objectDetect.py

import cv2
 
#img = cv2.imread('/home/pyarena/python/OpenCV/objectDetection/image1.jpg')
 
cap = cv2.VideoCapture(-1)
cap.set(3, 640)#
cap.set(4, 480)#
 
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'
weightPath = 'frozen_inference_graph.pb'
 
 
 
net = cv2.dnn_DetectionModel(weightPath, 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=0.5)
   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.imshow('output', img)
   cv2.waitKey(1)