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The objective of the program given is to detect object of interest(Car) in video frames and to keep tracking the same object. This is an example of how to detect vehicles in Python.
Why Vehicle Detection?
- The startling losses both in human lives and finance caused by vehicle accidents.
- Detecting vehicles in images acquired from a moving platform is a challenging problem.
Steps to download the requirements below:
- Download Python 2.7.x version, numpy and OpenCV 2.4.x version.Check if your Windows either 32 bit or 64 bit is compatible and install accordingly.
sudo apt-get install python pip install numpy
- install OpenCV from here
- Make sure that numpy is running in your python then try to install opencv.
- Put the cars.xml file in the same folder. Save this as .xml file.
- Download this video from here as input
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# OpenCV Python program to detect cars in video frame # import libraries of python OpenCV import cv2 # capture frames from a video cap = cv2.VideoCapture('video.avi') # Trained XML classifiers describes some features of some object we want to detect car_cascade = cv2.CascadeClassifier('cars.xml') # loop runs if capturing has been initialized. 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) # Display frames in a window cv2.imshow('video2', frames) # Wait for Esc key to stop if cv2.waitKey(33) == 27: break # De-allocate any associated memory usage cv2.destroyAllWindows() |
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