jashliao 用 VC++ 實現 fanfuhan OpenCV 教學018 ~ opencv-018-彩色/灰階 圖像 直方圖均衡化 藉此提高圖像的質量(對比/清晰)

資料來源: https://fanfuhan.github.io/

https://fanfuhan.github.io/2019/03/29/opencv-018/

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

https://github.com/jash-git/jashliao-implements-FANFUHAN-OPENCV-with-VC

★前言:

★主題:

    圖像直方圖均衡化可以用於圖像增強，對輸入圖像進行直方圖均衡化處理，增強後續對象檢測的準確率，在OpenCV人臉檢測的代碼演示中已經很常見。

    圖像也經常通過直方圖均衡化來提升圖像質量。

    OPENCV計算直方圖均衡化函數(equalizeHist)，其介紹如下所列:
        void cv::equalizeHist(cv::Mat src, cv::Mat dst);
        src : source src must be a single-channel, 8-bit image.
        dst : Output image

★C++

// VC_FANFUHAN_OPENCV018.cpp : 定義主控台應用程式的進入點。
//
/*
// Debug | x32
通用屬性
| C/C++
|	| 一般
|		| 其他 Include 目錄 -> C:\opencv\build\include
|
| 連結器
| 	|一一般
|		|  其他程式庫目錄 -> C:\opencv\build\x64\vc15\lib
|
| 	|一輸入
|		| 其他相依性 -> opencv_world411d.lib;%(AdditionalDependencies)
// Releas | x64
組態屬性
| C/C++
|	| 一般
|		| 其他 Include 目錄 -> C:\opencv\build\include
|
| 連結器
| 	|一般
|		| 其他程式庫目錄 -> C:\opencv\build\x64\vc15\lib
|
| 	|一輸入
|		| 其他相依性 -> opencv_world411.lib;%(AdditionalDependencies)
*/
#include "stdafx.h"
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace std;
using namespace cv;

void showHistogram(InputArray src, cv::String StrTitle);

void pause()
{
	printf("Press Enter key to continue...");
	fgetc(stdin);
}
int main()
{
	Mat src = imread("../../images/test.png");
	if (src.empty())
	{
		cout << "could not load image.." << endl;
		pause();
		return -1;
	}
	else
	{
		imshow("input", src);
		showHistogram(src, "Histogram");

		vector<Mat> mv; // mv用于存储图像分离后各通道像素
		split(src, mv);
		Mat B, G, R;
		equalizeHist(mv[0], B);
		equalizeHist(mv[1], G);
		equalizeHist(mv[2], R);
		vector<Mat> combined;
		combined.push_back(B);
		combined.push_back(G);
		combined.push_back(R);
		Mat dst_bgr;
		merge(combined, dst_bgr);
		imshow("input_eq", dst_bgr);
		showHistogram(dst_bgr, "Histogram_eq");

		Mat src_gray, dst_gray;
		cvtColor(src, src_gray, COLOR_BGR2GRAY);
		imshow("input_gray", src_gray);
		showHistogram(src_gray, "Histogram_gray");

		equalizeHist(src_gray, dst_gray);
		imshow("input_gray_eq", dst_gray);
		showHistogram(dst_gray, "Histogram_gray_eq");

		waitKey(0);
	}

	return 0;
}
void showHistogram(InputArray src, cv::String StrTitle)
{
	bool blnGray = false;
	if (src.channels() == 1)
	{
		blnGray = true;
	}

	// 三通道/單通道 直方圖 紀錄陣列
	vector<Mat> bgr_plane;
	vector<Mat> gray_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, hist;
	// 计算三通道直方图
	if (blnGray)
	{
		split(src, gray_plane);
		calcHist(&gray_plane[0], 1, 0, Mat(), hist, 1, bins, ranges);
	}
	else
	{
		split(src, bgr_plane);
		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
	if (blnGray)
	{
		normalize(hist, hist, 0, histImage.rows, NORM_MINMAX, -1);
	}
	else
	{
		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)
	{
		if (blnGray)
		{
			line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(hist.at<float>(i - 1))),
				Point(bin_w * (i), hist_h - cvRound(hist.at<float>(i))), Scalar(255, 255, 255),
				2, 8, 0);
		}
		else
		{
			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(StrTitle, histImage);
}

★Python

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.show()


src = cv.imread("../images/test.png")
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", gray)
dst = cv.equalizeHist(gray)
cv.imshow("eh", dst)

custom_hist(gray)
custom_hist(dst)

cv.waitKey(0)
cv.destroyAllWindows()

★結果圖:

★延伸說明/重點回顧: