The Edge Impulse object detection model (FOMO) is effective at classifying objects and very lightweight (can run on MCUs). It does not however have any object persistence between frames. One common use of computer vision is for object counting- in order to achieve this you will need to add in some extra logic when deploying.
This notebook takes you through how to count objects using the linux deployment block (and provides some pointers for how to achieve similar logic other firmware deployment options).
1. Download the linux deployment .eim for your project
To run your model locally, you need to deploy to a linux target in your project. First you need to enable all linux targets. Head to the deployment screen and click "Linux Boards" then in the following pop-up select "show all Linux deployment options on this page":
Then download the linux/mac target which is relevant to your machine:
Finally, follow the instructions shown as a pop-up to make your .eim file executable (for example for MacOS):
Open a terminal window and navigate to the folder where you downloaded this model.
Mark the model as executable: chmod +x path-to-model.eim
Remove the quarantine flag: xattr -d com.apple.quarantine ./path-to-model.eim
2. Object Detection
Dependencies
Ensure you have these libraries installed before starting:
This program will run object detection on an input video file and count the objects going upwards which pass a threshold (TOP_Y). The sensitivity can be tuned with the number of columns (NUM_COLS) and the DETECT_FACTOR which is the factor of width/height of the object used to determine object permanence between frames.
Ensure you have added the relevant paths to your model file and video file:
modelfile = '/path/to/modelfile.eim'
videofile = '/path/to/video.mp4'
import cv2import osimport timeimport sys, getoptimport numpy as npfrom edge_impulse_linux.image import ImageImpulseRunnermodelfile ='/path/to/modelfile.eim'videofile ='/path/to/video.mp4'runner =None# if you don't want to see a video preview, set this to Falseshow_camera =Trueif (sys.platform =='linux'andnot os.environ.get('DISPLAY')): show_camera =Falseprint('MODEL: '+ modelfile)withImageImpulseRunner(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()defgetFrame(sec): vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000) hasFrames,image = vidcap.read()if hasFrames:return imageelse: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 _ inrange(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 _ inrange(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 thresholdif 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_blobsif (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()
2.2 Run Object Counting on a webcam stream
This program will run object detection on a webcam port and count the objects going upwards which pass a threshold (TOP_Y). The sensitivity can be tuned with the number of columns (NUM_COLS) and the DETECT_FACTOR which is the factor of width/height of the object used to determine object permanence between frames.
Ensure you have added the relevant paths to your model file and video file:
modelfile = '/path/to/modelfile.eim'
[OPTIONAL] camera_port = '/camera_port'
import cv2import osimport sys, getoptimport signalimport timefrom edge_impulse_linux.image import ImageImpulseRunnermodelfile ='/path/to/modelfile.eim'# If you have multiple webcams, replace None with the camera port you desire, get_webcams() can help find thiscamera_port =Nonerunner =None# if you don't want to see a camera preview, set this to Falseshow_camera =Trueif (sys.platform =='linux'andnot os.environ.get('DISPLAY')): show_camera =Falsedefnow():returnround(time.time() *1000)defget_webcams(): port_ids = []for port inrange(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_idsdefsigint_handler(sig,frame):print('Interrupted')if (runner): runner.stop() sys.exit(0)signal.signal(signal.SIGINT, sigint_handler)print('MODEL: '+ modelfile)withImageImpulseRunner(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()iflen(port_ids)==0:raiseException('Cannot find any webcams')iflen(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:raiseException("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 _ inrange(NUM_COLS)]for res, img in runner.classifier(videoCaptureDeviceId):# Initialize list of current blobs current_blobs = [[] for _ inrange(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 thresholdif 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_blobsif (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()+100finally:if (runner): runner.stop()
3. Deploying to MCU firmware
While running object counting on linux hardware is fairly simple, it would be more useful to be able to deploy this to one of the firmware targets. This method varies per target but broadly speaking it is simple to add the object counting logic into existing firmware.
Here are the main steps:
1. Find and clone the Edge Impulse firmware repository for your target hardware
This can be found on our github pages e.g. https://github.com/edgeimpulse/firmware-arduino-nicla-vision
2. Deploy your model to a C++ library
You'll need to replace the "edge-impulse-sdk", "model-parameters" and "tflite-model" folders within the cloned firmware with the ones you've just downloaded for your model.
3. Find the object detection bounding boxes printout code in your firmware
This will be in a .h or similar file somewhere in the firmware. Likely in the ei_image_nn.h file. It can be found by searching for these lines:
The following lines must be added into the logic in these files (For code itself see below, diff for clarity). Firstly these variables must be instantiated:
Then this logic must be inserted into the bounding box printing logic here:
Full code example for nicla vision (src/inference/ei_run_camera_impulse.cpp):
/* 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 columnsstatic 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 variablesstd::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 intei_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!return0;}void ei_run_impulse(void){switch(state){ case INFERENCE_STOPPED:// nothing to doreturn; 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 successfulif(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 debuggingif(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 predictionsei_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 == 1bool 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 isinint 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 thresholdif (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);}}#elsefor (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 == 1ei_printf(" anomaly score: %.3f\n", result.anomaly);#endif#endifif (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//ifnot 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;}boolis_inference_running(void){return(state != INFERENCE_STOPPED);}#endif /* defined(EI_CLASSIFIER_SENSOR) && EI_CLASSIFIER_SENSOR == EI_CLASSIFIER_SENSOR_CAMERA */// AT+RUNIMPULSE
4. Build your firmware locally and flash to your device
Follow the instructions in the README.md file for the firmware repo you have been working in.
5. Run your impulse on device
Use the command below to see on-device inference (follow the local link to see bounding boxes and count output in the browser)