main.py

import cv2

thres = 0.45  # Threshold to detect object

cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
cap.set(10, 70)

classNames = []
classFile = 'coco.names'
with open(classFile,'rt') as f:
    classNames = f.read().rstrip('\n').split('\n')

    configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
    weightsPath = 'frozen_inference_graph.pb'

    net = cv2.dnn_DetectionModel(weightsPath, configPath)
    net.setInputSize(320, 320)
    net.setInputScale(1.0 / 127.5)
    net.setInputMean((127.5, 127.5, 127.5))
    net.setInputSwapRB(True)

    while True:
        success, img = cap.read()
        classIds, confs, bbox = net.detect(img, confThreshold=thres)
        print(classIds, bbox)

        if len(classIds) != 0:
            for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
                cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
                cv2.putText(img, classNames[classId-1].upper(),(box[0]+10,box[1]+30),
                            cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
                cv2.putText(img, str(round(confidence * 100, 2)), (box[0]+200,box[1]+30),
                                                                   cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)

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