fanfuhan OpenCV 教學113 ~ opencv-113-利用KMeans圖像分割進行主色彩提取 [顏色 數量統計]

資料來源: https://fanfuhan.github.io/

https://fanfuhan.github.io/2019/05/24/opencv-113/

GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV

KMeans分割會計算出每個聚類的預定值，根據這個可以得到圖像的主色彩RGB分佈多少，得到各種色彩在圖像中的比重，替換出圖像對應的取色卡！這個方面在紡織與填色方面特別有用！主要步驟顯示如下：

 01.讀入圖像建立KMenas樣本
 02.使用KMeans圖像分割，指定分類數

 03.統計各個聚類占總預期比率，根據比率建立色卡！

C++

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
	Mat src = imread("D:/images/master.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input image", WINDOW_AUTOSIZE);
	imshow("input image", src);

	int width = src.cols;
	int height = src.rows;
	int dims = src.channels();

	// 初始化定义
	int sampleCount = width*height;
	int clusterCount = 4;
	Mat labels;
	Mat centers;

	// RGB 数据转换到样本数据
	Mat sample_data = src.reshape(3, sampleCount);
	Mat data;
	sample_data.convertTo(data, CV_32F);

	// 运行K-Means
	TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);
	kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);

	Mat card = Mat::zeros(Size(width, 50), CV_8UC3);
	vector<float> clusters(clusterCount);
	for (int i = 0; i < labels.rows; i++) {
		clusters[labels.at<int>(i, 0)]++;
	}
	for (int i = 0; i < clusters.size(); i++) {
		clusters[i] = clusters[i] / sampleCount;
	}
	int x_offset = 0;
	for (int x = 0; x < clusterCount; x++) {
		Rect rect;
		rect.x = x_offset;
		rect.y = 0;
		rect.height = 50;
		rect.width = round(clusters[x] * width);
		x_offset += rect.width;
		int b = centers.at<float>(x, 0);
		int g = centers.at<float>(x, 1);
		int r = centers.at<float>(x, 2);
		rectangle(card, rect, Scalar(b, g, r), -1, 8, 0);
	}

	imshow("Image Color Card", card);
	waitKey(0);
	return 0;
}

Python

"""
利用KMeans图像分割进行主色彩提取
"""

import cv2 as cv
import numpy as np

image = cv.imread('images/toux.jpg')
cv.imshow("input", image)
h, w, ch = image.shape

# 构建图像数据
data = image.reshape((-1, 3))
data = np.float32(data)

# 图像分割
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 10, 1.0)
num_clusters = 4
ret, label, center = cv.kmeans(data, num_clusters, None, criteria, num_clusters, cv.KMEANS_RANDOM_CENTERS)

# 生成主色彩条形卡片
card = np.zeros((50, w, 3), dtype=np.uint8)
clusters = np.zeros([4], dtype=np.int32)
for i in range(len(label)):
    clusters[label[i][0]] += 1
# 计算各类别像素的比率
clusters = np.float32(clusters) / float(h*w)
center = np.int32(center)
x_offset = 0
for c in range(num_clusters):
    dx = np.int(clusters[c] * w)
    b = center[c][0]
    g = center[c][1]
    r = center[c][2]
    cv.rectangle(card, (x_offset, 0), (x_offset+dx, 50),
                 (int(b),int(g),int(r)), -1)
    x_offset += dx

cv.imshow("color table", card)

cv.waitKey(0)
cv.destroyAllWindows()