realtime_obj_detection.py

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import cv2
import time

ap=argparse.ArgumentParser()
ap.add_argument("-p","--prototxt",required=True,help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m","--model",required=True,help="path to Caffe pre-trained model")
ap.add_argument("-c","--confidence",type=float,default=0.2,help="minimum probability to filter weak detections")
args=vars(ap.parse_args())

CLASSES=["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"]
COLORS=np.random.uniform(0,255,size=(len(CLASSES),3))

print("----Loading model------")
net=cv2.dnn.readNetFromCaffe(args["prototxt"],args["model"])

print("----starting video stream-----")
vs=VideoStream(src=0).start()
time.sleep(2.0)
fps=FPS().start()

while(True):
	frame=vs.read()
	frame=imutils.resize(frame,width=400)
	h,w=frame.shape[:2]
	blob=cv2.dnn.blobFromImage(cv2.resize(frame,(300,300)),0.007843,(300,300),127.5)
	net.setInput(blob)
	detections=net.forward()
	for i in np.arange(0,detections.shape[2]):
		confidence=detections[0,0,i,2]
		if confidence > args["confidence"]:
			idx=int(detections[0,0,i,1])
			box=detections[0,0,i,3:7]*np.array([w,h,w,h])
			(startX,startY,endX,endY)=box.astype("int")
			label="{}: {:.2f}%".format(CLASSES[idx],confidence*100)
			cv2.rectangle(frame,(startX,startY),(endX,endY),COLORS[idx],2)
			y=startY-15 if startY - 15 > 15 else startY +15
			cv2.putText(frame,label,(startX,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,COLORS[idx],2)

	cv2.imshow("Frame",frame)
	key=cv2.waitKey(1) & 0xFF
	if key==ord("q"):
		break
	fps.update()

fps.stop()
print("Elapsed time:{:.2f}".format(fps.elapsed()))
print("Approx FPS :{:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
vs.stop()