Count objects using FOMO

! pip install edge_impulse_linux
! pip install numpy
! pip install opencv-python

import cv2
import os
import time
import sys, getopt
import numpy as np
from edge_impulse_linux.image import ImageImpulseRunner


modelfile = '/path/to/modelfile.eim'
videofile = '/path/to/video.mp4'


runner = None
# if you don't want to see a video preview, set this to False
show_camera = True
if (sys.platform == 'linux' and not os.environ.get('DISPLAY')):
    show_camera = False
print('MODEL: ' + modelfile)

with ImageImpulseRunner(modelfile) as runner:
    try:
        model_info = runner.init()
        print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
        labels = model_info['model_parameters']['labels']
        count = 0
        vidcap = cv2.VideoCapture(videofile)
        sec = 0
        start_time = time.time()

        def getFrame(sec):
            vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000)
            hasFrames,image = vidcap.read()
            if hasFrames:
                return image
            else:
                print('Failed to load frame', videofile)
                exit(1)


        img = getFrame(sec)
        
    
        # Define the top of the image and the number of columns
        TOP_Y = 30
        NUM_COLS = 5
        COL_WIDTH = int(vidcap.get(3) / NUM_COLS)
        # Define the factor of the width/height which determines the threshold
        # for detection of the object's movement between frames:
        DETECT_FACTOR = 1.5

        # Initialize variables
        count = [0] * NUM_COLS
        countsum = 0
        previous_blobs = [[] for _ in range(NUM_COLS)]
        
        while img.size != 0:
            # imread returns images in BGR format, so we need to convert to RGB
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

            # get_features_from_image also takes a crop direction arguments in case you don't have square images
            features, cropped = runner.get_features_from_image(img)
            img2 = cropped
            COL_WIDTH = int(np.shape(cropped)[0]/NUM_COLS)

            # the image will be resized and cropped, save a copy of the picture here
            # so you can see what's being passed into the classifier
            cv2.imwrite('debug.jpg', cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))

            res = runner.classify(features)
            # Initialize list of current blobs
            current_blobs = [[] for _ in range(NUM_COLS)]
            
            if "bounding_boxes" in res["result"].keys():
                print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
                for bb in res["result"]["bounding_boxes"]:
                    print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                    img2 = cv2.rectangle(cropped, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)

                        # Check which column the blob is in
                    col = int(bb['x'] / COL_WIDTH)

                    # Check if blob is within DETECT_FACTOR*h of a blob detected in the previous frame and treat as the same object
                    for blob in previous_blobs[col]:
                        if abs(bb['x'] - blob[0]) < DETECT_FACTOR * (bb['width'] + blob[2]) and abs(bb['y'] - blob[1]) < DETECT_FACTOR * (bb['height'] + blob[3]):
                        # Check this blob has "moved" across the Y threshold
                            if blob[1] >= TOP_Y and bb['y'] < TOP_Y:
                                # Increment count for this column if blob has left the top of the image
                                count[col] += 1
                                countsum += 1
                    # Add current blob to list
                    current_blobs[col].append((bb['x'], bb['y'], bb['width'], bb['height']))



            # Update previous blobs
            previous_blobs = current_blobs

            if (show_camera):
                im2 = cv2.resize(img2, dsize=(800,800))
                cv2.putText(im2, f'{countsum} items passed', (15,750), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
                cv2.imshow('edgeimpulse', cv2.cvtColor(im2, cv2.COLOR_RGB2BGR))
                print(f'{count}')
                if cv2.waitKey(1) == ord('q'):
                    break

            sec = time.time() - start_time
            sec = round(sec, 2)
            # print("Getting frame at: %.2f sec" % sec)
            img = getFrame(sec)
    finally:
        if (runner):
            print(f'{countsum} Items Left Conveyorbelt')
            runner.stop()

import cv2
import os
import sys, getopt
import signal
import time
from edge_impulse_linux.image import ImageImpulseRunner

modelfile = '/path/to/modelfile.eim'
# If you have multiple webcams, replace None with the camera port you desire, get_webcams() can help find this
camera_port = None


runner = None
# if you don't want to see a camera preview, set this to False
show_camera = True
if (sys.platform == 'linux' and not os.environ.get('DISPLAY')):
    show_camera = False

def now():
    return round(time.time() * 1000)

def get_webcams():
    port_ids = []
    for port in range(5):
        print("Looking for a camera in port %s:" %port)
        camera = cv2.VideoCapture(port)
        if camera.isOpened():
            ret = camera.read()[0]
            if ret:
                backendName =camera.getBackendName()
                w = camera.get(3)
                h = camera.get(4)
                print("Camera %s (%s x %s) found in port %s " %(backendName,h,w, port))
                port_ids.append(port)
            camera.release()
    return port_ids

def sigint_handler(sig, frame):
    print('Interrupted')
    if (runner):
        runner.stop()
    sys.exit(0)

signal.signal(signal.SIGINT, sigint_handler)


print('MODEL: ' + modelfile)



with ImageImpulseRunner(modelfile) as runner:
    try:
        model_info = runner.init()
        print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
        labels = model_info['model_parameters']['labels']
        if camera_port:
            videoCaptureDeviceId = int(args[1])
        else:
            port_ids = get_webcams()
            if len(port_ids) == 0:
                raise Exception('Cannot find any webcams')
            if len(port_ids)> 1:
                raise Exception("Multiple cameras found. Add the camera port ID as a second argument to use to this script")
            videoCaptureDeviceId = int(port_ids[0])

        camera = cv2.VideoCapture(videoCaptureDeviceId)
        ret = camera.read()[0]
        if ret:
            backendName = camera.getBackendName()
            w = camera.get(3)
            h = camera.get(4)
            print("Camera %s (%s x %s) in port %s selected." %(backendName,h,w, videoCaptureDeviceId))
            camera.release()
        else:
            raise Exception("Couldn't initialize selected camera.")

        next_frame = 0 # limit to ~10 fps here
        
        # Define the top of the image and the number of columns
        TOP_Y = 100
        NUM_COLS = 5
        COL_WIDTH = int(w / NUM_COLS)
        # Define the factor of the width/height which determines the threshold
        # for detection of the object's movement between frames:
        DETECT_FACTOR = 1.5

        # Initialize variables
        count = [0] * NUM_COLS
        countsum = 0
        previous_blobs = [[] for _ in range(NUM_COLS)]

        

        for res, img in runner.classifier(videoCaptureDeviceId):
            # Initialize list of current blobs
            current_blobs = [[] for _ in range(NUM_COLS)]
            
            if (next_frame > now()):
                time.sleep((next_frame - now()) / 1000)

            if "bounding_boxes" in res["result"].keys():
                print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
                for bb in res["result"]["bounding_boxes"]:
                    print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                    img = cv2.rectangle(img, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)

                        # Check which column the blob is in
                    col = int(bb['x'] / COL_WIDTH)
                    # Check if blob is within DETECT_FACTOR*h of a blob detected in the previous frame and treat as the same object
                    for blob in previous_blobs[col]:
                        print(abs(bb['x'] - blob[0]) < DETECT_FACTOR * (bb['width'] + blob[2]))
                        print(abs(bb['y'] - blob[1]) < DETECT_FACTOR * (bb['height'] + blob[3]))
                        if abs(bb['x'] - blob[0]) < DETECT_FACTOR * (bb['width'] + blob[2]) and abs(bb['y'] - blob[1]) < DETECT_FACTOR * (bb['height'] + blob[3]):
                        # Check this blob has "moved" across the Y threshold
                            if blob[1] >= TOP_Y and bb['y'] < TOP_Y:
                                # Increment count for this column if blob has left the top of the image
                                count[col] += 1
                                countsum += 1
                    # Add current blob to list
                    current_blobs[col].append((bb['x'], bb['y'], bb['width'], bb['height']))
                
            # Update previous blobs
            previous_blobs = current_blobs

            if (show_camera):
                im2 = cv2.resize(img, dsize=(800,800))
                cv2.putText(im2, f'{countsum} items passed', (15,750), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
                cv2.imshow('edgeimpulse', cv2.cvtColor(im2, cv2.COLOR_RGB2BGR))
                print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))

                if cv2.waitKey(1) == ord('q'):
                    break

            next_frame = now() + 100
    finally:
        if (runner):
            runner.stop()

#if EI_CLASSIFIER_OBJECT_DETECTION == 1
        bool bb_found = result.bounding_boxes[0].value > 0;

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

/* Include ----------------------------------------------------------------- */
#include "model-parameters/model_metadata.h"
#include "ei_device_lib.h"

#if defined(EI_CLASSIFIER_SENSOR) && EI_CLASSIFIER_SENSOR == EI_CLASSIFIER_SENSOR_CAMERA

#include "edge-impulse-sdk/classifier/ei_run_classifier.h"
#include "edge-impulse-sdk/dsp/image/image.hpp"
#include "ei_camera.h"
#include "firmware-sdk/at_base64_lib.h"
#include "firmware-sdk/jpeg/encode_as_jpg.h"
#include "firmware-sdk/ei_device_interface.h"
#include "stdint.h"
#include "ei_device_nicla_vision.h"
#include "ei_run_impulse.h"

#include <ea_malloc.h>

#define DWORD_ALIGN_PTR(a)   ((a & 0x3) ?(((uintptr_t)a + 0x4) & ~(uintptr_t)0x3) : a)
#define ALIGN_PTR(p,a)   ((p & (a-1)) ?(((uintptr_t)p + a) & ~(uintptr_t)(a-1)) : p)

typedef enum {
    INFERENCE_STOPPED,
    INFERENCE_WAITING,
    INFERENCE_SAMPLING,
    INFERENCE_DATA_READY
} inference_state_t;

static inference_state_t state = INFERENCE_STOPPED;
static uint64_t last_inference_ts = 0;

static bool debug_mode = false;
static bool continuous_mode = false;

static uint8_t *snapshot_buf = nullptr;
static uint32_t snapshot_buf_size;

static ei_device_snapshot_resolutions_t snapshot_resolution;
static ei_device_snapshot_resolutions_t fb_resolution;

static bool resize_required = false;
static uint32_t inference_delay;

 // Define the top of the image and the number of columns
static int TOP_Y = 50;
static int NUM_COLS = 5;
static int COL_WIDTH = EI_CLASSIFIER_INPUT_WIDTH / NUM_COLS;
static int MAX_ITEMS = 10;

// Define the factor of the width/height which determines the threshold
// for detection of the object's movement between frames:
static float DETECT_FACTOR = 1.5;

// Initialize variables
std::vector<int> count(NUM_COLS, 0);
int countsum =0;
int notfoundframes = 0;
std::vector<std::vector<ei_impulse_result_bounding_box_t> > previous_blobs(NUM_COLS);

static int ei_camera_get_data(size_t offset, size_t length, float *out_ptr)
{
    // we already have a RGB888 buffer, so recalculate offset into pixel index
    size_t pixel_ix = offset * 3;
    size_t pixels_left = length;
    size_t out_ptr_ix = 0;

    while (pixels_left != 0) {
        out_ptr[out_ptr_ix] = (snapshot_buf[pixel_ix] << 16) + (snapshot_buf[pixel_ix + 1] << 8) + snapshot_buf[pixel_ix + 2];

        // go to the next pixel
        out_ptr_ix++;
        pixel_ix+=3;
        pixels_left--;
    }

    // and done!
    return 0;
}

void ei_run_impulse(void)
{
    switch(state) {
        case INFERENCE_STOPPED:
            // nothing to do
            return;
        case INFERENCE_WAITING:
            if(ei_read_timer_ms() < (last_inference_ts + inference_delay)) {
                return;
            }
            state = INFERENCE_DATA_READY;
            break;
        case INFERENCE_SAMPLING:
        case INFERENCE_DATA_READY:
            if(continuous_mode == true) {
                state = INFERENCE_WAITING;
            }
            break;
        default:
            break;
    }

    snapshot_buf = (uint8_t*)ea_malloc(snapshot_buf_size + 32);
    snapshot_buf = (uint8_t *)ALIGN_PTR((uintptr_t)snapshot_buf, 32);

    // check if allocation was successful
    if(snapshot_buf == nullptr) {
        ei_printf("ERR: Failed to allocate snapshot buffer!\n");
        return;
    }

    EiCameraNiclaVision *camera = static_cast<EiCameraNiclaVision*>(EiCameraNiclaVision::get_camera());

    ei_printf("Taking photo...\n");

    bool isOK = camera->ei_camera_capture_rgb888_packed_big_endian(snapshot_buf, snapshot_buf_size);
    if (!isOK) {
        return;
    }

    if (resize_required) {
        ei::image::processing::crop_and_interpolate_rgb888(
            snapshot_buf,
            fb_resolution.width,
            fb_resolution.height,
            snapshot_buf,
            snapshot_resolution.width,
            snapshot_resolution.height);
    }

    ei::signal_t signal;
    signal.total_length = EI_CLASSIFIER_INPUT_WIDTH * EI_CLASSIFIER_INPUT_HEIGHT;
    signal.get_data = &ei_camera_get_data;

    // Print framebuffer as JPG during debugging
    if(debug_mode) {
        ei_printf("Begin output\n");

        size_t jpeg_buffer_size = EI_CLASSIFIER_INPUT_WIDTH * EI_CLASSIFIER_INPUT_HEIGHT >= 128 * 128 ?
            8192 * 3 :
            4096 * 4;
        uint8_t *jpeg_buffer = NULL;
        jpeg_buffer = (uint8_t*)ei_malloc(jpeg_buffer_size);
        if (!jpeg_buffer) {
            ei_printf("ERR: Failed to allocate JPG buffer\r\n");
            return;
        }

        size_t out_size;
        int x = encode_rgb888_signal_as_jpg(&signal, EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT, jpeg_buffer, jpeg_buffer_size, &out_size);
        if (x != 0) {
            ei_printf("Failed to encode frame as JPEG (%d)\n", x);
            return;
        }

        ei_printf("Framebuffer: ");
        base64_encode((char*)jpeg_buffer, out_size, ei_putc);
        ei_printf("\r\n");

        if (jpeg_buffer) {
            ei_free(jpeg_buffer);
        }
    }

    // run the impulse: DSP, neural network and the Anomaly algorithm
    ei_impulse_result_t result = { 0 };

    EI_IMPULSE_ERROR ei_error = run_classifier(&signal, &result, false);
    if (ei_error != EI_IMPULSE_OK) {
        ei_printf("ERR: Failed to run impulse (%d)\n", ei_error);
        ea_free(snapshot_buf);
        return;
    }
    ea_free(snapshot_buf);

    // print the predictions
    ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms., Count: %d ): \n",
                result.timing.dsp, result.timing.classification,result.timing.anomaly, countsum);
    
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
    bool bb_found = result.bounding_boxes[0].value > 0;
    std::vector<std::vector<ei_impulse_result_bounding_box_t> > current_blobs(NUM_COLS);
    for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
        auto bb = result.bounding_boxes[ix];
        if (bb.value == 0) {
            continue;
        }
        // Check which column the blob is in
        int col = int(bb.x / COL_WIDTH);
        // Check if blob is within DETECT_FACTOR*h of a blob detected in the previous frame and treat as the same object
        for (auto blob : previous_blobs[col]) {
            if (abs(int(bb.x - blob.x)) < DETECT_FACTOR * (bb.width + blob.width) && abs(int(bb.y - blob.y)) < DETECT_FACTOR * (bb.height + blob.height)) {
                // Check this blob has "moved" across the Y threshold
                if (blob.y >= TOP_Y && bb.y < TOP_Y) {
                    // Increment count for this column if blob has left the top of the image
                    count[col]++;
                    countsum++;
                }
            }
        }
        // Add current blob to list
        current_blobs[col].push_back(bb);
        ei_printf("    %s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
    }
    previous_blobs = std::move(current_blobs);
    if (bb_found) { 
        ei_printf("    Count: %d\n",countsum);
        notfoundframes = 0;
    }
    else {
        notfoundframes ++;
        if (notfoundframes == 1){
            ei_printf("    No objects found\n");
        }
        else {
            ei_printf("    Count: %d\n",countsum);
        }
    }
    
#else
    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
#endif

    if (debug_mode) {
        ei_printf("\r\n----------------------------------\r\n");
        ei_printf("End output\r\n");
    }

    if(continuous_mode == false) {
        ei_printf("Starting inferencing in %d seconds...\n", inference_delay / 1000);
        last_inference_ts = ei_read_timer_ms();
        state = INFERENCE_WAITING;
    }
}

void ei_start_impulse(bool continuous, bool debug, bool use_max_uart_speed)
{
    snapshot_resolution.width = EI_CLASSIFIER_INPUT_WIDTH;
    snapshot_resolution.height = EI_CLASSIFIER_INPUT_HEIGHT;

    debug_mode = debug;
    continuous_mode = (debug) ? true : continuous;

    EiDeviceNiclaVision* dev = static_cast<EiDeviceNiclaVision*>(EiDeviceNiclaVision::get_device());
    EiCameraNiclaVision *camera = static_cast<EiCameraNiclaVision*>(EiCameraNiclaVision::get_camera());

    // check if minimum suitable sensor resolution is the same as 
    // desired snapshot resolution
    // if not we need to resize later
    fb_resolution = camera->search_resolution(snapshot_resolution.width, snapshot_resolution.height);

    if (snapshot_resolution.width != fb_resolution.width || snapshot_resolution.height != fb_resolution.height) {
        resize_required = true;
    }

    if (!camera->init(snapshot_resolution.width, snapshot_resolution.height)) {
        ei_printf("Failed to init camera, check if camera is connected!\n");
        return;
    }

    snapshot_buf_size = fb_resolution.width * fb_resolution.height * 3;

    // summary of inferencing settings (from model_metadata.h)
    ei_printf("Inferencing settings:\n");
    ei_printf("\tImage resolution: %dx%d\n", EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT);
    ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
    ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0]));

    if(continuous_mode == true) {
        inference_delay = 0;
        state = INFERENCE_DATA_READY;
    }
    else {
        inference_delay = 2000;
        last_inference_ts = ei_read_timer_ms();
        state = INFERENCE_WAITING;
        ei_printf("Starting inferencing in %d seconds...\n", inference_delay / 1000);
    }

    if (debug_mode) {
        ei_printf("OK\r\n");
        ei_sleep(100);
        dev->set_max_data_output_baudrate();
        ei_sleep(100);
    }

    while(!ei_user_invoke_stop_lib()) {
        ei_run_impulse();
        ei_sleep(1);
    }

    ei_stop_impulse();

    if (debug_mode) {
        ei_printf("\r\nOK\r\n");
        ei_sleep(100);
        dev->set_default_data_output_baudrate();
        ei_sleep(100);
    }

}

void ei_stop_impulse(void)
{
    state = INFERENCE_STOPPED;
}

bool is_inference_running(void)
{
    return (state != INFERENCE_STOPPED);
}

#endif /* defined(EI_CLASSIFIER_SENSOR) && EI_CLASSIFIER_SENSOR == EI_CLASSIFIER_SENSOR_CAMERA */

// AT+RUNIMPULSE

edge-impulse-run-impulse --debug