Computer vision:Vehicle detection and counting using openCV | Arabic
import cv2
import numpy as np
from time import sleep
largura_min = 80
altura_min = 80
offset = 6
pos_linha = 550
# FPS to vĂdeo
delay = 60
detec = []
carros = 0
def pega_centro(x, y, w, h):
x1 = int(w / 2)
y1 = int(h / 2)
cx = x + x1
cy = y + y1
return cx, cy
# video source input
cap = cv2.VideoCapture('video.mp4')
subtracao = cv2.bgsegm.createBackgroundSubtractorMOG()
while True:
ret, frame1 = cap.read()
tempo = float(1/delay)
sleep(tempo)
grey = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(grey, (3, 3), 5)
img_sub = subtracao.apply(blur)
dilat = cv2.dilate(img_sub, np.ones((5, 5)))
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
dilatada = cv2.morphologyEx(dilat, cv2. MORPH_CLOSE, kernel)
dilatada = cv2.morphologyEx(dilatada, cv2. MORPH_CLOSE, kernel)
contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.line(frame1, (25, pos_linha), (1200, pos_linha), (176, 130, 39), 2)
for(i, c) in enumerate(contorno):
(x, y, w, h) = cv2.boundingRect(c)
validar_contorno = (w >= largura_min) and (h >= altura_min)
if not validar_contorno:
continue
cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
centro = pega_centro(x, y, w, h)
detec.append(centro)
cv2.circle(frame1, centro, 4, (0, 0, 255), -1)
for (x, y) in detec:
if (y < (pos_linha + offset)) and (y > (pos_linha-offset)):
carros += 1
cv2.line(frame1, (25, pos_linha), (1200, pos_linha), (0, 127, 255), 3)
detec.remove((x, y))
print("No. of cars detected : " + str(carros))
cv2.putText(frame1, "VEHICLE COUNT : "+str(carros), (320, 70), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 4)
cv2.imshow("Video Original", frame1)
cv2.imshow(" Detectar ", dilatada)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
cap.release()
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