open_cv_object_deaction.py

import cv2 
def get_object_name(preview=0):
	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 = r'C:\Users\IamTheHimansh\Documents\BILLROPY\coco.names'
	with open(classFile,'rt') as f:
		classNames = f.read().rstrip('\n').split('\n')
	# print(classNames)
	configPath = r'C:\Users\IamTheHimansh\Documents\BILLROPY\ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
	weightsPath = r'C:\Users\IamTheHimansh\Documents\BILLROPY\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)
	finded_obj=[]
	# 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=(255,255,0),thickness=2)
			cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
			cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0),2)
			cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
			cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0),2)
			print(classNames[classId-1].lower()+" Founded")
			finded_obj.append(classNames[classId-1].lower())
	else:
		print("Nothing Founded")
	if preview==1:
		cv2.imshow("Output",img)
		cv2.waitKey()
	elif preview==0:
		pass
	else :
		print(f"ValueError:- Wanted 0 or 1 but given {preview} \n  The value for Preview  is only 0 or 1 . 1 mean on And 0 means off.")
	return finded_obj
	



if __name__ == '__main__':
	print(get_object_name(1))