import cv2

import numpy

import matplotlib

from ObjectTracker import CentroidTracker

def rescale_frame(frame, percent=.75):

width = int(frame.shape[1] * percent)

height = int(frame.shape[0] * percent)

dim = (width, height)

return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)

def filter_mask(img, a=None):

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))

closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)

dilation = cv2.dilate(opening, kernel, iterations=1)

retval, threshold = cv2.threshold(opening, 155, 255, cv2.THRESH_BINARY)

#threshold = cv2.GaussianBlur(threshold, (5, 5), 1)

return threshold

def detect_vehicles(fg_mask, min_contour_width=20, min_contour_height=30):

matches = []

# finding external contours

contours, hierarchy = cv2.findContours(

fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# filtering by with, height

for (i, contour) in enumerate(contours):

(x, y, w, h) = cv2.boundingRect(contour)

contour_valid = (w >= min_contour_width) and (h >= min_contour_height)

if not contour_valid:

continue

# getting center of the bounding box

centroid = get_centroid(x, y, w, h)

matches.append(((x, y, w, h), centroid))

return matches

def get_centroid(x, y, w, h):

x1 = int(w / 2)

y1 = int(h / 2)

cx = x + x1

cy = y + y1

return (cx, cy)

try:

vidFile = cv2.VideoCapture('AlibiShort.mp4')

except:

print("problem opening input stream")

if not vidFile.isOpened():

print("capture stream not open")

size = 0.6

x1 = int(612*size)

x2 = int(1510*size)

y1 = int(210*size)

y2 = int(938*size)

fgbg = cv2.createBackgroundSubtractorKNN(dist2Threshold=1000.0)

nFrames = int(vidFile.get(cv2.CAP_PROP_FRAME_COUNT))

fps = vidFile.get(cv2.CAP_PROP_FPS)

ret, frame = vidFile.read()

h, w = frame.shape[:2]

det_zone = (100, 330, 600, 430)

print(det_zone)

ct = CentroidTracker(detection_zone=det_zone)

while ret:

patch = rescale_frame(frame, percent=size)

fgmask = fgbg.apply(patch)

filtered_img = filter_mask(fgmask)

squares = detect_vehicles(filtered_img)

cars = ct.update(squares)

for key, car in cars.items():

start_point = (car[0][0], car[0][1])

end_point = (car[0][0]+car[0][2], car[0][1]+car[0][3])

patch = cv2.rectangle(patch, start_point, end_point,

color=(100, 45, 255), thickness=2)

patch = cv2.putText(patch, f'carId:{key}', start_point,

fontFace=4, fontScale=0.6, color=(0, 0, 0), thickness=1)

patch = cv2.putText(patch, f'Car passed:{ct.count}', (

20, 20), fontFace=4, fontScale=0.6, color=(0, 0, 0), thickness=1)

patch = cv2.rectangle(patch, (det_zone[0], det_zone[1]), (

det_zone[2], det_zone[3]), color=(78, 252, 3), thickness=1)

cv2.imshow("frameWindow", patch)

#cv2.imshow("fgmask", filtered_img)

cv2.waitKey(int(1000/fps))

ret, frame = vidFile.read()

if cv2.waitKey(20) & 0xFF == ord('q'):

break