fanfuhan OpenCV 教學010 ~ opencv-010-圖像像素值統計及應用（普通圖像轉化為二值化圖像）[將彩色圖的相似顏色都標成相同區域，簡易分割圖片方式] – jashlia

fanfuhan OpenCV 教學010 ~ opencv-010-圖像像素值統計及應用（普通圖像轉化為二值化圖像）[將彩色圖的相似顏色都標成相同區域，簡易分割圖片方式]

資料來源: https://fanfuhan.github.io/

https://fanfuhan.github.io/2019/03/25/opencv-010/

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

C++

#include 
#include 

using namespace std;
using namespace cv;

/*
 * 图像像素值统计及应用（普通图像转化为二值图像）
 */
int main() {
    Mat src_bgr = imread("../images/test.png");
    Mat src_gray;
    cvtColor(src_bgr, src_gray, COLOR_BGR2GRAY);
    if (src_bgr.empty() || src_gray.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow("input_bgr", src_bgr);

    // 计算灰度图像的最大最小值
    double minVal, maxVal;
    Point minLoc, maxLoc;
    minMaxLoc(src_gray, &minVal, &maxVal, &minLoc, &maxLoc);
    cout << "paramenters of src_gray:" << endl;
    printf("min:%.2f, max:%.2f \n", minVal, maxVal);
    printf("min loc: (%d, %d) \n", minLoc.x, minLoc.y);
    printf("max loc: (%d, %d) \n", maxLoc.x, maxLoc.y);

    // 普通图像转二值图像
    Mat mean, stddev;
    meanStdDev(src_bgr, mean, stddev);
    cout << "paramenters of src_bgr:" << endl;
    printf("blue channel mean:%.2f, stddev: %.2f \n", mean.at(0, 0), stddev.at(0, 0));
    printf("green channel mean:%.2f, stddev: %.2f \n", mean.at(1, 0), stddev.at(1, 0));
    printf("red channel mean:%.2f, stddev: %.2f \n", mean.at(2, 0), stddev.at(2, 0));
    for (int row = 0; row < src_bgr.rows; ++row) {
        for (int col = 0; col < src_bgr.cols; ++col) {
            Vec3b bgr = src_bgr.at(row, col);
            bgr[0] = bgr[0] < mean.at(0, 0) ? 0 : 255;
            bgr[1] = bgr[1] < mean.at(1, 0) ? 0 : 255;
            bgr[2] = bgr[2] < mean.at(2, 0) ? 0 : 255;
            src_bgr.at(row, col) = bgr;
        }
    }
    imshow("binary", src_bgr);

    waitKey(0);
    return 0;
}

Python

import cv2 as cv
import numpy as np

src = cv.imread("../images/test.png", cv.IMREAD_GRAYSCALE)
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)

min, max, minLoc, maxLoc = cv.minMaxLoc(src)
print("min: %.2f, max: %.2f"% (min, max))
print("min loc: ", minLoc)
print("max loc: ", maxLoc)

means, stddev = cv.meanStdDev(src)
print("mean: %.2f, stddev: %.2f"% (means, stddev))
src[np.where(src < means)] = 0
src[np.where(src > means)] = 255
cv.imshow("binary", src)

cv.waitKey(0)
cv.destroyAllWindows()