AI Audio Classifier Recycle Bin

/* AI Audio Classifier Recycle Bin
 * License: Apache-2.0
 * Modification by: Samuel Alexander
 * Based on the nano_ble33_sense_microphone example
 * Below is the license statement from the base software
/*

/* Edge Impulse ingestion SDK
 * Copyright (c) 2022 EdgeImpulse Inc.
 *
 * 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.
 *
 */

// If your target is limited in memory remove this macro to save 10K RAM
#define EIDSP_QUANTIZE_FILTERBANK   0

/*
 ** NOTE: If you run into TFLite arena allocation issue.
 **
 ** This may be due to may dynamic memory fragmentation.
 ** Try defining "-DEI_CLASSIFIER_ALLOCATION_STATIC" in boards.local.txt (create
 ** if it doesn't exist) and copy this file to
 ** `<ARDUINO_CORE_INSTALL_PATH>/arduino/hardware/<mbed_core>/<core_version>/`.
 **
 ** See
 ** (https://support.arduino.cc/hc/en-us/articles/360012076960-Where-are-the-installed-cores-located-)
 ** to find where Arduino installs cores on your machine.
 **
 ** If the problem persists then there's not enough memory for this model and application.
 */

/* Includes ---------------------------------------------------------------- */
#include <PDM.h>
#include <Edge_AI_Recycle_Bin_inferencing.h>
#include <AccelStepper.h>
#include <Servo.h>

/** Audio buffers, pointers and selectors */
typedef struct {
    int16_t *buffer;
    uint8_t buf_ready;
    uint32_t buf_count;
    uint32_t n_samples;
} inference_t;

static inference_t inference;
static signed short sampleBuffer[2048];
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal

/**
 * @brief      Arduino setup function
 */

AccelStepper stepper1(1, 2, 3); // (Typeof driver: with 2 pins, STEP, DIR)

int compartment[] = {925, 2775, 0, 4625, 6475}; //based on our EI model (Bottle, Can, Noise, Paper, Pong)
const int HALL = 5;

Servo trapdoor;
int pos = 0;
float threshold = 0.8;
int selected = 2; //default as Noise

void setup()
{
    // put your setup code here, to run once:
    Serial.begin(115200);
    pinMode(HALL, INPUT);
    trapdoor.attach(A5);
    
    // stepper motor calibration
    stepper1.setMaxSpeed(2000); // Set maximum speed value for the stepper
    stepper1.setAcceleration(1000); // Set acceleration value for the stepper
    while(digitalRead(HALL)){
      stepper1.setSpeed(-400); // Set the current position to 0 steps
      stepper1.runSpeed();
      Serial.println("homing...");
    }
    delay(100);
    stepper1.setCurrentPosition(0);
    //end of stepper motor calibration


    // servo motor calibration
  for (pos = 11; pos <= 90; pos += 1) { // goes from 0 degrees to 180 degrees
    // in steps of 1 degree
    trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
    delay(20);                       // waits 15 ms for the servo to reach the position
  }
  for (pos = 90; pos >= 11; pos -= 1) { // goes from 180 degrees to 0 degrees
    trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
    delay(20);                       // waits 15 ms for the servo to reach the position
  }
    // end of servo motor calibration
    delay(500); //Delay so the sound of servo is not captured



    

    // comment out the below line to cancel the wait for USB connection (needed for native USB)
    //while (!Serial);
    Serial.println("Edge Impulse Inferencing Demo");

    // summary of inferencing settings (from model_metadata.h)
    ei_printf("Inferencing settings:\n");
    ei_printf("\tInterval: %.2f ms.\n", (float)EI_CLASSIFIER_INTERVAL_MS);
    ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
    ei_printf("\tSample length: %d ms.\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT / 16);
    ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0]));

    if (microphone_inference_start(EI_CLASSIFIER_RAW_SAMPLE_COUNT) == false) {
        ei_printf("ERR: Could not allocate audio buffer (size %d), this could be due to the window length of your model\r\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT);
        return;
    }
}

/**
 * @brief      Arduino main function. Runs the inferencing loop.
 */
void loop()
{
    //ei_printf("Starting inferencing in 2 seconds...\n");

    //delay(2000);

    ei_printf("Recording...\n");

    bool m = microphone_inference_record();
    if (!m) {
        ei_printf("ERR: Failed to record audio...\n");
        return;
    }

    ei_printf("Recording done\n");

    signal_t signal;
    signal.total_length = EI_CLASSIFIER_RAW_SAMPLE_COUNT;
    signal.get_data = &microphone_audio_signal_get_data;
    ei_impulse_result_t result = { 0 };

    EI_IMPULSE_ERROR r = run_classifier(&signal, &result, debug_nn);
    if (r != EI_IMPULSE_OK) {
        ei_printf("ERR: Failed to run classifier (%d)\n", r);
        return;
    }

    // print the predictions
    ei_printf("Predictions ");
    ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
        result.timing.dsp, result.timing.classification, result.timing.anomaly);
    ei_printf(": \n");
    for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        ei_printf("    %s: %.5f\n", result.classification[ix].label, result.classification[ix].value);
        if (result.classification[ix].value > threshold){
          ei_printf("\nYOUR TRASH IS: %s\n", result.classification[ix].label);
          selected = ix;
        }
    }
    // The order is Bottle, Can, Noise, Paper, Ping-pong
    // if BOTTLE selected
    if (selected == 0){
      stepper1.moveTo(compartment[selected]);
      stepper1.runToPosition();
      for (pos = 11; pos <= 90; pos += 1) { // goes from 0 degrees to 180 degrees
        // in steps of 1 degree
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      for (pos = 90; pos >= 11; pos -= 1) { // goes from 180 degrees to 0 degrees
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      delay(500);
    }
    // if CAN selected
    else if (selected == 1){
      stepper1.moveTo(compartment[selected]);
      stepper1.runToPosition();
      for (pos = 11; pos <= 90; pos += 1) { // goes from 0 degrees to 180 degrees
        // in steps of 1 degree
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      for (pos = 90; pos >= 11; pos -= 1) { // goes from 180 degrees to 0 degrees
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      delay(500);
    }
    // if PAPER selected
    else if (selected == 3){
      stepper1.moveTo(compartment[selected]);
      stepper1.runToPosition();
      for (pos = 11; pos <= 90; pos += 1) { // goes from 0 degrees to 180 degrees
        // in steps of 1 degree
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      for (pos = 90; pos >= 11; pos -= 1) { // goes from 180 degrees to 0 degrees
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      delay(500);
    }
    // if Ping Pong selected
    else if (selected == 4){
      stepper1.moveTo(compartment[selected]);
      stepper1.runToPosition();
      for (pos = 11; pos <= 90; pos += 1) { // goes from 0 degrees to 180 degrees
        // in steps of 1 degree
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      for (pos = 90; pos >= 11; pos -= 1) { // goes from 180 degrees to 0 degrees
        trapdoor.write(pos);              // tell servo to go to position in variable 'pos'
        delay(20);                       // waits 15 ms for the servo to reach the position
      }
      delay(500);
    }
    // if NOISE selected
    else {
      //no statement
    }



#if EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf("    anomaly score: %.3f\n", result.anomaly);
#endif
}

/**
 * @brief      PDM buffer full callback
 *             Get data and call audio thread callback
 */
static void pdm_data_ready_inference_callback(void)
{
    int bytesAvailable = PDM.available();

    // read into the sample buffer
    int bytesRead = PDM.read((char *)&sampleBuffer[0], bytesAvailable);

    if (inference.buf_ready == 0) {
        for(int i = 0; i < bytesRead>>1; i++) {
            inference.buffer[inference.buf_count++] = sampleBuffer[i];

            if(inference.buf_count >= inference.n_samples) {
                inference.buf_count = 0;
                inference.buf_ready = 1;
                break;
            }
        }
    }
}

/**
 * @brief      Init inferencing struct and setup/start PDM
 *
 * @param[in]  n_samples  The n samples
 *
 * @return     { description_of_the_return_value }
 */
static bool microphone_inference_start(uint32_t n_samples)
{
    inference.buffer = (int16_t *)malloc(n_samples * sizeof(int16_t));

    if(inference.buffer == NULL) {
        return false;
    }

    inference.buf_count  = 0;
    inference.n_samples  = n_samples;
    inference.buf_ready  = 0;

    // configure the data receive callback
    PDM.onReceive(&pdm_data_ready_inference_callback);

    PDM.setBufferSize(4096);

    // initialize PDM with:
    // - one channel (mono mode)
    // - a 16 kHz sample rate
    if (!PDM.begin(1, EI_CLASSIFIER_FREQUENCY)) {
        ei_printf("Failed to start PDM!");
        microphone_inference_end();

        return false;
    }

    // set the gain, defaults to 20
    PDM.setGain(80);

    return true;
}

/**
 * @brief      Wait on new data
 *
 * @return     True when finished
 */
static bool microphone_inference_record(void)
{
    inference.buf_ready = 0;
    inference.buf_count = 0;

    while(inference.buf_ready == 0) {
        delay(10);
    }

    return true;
}

/**
 * Get raw audio signal data
 */
static int microphone_audio_signal_get_data(size_t offset, size_t length, float *out_ptr)
{
    numpy::int16_to_float(&inference.buffer[offset], out_ptr, length);

    return 0;
}

/**
 * @brief      Stop PDM and release buffers
 */
static void microphone_inference_end(void)
{
    PDM.end();
    free(inference.buffer);
}

#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_MICROPHONE
#error "Invalid model for current sensor."
#endif