Person detection model

With the tensorflow lite library download for the arduino nano 33 ble sense, there is an example sketch of a person detection model which is doing image classification with an Arducam OV2640. Is it possible to replace the person detection trained_tflite model and lable information from a c++ library deployment from edge impulse to have a custom image model that will run on the arduino nano 33 ble sense?

@timlester Sure, if you follow the Adding sight to your sensors tutorial you can export as OpenMV library. That gives you a tflite file and a labels.txt file.

Hi Jan, from what I can tell there is no direct deployment of an image classification model to an Arduino nano 33 ble sense from edge impulse. So would editing the tflite ‘person detection’ model with the files from an OpenMV library deployment be the best approach at getting the nano 33 ble sense to do image classification or am I over complicating this?

Ah scrap that then. No, the easiest way is just deploy to Arduino library, then following the steps in Then just hook the camera output to the run_impulse function and this works.

You say that like it’s easy, lol…two days later and I’m still trying. When I go the tensorflow lite model solution by changing the model and labels, I can tell that it’s detecting the images but its not outputting the probabilities of each label. I’m assuming the C source file output of the edge impulse model is directly compatible with the tensorflow lite example model?

I’d prefer the route you suggested and stay within edge impulse deployments but when I try the arduino library deployment from edge impulse and try routing camera output to run_impulse I get hung up trying to port the camera output through the

std::function<void(float*, size_t)> data_fn

of the run_impulse function. I can find no documentation on the run_impulse function. Every search just pulls up run_classifier examples, but I know that the camera data must go through the run_impulse function to be processed for the run_classifier function. Is there any documentation on this that I’m missing? I mostly develop .net apps, so forgive me if I’m missing something simple.

Hi @timlester,

I would suggest to start with the static_buffer Arduino example once you imported the library.
The features[] array contains RGB values of your image (you can grab an example of raw features values in your Live Classification tab).
Once it works with the current static definition, you can dynamically fill the features array in the loop function based on your Arducam output.

Side note: if you wish to train an image recognition model within our pipeline, you will need to decrease image size to 48*48 as the Arduino BLE board is limited in RAM.


@timlester sorry where I said run_impulse I meant run_classifier. Sorry for the confusion!

@janjongboom , @aurel

I was able to get up to about a 55 x 55 (RGB) running the static buffer deployed as Quantized (int8) with EON compiler. Any larger and I would get the Failed to allocate TFLite arena error and such. I stole the jpeg decoder resize code for the Arducam from the tensorflow lite person detection model to resize the image and load the ‘features[]’ (image_datax[PPIXELS]) array in the loop. I seem to be having some success with this code by chance it helps anyone. I’m only training on about 600ish images. If you see where I am in error let me know because C/C++ is not my native language…

BTW: I left the 16 bit to RGB565 conversion and grayscale conversion from the original sketch in but commented out in case anyone needs them.

#include <anit-hog_feeder_inference.h>

#include <SPI.h>

#include <Wire.h>

#include <memorysaver.h>

#include <ArduCAM.h>

#include <JPEGDecoder.h>

#include <stdint.h>

#if !(defined OV2640_MINI_2MP_PLUS)

#error Please select the hardware platform and camera module in the Arduino/libraries/ArduCAM/memorysaver.h


#define MAX_JPEG_BYTES 5000

#define PPIXELS 3025

#define CS 7

#define img_sz 55

ArduCAM myCAM(OV2640, CS);

float image_datax[PPIXELS];

enum imgcaptured


    not_captured = 0,

    captured = 1


int raw_feature_get_data(size_t offset, size_t length, float *out_ptr)


    memcpy(out_ptr, image_datax + offset, length * sizeof(float));

    return 0;


void setup()


    // put your setup code here, to run once:



    // Configure the CS pin

    pinMode(CS, OUTPUT);

    digitalWrite(CS, HIGH);


    // initialize SPI


    // Reset the CPLD

    myCAM.write_reg(0x07, 0x80);


    myCAM.write_reg(0x07, 0x00);


    // Test whether we can communicate with Arducam via SPI

    myCAM.write_reg(ARDUCHIP_TEST1, 0x55);

    uint8_t test;

    test = myCAM.read_reg(ARDUCHIP_TEST1);

    if (test != 0x55)


        Serial.println("Can't communicate with Arducam");



    // Use JPEG capture mode, since it allows us to specify

    // a resolution smaller than the full sensor frame



    // Specify the smallest possible resolution




void loop()


    imgcaptured captureimg = capture_resize_image();

    if (captureimg == captured)


        if (sizeof(image_datax) / sizeof(float) != EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE)


             Serial.println("array size error");



        ei_impulse_result_t result = {0};

        // the features are stored into flash, and we don't want to load everything into RAM

        signal_t features_signal;

        features_signal.total_length = sizeof(image_datax) / sizeof(image_datax[0]);

        features_signal.get_data = &raw_feature_get_data;

        // invoke the impulse

        EI_IMPULSE_ERROR res = run_classifier(&features_signal, &result, false /* debug */);

        if (res != 0)


            Serial.println("res != 0");



        bool identifiedd = false;

        for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++)


            float valuee = result.classification[ix].value;

            if (valuee > 0.6)


                if (strcmp(result.classification[ix].label, "hog") == 0)


                    Serial.print("hog - ");


                    identifiedd = true;




                    Serial.print("not_hog - ");


                    identifiedd = true;




        if (!identifiedd)









imgcaptured capture_resize_image()


    uint8_t jpeg_buffer[MAX_JPEG_BYTES] = {0};

    uint32_t jpeg_length = 0;



    // Start capture


    // Wait for indication that it is done

    while (!myCAM.get_bit(ARDUCHIP_TRIG, CAP_DONE_MASK))



    Serial.println("Image captured");


    // Clear the capture done flag


    jpeg_length = myCAM.read_fifo_length();

    Serial.println("Reading %d bytes from Arducam");

    // Ensure there's not too much data for our buffer

    if (jpeg_length > MAX_JPEG_BYTES)


        Serial.println("Too many bytes in FIFO buffer (%d)");

        return not_captured;


    if (jpeg_length == 0)


        Serial.println("No data in Arducam FIFO buffer");

        return not_captured;




    for (int index = 0; index < jpeg_length; index++)


        jpeg_buffer[index] = SPI.transfer(0x00);



    Serial.println("Finished reading");


    JpegDec.decodeArray(jpeg_buffer, jpeg_length);

    // Crop the image by keeping a certain number of MCUs in each dimension

    const int keep_x_mcus = img_sz / JpegDec.MCUWidth;

    const int keep_y_mcus = img_sz / JpegDec.MCUHeight;

    // Calculate how many MCUs we will throw away on the x axis

    const int skip_x_mcus = JpegDec.MCUSPerRow - keep_x_mcus;

    // Roughly center the crop by skipping half the throwaway MCUs at the

    // beginning of each row

    const int skip_start_x_mcus = skip_x_mcus / 2;

    // Index where we will start throwing away MCUs after the data

    const int skip_end_x_mcu_index = skip_start_x_mcus + keep_x_mcus;

    // Same approach for the columns

    const int skip_y_mcus = JpegDec.MCUSPerCol - keep_y_mcus;

    const int skip_start_y_mcus = skip_y_mcus / 2;

    const int skip_end_y_mcu_index = skip_start_y_mcus + keep_y_mcus;

    uint16_t *pImg;

    uint16_t color;

    int indexx = 0;

    // Loop over the MCUs

    while (


        // Skip over the initial set of rows

        if (JpegDec.MCUy < skip_start_y_mcus)




        // Skip if we're on a column that we don't want

        if (JpegDec.MCUx < skip_start_x_mcus ||

            JpegDec.MCUx >= skip_end_x_mcu_index)




        // Skip if we've got all the rows we want

        if (JpegDec.MCUy >= skip_end_y_mcu_index)




        // Pointer to the current pixel

        pImg = JpegDec.pImage;

        // The x and y indexes of the current MCU, ignoring the MCUs we skip

        int relative_mcu_x = JpegDec.MCUx - skip_start_x_mcus;

        int relative_mcu_y = JpegDec.MCUy - skip_start_y_mcus;

        // The coordinates of the top left of this MCU when applied to the output

        // image

        int x_origin = relative_mcu_x * JpegDec.MCUWidth;

        int y_origin = relative_mcu_y * JpegDec.MCUHeight;

        // Loop through the MCU's rows and columns

        for (int mcu_row = 0; mcu_row < JpegDec.MCUHeight; mcu_row++)


            // The y coordinate of this pixel in the output index

            int current_y = y_origin + mcu_row;

            for (int mcu_col = 0; mcu_col < JpegDec.MCUWidth; mcu_col++)


                // Read the color of the pixel as 16-bit integer

                color = *pImg++;

                //Extract the color values (5 red bits, 6 green, 5 blue)

                // uint8_t r, g, b;

                // r = ((color & 0xF800) >> 11) * 8;

                // g = ((color & 0x07E0) >> 5) * 4;

                // b = ((color & 0x001F) >> 0) * 8;

                // Convert to grayscale by calculating luminance

                // See for magic numbers

                //float gray_value = (0.2126 * r) + (0.7152 * g) + (0.0722 * b);

                // The x coordinate of this pixel in the output image

                int current_x = x_origin + mcu_col;

                // The index of this pixel in our flat output buffer

                indexx = (current_y * img_sz) + current_x;

                // image_data[indexx] = gray_value;

                image_datax[indexx] = (float)color;




    return captured;