# import the necessary packages
import numpy as np
import argparse
import cv2
from shapely.geometry import Polygon
import time
import threading
import math

def task():
  print("Timer object is getting executed...*****************************************")

t = threading.Timer(5, task)
temp=0

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--video", required=True,
	help="path to input video")
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())

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
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))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])



# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
# image = cv2.imread(args["image"])
cap = cv2.VideoCapture(args["video"])
counter=0
timer=0
frequency_error=0
ret, image = cap.read()
skip_frames=2
illegal=False
alert=False
illegal_detection_percent=30
alarm_time=1    # In Seconds

while (ret):
	counter+=1
	ret, image = cap.read()

	if counter%skip_frames==0:
		# print(counter)
		# ret, image = cap.read()
		
		


		(h, w) = image.shape[:2]
		# cv2.imwrite("C:\Users\qmobi\Desktop\Python\Object-Detection-Tutorial-master\images\parking.jpg", image)
		blob = cv2.dnn.blobFromImage(cv2.resize(image, (318, 260)), 0.007843, (300, 300), 127.5)

		#draw polygon
		# top-left, buttom-left, buttom-right, top-right
		pts = np.array([[250,130],[230,180],[380,205],[405,150]], np.int32)

		pts_reshape = pts.reshape((-1,1,2))
		image = cv2.polylines(image,[pts_reshape],True,(0,255,255))
		

		# pass the blob through the net work and obtain the detections and predictions
		# print("[INFO] computing object detections...")
		net.setInput(blob)
		detections = net.forward()
		# loop over the detections
		# print( detections.shape[2])
		# print(detections.shape[2])
		
		for i in np.arange(0, detections.shape[2]):
			
			
			# extract the confidence (i.e., probability) associated with the prediction
			confidence = detections[0, 0, i, 2]
			# filter out weak detections by ensuring the `confidence` is greater than the minimum confidence
			if confidence > args["confidence"]:

				

				# extract the index of the class label from the `detections`,
				# then compute the (x, y)-coordinates of the bounding box for the object
				idx = int(detections[0, 0, i, 1])
				
				if(idx==7 ): #dedtect only cars 

					box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
					(startX, startY, endX, endY) = box.astype("int")

					#calculate polygon coordinatef from detection points
					ax=startX
					ay=startY
					bx=startX
					by=endY
					cx=endX
					cy=endY
					dx=endX
					dy=startY
					
					detected_pts = np.array([[ax,ay],[bx,by],[cx,cy],[dx,dy]], np.int32)
					# detected_pts_reshape = detected_pts.reshape((-1,1,2))
					# image = cv2.polylines(image,[detected_pts_reshape],True,(0,0,255))

					# find collision ````````````````````````````````````````````

					p1=Polygon(pts)
					p2=Polygon(detected_pts)
					p3=p2.intersection(p1)
					# print(p3) # result: POLYGON ((0.5 0.5, 1 1, 1 0, 0.5 0.5))
					detection_percentage=p3.area*100/p1.area
					# print(detection_percentage,"%") # result: 0.25

					
					if(detection_percentage>=illegal_detection_percent):
						illegal=True
						
						# temp+=skip_frames
						# if temp>150:
						# 	print("*********************Car is on wrong parking**************************")
					# else:
					# 	temp=0


					# ``````````````````````````````````````````````````````````

					# display the prediction
					label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
					# print("[INFO] {}".format(label))
					cv2.rectangle(image, (startX, startY), (endX, endY),
						COLORS[idx], 2)
					y = startY - 15 if startY - 15 > 15 else startY + 15
					cv2.putText(image, label+" Illegal:"+str(detection_percentage)[0:2]+"%", (startX, y),
						cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2)
					

			# end of if condition
			# Define Alarm Time in seconds
			# alarm_time=5
			time_check=(30*alarm_time)/skip_frames

			# print("time",time_check)
			
			if illegal==True:
				frequency_error=0
				timer+=1
				# illegal=False
				# print(timer)
				
				if math.floor(timer/100)>=time_check:
					alert=True
					print("************ wrong Parking ********",counter)
					illegal=False

			else:
				frequency_error+=1
				timer+=1

				if(frequency_error==5):
					timer=0
					illegal=False
					alert=False
					# print("************ Its Ok ********")
					frequency_error=0
				# else:
					


			# show the output image
			if alert==True:
				cv2.putText(image, "Illegal Parking Alert !!!******", (5, 25),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2)
				cv2.imshow("Output", image)
				cv2.waitKey(1)	
			else:
				cv2.imshow("Output", image)
				cv2.waitKey(1)




	

# USAGE
# python deep.py --video images/example_01.jpg --prototxt MobileNetSSD.prototxt.txt --model NetSSD.caffemodel


