Vehicle (car) Detection in Real-Time and Recorded Videos in Python — Windows and macOS

Published in

Analytics Vidhya

5 min readJan 2, 2020

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 project, we will learn how to build a car tracking system in python for both recorded and live cam streamed videos.

Before we begin, we need to install the OpenCV (Open Source Computer Vision) library which is built to help developers carry out tasks related to computer vision. We need to do a pip install for the OpenCV library.

install opencv-python

Let us now build the system in Python

We have the required libraries installed. The way the technology works is that we train the model on various image parameters of the object to be detected (car in this case), which is used to identify the object in our target.

Think of it as the train and test datasets of any machine learning model.

In this case:

Train dataset: .xml files which capture the image details of the target object

Test dataset: Live stream video/ Recorded video

The link to the full code can be found at the end of this article. I’ll explain the code in steps and blocks to help you understand how it works:

Step 1: Open Spyder

Step 2: Import the library

import cv2

Step 3: Reference the input to your webcam or to the video file saved on your hard drive (mp4 format)

Webcam: cap = cv2.VideoCapture(0)
Video: cap = cv2.VideoCapture(<enter file path.mp4>)

Step 4: We will use a pre-trained .xml file which has data on cars built using individual images. You can download the file here

car_cascade = cv2.CascadeClassifier(<enter file path>/cars.xml’)

Step 5: The video is divided into frames and the code reads one frame at a time. In each frame, we detect the location of the car in the frame using the APIs which we have imported above. For each car detected, we locate the coordinates and draw a rectangle around it and release the video to the viewer.

The full code is shown below — explanations follow below the code

while True:
 # reads frames from a video
 ret, frames = cap.read()
 # convert to gray scale of each frames
 gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
 # Detects cars of different sizes in the input image
 cars = car_cascade.detectMultiScale( gray, 1.1, 1)
 # To draw a rectangle in each cars
 for (x,y,w,h) in cars:
 cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2)
 font = cv2.FONT_HERSHEY_DUPLEX
 cv2.putText(frames, ‘Car’, (x + 6, y — 6), font, 0.5, (0, 0, 255), 1)
 # Display frames in a window
 cv2.imshow(‘Car Detection’, frames)
 # Wait for Enter key to stop
 if cv2.waitKey(33) == 13:
 break

Block 1:

# reads frames from a video
 ret, frames = cap.read()
 # convert to gray scale of each frames
 gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)

The video is read in individual frames. Next, the frame is converted to grayscale which helps in detecting the car quickly. The reason why the image is converted to grayscale is that the trained dataset is built in grayscale to reduce the size of the file.

Block 2:

# Detects cars of different sizes in the input image
 cars = car_cascade.detectMultiScale( gray, 1.1, 1) # To draw a rectangle in each cars
 for (x,y,w,h) in cars:
 cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2)
 font = cv2.FONT_HERSHEY_DUPLEX
 cv2.putText(frames, ‘Car’, (x + 6, y — 6), font, 0.5, (0, 0, 255), 
 1)

The first section of the code detects the cars in the frame and stores their coordinates (x, y axes, and the width and height of the car). The second section draws a rectangle around the area where the car is detected and displays the text ‘Car’ above the rectangle. You can change the font of the text and the code (0, 0, 255) is the color code of the rectangle and the text in B-G-R sequence.

Block 3:

# Display frames in a window
 cv2.imshow(‘Car Detection’, frames)
# Wait for Enter key to stop
 if cv2.waitKey(33) == 13:
 break

The resulting image (frame) is released to the viewer and the loop continues to run until the user hits the Enter key on the keyboard.

Step 6: All captured videos must be released.

cap.release()
cv2.destroyAllWindows()

Run the program in the command line

The next step is to save the file in .py format and run it in command line/Anaconda prompt.

I ran it in Anaconda prompt by first navigating to the folder using the command cd.

cd <folder path>

Run the python file

python filename.py

You will see a pop-up window with the video playing. The video might be slow and it is because the number of frames is usually large in OpenCV. However, if you save the video on your hard drive, the written video is not slow and matches the fps (frames per second) of the input video.

You can download more royalty-free videos here.

Eureka! You have successfully built a setup which detects cars in real-time and in video recordings.

Play around with videos with vintage or concept cars and share your story in the comments section below.

Facing issues? Post your query.

Use Cases

Sensors at parking stations can identify defaulters
Detecting the speed of cars
Identify defaulters who speed up the car while pedestrians cross the road

Codes

chandravenky/Computer-Vision---Object-Detection-in-Python

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