#This code you have to work on and you have to take the video as input 
#python video_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
#  python video_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2

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

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

print("[INFO] 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("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

cv2.destroyAllWindows()
vs.stop()
