Deploying Ei on Adafruit Feather (Arduino IDE)

Help
#1

Hi,
I`m trying to deploy Ei Classifier for a continuous audio recognition on Adafruit Feather Sense , I copied the platform.local.txt , created 2 buffers…
I got this error

error
error1920×173 8.5 KB

#3 
/* Edge Impulse Arduino examples
  • Copyright © 2021 EdgeImpulse Inc.
  • Permission is hereby granted, free of charge, to any person obtaining a copy
  • of this software and associated documentation files (the “Software”), to deal
  • in the Software without restriction, including without limitation the rights
  • to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  • copies of the Software, and to permit persons to whom the Software is
  • furnished to do so, subject to the following conditions:
  • The above copyright notice and this permission notice shall be included in
  • all copies or substantial portions of the Software.
  • THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  • IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  • FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  • AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  • LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  • OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  • SOFTWARE.
    */

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

/**

/* Includes ---------------------------------------------------------------- */
#include <PDM.h>
#include <BirdBoard_Test1_inferencing.h>
#include “edge-impulse-sdk/dsp/numpy.hpp”
#define BUFFER_LENGTH EI_CLASSIFIER_SLICE_SIZE //EI_CLASSIFIER_RAW_SAMPLE_COUNT

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

static inference_t inference;
static bool record_ready = false;
static signed short sampleBuffer[2048];
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
static int samples_saved = 0;
static int print_results = -(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW);

/**

  • @brief Arduino setup function
    */
    void setup()
    {
    // put your setup code here, to run once:
    Serial.begin(115200);

    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]));

    run_classifier_init();
    if (microphone_inference_start(EI_CLASSIFIER_SLICE_SIZE) == false) {
    ei_printf(“ERR: Failed to setup audio sampling\r\n”);
    return;
    }
    }

/**

  • @brief Arduino main function. Runs the inferencing loop.
    */
    void loop()
    {
    bool m = microphone_inference_record();
    if (!m) {
    ei_printf(“ERR: Failed to record audio…\n”);
    return;
    }
    inference.use_buffer = !inference.use_buffer;
    inference.buf_ready = 0;
    inference.buf_count = 0;
    ei_printf(“Recording done\n”);

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

    EI_IMPULSE_ERROR r = run_classifier_continuous(&signal, &result, debug_nn);
    if (r != EI_IMPULSE_OK) {
    ei_printf(“ERR: Failed to run classifier (%d)\n”, r);
    return;
    }
    ei_printf(“Amount already recorded: %d of %d, is it done: %d\n”,inference.buf_count, inference.n_samples, inference.buf_ready);

    if (++print_results >= (EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)) {
    // 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 EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf(" anomaly score: %.3f\n", result.anomaly);
    #endif

     print_results = 0;

    }

}

/**

  • @brief Printf function uses vsnprintf and output using Arduino Serial

  • @param[in] format Variable argument list
    */
    void ei_printf(const char *format, …) {
    static char print_buf[1024] = { 0 };

    va_list args;
    va_start(args, format);
    int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
    va_end(args);

    if (r > 0) {
    Serial.write(print_buf);
    }
    }

/**

  • @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 (record_ready == true || inference.buf_ready == 1) {
if (inference.buf_ready != 1) {
    for(int i = 0; i < bytesRead>>1; i++) {
        if (inference.use_buffer) {
          inference.buffer[inference.buf_count++] = sampleBuffer[i];
        } else {
          inference.other_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) {
    ei_printf(“Fnot engough mem 1”);
    return false;
    }

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

    inference.other_buffer = (int16_t *)malloc(n_samples * sizeof(int16_t));

    if(inference.other_buffer == NULL) {
    ei_printf(“not enough mem 2!”);
    return false;
    }

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

    // optionally set the gain, defaults to 20
    PDM.setGain(60);

    //ei_printf(“Sector size: %d nblocks: %d\r\n”, ei_nano_fs_get_block_size(), n_sample_blocks);
    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!”);
    }

    record_ready = true;

    return true;
    }

/**

  • @brief Wait on new data

  • @return True when finished
    */
    static bool microphone_inference_record(void)
    {
    bool ret = true;

    if (inference.buf_ready == 1) {
    ei_printf(
    "Error sample buffer overrun. Decrease the number of slices per model window "
    “(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)\n”);
    ret = false;
    }

    while (inference.buf_ready == 0) {
    delay(1);
    }

    inference.buf_ready = 0;

    return ret;
    }

/**

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

    } else {
    numpy::int16_to_float(&inference.other_buffer[offset], out_ptr, length);

    }

    return 0;
    }

/**

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

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

#4

Hi @Junior.F,

Please note that only the board listed in the library name (e.g. Arduino Nano 33 BLE Sense) will work. We can’t guarantee that other boards are configured the same in Arduino.

It looks like the Adafruit nRF52 boards do not have some of the functions required by the Edge Impulse library code. As a result, I recommend trying the workaround found in this thread: https://forum.edgeimpulse.com/t/error-with-arduino-library-on-adafruit-nrf-board/422/13.

1 Like