Merge branch 'main' of https://github.com/djdunc/casa0018

.gitignore

@@ -0,0 +1 @@
+.DS_Store

Assessment/Projects/Final Project/README.md

@@ -1,2 +1,19 @@
 # Final Project
-describe what is in these folders...
+
+
+
+Hints on what should go into this page (note each three of these sections are weighted evenly):
+
+### Definition of problem being solved 
+(project overview, research question, data available, outcomes anticipated, application design) 
+
+### Documentation of experiments and results 
+(model training results, description of training runs, model architecture choices, visual record of experiments) 
+
+### Critical reflection and learning from experiments 
+(observations from experiments, factors incluencing results, potential improvements, weaknesses, feedback from reviews) 
+
+
+
+### Note: 
+*You can choose what tools you use to write up and document your project - your final submission will be a pdf document being uploaded via Moodle, however we would also expect to see a link through to your GitHub repository where you data, results etc. are documented.*

Assessment/Report/README.md

@@ -1,6 +1,6 @@
 # Report title goes here
 
-name of author
+name of author, link to github repo with project work in / link to Edge Impulse projects
 
 ## Introduction
 - an overview of what the project does
@@ -55,4 +55,4 @@ I, AUTHORS NAME HERE, confirm that the work presented in this assessment is my o
 
 *Digitally Sign by typing your name here*
 
-ASSESSMENT DATE
+ASSESSMENT DATE

Week4/arduino/micro_speech_CASA/arduino_audio_provider.cpp

@@ -1,19 +1,6 @@
-/* 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.
-==============================================================================*/
-
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+/* 
+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.
@@ -26,7 +13,7 @@ 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.
-==============================================================================*/
+*/
 
 #if defined(ARDUINO) && !defined(ARDUINO_ARDUINO_NANO33BLE)
 #define ARDUINO_EXCLUDE_CODE
@@ -36,7 +23,8 @@ limitations under the License.
 
 #include "audio_provider.h"
 
-#include "PDM.h"
+#include "PDM.h" // https://www.arduino.cc/en/Reference/PDM Pulse Density Modulation
+
 #include "micro_features_micro_model_settings.h"
 
 namespace {

Week4/arduino/micro_speech_CASA/micro_features_micro_features_generator.cpp

@@ -13,6 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+
+
+/*
+  Performs the FFT and returns the audio frequency information Ch.7 p145
+*/
+
+
 #include "micro_features_micro_features_generator.h"
 
 #include <cmath>

Week4/arduino/micro_speech_CASA/micro_features_micro_features_generator.h

@@ -13,6 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+
+/*
+  Performs the FFT and returns the audio frequency information Ch.7 p145
+*/
+
+
 #ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
 #define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
 

Week5/arduino/CASA_person_detection_experimental_TF2.4.0/CASA_person_detection_experimental_TF2.4.0.ino

@@ -0,0 +1,121 @@
+/* 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.h>
+
+#include "main_functions.h"
+
+#include "detection_responder.h"
+#include "image_provider.h"
+#include "model_settings.h"
+#include "person_detect_model_data.h"
+#include "tensorflow/lite/micro/kernels/micro_ops.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/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* input = nullptr;
+
+// In order to use optimized tensorflow lite kernels, a signed int8 quantized
+// model is preferred over the legacy unsigned model format. This means that
+// throughout this project, input images must be converted from unisgned to
+// signed format. The easiest and quickest way to convert from unsigned to
+// signed 8-bit integers is to subtract 128 from the unsigned value to get a
+// signed value.
+
+// An area of memory to use for input, output, and intermediate arrays.
+constexpr int kTensorArenaSize = 136 * 1024;
+static uint8_t tensor_arena[kTensorArenaSize];
+}  // 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.
+  // 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_person_detect_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    TF_LITE_REPORT_ERROR(error_reporter,
+                         "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::AllOpsResolver resolver;
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroMutableOpResolver<5> micro_op_resolver;
+  micro_op_resolver.AddAveragePool2D();
+  micro_op_resolver.AddConv2D();
+  micro_op_resolver.AddDepthwiseConv2D();
+  micro_op_resolver.AddReshape();
+  micro_op_resolver.AddSoftmax();
+
+  // Build an interpreter to run the model with.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroInterpreter static_interpreter(
+      model, micro_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) {
+    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+    return;
+  }
+
+  // Get information about the memory area to use for the model's input.
+  input = interpreter->input(0);
+}
+
+// The name of this function is important for Arduino compatibility.
+void loop() {
+
+  // Get image from provider.
+  if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
+                            input->data.int8)) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
+  }
+
+  // Run the model on this input and make sure it succeeds.
+  if (kTfLiteOk != interpreter->Invoke()) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
+  }
+
+  TfLiteTensor* output = interpreter->output(0);
+
+  // Process the inference results.
+  int8_t person_score = output->data.uint8[kPersonIndex];
+  int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
+  RespondToDetection(error_reporter, person_score, no_person_score);
+}

Week5/arduino/CASA_person_detection_experimental_TF2.4.0/arduino_detection_responder.cpp

@@ -0,0 +1,56 @@
+/* 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 "detection_responder.h"
+
+#include "Arduino.h"
+
+// Flash the blue LED after each inference
+void RespondToDetection(tflite::ErrorReporter* error_reporter,
+                        int8_t person_score, int8_t no_person_score) {
+  static bool is_initialized = false;
+  if (!is_initialized) {
+    // Pins for the built-in RGB LEDs on the Arduino Nano 33 BLE Sense
+    pinMode(LEDR, OUTPUT);
+    pinMode(LEDG, OUTPUT);
+    pinMode(LEDB, OUTPUT);
+    is_initialized = true;
+  }
+
+  // Note: The RGB LEDs on the Arduino Nano 33 BLE
+  // Sense are on when the pin is LOW, off when HIGH.
+
+  // Switch the person/not person LEDs off
+  digitalWrite(LEDG, HIGH);
+  digitalWrite(LEDR, HIGH);
+
+  // Flash the blue LED after every inference.
+  digitalWrite(LEDB, LOW);
+  delay(100);
+  digitalWrite(LEDB, HIGH);
+
+  // Switch on the green LED when a person is detected,
+  // the red when no person is detected
+  if (person_score > no_person_score) {
+    digitalWrite(LEDG, LOW);
+    digitalWrite(LEDR, HIGH);
+  } else {
+    digitalWrite(LEDG, HIGH);
+    digitalWrite(LEDR, LOW);
+  }
+
+  TF_LITE_REPORT_ERROR(error_reporter, "Person score: %d No person score: %d",
+                       person_score, no_person_score);
+}