目標

在本教程中，您將學習如何

使用函式 cv::compareHist 獲取一個數值引數，該引數表示兩個直方圖的匹配程度。
使用不同的度量標準來比較直方圖

理論

為了比較兩個直方圖（ \(H_{1}\) 和 \(H_{2}\) ），首先我們必須選擇一個度量標準（ \(d(H_{1}, H_{2})\)）來表示兩個直方圖的匹配程度。
OpenCV 實現了 cv::compareHist 函式來執行比較。它還提供了 4 種不同的度量標準來計算匹配度。
1. 相關性 ( 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 \(N\) 是直方圖 bin 的總數。
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. 巴氏距離 ( cv::HISTCMP_BHATTACHARYYA )
  \[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\]

程式碼

此程式的作用是什麼？
- 載入一張基礎影像和兩張測試影像，用於與基礎影像進行比較。
- 生成一張影像，它是基礎影像的下半部分。
- 將影像轉換為 HSV 格式
- 計算所有影像的 H-S 直方圖，並對其進行歸一化以便比較。
- 比較基礎影像的直方圖與兩張測試影像的直方圖、基礎影像下半部分的直方圖以及基礎影像自身的直方圖。
- 顯示獲得的數值匹配引數。

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#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| | 列印幫助資訊. }"

"{ @input1 |Histogram_Comparison_Source_0.jpg | 輸入影像 1 的路徑。 }"

"{ @input2 |Histogram_Comparison_Source_1.jpg | 輸入影像 2 的路徑。 }"

"{ @input3 |Histogram_Comparison_Source_2.jpg | 輸入影像 3 的路徑。 }";

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 << "無法開啟或找不到影像！\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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(2) : "

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

}

cout << "完成 \n";

return 0;

}

bool empty() const
如果陣列沒有元素，則返回 true。
int64_t int64

cv::Mat
n 維密集陣列類
定義 mat.hpp:830

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

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

cv::Mat::rows
int rows
行列數，或當矩陣維度超過 2 時為 (-1, -1)
定義 mat.hpp:2165

指定序列的連續子序列（切片）的模板類。更多...
指定序列連續子序列（切片）的模板類。
定義 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())
對陣列的範數或值範圍進行歸一化。

cv::String
std::string String
定義 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)
計算一組陣列的直方圖。

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

highgui.hpp

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

imgcodecs.hpp

imgproc.hpp

cv
定義 core.hpp:107

std
STL 名稱空間。

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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("您必須提供 3 個引數，它們對應於 3 個影像的路徑。");

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("無法讀取影像");

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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(2) : " + baseBase + " / " + baseHalf

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

}

}

}

public class CompareHistDemo {

public static void main(String[] args) {

// 載入本地 OpenCV 庫

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

new CompareHist().run(args);

}

}

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from __future__ import print_function

from __future__ import division

import cv2 as cv

import numpy as np

import argparse

parser = argparse.ArgumentParser(description='直方圖比較教程程式碼。')

parser.add_argument('--input1', help='輸入影像 1 的路徑。')

parser.add_argument('--input2', help='輸入影像 2 的路徑。')

parser.add_argument('--input3', help='輸入影像 3 的路徑。')

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('無法開啟或找不到影像！')

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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(2) :',\

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

cv::imread
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
從檔案載入影像。

cv::cvtColor
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0, AlgorithmHint hint=cv::ALGO_HINT_DEFAULT)
將影像從一個顏色空間轉換為另一個顏色空間。

解釋

載入基礎影像 (src_base) 和另外兩張測試影像

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 << "無法開啟或找不到影像！\n" << endl;

parser.printMessage();

return -1;

}

Java

if (args.length != 3) {

System.err.println("您必須提供 3 個引數，它們對應於 3 個影像的路徑。");

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("無法讀取影像");

System.exit(0);

}

Python

parser = argparse.ArgumentParser(description='直方圖比較教程程式碼。')

parser.add_argument('--input1', help='輸入影像 1 的路徑。')

parser.add_argument('--input2', help='輸入影像 2 的路徑。')

parser.add_argument('--input3', help='輸入影像 3 的路徑。')

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('無法開啟或找不到影像！')

exit(0)
將它們轉換為 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，建立一個基礎影像的下半部分影像（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:,:]
初始化計算直方圖的引數（bin 數量、範圍以及 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]
計算基礎影像、兩張測試影像和基礎影像下半部分的直方圖

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)
依次應用 4 種比較方法，比較基礎影像 (hist_base) 的直方圖與其他直方圖

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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(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, 基礎-下半部分, 基礎-測試(1), 基礎-測試(2) :',\

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

結果

我們使用以下影像作為輸入：

其中第一張是基礎影像（用於與其他影像比較），另外兩張是測試影像。我們還將第一張影像與自身以及與基礎影像的下半部分進行比較。

當我們將基礎影像直方圖與自身比較時，應該會得到完美匹配。此外，與基礎影像下半部分的直方圖相比，它應該呈現出高匹配度，因為它們都來自同一來源。對於另外兩張測試影像，我們可以觀察到它們具有非常不同的光照條件，因此匹配度應該不會很好。

以下是我們使用 OpenCV 3.4.1 獲得的數值結果

方法	基礎 - 基礎	基礎 - 下半部分	基礎 - 測試 1	基礎 - 測試 2
相關性	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
巴氏距離	0.000000	0.237887	0.679826	0.874173

對於相關性和交叉方法，度量值越高，匹配越準確。正如我們所見，基礎-基礎匹配度最高，符合預期。我們還可以觀察到，基礎-下半部分匹配度位居第二（正如我們預測的）。對於另外兩種度量標準，結果越小，匹配度越好。我們可以觀察到，測試 1 和測試 2 與基礎影像的匹配度較差，這也符合預期。


原始作者	Ana Huamán
相容性	OpenCV >= 3.0

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