How to reduce the number of colors in an image with OpenCV?

Question

I have a set of image files, and I want to reduce the number of colors of them to 64. How can I do this with OpenCV?

I need this so I can work with a 64-sized image histogram. I'm implementing CBIR techniques

What I want is color quantization to a 4-bit palette.

Answer 1

You might consider K-means, yet in this case it will most likely be extremely slow. A better approach might be doing this "manually" on your own. Let's say you have image of type CV_8UC3, i.e. an image where each pixel is represented by 3 RGB values from 0 to 255 (Vec3b). You might "map" these 256 values to only 4 specific values, which would yield 4 x 4 x 4 = 64 possible colors.

I've had a dataset, where I needed to make sure that dark = black, light = white and reduce the amount of colors of everything between. This is what I did (C++):

inline uchar reduceVal(const uchar val) {     if (val < 64) return 0;     if (val < 128) return 64;     return 255; }  void processColors(Mat& img) {     uchar* pixelPtr = img.data;     for (int i = 0; i < img.rows; i++)     {         for (int j = 0; j < img.cols; j++)         {             const int pi = i*img.cols*3 + j*3;             pixelPtr[pi + 0] = reduceVal(pixelPtr[pi + 0]); // B             pixelPtr[pi + 1] = reduceVal(pixelPtr[pi + 1]); // G             pixelPtr[pi + 2] = reduceVal(pixelPtr[pi + 2]); // R         }     } } 

causing [0,64) to become 0, [64,128) -> 64 and [128,255) -> 255, yielding 27 colors:

To me this seems to be neat, perfectly clear and faster than anything else mentioned in other answers.

You might also consider reducing these values to one of the multiples of some number, let's say:

inline uchar reduceVal(const uchar val) {     if (val < 192) return uchar(val / 64.0 + 0.5) * 64;     return 255; } 

which would yield a set of 5 possible values: {0, 64, 128, 192, 255}, i.e. 125 colors.

Answer 2

This subject was well covered on OpenCV 2 Computer Vision Application Programming Cookbook:

Chapter 2 shows a few reduction operations, one of them demonstrated here in C++ and later in Python:

#include <iostream> #include <vector>  #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>   void colorReduce(cv::Mat& image, int div=64) {         int nl = image.rows;                    // number of lines     int nc = image.cols * image.channels(); // number of elements per line      for (int j = 0; j < nl; j++)     {         // get the address of row j         uchar* data = image.ptr<uchar>(j);          for (int i = 0; i < nc; i++)         {             // process each pixel             data[i] = data[i] / div * div + div / 2;         }     } }  int main(int argc, char* argv[]) {        // Load input image (colored, 3-channel, BGR)     cv::Mat input = cv::imread(argv[1]);     if (input.empty())     {         std::cout << "!!! Failed imread()" << std::endl;         return -1;     }       colorReduce(input);      cv::imshow("Color Reduction", input);        cv::imwrite("output.jpg", input);        cv::waitKey(0);      return 0; } 

Below you can find the input image (left) and the output of this operation (right):

The equivalent code in Python would be the following: (credits to @eliezer-bernart)

import cv2 import numpy as np  input = cv2.imread('castle.jpg')  # colorReduce() div = 64 quantized = input // div * div + div // 2  cv2.imwrite('output.jpg', quantized)