Vehicle Detection and Counting System using OpenCV

Now we will use another image, i.e. bus image, and we will fetch this image from the internet. We will resize the image, store it as a NumPy array, and convert it into grayscale.

# Reading image form url
image2 = Image.open(requests.get('https://qph.fs.quoracdn.net/main-qimg-b5c4e39dcd48dddd9e609e6022f74d85', stream=True).raw)
image2 = image2.resize((450,250))
image_arr2 = np.array(image2)
grey2 = cv2.cvtColor(image_arr2,cv2.COLOR_BGR2GRAY)

We are working with the bus image here, so we require the cascade to detect the bus. We will use Bus_front cascade to identify buses from the image, and we will perform a similar operation as performed above.

bus_cascade_src = 'Bus_front.xml'
bus_cascade = cv2.CascadeClassifier(bus_cascade_src)
bus = bus_cascade.detectMultiScale(grey2, 1.1, 1)

As we did in car cascading similarly, we will be performing the same contour operations on the bus image and create a rectangle around the bus if detected any.

cnt = 0
for (x,y,w,h) in bus:
    cv2.rectangle(image_arr2,(x,y),(x+w,y+h),(255,0,0),2)
    cnt += 1
print(cnt, " bus's found")
Image.fromarray(image_arr2)

Output:

1  bus's found

Now we will perform the vehicle detection and counting in a video. For creating the output video from frames or images, we need cv2.VideoWriter() method. It takes the path with extension as the first parameter; the second parameter is the codec for the output format, then we also need to pass frames per second and height and width.

cascade_src = 'cars.xml'
video_src = 'Cars.mp4'

cap = cv2.VideoCapture(video_src)
car_cascade = cv2.CascadeClassifier(cascade_src)
video = cv2.VideoWriter('result.avi',cv2.VideoWriter_fourcc(*'DIVX'), 15, (450,250))

Now we will read frames one by one from the input video, convert them into grayscale, and use car cascade to detect all cars in that particular frame. In the end, we write this video using video.write() method and video.release() will save this video to the given path.

while True:
    ret, img = cap.read()

    if (type(img) == type(None)):
        break

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cars = car_cascade.detectMultiScale(gray, 1.1, 2)

    for (x,y,w,h) in cars:
        cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)

video.write(img) 
video.release()