main.py

import cv2
import numpy as np
import cvzone
thres = 0.45 # Threshold to detect object
nms_threshold = 0.2
cap = cv2.VideoCapture(0)
cap.set(3,1280)
cap.set(4,720)
# cap.set(10,150)

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

#print(classNames)
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))
    # print(type(confs[0]))
    # print(confs)

    indices = cv2.dnn.NMSBoxes(bbox,confs,thres,nms_threshold)
    #print(indices)
    # 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)
    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=3)
        cvzone.putTextRect(img, classNames[classIds[i][0]-1].upper(), (box[0]+10,box[1]+30))
        # 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)
    if cv2.waitKey(5) & 0xFF == 27:
        break