In this quick post I wanted to share a very popular and easy way of detecting faces using Haar cascades in OpenCV and Python.
First of all make sure you have OpenCV installed. You can install it using pip: pip install opencv-python
Face detection using Haar cascades is a machine learning based approach where a cascade function is trained with a set of input data. OpenCV already contains many pre-trained classifiers for face, eyes, smiles, etc.. Today we will be using the face classifier. You can experiment with other classifiers as well.
To detect faces in images:
A few things to note:
- The detection works only on grayscale images. So it is important to convert the color image to grayscale. (line 8)
- detectMultiScale function (line 10) is used to detect the faces. It takes 3 arguments — the input image, 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. You can read about it in detail here. You may have to tweak these values to get the best results.
- faces contains a list of coordinates for the rectangular regions where faces were found. We use these coordinates to draw the rectangles in our image.
The only difference here is that we use an infinite loop to loop through each frame in the video. We use cap.read() to read each frame. The first value returned is a flag that indicates if the frame was read correctly or not. We don’t need it. The second value returned is the still frame on which we will be performing the detection.
Find the code here: https://github.com/adarsh1021/facedetection
Also, you can see the video tutorial here.
Note: This article was originally published on towardsdatascience.com, and kindly contributed to DPhi to spread the knowledge.
Featured Image Credit – https://www.kairos.com/blog/face-detection-explained
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