YOLOV3

import  cv2
import numpy as np

cap = cv2.VideoCapture(0)
whT = 320
confThreshold = 0.5
nmsThreshold = 0.3

classesFilePath = 'coco.names'
classNames = []

with open(classesFilePath, 'rt') as f:
    classNames = f.read().rstrip('\n').split('\n')

modelConfiguration = 'yolov3-320.cfg'
modelWeights = 'yolov3-320.weights'

net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

def findObjects(outputs, img):
    hT, wT, cT = img.shape
    bbox = []   #x, y, w, h
    classIds = []
    confs = []

    for output in outputs:
        for detection in output:
            scores = detection[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > confThreshold:
                w, h = int(detection[2]*wT), int(detection[3]*hT)
                x, y = int((detection[0]*wT - w/2)), int(detection[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, nms_threshold=nmsThreshold)

    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'{classNames[classIds[i]].upper()} {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()
    #print(layerNames)
    outputNames = [layerNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    #print(outputNames)
    #print(net.getUnconnectedOutLayers())

    outputs = net.forward(outputNames)
    #print(outputs[0].shape)    320*85
    #print(outputs[1].shape)    1200*85
    #print(outputs[2].shape)    4800*85
    #print(outputs[0][0])

    findObjects(outputs, img)

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break