Car-Detection-OpenCV

Vehicle detection is one of the widely used features by companies and organizations these days. This technology uses computer vision to detect different types of vehicles in a video or real-time via a camera. It finds its applications in traffic control, car tracking, creating parking sensors and many more.

In this repository, we will learn how to build a car detecting system in python for both recorded and live cam streamed videos.

Installation

First of all make sure you have Python installed in your system.

Secondly you have to install pip. It can be installed easily using command prompt.

python -m pip install

The last requirement is the OpenCV module. It can be installed using pip.

pip install opencv-python

Detecting cars in Video/Image

Importing necessary Python & OpenCV libraries.

import cv2

Capturing Video

videoCapture is used to capture videos.

For capturing video in the real time using external camera-

  cap = cv2.VideoCapture(1)
  

For capturing or importing video from the saved files-

  cap = cv2.VideoCapture('video path')

INPUT VIDEO-

https://user-images.githubusercontent.com/86667690/127769236-c6c65f7f-1450-4d14-b150-42b0e5077dc9.mp4

Using prebuilt XML classifiers

These are the pre-trained Classifiers that can be directly used.

There are various Cascade classifiers available that can be used according to the requirement.

car_cascade = cv2.CascadeClassifier('haarcascade_cars.xml')
  

Loop Condition

Applying Loop Condition because of the continuity of the video.

while True:

Capturing frame by frame.

ret, frame = cap.read()

Converting image to gray scale

Converting video into gray scale of each frames.

The detection effectively works only on grayscale images/frames. So it is important to convert the colour frames to grayscale.

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

Detecting cars

detectMultiScale function is used to detect the cars. It takes 3 arguments — the input image/frame, scaleFactor and minNeighbours. scaleFactor specifies how much the image size is reduced with each scale. minNeighbours specifies how many neighbors each candidate rectangle should have to retain it.

cars = car_cascade.detectMultiScale(gray, 1.1, 3)

cars contains a list of coordinates for the rectangular regions where cars were found. We use these coordinates to draw the rectangles in our image/video.

for (x,y,w,h) in cars:

    cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)

Displaying the video in real time scenario.

    cv2.imshow('video', frame)
    
    crop_img = frame[y:y+h,x:x+w]
    

Stopping the program if the Q key is pressed.

if cv2.waitKey(25) & 0xFF == ord('q'):
    
    break

Release the VideoCapture object.

cap.release()

Closing all the frames.

cv2.destroyAllWindows()

OUTPUT RESULT-

Counting cars in Video/Real Time

Importing necessary Python & OpenCV libraries.

import cv2

import numpy as np

Capturing Video

videoCapture is used to capture videos.

For capturing video in the real time using external camera-

  cap = cv2.VideoCapture(1)
  

For capturing or importing video from the saved files-

  cap = cv2.VideoCapture('video path')    

https://user-images.githubusercontent.com/86667690/127770208-c6794c92-a82c-4871-a6d3-0d8490f02e18.mp4

Video frame rate

Adjusting Frame rate of the Video

fps = cap.set(cv2.CAP_PROP_FPS,1)
       

Setting some prior values

Setting minimum contour width.

min_contour_width=40  #40

Setting minimum contour height.

min_contour_height=40 

offset=10   

line_height=550  

matches =[]

cars=0 

Defining Function

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

  x1 = int(w / 2)
  
  y1 = int(h / 2)

  cx = x + x1
  
  cy = y + y1
  
  return cx,cy



cap.set(3,1920)

cap.set(4,1080)
 

..........currently under work sorry for the inconvience