speed_check.py

import cv2
import dlib
import time
import threading
import math
import os
import requests
from pprint import pprint
import json
from flask import Response
from flask import Flask
from flask import render_template
from threading import Thread, Condition
import random
import queue
import ConfigParser


config=ConfigParser.ConfigParser()
configFilePath=r'.\config.json'
config.read(configFilePath)

carCascade = cv2.CascadeClassifier('myhaar.xml')
video = cv2.VideoCapture('../short-test.mp4')
PATH="P:/hackthon/images"
FILE_PATH="P:/hackthon/list.txt"
WIDTH = config['WIDTH']
HEIGHT = config['HEIGHT']
MIN_WIDTH=config['MIN_WIDTH']
MIN_HEIGHT=config['MIN_HEIGHT']
API_KEY=config['API_KEY']
SPEED_LIMIT=0
MAX_NUM_THREADS=4





lock=threading.Lock()
vehicles=queue.Queue(maxsize=50)

app = Flask(__name__)
resultImage=[]


def overspeeding():

	

		

	while True:
		
		print("Started running overspeeding")
		if(vehicles.empty()):
			print("Running Thread Overspeeding")
			time.sleep(5)
			continue

		car_id=vehicles.get()
		print("popped car:"+str(car_id))
		with open(PATH+'/'+str(car_id)+'car.jpg', 'rb') as fp:
			response = requests.post(
        'https://api.platerecognizer.com/v1/plate-reader/',
         # Optional
        files=dict(upload=fp),
        headers={'Authorization':'Token '+API_KEY})
		res=response.json()
		
		if(not len(res['results'])):
			print ("plate not recognised")
			break;
		else:
			print( res['results'][0]['plate'] )
			print(res['results'][0]['vehicle'])

	# print(type(res)) #type == dictionary
	# print(type(res['results'])) # type==list
	# print(len(res['results']))
	# print(type( res['results'][0]['box'] ))
		

		
	
	
	
  
def estimateSpeed(location1, location2):
	d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
	# ppm = location2[2] / carWidht
	ppm = 8.8
	d_meters = d_pixels / ppm
	#print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
	fps = 18
	speed = d_meters * fps * 3.6
	return speed
	

def trackMultipleObjects():

	global resultImage

	rectangleColor = (0, 255, 0)
	frameCounter = 0
	currentCarID = 0
	fps = 0
	
	carTracker = {}
	carNumbers = {}
	carLocation1 = {}
	carLocation2 = {}
	speed = [None] * 1000
	
	# Write output to video file
	# out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (WIDTH,HEIGHT))


	while True:
		file=open(FILE_PATH, "w+")
		start_time = time.time()
		rc, image = video.read()
		if type(image) == type(None):
			break
		
		image = cv2.resize(image, (WIDTH, HEIGHT))
		resultImage = image.copy()
		
		frameCounter = frameCounter + 1
		
		carIDtoDelete = []

		for carID in carTracker.keys():
			trackingQuality = carTracker[carID].update(image)
			
			if trackingQuality < 7:
				carIDtoDelete.append(carID)
				
		for carID in carIDtoDelete:
			# print ('Removing carID ' + str(carID) + ' from list of trackers.')
			# print ('Removing carID ' + str(carID) + ' previous location.')
			# print ('Removing carID ' + str(carID) + ' current location.')
			carTracker.pop(carID, None)
			carLocation1.pop(carID, None)
			carLocation2.pop(carID, None)
		
		if not (frameCounter % 10):
			gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
			cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
			
			for (_x, _y, _w, _h) in cars:
				x = int(_x)
				y = int(_y)
				w = int(_w)
				h = int(_h)
			
				x_bar = x + 0.5 * w
				y_bar = y + 0.5 * h
				
				matchCarID = None
			
				for carID in carTracker.keys():
					trackedPosition = carTracker[carID].get_position()
					
					t_x = int(trackedPosition.left())
					t_y = int(trackedPosition.top())
					t_w = int(trackedPosition.width())
					t_h = int(trackedPosition.height())
					
					t_x_bar = t_x + 0.5 * t_w
					t_y_bar = t_y + 0.5 * t_h
				
					if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
						matchCarID = carID
				
				if matchCarID is None and w>MIN_WIDTH and h>MIN_HEIGHT :

					
					print ('Creating new tracker ' + str(currentCarID))

					#cropping the detected vehicle and saving it in images folder
					box=(x,y,w,h)
					crop=image[y:y+h,x:x+w]
					cv2.imwrite(os.path.join(PATH, str(currentCarID)+'car.jpg'), crop)
					tracker = dlib.correlation_tracker()
					tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
					
					carTracker[currentCarID] = tracker
					carLocation1[currentCarID] = [x, y, w, h]

					currentCarID = currentCarID + 1
		
		#cv2.line(resultImage,(0,480),(1280,480),(255,0,0),5)


		for carID in carTracker.keys():
			trackedPosition = carTracker[carID].get_position()
					
			t_x = int(trackedPosition.left())
			t_y = int(trackedPosition.top())
			t_w = int(trackedPosition.width())
			t_h = int(trackedPosition.height())
			
			cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4)
			
			# speed estimation
			carLocation2[carID] = [t_x, t_y, t_w, t_h]
		
		end_time = time.time()
		
		if not (end_time == start_time):
			fps = 1.0/(end_time - start_time)
		
		cv2.putText(resultImage, 'FPS: ' + str(int(fps)), (620, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)


		for i in carLocation1.keys():	
			if frameCounter % 1 == 0:
				[x1, y1, w1, h1] = carLocation1[i]
				[x2, y2, w2, h2] = carLocation2[i]
		
				# print 'previous location: ' + str(carLocation1[i]) + ', current location: ' + str(carLocation2[i])
				carLocation1[i] = [x2, y2, w2, h2]
		
				# print 'new previous location: ' + str(carLocation1[i])
				if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
					if (speed[i] == None or speed[i] == 0) and y1 >= 275 and y1 <= 285:
						speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2])
						if int(speed[i])>SPEED_LIMIT:
							vehicles.put(i)
							print("Pushed Overspeeding Vehicle"+str(i))
							# overspeeding()
					#if y1 > 275 and y1 < 285:
					if speed[i] != None and y1 >= 180:
						cv2.putText(resultImage, str(int(speed[i])) + " km/hr", (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 255, 255), 2)
						
					#print ('CarID ' + str(i) + ': speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')

					#else:
					#	cv2.putText(resultImage, "Far Object", (int(x1 + w1/2), int(y1)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)

						#print ('CarID ' + str(i) + ' Location1: ' + str(carLocation1[i]) + ' Location2: ' + str(carLocation2[i]) + ' speed is ' + str("%.2f" % round(speed[i], 0)) + ' km/h.\n')
		cv2.imshow('result', resultImage)

		# Write the frame into the file 'output.avi'
		#out.write(resultImage)


		if cv2.waitKey(33) == 27:
			break
	
	cv2.destroyAllWindows()
	
def generate():

	# grab global references to the output frame and lock variables
	
	# loop over frames from the output stream
	global resultImage, lock

	while True:

		with lock:
		# wait until the lock is acquired
		
			# check if the output frame is available, otherwise skip
			# the iteration of the loop
			
			if resultImage is None:
				continue

			# encode the frame in JPEG format
			(flag, encodedImage) = cv2.imencode(".jpg", resultImage)

			# ensure the frame was successfully encoded
			if not flag:
				continue

		# yield the output frame in the byte format
		yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + 
			bytearray(encodedImage) + b'\r\n')

@app.route("/overspeed")
def video_feed():
	# return the response generated along with the specific media
	# type (mime type)
	return Response(generate(),
		mimetype = "multipart/x-mixed-replace; boundary=frame")

    




if __name__ == '__main__':

	t = threading.Thread(target=trackMultipleObjects)
	t.daemon = True
	t.start()

	t2=threading.Thread(target=overspeeding)
	t2.daemon=True
	t2.start()
	# trackMultipleObjects()

	app.run(host='127.0.0.1', port='3001', debug=True,threaded=True, use_reloader=False)

	# trackMultipleObjects()
	# p=ProducerThread(name='producer')
	# c=ConsumerThread(name="consumer")

	# # p.start()
	# c.start()


	# Thread(target=trackMultipleObjects).start()
	# Thread(target=overspeeding).start()