main.py

import cv2
import numpy as np

cap = cv2.VideoCapture(0)
whT = 320
confThreshold = 0.5

with open("coco.names", 'r') as f:
    classes = [line.strip() for line in f.readlines()]

net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)


def find_objects(outputs, img):
    hT, wT, cT = img.shape
    bbox = []
    classIds = []
    confs = []

    for output in outputs:
        for det in output:
            scores = det[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > confThreshold:
                w, h = int(det[2] * wT), int(det[3] * hT)
                x, y = int((det[0] * wT) - w / 2), int((det[1] * hT) - h / 2)
                bbox.append([x, y, w, h])
                classIds.append(classId)
                confs.append(float(confidence))
    print(len(bbox))
    indices = cv2.dnn.NMSBoxes(bbox, confs, confThreshold, 0.3)

    for i in indices:
        i = i[0]
        box = bbox[i]
        x, y, w, h = box[0], box[1], box[2], box[3]
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 2)
        cv2.putText(img, f"{classes[classIds[i]]} {int(confs[i] * 100)}%", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
                    (255, 0, 255), 2)


while True:
    success, img = cap.read()

    blob = cv2.dnn.blobFromImage(img, 1 / 255, (whT, whT), [0, 0, 0], 1, crop=False)
    net.setInput(blob)

    layerNames = net.getLayerNames()
    outputNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    outputs = net.forward(outputNames)
    find_objects(outputs, img)

    cv2.imshow("image", img)
    cv2.waitKey(1)