main.py

import cv2
import numpy as np

thres=0.45
nms_threshold=0.2
cap=cv2.VideoCapture("1.mp4")
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)
    bbox=list(bbox)
    confs=list(np.array(confs).reshape(1,-1)[0])
    confs=list(map(float,confs))
    indices=cv2.dnn.NMSBoxes(bbox,confs,thres,nms_threshold)

    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,h+y),color=(0,255,0),thickness=2)
        cv2.putText(img,classNames[classIds[i][0]-1].upper(),(box[0]+10,box[1]+30),
                    cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
    cv2.imshow("Output",img)
    cv2.waitKey(1)