目标

在本教程中，您将学习如何

使用函数 cv::compareHist 获取一个数值参数，该参数表示两个直方图的匹配程度。
使用不同的度量标准来比较直方图

理论

为了比较两个直方图（ \(H_{1}\) 和 \(H_{2}\) ），首先我们必须选择一个度量标准（ \(d(H_{1}, H_{2})\)）来表示两个直方图的匹配程度。
OpenCV 实现了 cv::compareHist 函数来执行比较。它还提供了 4 种不同的度量标准来计算匹配度。
1. 相关性 ( cv::HISTCMP_CORREL )
  \[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\]
  其中
  \[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\]
  其中 \(N\) 是直方图 bin 的总数。
2. 卡方 ( cv::HISTCMP_CHISQR )
  \[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\]
3. 交叉 ( method=cv::HISTCMP_INTERSECT )
  \[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\]
4. 巴氏距离 ( cv::HISTCMP_BHATTACHARYYA )
  \[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\]

代码

此程序的作用是什么？
- 加载一张基础图像和两张测试图像，用于与基础图像进行比较。
- 生成一张图像，它是基础图像的下半部分。
- 将图像转换为 HSV 格式
- 计算所有图像的 H-S 直方图，并对其进行归一化以便比较。
- 比较基础图像的直方图与两张测试图像的直方图、基础图像下半部分的直方图以及基础图像自身的直方图。
- 显示获得的数值匹配参数。

可下载代码: 点击此处
代码速览

#include "opencv2/imgcodecs.hpp"

#include "opencv2/highgui.hpp"

#include "opencv2/imgproc.hpp"

#include <iostream>

using namespace std;

using namespace cv;

const char* keys =

"{ help h| | 打印帮助信息. }"

"{ @input1 |Histogram_Comparison_Source_0.jpg | 输入图像 1 的路径。 }"

"{ @input2 |Histogram_Comparison_Source_1.jpg | 输入图像 2 的路径。 }"

"{ @input3 |Histogram_Comparison_Source_2.jpg | 输入图像 3 的路径。 }";

int main( int argc, char** argv )

{

CommandLineParser parser( argc, argv, keys );

samples::addSamplesDataSearchSubDirectory( "doc/tutorials/imgproc/histograms/histogram_comparison/images" );

Mat src_base = imread(samples::findFile( parser.get<String>( "@input1" ) ) );

Mat src_test1 = imread(samples::findFile( parser.get<String>( "@input2" ) ) );

Mat src_test2 = imread(samples::findFile( parser.get<String>( "@input3" ) ) );

if( src_base.empty() || src_test1.empty() || src_test2.empty() )

{

cout << "无法打开或找不到图像！\n" << endl;

parser.printMessage();

return -1;

}

Mat hsv_base, hsv_test1, hsv_test2;

cvtColor( src_base, hsv_base, COLOR_BGR2HSV );

cvtColor( src_test1, hsv_test1, COLOR_BGR2HSV );

cvtColor( src_test2, hsv_test2, COLOR_BGR2HSV );

Mat hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows ), Range( 0, hsv_base.cols ) );

int h_bins = 50, s_bins = 60;

int histSize[] = { h_bins, s_bins };

// hue varies from 0 to 179, saturation from 0 to 255

float h_ranges[] = { 0, 180 };

float s_ranges[] = { 0, 256 };

const float* ranges[] = { h_ranges, s_ranges };

// Use the 0-th and 1-st channels

int channels[] = { 0, 1 };

Mat hist_base, hist_half_down, hist_test1, hist_test2;

calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );

normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );

normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );

normalize( hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );

normalize( hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat() );

for( int compare_method = 0; compare_method < 4; compare_method++ )

{

double base_base = compareHist( hist_base, hist_base, compare_method );

double base_half = compareHist( hist_base, hist_half_down, compare_method );

double base_test1 = compareHist( hist_base, hist_test1, compare_method );

double base_test2 = compareHist( hist_base, hist_test2, compare_method );

cout << "方法 " << compare_method << " 完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) : "

<< base_base << " / " << base_half << " / " << base_test1 << " / " << base_test2 << endl;

}

cout << "完成 \n";

return 0;

}

bool empty() const
如果数组没有元素，则返回 true。
int64_t int64

cv::Mat
n 维密集数组类
定义 mat.hpp:830

cv::Mat::cols
int cols
定义 mat.hpp:2165

定义 interface.h:61
cv::getTickFrequency
double getTickFrequency()

cv::Mat::rows
int rows
行列数，或当矩阵维度超过 2 时为 (-1, -1)
定义 mat.hpp:2165

指定序列的连续子序列（切片）的模板类。更多...
指定序列连续子序列（切片）的模板类。
定义 types.hpp:630

cv::normalize
void normalize(InputArray src, InputOutputArray dst, double alpha=1, double beta=0, int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray())
对数组的范数或值范围进行归一化。

cv::String
std::string String
定义 cvstd.hpp:151

cv::calcHist
void calcHist(const Mat *images, int nimages, const int *channels, InputArray mask, OutputArray hist, int dims, const int *histSize, const float **ranges, bool uniform=true, bool accumulate=false)
计算一组数组的直方图。

cv::compareHist
double compareHist(InputArray H1, InputArray H2, int method)
Compares two histograms.

highgui.hpp

main
int main(int argc, char *argv[])
定义 highgui_qt.cpp:3

imgcodecs.hpp

imgproc.hpp

cv
定义 core.hpp:107

std
STL 命名空间。

可下载代码: 点击此处
代码速览
import java.util.Arrays;

import java.util.List;

import org.opencv.core.Core;

import org.opencv.core.Mat;

import org.opencv.core.MatOfFloat;

import org.opencv.core.MatOfInt;

import org.opencv.core.Range;

import org.opencv.imgcodecs.Imgcodecs;

import org.opencv.imgproc.Imgproc;

class CompareHist {

public void run(String[] args) {

if (args.length != 3) {

System.err.println("您必须提供 3 个参数，它们对应于 3 个图像的路径。");

System.exit(0);

}

Mat srcBase = Imgcodecs.imread(args[0]);

Mat srcTest1 = Imgcodecs.imread(args[1]);

Mat srcTest2 = Imgcodecs.imread(args[2]);

if (srcBase.empty() || srcTest1.empty() || srcTest2.empty()) {

System.err.println("无法读取图像");

System.exit(0);

}

Mat hsvBase = new Mat(), hsvTest1 = new Mat(), hsvTest2 = new Mat();

Imgproc.cvtColor( srcBase, hsvBase, Imgproc.COLOR_BGR2HSV );

Imgproc.cvtColor( srcTest1, hsvTest1, Imgproc.COLOR_BGR2HSV );

Imgproc.cvtColor( srcTest2, hsvTest2, Imgproc.COLOR_BGR2HSV );

Mat hsvHalfDown = hsvBase.submat( new Range( hsvBase.rows()/2, hsvBase.rows() - 1 ), new Range( 0, hsvBase.cols() - 1 ) );

int hBins = 50, sBins = 60;

int[] histSize = { hBins, sBins };

// hue varies from 0 to 179, saturation from 0 to 255

float[] ranges = { 0, 180, 0, 256 };

// Use the 0-th and 1-st channels

int[] channels = { 0, 1 };

Mat histBase = new Mat(), histHalfDown = new Mat(), histTest1 = new Mat(), histTest2 = new Mat();

List<Mat> hsvBaseList = Arrays.asList(hsvBase);

Imgproc.calcHist(hsvBaseList, new MatOfInt(channels), new Mat(), histBase, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histBase, histBase, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvHalfDownList = Arrays.asList(hsvHalfDown);

Imgproc.calcHist(hsvHalfDownList, new MatOfInt(channels), new Mat(), histHalfDown, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histHalfDown, histHalfDown, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvTest1List = Arrays.asList(hsvTest1);

Imgproc.calcHist(hsvTest1List, new MatOfInt(channels), new Mat(), histTest1, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histTest1, histTest1, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvTest2List = Arrays.asList(hsvTest2);

Imgproc.calcHist(hsvTest2List, new MatOfInt(channels), new Mat(), histTest2, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histTest2, histTest2, 0, 1, Core.NORM_MINMAX);

for( int compareMethod = 0; compareMethod < 4; compareMethod++ ) {

double baseBase = Imgproc.compareHist( histBase, histBase, compareMethod );

double baseHalf = Imgproc.compareHist( histBase, histHalfDown, compareMethod );

double baseTest1 = Imgproc.compareHist( histBase, histTest1, compareMethod );

double baseTest2 = Imgproc.compareHist( histBase, histTest2, compareMethod );

System.out.println("方法 " + compareMethod + " 完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) : " + baseBase + " / " + baseHalf

+ " / " + baseTest1 + " / " + baseTest2);

}

}

}

public class CompareHistDemo {

public static void main(String[] args) {

// 加载本地 OpenCV 库

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

new CompareHist().run(args);

}

}

可下载代码: 点击此处
代码速览
from __future__ import print_function

from __future__ import division

import cv2 as cv

import numpy as np

import argparse

parser = argparse.ArgumentParser(description='直方图比较教程代码。')

parser.add_argument('--input1', help='输入图像 1 的路径。')

parser.add_argument('--input2', help='输入图像 2 的路径。')

parser.add_argument('--input3', help='输入图像 3 的路径。')

args = parser.parse_args()

src_base = cv.imread(args.input1)

src_test1 = cv.imread(args.input2)

src_test2 = cv.imread(args.input3)

if src_base is None or src_test1 is None or src_test2 is None

print('无法打开或找不到图像！')

exit(0)

hsv_base = cv.cvtColor(src_base, cv.COLOR_BGR2HSV)

hsv_test1 = cv.cvtColor(src_test1, cv.COLOR_BGR2HSV)

hsv_test2 = cv.cvtColor(src_test2, cv.COLOR_BGR2HSV)

hsv_half_down = hsv_base[hsv_base.shape[0]//2:,:]

h_bins = 50

s_bins = 60

histSize = [h_bins, s_bins]

# hue varies from 0 to 179, saturation from 0 to 255

h_ranges = [0, 180]

s_ranges = [0, 256]

ranges = h_ranges + s_ranges # concat lists

# Use the 0-th and 1-st channels

channels = [0, 1]

hist_base = cv.calcHist([hsv_base], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_base, hist_base, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_half_down = cv.calcHist([hsv_half_down], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_half_down, hist_half_down, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_test1 = cv.calcHist([hsv_test1], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_test1, hist_test1, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_test2 = cv.calcHist([hsv_test2], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_test2, hist_test2, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

for compare_method in range(4)

base_base = cv.compareHist(hist_base, hist_base, compare_method)

base_half = cv.compareHist(hist_base, hist_half_down, compare_method)

base_test1 = cv.compareHist(hist_base, hist_test1, compare_method)

base_test2 = cv.compareHist(hist_base, hist_test2, compare_method)

print('方法:', compare_method, '完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) :',\

base_base, '/', base_half, '/', base_test1, '/', base_test2)

cv::imread
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
从文件加载图像。

cv::cvtColor
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0, AlgorithmHint hint=cv::ALGO_HINT_DEFAULT)
将图像从一个颜色空间转换为另一个颜色空间。

解释

加载基础图像 (src_base) 和另外两张测试图像

C++

CommandLineParser parser( argc, argv, keys );

samples::addSamplesDataSearchSubDirectory( "doc/tutorials/imgproc/histograms/histogram_comparison/images" );

Mat src_base = imread(samples::findFile( parser.get<String>( "@input1" ) ) );

Mat src_test1 = imread(samples::findFile( parser.get<String>( "@input2" ) ) );

Mat src_test2 = imread(samples::findFile( parser.get<String>( "@input3" ) ) );

if( src_base.empty() || src_test1.empty() || src_test2.empty() )

{

cout << "无法打开或找不到图像！\n" << endl;

parser.printMessage();

return -1;

}

Java

if (args.length != 3) {

System.err.println("您必须提供 3 个参数，它们对应于 3 个图像的路径。");

System.exit(0);

}

Mat srcBase = Imgcodecs.imread(args[0]);

Mat srcTest1 = Imgcodecs.imread(args[1]);

Mat srcTest2 = Imgcodecs.imread(args[2]);

if (srcBase.empty() || srcTest1.empty() || srcTest2.empty()) {

System.err.println("无法读取图像");

System.exit(0);

}

Python

parser = argparse.ArgumentParser(description='直方图比较教程代码。')

parser.add_argument('--input1', help='输入图像 1 的路径。')

parser.add_argument('--input2', help='输入图像 2 的路径。')

parser.add_argument('--input3', help='输入图像 3 的路径。')

args = parser.parse_args()

src_base = cv.imread(args.input1)

src_test1 = cv.imread(args.input2)

src_test2 = cv.imread(args.input3)

if src_base is None or src_test1 is None or src_test2 is None

print('无法打开或找不到图像！')

exit(0)
将它们转换为 HSV 格式

C++

Mat hsv_base, hsv_test1, hsv_test2;

cvtColor( src_base, hsv_base, COLOR_BGR2HSV );

cvtColor( src_test1, hsv_test1, COLOR_BGR2HSV );

cvtColor( src_test2, hsv_test2, COLOR_BGR2HSV );

Java

Mat hsvBase = new Mat(), hsvTest1 = new Mat(), hsvTest2 = new Mat();

Imgproc.cvtColor( srcBase, hsvBase, Imgproc.COLOR_BGR2HSV );

Imgproc.cvtColor( srcTest1, hsvTest1, Imgproc.COLOR_BGR2HSV );

Imgproc.cvtColor( srcTest2, hsvTest2, Imgproc.COLOR_BGR2HSV );

Python

hsv_base = cv.cvtColor(src_base, cv.COLOR_BGR2HSV)

hsv_test1 = cv.cvtColor(src_test1, cv.COLOR_BGR2HSV)

hsv_test2 = cv.cvtColor(src_test2, cv.COLOR_BGR2HSV)
此外，创建一个基础图像的下半部分图像（HSV 格式）

C++

Mat hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows ), Range( 0, hsv_base.cols ) );

Java

Mat hsvHalfDown = hsvBase.submat( new Range( hsvBase.rows()/2, hsvBase.rows() - 1 ), new Range( 0, hsvBase.cols() - 1 ) );

Python

hsv_half_down = hsv_base[hsv_base.shape[0]//2:,:]
初始化计算直方图的参数（bin 数量、范围以及 H 和 S 通道）。

C++

int h_bins = 50, s_bins = 60;

int histSize[] = { h_bins, s_bins };

// hue varies from 0 to 179, saturation from 0 to 255

float h_ranges[] = { 0, 180 };

float s_ranges[] = { 0, 256 };

const float* ranges[] = { h_ranges, s_ranges };

// Use the 0-th and 1-st channels

int channels[] = { 0, 1 };

Java

int hBins = 50, sBins = 60;

int[] histSize = { hBins, sBins };

// hue varies from 0 to 179, saturation from 0 to 255

float[] ranges = { 0, 180, 0, 256 };

// Use the 0-th and 1-st channels

int[] channels = { 0, 1 };

Python

h_bins = 50

s_bins = 60

histSize = [h_bins, s_bins]

# hue varies from 0 to 179, saturation from 0 to 255

h_ranges = [0, 180]

s_ranges = [0, 256]

ranges = h_ranges + s_ranges # concat lists

# Use the 0-th and 1-st channels

channels = [0, 1]
计算基础图像、两张测试图像和基础图像下半部分的直方图

C++

Mat hist_base, hist_half_down, hist_test1, hist_test2;

calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );

normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );

normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );

normalize( hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat() );

calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );

normalize( hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat() );

Java

Mat histBase = new Mat(), histHalfDown = new Mat(), histTest1 = new Mat(), histTest2 = new Mat();

List<Mat> hsvBaseList = Arrays.asList(hsvBase);

Imgproc.calcHist(hsvBaseList, new MatOfInt(channels), new Mat(), histBase, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histBase, histBase, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvHalfDownList = Arrays.asList(hsvHalfDown);

Imgproc.calcHist(hsvHalfDownList, new MatOfInt(channels), new Mat(), histHalfDown, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histHalfDown, histHalfDown, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvTest1List = Arrays.asList(hsvTest1);

Imgproc.calcHist(hsvTest1List, new MatOfInt(channels), new Mat(), histTest1, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histTest1, histTest1, 0, 1, Core.NORM_MINMAX);

List<Mat> hsvTest2List = Arrays.asList(hsvTest2);

Imgproc.calcHist(hsvTest2List, new MatOfInt(channels), new Mat(), histTest2, new MatOfInt(histSize), new MatOfFloat(ranges), false);

Core.normalize(histTest2, histTest2, 0, 1, Core.NORM_MINMAX);

Python

hist_base = cv.calcHist([hsv_base], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_base, hist_base, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_half_down = cv.calcHist([hsv_half_down], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_half_down, hist_half_down, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_test1 = cv.calcHist([hsv_test1], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_test1, hist_test1, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)

hist_test2 = cv.calcHist([hsv_test2], channels, None, histSize, ranges, accumulate=False)

cv.normalize(hist_test2, hist_test2, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)
依次应用 4 种比较方法，比较基础图像 (hist_base) 的直方图与其他直方图

C++

for( int compare_method = 0; compare_method < 4; compare_method++ )

{

double base_base = compareHist( hist_base, hist_base, compare_method );

double base_half = compareHist( hist_base, hist_half_down, compare_method );

double base_test1 = compareHist( hist_base, hist_test1, compare_method );

double base_test2 = compareHist( hist_base, hist_test2, compare_method );

cout << "方法 " << compare_method << " 完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) : "

<< base_base << " / " << base_half << " / " << base_test1 << " / " << base_test2 << endl;

}

Java

for( int compareMethod = 0; compareMethod < 4; compareMethod++ ) {

double baseBase = Imgproc.compareHist( histBase, histBase, compareMethod );

double baseHalf = Imgproc.compareHist( histBase, histHalfDown, compareMethod );

double baseTest1 = Imgproc.compareHist( histBase, histTest1, compareMethod );

double baseTest2 = Imgproc.compareHist( histBase, histTest2, compareMethod );

System.out.println("方法 " + compareMethod + " 完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) : " + baseBase + " / " + baseHalf

+ " / " + baseTest1 + " / " + baseTest2);

}

Python

for compare_method in range(4)

base_base = cv.compareHist(hist_base, hist_base, compare_method)

base_half = cv.compareHist(hist_base, hist_half_down, compare_method)

base_test1 = cv.compareHist(hist_base, hist_test1, compare_method)

base_test2 = cv.compareHist(hist_base, hist_test2, compare_method)

print('方法:', compare_method, '完美匹配, 基础-下半部分, 基础-测试(1), 基础-测试(2) :',\

base_base, '/', base_half, '/', base_test1, '/', base_test2)

结果

我们使用以下图像作为输入：

其中第一张是基础图像（用于与其他图像比较），另外两张是测试图像。我们还将第一张图像与自身以及与基础图像的下半部分进行比较。

当我们将基础图像直方图与自身比较时，应该会得到完美匹配。此外，与基础图像下半部分的直方图相比，它应该呈现出高匹配度，因为它们都来自同一来源。对于另外两张测试图像，我们可以观察到它们具有非常不同的光照条件，因此匹配度应该不会很好。

以下是我们使用 OpenCV 3.4.1 获得的数值结果

方法	基础 - 基础	基础 - 下半部分	基础 - 测试 1	基础 - 测试 2
相关性	1.000000	0.880438	0.20457	0.0664547
卡方	0.000000	4.6834	2697.98	4763.8
交叉	18.8947	13.022	5.44085	2.58173
巴氏距离	0.000000	0.237887	0.679826	0.874173

对于相关性和交叉方法，度量值越高，匹配越准确。正如我们所见，基础-基础匹配度最高，符合预期。我们还可以观察到，基础-下半部分匹配度位居第二（正如我们预测的）。对于另外两种度量标准，结果越小，匹配度越好。我们可以观察到，测试 1 和测试 2 与基础图像的匹配度较差，这也符合预期。


原始作者	Ana Huamán
兼容性	OpenCV >= 3.0

目录

目标

理论

代码

解释

结果