opencv-017-图像直方图

发表于 | 分类于

知识点

图像直方图的解释
图像直方图是图像像素值的统计学特征、计算代价较小，具有图像平移、旋转、缩放不变性等众多优点，广泛地应用于图像处理的各个领域，特别是灰度图像的阈值分割、基于颜色的图像检索以及图像分类、反向投影跟踪。常见的分为

  • 灰度直方图
  • 颜色直方图

Bins是指直方图的大小范围， 对于像素值取值在0～255之间的，最少有256个bin，此外还可以有16、32、48、128等，256除以bin的大小应该是整数倍。

OpenCV中相关API
calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges);
cv.calcHist([image], [i], None, [256], [0, 256])

代码（c++,python）

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
#include <iostream>
#include <opencv2/opencv.hpp>
#include <vector>

using namespace std;
using namespace cv;

const int bins = 256;
Mat src;
const char *winTitle = "input image";

void showHistogram();

/*
 * 图像直方图
 */
int main() {
    src = imread("../images/test.png");
    if (src.empty()) {
        cout << "could not load image.." << endl;
    }
    imshow(winTitle, src);
    showHistogram();

    waitKey(0);
    return 0;
}

void showHistogram() {
    // 三通道分离
    vector<Mat> bgr_plane;
    split(src, bgr_plane);
    // 定义参数变量
    const int channels[1] = {0};
    const int bins[1] = {256};
    float hranges[2] = {0, 255};
    const float *ranges[1] = {hranges};
    Mat b_hist, g_hist, r_hist;
    // 计算三通道直方图
    calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges);
    calcHist(&bgr_plane[1], 1, 0, Mat(), g_hist, 1, bins, ranges);
    calcHist(&bgr_plane[2], 1, 0, Mat(), r_hist, 1, bins, ranges);
    /*
     * 显示直方图
     */
    int hist_w = 512;
    int hist_h = 400;
    int bin_w = cvRound((double) hist_w / bins[0]);
    Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3);
    // 归一化直方图数据
    normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1);
    normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1);
    normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1);
    // 绘制直方图曲线
    for (int i = 1; i < bins[0]; ++i) {
        line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))),
             Point(bin_w * (i), hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0),
             2, 8, 0);
        line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))),
             Point(bin_w * (i), hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0),
             2, 8, 0);
        line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))),
             Point(bin_w * (i), hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255),
             2, 8, 0);

    }
    imshow("Histogram", histImage);
}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt


def custom_hist(gray):
    h, w = gray.shape
    hist = np.zeros([256], dtype=np.int32)
    for row in range(h):
        for col in range(w):
            pv = gray[row, col]
            hist[pv] += 1

    y_pos = np.arange(0, 256, 1, dtype=np.int32)
    plt.bar(y_pos, hist, align='center', color='r', alpha=0.5)
    plt.xticks(y_pos, y_pos)
    plt.ylabel('Frequency')
    plt.title('Histogram')

    # plt.plot(hist, color='r')
    # plt.xlim([0, 256])
    plt.show()


def image_hist(image):
    cv.imshow("input", image)
    color = ('blue', 'green', 'red')
    for i, color in enumerate(color):
        hist = cv.calcHist([image], [i], None, [256], [0, 256])
        plt.plot(hist, color=color)
        plt.xlim([0, 256])
    plt.show()


src = cv.imread("../images/test.png")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
cv.imshow("input", gray)
#custom_hist(gray)
image_hist(src)
cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github