Python | Grayscaling of Images using OpenCV
Grayscaling is the process of converting an image from other color spaces e.g. RGB, CMYK, HSV, etc. to shades of gray. It varies between complete black and complete white.
Importance of grayscaling
- Dimension reduction: For example, In RGB images there are three color channels and has three dimensions while grayscale images are single-dimensional.
- Reduces model complexity: Consider training neural article on RGB images of 10x10x3 pixel. The input layer will have 300 input nodes. On the other hand, the same neural network will need only 100 input nodes for grayscale images.
- For other algorithms to work: Many algorithms are customized to work only on grayscale images e.g. Canny edge detection function pre-implemented in OpenCV library works on Grayscale images only.
Let’s learn the different image processing methods to convert a colored image into a grayscale image.
Method 1: Using the cv2.cvtColor() function
Method 2: Using the cv2.imread() function with flag = zero
Method 3: Using the pixel manipulation (Average method)
Hope you have understood the above discussed image processing techniques to convert a colored image into a grayscale image in Python!
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