many_new_slices) {
+  if (feature_size_ != kFeatureElementCount) {
+    error_reporter->Report("Requested feature_data_ size %d doesn't match %d",
+                           feature_size_, kFeatureElementCount);
+    return kTfLiteError;
+  }
+
+  // Quantize the time into steps as long as each window stride, so we can
+  // figure out which audio data we need to fetch.
+  const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
+  const int current_step = (time_in_ms / kFeatureSliceStrideMs);
+
+  int slices_needed = current_step - last_step;
+  // If this is the first call, make sure we don't use any cached information.
+  if (is_first_run_) {
+    TfLiteStatus init_status = InitializeMicroFeatures(error_reporter);
+    if (init_status != kTfLiteOk) {
+      return init_status;
+    }
+    is_first_run_ = false;
+    slices_needed = kFeatureSliceCount;
+  }
+  if (slices_needed > kFeatureSliceCount) {
+    slices_needed = kFeatureSliceCount;
+  }
+  *how_many_new_slices = slices_needed;
+
+  const int slices_to_keep = kFeatureSliceCount - slices_needed;
+  const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
+  // If we can avoid recalculating some slices, just move the existing data
+  // up in the spectrogram, to perform something like this:
+  // last time = 80ms          current time = 120ms
+  // +-----------+             +-----------+
+  // | data@20ms |         --> | data@60ms |
+  // +-----------+       --    +-----------+
+  // | data@40ms |     --  --> | data@80ms |
+  // +-----------+   --  --    +-----------+
+  // | data@60ms | --  --      |  <empty>  |
+  // +-----------+   --        +-----------+
+  // | data@80ms | --          |  <empty>  |
+  // +-----------+             +-----------+
+  if (slices_to_keep > 0) {
+    for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
+      uint8_t* dest_slice_data =
+          feature_data_ + (dest_slice * kFeatureSliceSize);
+      const int src_slice = dest_slice + slices_to_drop;
+      const uint8_t* src_slice_data =
+          feature_data_ + (src_slice * kFeatureSliceSize);
+      for (int i = 0; i < kFeatureSliceSize; ++i) {
+        dest_slice_data[i] = src_slice_data[i];
+      }
+    }
+  }
+  // Any slices that need to be filled in with feature data have their
+  // appropriate audio data pulled, and features calculated for that slice.
+  if (slices_needed > 0) {
+    for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount;
+         ++new_slice) {
+      const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
+      const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
+      int16_t* audio_samples = nullptr;
+      int audio_samples_size = 0;
+      // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
+      GetAudioSamples(error_reporter, (slice_start_ms > 0 ? slice_start_ms : 0),
+                      kFeatureSliceDurationMs, &audio_samples_size,
+                      &audio_samples);
+      if (audio_samples_size < kMaxAudioSampleSize) {
+        error_reporter->Report("Audio data size %d too small, want %d",
+                               audio_samples_size, kMaxAudioSampleSize);
+        return kTfLiteError;
+      }
+      uint8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
+      size_t num_samples_read;
+      TfLiteStatus generate_status = GenerateMicroFeatures(
+          error_reporter, audio_samples, audio_samples_size, kFeatureSliceSize,
+          new_slice_data, &num_samples_read);
+      if (generate_status != kTfLiteOk) {
+        return generate_status;
+      }
+    }
+  }
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/feature_provider.h

@@ -0,0 +1,52 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Binds itself to an area of memory intended to hold the input features for an
+// audio-recognition neural network model, and fills that data area with the
+// features representing the current audio input, for example from a microphone.
+// The audio features themselves are a two-dimensional array, made up of
+// horizontal slices representing the frequencies at one point in time, stacked
+// on top of each other to form a spectrogram showing how those frequencies
+// changed over time.
+class FeatureProvider {
+ public:
+  // Create the provider, and bind it to an area of memory. This memory should
+  // remain accessible for the lifetime of the provider object, since subsequent
+  // calls will fill it with feature data. The provider does no memory
+  // management of this data.
+  FeatureProvider(int feature_size, uint8_t* feature_data);
+  ~FeatureProvider();
+
+  // Fills the feature data with information from audio inputs, and returns how
+  // many feature slices were updated.
+  TfLiteStatus PopulateFeatureData(tflite::ErrorReporter* error_reporter,
+                                   int32_t last_time_in_ms, int32_t time_in_ms,
+                                   int* how_many_new_slices);
+
+ private:
+  int feature_size_;
+  uint8_t* feature_data_;
+  // Make sure we don't try to use cached information if this is the first call
+  // into the provider.
+  bool is_first_run_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/main_functions.h

@@ -0,0 +1,28 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+void setup();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+void loop();
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/micro_features_generator.cpp

@@ -0,0 +1,102 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_features_generator.h"
+
+#include <cmath>
+#include <cstring>
+
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
+#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"
+
+// Configure FFT to output 16 bit fixed point.
+#define FIXED_POINT 16
+
+namespace {
+
+FrontendState g_micro_features_state;
+bool g_is_first_time = true;
+
+}  // namespace
+
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter) {
+  FrontendConfig config;
+  config.window.size_ms = kFeatureSliceDurationMs;
+  config.window.step_size_ms = kFeatureSliceStrideMs;
+  config.noise_reduction.smoothing_bits = 10;
+  config.filterbank.num_channels = kFeatureSliceSize;
+  config.filterbank.lower_band_limit = 125.0;
+  config.filterbank.upper_band_limit = 7500.0;
+  config.noise_reduction.smoothing_bits = 10;
+  config.noise_reduction.even_smoothing = 0.025;
+  config.noise_reduction.odd_smoothing = 0.06;
+  config.noise_reduction.min_signal_remaining = 0.05;
+  config.pcan_gain_control.enable_pcan = 1;
+  config.pcan_gain_control.strength = 0.95;
+  config.pcan_gain_control.offset = 80.0;
+  config.pcan_gain_control.gain_bits = 21;
+  config.log_scale.enable_log = 1;
+  config.log_scale.scale_shift = 6;
+  if (!FrontendPopulateState(&config, &g_micro_features_state,
+                             kAudioSampleFrequency)) {
+    error_reporter->Report("FrontendPopulateState() failed");
+    return kTfLiteError;
+  }
+  g_is_first_time = true;
+  return kTfLiteOk;
+}
+
+// This is not exposed in any header, and is only used for testing, to ensure
+// that the state is correctly set up before generating results.
+void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) {
+  for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
+    g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
+  }
+}
+
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read) {
+  const int16_t* frontend_input;
+  if (g_is_first_time) {
+    frontend_input = input;
+    g_is_first_time = false;
+  } else {
+    frontend_input = input + 160;
+  }
+  FrontendOutput frontend_output = FrontendProcessSamples(
+      &g_micro_features_state, frontend_input, input_size, num_samples_read);
+
+  for (int i = 0; i < frontend_output.size; ++i) {
+    // These scaling values are derived from those used in input_data.py in the
+    // training pipeline.
+    constexpr int32_t value_scale = (10 * 255);
+    constexpr int32_t value_div = (256 * 26);
+    int32_t value =
+        ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
+        value_div;
+    if (value < 0) {
+      value = 0;
+    }
+    if (value > 255) {
+      value = 255;
+    }
+    output[i] = value;
+  }
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/micro_features_generator.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Sets up any resources needed for the feature generation pipeline.
+TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter);
+
+// Converts audio sample data into a more compact form that's appropriate for
+// feeding into a neural network.
+TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
+                                   const int16_t* input, int input_size,
+                                   int output_size, uint8_t* output,
+                                   size_t* num_samples_read);
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/micro_model_settings.cpp

@@ -0,0 +1,23 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "micro_model_settings.h"
+
+const char* kCategoryLabels[kCategoryCount] = {
+    "silence",
+    "unknown",
+    "yes",
+    "no",
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/micro_model_settings.h

@@ -0,0 +1,41 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
+
+// Keeping these as constant expressions allow us to allocate fixed-sized arrays
+// on the stack for our working memory.
+
+// The size of the input time series data we pass to the FFT to produce the
+// frequency information. This has to be a power of two, and since we're dealing
+// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
+constexpr int kMaxAudioSampleSize = 512;
+constexpr int kAudioSampleFrequency = 16000;
+
+// All of these values are derived from the values used during model training,
+// if you change your model you'll need to update these constants.
+constexpr int kFeatureSliceSize = 40;
+constexpr int kFeatureSliceCount = 49;
+constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
+constexpr int kFeatureSliceStrideMs = 20;
+constexpr int kFeatureSliceDurationMs = 30;
+
+constexpr int kCategoryCount = 4;
+constexpr int kSilenceIndex = 0;
+constexpr int kUnknownIndex = 1;
+extern const char* kCategoryLabels[kCategoryCount];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/micro_speech_M5StackFire.ino

@@ -0,0 +1,196 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+  ==============================================================================*/
+
+#include <TensorFlowLite_ESP32.h>
+
+#include "main_functions.h"
+
+#include "audio_provider.h"
+#include "command_responder.h"
+#include "feature_provider.h"
+#include "micro_model_settings.h"
+#include "tiny_conv_micro_features_model_data.h"
+#include "recognize_commands.h"
+#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
+#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* model_input = nullptr;
+FeatureProvider* feature_provider = nullptr;
+RecognizeCommands* recognizer = nullptr;
+int32_t previous_time = 0;
+
+// Create an area of memory to use for input, output, and intermediate arrays.
+// The size of this will depend on the model you're using, and may need to be
+// determined by experimentation.
+constexpr int kTensorArenaSize = 10 * 1024;
+uint8_t tensor_arena[kTensorArenaSize];
+}  // namespace
+
+QueueHandle_t xQueueAudioWave;
+#define QueueAudioWaveSize 32
+
+// The name of this function is important for Arduino compatibility.
+void setup() {
+  xQueueAudioWave = xQueueCreate(QueueAudioWaveSize, sizeof(int16_t));
+  
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_tiny_conv_micro_features_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    error_reporter->Report(
+      "Model provided is schema version %d not equal "
+      "to supported version %d.",
+      model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  //
+  // tflite::ops::micro::AllOpsResolver resolver;
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+    tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
+  micro_mutable_op_resolver.AddBuiltin(
+    tflite::BuiltinOperator_FULLY_CONNECTED,
+    tflite::ops::micro::Register_FULLY_CONNECTED());
+  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
+                                       tflite::ops::micro::Register_SOFTMAX());
+
+  // Build an interpreter to run the model with.
+  static tflite::MicroInterpreter static_interpreter(
+    model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
+    error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    error_reporter->Report("AllocateTensors() failed");
+    return;
+  }
+
+  // Get information about the memory area to use for the model's input.
+  model_input = interpreter->input(0);
+  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
+      (model_input->dims->data[1] != kFeatureSliceCount) ||
+      (model_input->dims->data[2] != kFeatureSliceSize) ||
+      (model_input->type != kTfLiteUInt8)) {
+    error_reporter->Report("Bad input tensor parameters in model");
+    return;
+  }
+
+  // Prepare to access the audio spectrograms from a microphone or other source
+  // that will provide the inputs to the neural network.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static FeatureProvider static_feature_provider(kFeatureElementCount,
+      model_input->data.uint8);
+  feature_provider = &static_feature_provider;
+
+  static RecognizeCommands static_recognizer(error_reporter);
+  recognizer = &static_recognizer;
+
+  previous_time = 0;
+
+  InitResponder();
+
+  Serial.printf("model_input->name          : %s\n", model_input->name);
+  Serial.printf("model_input->type          : %d\n", model_input->type);
+  Serial.printf("model_input->bytes         : %d\n", model_input->bytes);
+  Serial.printf("model_input->dims->size    : %d\n", model_input->dims->size);
+  Serial.printf("model_input->dims->data[0] : %d\n", model_input->dims->data[0]); // 1
+  Serial.printf("model_input->dims->data[1] : %d\n", model_input->dims->data[1]); // kFeatureSliceCount
+  Serial.printf("model_input->dims->data[2] : %d\n", model_input->dims->data[2]); // kFeatureSliceSize
+}
+
+// The name of this function is important for Arduino compatibility.
+void loop() {
+
+  int16_t wave = 0;
+  for (int i = 0; i < QueueAudioWaveSize; i++) {
+    if (xQueueReceive(xQueueAudioWave, &wave, 0) == pdTRUE) {
+      drawWave(wave);
+    }
+  }  
+
+  // Fetch the spectrogram for the current time.
+  const int32_t current_time = LatestAudioTimestamp();
+  int how_many_new_slices = 0;
+  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
+                                  error_reporter, previous_time, current_time, &how_many_new_slices);
+  if (feature_status != kTfLiteOk) {
+    error_reporter->Report("Feature generation failed");
+    delay(1);
+    return;
+  }
+  previous_time = current_time;
+  // If no new audio samples have been received since last time, don't bother
+  // running the network model.
+  if (how_many_new_slices == 0) {
+    delay(1);
+    return;
+  }
+
+  // Run the model on the spectrogram input and make sure it succeeds.
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    error_reporter->Report("Invoke failed");
+    delay(1);
+    return;
+  }
+
+  // Obtain a pointer to the output tensor
+  TfLiteTensor* output = interpreter->output(0);
+  // Determine whether a command was recognized based on the output of inference
+  const char* found_command = nullptr;
+  uint8_t score = 0;
+  bool is_new_command = false;
+  TfLiteStatus process_status = recognizer->ProcessLatestResults(
+                                  output, current_time, &found_command, &score, &is_new_command);
+  if (process_status != kTfLiteOk) {
+    error_reporter->Report("RecognizeCommands::ProcessLatestResults() failed");
+    delay(1);
+    return;
+  }
+  // Do something based on the recognized command. The default implementation
+  // just prints to the error console, but you should replace this with your
+  // own function for a real application.
+  RespondToCommand(error_reporter, current_time, found_command, score,
+                   is_new_command);
+
+  drawInput(model_input->data.uint8);
+
+  delay(1);
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/no_micro_features_data.cpp

@@ -0,0 +1,165 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "no_micro_features_data.h"
+
+/* File automatically created by
+ * tensorflow/examples/speech_commands/wav_to_features.py \
+ * --sample_rate=16000 \
+ * --clip_duration_ms=1000 \
+ * --window_size_ms=30 \
+ * --window_stride_ms=20 \
+ * --feature_bin_count=40 \
+ * --quantize=1 \
+ * --preprocess="micro" \
+ * --input_wav="speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav" \
+ * --output_c_file="/tmp/no_micro_features_data.cc" \
+ */
+
+const int g_no_micro_f9643d42_nohash_4_width = 40;
+const int g_no_micro_f9643d42_nohash_4_height = 49;
+const unsigned char g_no_micro_f9643d42_nohash_4_data[] = {
+    230, 205, 191, 203, 202, 181, 180, 194, 205, 187, 183, 197, 203, 198, 196,
+    186, 202, 159, 151, 126, 110, 138, 141, 142, 137, 148, 133, 120, 110, 126,
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+    141, 172, 110, 175, 167, 180, 156, 213, 188, 192, 179, 213, 205, 204, 174,
+    200, 147, 162, 181, 203, 167, 198, 187, 210, 164, 196, 169, 189, 168, 224,
+    198, 213, 204, 198, 195, 230, 211, 221, 197, 208, 0,   0,   0,   85,  90,
+    167, 130, 175, 173, 203, 164, 193, 144, 170, 145, 185, 148, 154, 139, 198,
+    159, 180, 171, 216, 174, 178, 161, 166, 136, 216, 184, 215, 197, 199, 190,
+    228, 195, 208, 51,  117, 0,   0,   0,   0,   0,   140, 51,  135, 154, 188,
+    155, 168, 0,   90,  0,   156, 85,  110, 0,   174, 90,  172, 154, 179, 99,
+    142, 166, 179, 157, 177, 95,  192, 142, 204, 198, 217, 147, 173, 0,   112,
+    0,   0,   0,   0,   0,   0,   0,   110, 0,   107, 0,   160, 0,   148, 95,
+    172, 0,   0,   0,   116, 0,   122, 114, 170, 0,   0,   0,   0,   0,   179,
+    110, 196, 85,  205, 183, 169, 0,   99,  0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   141, 0,   112, 0,   0,   0,   134, 0,   0,   0,   0,
+    0,   0,   0,   139, 0,   0,   0,   0,   112, 186, 78,  163, 0,   169, 128,
+    174, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   95,
+    0,   105, 0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   95,  0,
+    0,   0,   0,   0,   0,   0,   119, 0,   164, 78,  0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   90,  0,   0,   68,
+    117, 0,   0,   0,   0,   0,   0,   0,   148, 0,   0,   0,   0,   0,   0,
+    0,   0,   0,   116, 0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   51,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   99,  0,   0,   0,   0,   0,   0,   0,   0,   0,   78,  0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+};

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/no_micro_features_data.h

@@ -0,0 +1,23 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
+
+extern const int g_no_micro_f9643d42_nohash_4_width;
+extern const int g_no_micro_f9643d42_nohash_4_height;
+extern const unsigned char g_no_micro_f9643d42_nohash_4_data[];
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/recognize_commands.cpp

@@ -0,0 +1,139 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "recognize_commands.h"
+
+#include <limits>
+
+RecognizeCommands::RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                                     int32_t average_window_duration_ms,
+                                     uint8_t detection_threshold,
+                                     int32_t suppression_ms,
+                                     int32_t minimum_count)
+    : error_reporter_(error_reporter),
+      average_window_duration_ms_(average_window_duration_ms),
+      detection_threshold_(detection_threshold),
+      suppression_ms_(suppression_ms),
+      minimum_count_(minimum_count),
+      previous_results_(error_reporter) {
+  previous_top_label_ = "silence";
+  previous_top_label_time_ = std::numeric_limits<int32_t>::min();
+}
+
+TfLiteStatus RecognizeCommands::ProcessLatestResults(
+    const TfLiteTensor* latest_results, const int32_t current_time_ms,
+    const char** found_command, uint8_t* score, bool* is_new_command) {
+  if ((latest_results->dims->size != 2) ||
+      (latest_results->dims->data[0] != 1) ||
+      (latest_results->dims->data[1] != kCategoryCount)) {
+    error_reporter_->Report(
+        "The results for recognition should contain %d elements, but there are "
+        "%d in an %d-dimensional shape",
+        kCategoryCount, latest_results->dims->data[1],
+        latest_results->dims->size);
+    return kTfLiteError;
+  }
+
+  if (latest_results->type != kTfLiteUInt8) {
+    error_reporter_->Report(
+        "The results for recognition should be uint8 elements, but are %d",
+        latest_results->type);
+    return kTfLiteError;
+  }
+
+  if ((!previous_results_.empty()) &&
+      (current_time_ms < previous_results_.front().time_)) {
+    error_reporter_->Report(
+        "Results must be fed in increasing time order, but received a "
+        "timestamp of %d that was earlier than the previous one of %d",
+        current_time_ms, previous_results_.front().time_);
+    return kTfLiteError;
+  }
+
+  // Add the latest results to the head of the queue.
+  previous_results_.push_back({current_time_ms, latest_results->data.uint8});
+
+  // Prune any earlier results that are too old for the averaging window.
+  const int64_t time_limit = current_time_ms - average_window_duration_ms_;
+  while ((!previous_results_.empty()) &&
+         previous_results_.front().time_ < time_limit) {
+    previous_results_.pop_front();
+  }
+
+  // If there are too few results, assume the result will be unreliable and
+  // bail.
+  const int64_t how_many_results = previous_results_.size();
+  const int64_t earliest_time = previous_results_.front().time_;
+  const int64_t samples_duration = current_time_ms - earliest_time;
+  if ((how_many_results < minimum_count_) ||
+      (samples_duration < (average_window_duration_ms_ / 4))) {
+    *found_command = previous_top_label_;
+    *score = 0;
+    *is_new_command = false;
+    return kTfLiteOk;
+  }
+
+  // Calculate the average score across all the results in the window.
+  int32_t average_scores[kCategoryCount];
+  for (int offset = 0; offset < previous_results_.size(); ++offset) {
+    PreviousResultsQueue::Result previous_result =
+        previous_results_.from_front(offset);
+    const uint8_t* scores = previous_result.scores_;
+    for (int i = 0; i < kCategoryCount; ++i) {
+      if (offset == 0) {
+        average_scores[i] = scores[i];
+      } else {
+        average_scores[i] += scores[i];
+      }
+    }
+  }
+  for (int i = 0; i < kCategoryCount; ++i) {
+    average_scores[i] /= how_many_results;
+  }
+
+  // Find the current highest scoring category.
+  int current_top_index = 0;
+  int32_t current_top_score = 0;
+  for (int i = 0; i < kCategoryCount; ++i) {
+    if (average_scores[i] > current_top_score) {
+      current_top_score = average_scores[i];
+      current_top_index = i;
+    }
+  }
+  const char* current_top_label = kCategoryLabels[current_top_index];
+
+  // If we've recently had another label trigger, assume one that occurs too
+  // soon afterwards is a bad result.
+  int64_t time_since_last_top;
+  if ((previous_top_label_ == kCategoryLabels[0]) ||
+      (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
+    time_since_last_top = std::numeric_limits<int32_t>::max();
+  } else {
+    time_since_last_top = current_time_ms - previous_top_label_time_;
+  }
+  if ((current_top_score > detection_threshold_) &&
+      ((current_top_label != previous_top_label_) ||
+       (time_since_last_top > suppression_ms_))) {
+    previous_top_label_ = current_top_label;
+    previous_top_label_time_ = current_time_ms;
+    *is_new_command = true;
+  } else {
+    *is_new_command = false;
+  }
+  *found_command = current_top_label;
+  *score = current_top_score;
+
+  return kTfLiteOk;
+}

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/recognize_commands.h

@@ -0,0 +1,156 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
+
+#include <cstdint>
+
+#include "tensorflow/lite/c/c_api_internal.h"
+#include "micro_model_settings.h"
+#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
+
+// Partial implementation of std::dequeue, just providing the functionality
+// that's needed to keep a record of previous neural network results over a
+// short time period, so they can be averaged together to produce a more
+// accurate overall prediction. This doesn't use any dynamic memory allocation
+// so it's a better fit for microcontroller applications, but this does mean
+// there are hard limits on the number of results it can store.
+class PreviousResultsQueue {
+ public:
+  PreviousResultsQueue(tflite::ErrorReporter* error_reporter)
+      : error_reporter_(error_reporter), front_index_(0), size_(0) {}
+
+  // Data structure that holds an inference result, and the time when it
+  // was recorded.
+  struct Result {
+    Result() : time_(0), scores_() {}
+    Result(int32_t time, uint8_t* scores) : time_(time) {
+      for (int i = 0; i < kCategoryCount; ++i) {
+        scores_[i] = scores[i];
+      }
+    }
+    int32_t time_;
+    uint8_t scores_[kCategoryCount];
+  };
+
+  int size() { return size_; }
+  bool empty() { return size_ == 0; }
+  Result& front() { return results_[front_index_]; }
+  Result& back() {
+    int back_index = front_index_ + (size_ - 1);
+    if (back_index >= kMaxResults) {
+      back_index -= kMaxResults;
+    }
+    return results_[back_index];
+  }
+
+  void push_back(const Result& entry) {
+    if (size() >= kMaxResults) {
+      error_reporter_->Report(
+          "Couldn't push_back latest result, too many already!");
+      return;
+    }
+    size_ += 1;
+    back() = entry;
+  }
+
+  Result pop_front() {
+    if (size() <= 0) {
+      error_reporter_->Report("Couldn't pop_front result, none present!");
+      return Result();
+    }
+    Result result = front();
+    front_index_ += 1;
+    if (front_index_ >= kMaxResults) {
+      front_index_ = 0;
+    }
+    size_ -= 1;
+    return result;
+  }
+
+  // Most of the functions are duplicates of dequeue containers, but this
+  // is a helper that makes it easy to iterate through the contents of the
+  // queue.
+  Result& from_front(int offset) {
+    if ((offset < 0) || (offset >= size_)) {
+      error_reporter_->Report("Attempt to read beyond the end of the queue!");
+      offset = size_ - 1;
+    }
+    int index = front_index_ + offset;
+    if (index >= kMaxResults) {
+      index -= kMaxResults;
+    }
+    return results_[index];
+  }
+
+ private:
+  tflite::ErrorReporter* error_reporter_;
+  static constexpr int kMaxResults = 50;
+  Result results_[kMaxResults];
+
+  int front_index_;
+  int size_;
+};
+
+// This class is designed to apply a very primitive decoding model on top of the
+// instantaneous results from running an audio recognition model on a single
+// window of samples. It applies smoothing over time so that noisy individual
+// label scores are averaged, increasing the confidence that apparent matches
+// are real.
+// To use it, you should create a class object with the configuration you
+// want, and then feed results from running a TensorFlow model into the
+// processing method. The timestamp for each subsequent call should be
+// increasing from the previous, since the class is designed to process a stream
+// of data over time.
+class RecognizeCommands {
+ public:
+  // labels should be a list of the strings associated with each one-hot score.
+  // The window duration controls the smoothing. Longer durations will give a
+  // higher confidence that the results are correct, but may miss some commands.
+  // The detection threshold has a similar effect, with high values increasing
+  // the precision at the cost of recall. The minimum count controls how many
+  // results need to be in the averaging window before it's seen as a reliable
+  // average. This prevents erroneous results when the averaging window is
+  // initially being populated for example. The suppression argument disables
+  // further recognitions for a set time after one has been triggered, which can
+  // help reduce spurious recognitions.
+  explicit RecognizeCommands(tflite::ErrorReporter* error_reporter,
+                             int32_t average_window_duration_ms = 1000,
+                             uint8_t detection_threshold = 200,
+                             int32_t suppression_ms = 1500,
+                             int32_t minimum_count = 3);
+
+  // Call this with the results of running a model on sample data.
+  TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results,
+                                    const int32_t current_time_ms,
+                                    const char** found_command, uint8_t* score,
+                                    bool* is_new_command);
+
+ private:
+  // Configuration
+  tflite::ErrorReporter* error_reporter_;
+  int32_t average_window_duration_ms_;
+  uint8_t detection_threshold_;
+  int32_t suppression_ms_;
+  int32_t minimum_count_;
+
+  // Working variables
+  PreviousResultsQueue previous_results_;
+  const char* previous_top_label_;
+  int32_t previous_top_label_time_;
+};
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/static_alloc.h

@@ -0,0 +1,32 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_
+
+// Checks to ensure that the C-style array passed in has a compile-time size of
+// at least the number of bytes requested. This doesn't work with raw pointers
+// since sizeof() doesn't know their actual length, so only use this to check
+// statically-allocated arrays with known sizes.
+#define STATIC_ALLOC_ENSURE_ARRAY_SIZE(A, N)                                 \
+  do {                                                                       \
+    if (sizeof(A) < (N)) {                                                   \
+      error_reporter->Report(#A " too small (%d bytes, wanted %d) at %s:%d", \
+                             sizeof(A), (N), __FILE__, __LINE__);            \
+      return 0;                                                              \
+    }                                                                        \
+  } while (0)
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_STATIC_ALLOC_H_

WiFiX-Enhanced/.pio/libdeps/esp32_enhanced/TensorFlowLite_ESP32/examples/OldExamples/micro_speech_M5StackFire/tiny_conv_micro_features_model_data.h

@@ -0,0 +1,27 @@
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i tiny_conv.tflite > tiny_conv_simple_features_model_data.cc
+
+#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
+
+extern const unsigned char g_tiny_conv_micro_features_model_data[];
+extern const int g_tiny_conv_micro_features_model_data_len;
+
+#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_