矩陣上的掩碼操作

目錄

• 我們的測試用例
• 程式碼
• 基本方法
• filter2D 函式

上一個教程： 使用OpenCV掃描影像、查詢表和時間測量
下一個教程： 影像操作

原始作者	Bernát Gábor
相容性	OpenCV >= 3.0

矩陣上的掩模操作非常簡單。其思想是根據掩模矩陣（也稱為核）重新計算影像中每個畫素的值。該掩模包含一些值，這些值將調整相鄰畫素（以及當前畫素）對新畫素值的影響程度。從數學角度來看，我們使用指定的值進行加權平均。

我們的測試用例

讓我們考慮一個影像對比度增強方法的問題。基本上，我們想對影像中的每個畫素應用以下公式：

\[I(i,j) = 5*I(i,j) - [ I(i-1,j) + I(i+1,j) + I(i,j-1) + I(i,j+1)]\]

\[\iff I(i,j)*M, \text{其中 } M = \bordermatrix{ _i\backslash ^j & -1 & 0 & +1 \cr -1 & 0 & -1 & 0 \cr 0 & -1 & 5 & -1 \cr +1 & 0 & -1 & 0 \cr }\]

第一種表示方法是使用公式，而第二種是透過使用掩模對第一種進行壓縮。使用掩模時，您將掩模矩陣的中心（在上述情況下由零零索引表示）放在您要計算的畫素上，並將畫素值與重疊的矩陣值相乘後求和。它們是相同的，但在大型矩陣的情況下，後一種表示方法更容易檢視。

程式碼

C++

您可以從此處下載此原始碼，或者在OpenCV原始碼庫的示例目錄中檢視samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp。

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
 
using namespace std;
using namespace cv;
 
static void help(char* progName)
{
cout << endl
<< "This program shows how to filter images with mask: the write it yourself and the"
<< "filter2d way. " << endl
<< "Usage:" << endl
<< progName << " [image_path -- default lena.jpg] [G -- grayscale] " << endl << endl;
}
 
 
void Sharpen(const Mat& myImage,Mat& Result);
 
int main( int argc, char* argv[])
{
help(argv[0]);
    const char* filename = argc >=2 ? argv[1] : "lena.jpg";
 
    Mat src, dst0, dst1;
 
    if (argc >= 3 && !strcmp("G", argv[2]))
src = imread( samples::findFile( filename ), IMREAD_GRAYSCALE);
    else
src = imread( samples::findFile( filename ), IMREAD_COLOR);
 
    if (src.empty())
    {
cerr << "Can't open image [" << filename << "]" << endl;
        return EXIT_FAILURE;
    }
 
namedWindow("Input", WINDOW_AUTOSIZE);
namedWindow("Output", WINDOW_AUTOSIZE);
 
imshow( "Input", src );
    double t = (double)getTickCount();
 
Sharpen( src, dst0 );
 
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Hand written function time passed in seconds: " << t << endl;
 
imshow( "Output", dst0 );
cv::Mat::empty
 
    Mat kernel = (Mat_<char>(3,3) << 0, -1, 0,
                                   -1,  5, -1,
                                    0, -1,  0);
 
t = (double)getTickCount();
 
filter2D( src, dst1, src.depth(), kernel );
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Built-in filter2D time passed in seconds: " << t << endl;
 
imshow( "Output", dst1 );
 
cv::Mat::empty
    return EXIT_SUCCESS;
}
void Sharpen(const Mat& myImage,Mat& Result)
{
    CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
 
    const int nChannels = myImage.channels();
Result.create(myImage.size(),myImage.type());
 
    for(int j = 1 ; j < myImage.rows-1; ++j)
    {
        const uchar* previous = myImage.ptr<uchar>(j - 1);
        const uchar* current = myImage.ptr<uchar>(j );
        const uchar* next = myImage.ptr<uchar>(j + 1);
 
        uchar* output = Result.ptr<uchar>(j);
 
        for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
        {
output[i] = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
        }
    }
 
Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
}

Java

您可以從此處下載此原始碼，或者在OpenCV原始碼庫的示例目錄中檢視samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java。

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
 
class MatMaskOperationsRun {
 
    public void run(String[] args) {
 
String filename = "../data/lena.jpg";
 
        int img_codec = Imgcodecs.IMREAD_COLOR;
        if (args.length != 0) {
filename = args[0];
            if (args.length >= 2 && args[1].equals("G"))
img_codec = Imgcodecs.IMREAD_GRAYSCALE;
        }
 
Mat src = Imgcodecs.imread(filename, img_codec);
 
        if (src.empty()) {
System.out.println("Can't open image [" + filename + "]");
System.out.println("Program Arguments: [image_path -- default ../data/lena.jpg] [G -- grayscale]");
System.exit(-1);
        }
 
HighGui.namedWindow("Input", HighGui.WINDOW_AUTOSIZE);
HighGui.namedWindow("Output", HighGui.WINDOW_AUTOSIZE);
 
HighGui.imshow( "Input", src );
        double t = System.currentTimeMillis();
 
Mat dst0 = sharpen(src, new Mat());
 
t = ((double) System.currentTimeMillis() - t) / 1000;
System.out.println("Hand written function time passed in seconds: " + t);
 
HighGui.imshow( "Output", dst0 );
HighGui.moveWindow("Output", 400, 400);
HighGui.waitKey();
 
Mat kern = new Mat(3, 3, CvType.CV_8S);
        int row = 0, col = 0;
kern.put(row, col, 0, -1, 0, -1, 5, -1, 0, -1, 0);
 
t = System.currentTimeMillis();
 
Mat dst1 = new Mat();
Imgproc.filter2D(src, dst1, src.depth(), kern);
t = ((double) System.currentTimeMillis() - t) / 1000;
System.out.println("Built-in filter2D time passed in seconds: " + t);
 
HighGui.imshow( "Output", dst1 );
 
HighGui.waitKey();
System.exit(0);
    }
 
    public static double saturate(double x) {
        return x > 255.0 ? 255.0 : (x < 0.0 ? 0.0 : x);
    }
 
    public Mat sharpen(Mat myImage, Mat Result) {
myImage.convertTo(myImage, CvType.CV_8U);
 
        int nChannels = myImage.channels();
Result.create(myImage.size(), myImage.type());
 
        for (int j = 1; j < myImage.rows() - 1; ++j) {
            for (int i = 1; i < myImage.cols() - 1; ++i) {
                double sum[] = new double[nChannels];
 
                for (int k = 0; k < nChannels; ++k) {
 
                    double top = -myImage.get(j - 1, i)[k];
                    double bottom = -myImage.get(j + 1, i)[k];
                    double center = (5 * myImage.get(j, i)[k]);
                    double left = -myImage.get(j, i - 1)[k];
                    double right = -myImage.get(j, i + 1)[k];
 
                    sum[k] = saturate(top + bottom + center + left + right);
                }
 
Result.put(j, i, sum);
            }
        }
 
Result.row(0).setTo(new Scalar(0));
Result.row(Result.rows() - 1).setTo(new Scalar(0));
Result.col(0).setTo(new Scalar(0));
Result.col(Result.cols() - 1).setTo(new Scalar(0));
 
        return Result;
    }
}
 
public class MatMaskOperations {
    public static void main(String[] args) {
        // 載入本地庫。
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        new MatMaskOperationsRun().run(args);
    }
}

Python

您可以從此處下載此原始碼，或者在OpenCV原始碼庫的示例目錄中檢視samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py。

from __future__ import print_function
import sys
import time
 
import numpy as np
import cv2 as cv
 
 
def is_grayscale(my_image)
    return len(my_image.shape) < 3
 
 
def saturated(sum_value)
    if sum_value > 255
sum_value = 255
    if sum_value < 0
sum_value = 0
 
    return sum_value
 
 
def sharpen(my_image)
    if is_grayscale(my_image)
height, width = my_image.shape
    else:
my_image = cv.cvtColor(my_image, cv.CV_8U)
height, width, n_channels = my_image.shape
 
result = np.zeros(my_image.shape, my_image.dtype)
    
    for j in range(1, height - 1)
        for i in range(1, width - 1)
            if is_grayscale(my_image)
sum_value = 5 * my_image[j, i] - my_image[j + 1, i] - my_image[j - 1, i] \
- my_image[j, i + 1] - my_image[j, i - 1]
result[j, i] = saturated(sum_value)
            else:
                for k in range(0, n_channels)
sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k] \
- my_image[j - 1, i, k] - my_image[j, i + 1, k]\
- my_image[j, i - 1, k]
result[j, i, k] = saturated(sum_value)
    
    return result
 
 
def main(argv)
filename = 'lena.jpg'
 
img_codec = cv.IMREAD_COLOR
    if argv
filename = sys.argv[1]
        if len(argv) >= 2 and sys.argv[2] == "G"
img_codec = cv.IMREAD_GRAYSCALE
 
src = cv.imread(cv.samples.findFile(filename), img_codec)
 
    if src is None
print("Can't open image [" + filename + "]")
print("Usage:")
print("mat_mask_operations.py [image_path -- default lena.jpg] [G -- grayscale]")
        return -1
 
    cv.namedWindow("Input", cv.WINDOW_AUTOSIZE)
    cv.namedWindow("Output", cv.WINDOW_AUTOSIZE)
 
    cv.imshow("Input", src)
t = round(time.time())
 
dst0 = sharpen(src)
 
t = (time.time() - t)
print("Hand written function time passed in seconds: %s" % t)
 
    cv.imshow("Output", dst0)
    cv.waitKey()
 
t = time.time()
    
kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
[0, -1, 0]], np.float32) # kernel should be floating point type
    
dst1 = cv.filter2D(src, -1, kernel)
    # ddepth = -1, means destination image has depth same as input image
    
 
t = (time.time() - t)
print("Built-in filter2D time passed in seconds: %s" % t)
 
    cv.imshow("Output", dst1)
 
    cv.waitKey(0)
    cv.destroyAllWindows()
    return 0
 
 
if __name__ == "__main__"
    main(sys.argv[1:])

基本方法

現在讓我們看看如何透過使用基本的畫素訪問方法或使用filter2D()函式來實現這一點。

這是一個可以實現此功能的函式

C++

void Sharpen(const Mat& myImage,Mat& Result)
{
    CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
 
    const int nChannels = myImage.channels();
Result.create(myImage.size(),myImage.type());
 
    for(int j = 1 ; j < myImage.rows-1; ++j)
    {
        const uchar* previous = myImage.ptr<uchar>(j - 1);
        const uchar* current = myImage.ptr<uchar>(j );
        const uchar* next = myImage.ptr<uchar>(j + 1);
 
        uchar* output = Result.ptr<uchar>(j);
 
        for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
        {
output[i] = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
        }
    }
 
Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
}

首先，我們確保輸入影像資料是無符號字元（unsigned char）格式。為此，我們使用CV_Assert函式（宏），當其中的表示式為假時，它會丟擲錯誤。

    CV_Assert(myImage.depth() == CV_8U); // accept only uchar images

Java

    public static double saturate(double x) {
        return x > 255.0 ? 255.0 : (x < 0.0 ? 0.0 : x);
    }
 
    public Mat sharpen(Mat myImage, Mat Result) {
myImage.convertTo(myImage, CvType.CV_8U);
 
        int nChannels = myImage.channels();
Result.create(myImage.size(), myImage.type());
 
        for (int j = 1; j < myImage.rows() - 1; ++j) {
            for (int i = 1; i < myImage.cols() - 1; ++i) {
                double sum[] = new double[nChannels];
 
                for (int k = 0; k < nChannels; ++k) {
 
                    double top = -myImage.get(j - 1, i)[k];
                    double bottom = -myImage.get(j + 1, i)[k];
                    double center = (5 * myImage.get(j, i)[k]);
                    double left = -myImage.get(j, i - 1)[k];
                    double right = -myImage.get(j, i + 1)[k];
 
                    sum[k] = saturate(top + bottom + center + left + right);
                }
 
Result.put(j, i, sum);
            }
        }
 
Result.row(0).setTo(new Scalar(0));
Result.row(Result.rows() - 1).setTo(new Scalar(0));
Result.col(0).setTo(new Scalar(0));
Result.col(Result.cols() - 1).setTo(new Scalar(0));
 
        return Result;
    }

首先，我們確保輸入影像資料是無符號8位格式。

myImage.convertTo(myImage, CvType.CV_8U);

Python

def is_grayscale(my_image)
    return len(my_image.shape) < 3
 
 
def saturated(sum_value)
    if sum_value > 255
sum_value = 255
    if sum_value < 0
sum_value = 0
 
    return sum_value
 
 
def sharpen(my_image)
    if is_grayscale(my_image)
height, width = my_image.shape
    else:
my_image = cv.cvtColor(my_image, cv.CV_8U)
height, width, n_channels = my_image.shape
 
result = np.zeros(my_image.shape, my_image.dtype)
    
    for j in range(1, height - 1)
        for i in range(1, width - 1)
            if is_grayscale(my_image)
sum_value = 5 * my_image[j, i] - my_image[j + 1, i] - my_image[j - 1, i] \
- my_image[j, i + 1] - my_image[j, i - 1]
result[j, i] = saturated(sum_value)
            else:
                for k in range(0, n_channels)
sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k] \
- my_image[j - 1, i, k] - my_image[j, i + 1, k]\
- my_image[j, i - 1, k]
result[j, i, k] = saturated(sum_value)
    
    return result

首先，我們確保輸入影像資料是無符號8位格式。

my_image = cv.cvtColor(my_image, cv.CV_8U)

我們建立一個與輸入影像大小和型別相同的輸出影像。如您在儲存一節中所見，根據通道數，我們可能有一個或多個子列。

C++

我們將透過指標遍歷它們，因此元素的總數取決於此（通道）數量。

    const int nChannels = myImage.channels();
Result.create(myImage.size(),myImage.type());

Java

        int nChannels = myImage.channels();
Result.create(myImage.size(), myImage.type());

Python

height, width, n_channels = my_image.shape
result = np.zeros(my_image.shape, my_image.dtype)

C++

我們將使用普通的C語言 `[]` 運算子來訪問畫素。由於我們需要同時訪問多行，因此我們將獲取每行的指標（前一行、當前行和下一行）。我們還需要另一個指標來儲存計算結果。然後，只需使用 `[]` 運算子訪問正確的項。為了將輸出指標向前移動，我們只需在每次操作後將其增加（一個位元組）。

    for(int j = 1 ; j < myImage.rows-1; ++j)
    {
        const uchar* previous = myImage.ptr<uchar>(j - 1);
        const uchar* current = myImage.ptr<uchar>(j );
        const uchar* next = myImage.ptr<uchar>(j + 1);
 
        uchar* output = Result.ptr<uchar>(j);
 
        for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
        {
output[i] = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
        }
    }

在影像的邊界處，上述表示法會導致不存在的畫素位置（例如負一 - 負一）。在這些點，我們的公式是未定義的。一個簡單的解決方案是，在這些點不應用核，例如，將邊界上的畫素設定為零。

Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));

Java

我們需要訪問多行和多列，這可以透過向當前中心(i,j)新增或減去1來實現。然後我們應用求和並將新值放入Result矩陣中。

        for (int j = 1; j < myImage.rows() - 1; ++j) {
            for (int i = 1; i < myImage.cols() - 1; ++i) {
                double sum[] = new double[nChannels];
 
                for (int k = 0; k < nChannels; ++k) {
 
                    double top = -myImage.get(j - 1, i)[k];
                    double bottom = -myImage.get(j + 1, i)[k];
                    double center = (5 * myImage.get(j, i)[k]);
                    double left = -myImage.get(j, i - 1)[k];
                    double right = -myImage.get(j, i + 1)[k];
 
                    sum[k] = saturate(top + bottom + center + left + right);
                }
 
Result.put(j, i, sum);
            }
        }

在影像的邊界處，上述表示法會導致不存在的畫素位置（例如(-1,-1)）。在這些點，我們的公式是未定義的。一個簡單的解決方案是，在這些點不應用核，例如，將邊界上的畫素設定為零。

Result.row(0).setTo(new Scalar(0));
Result.row(Result.rows() - 1).setTo(new Scalar(0));
Result.col(0).setTo(new Scalar(0));
Result.col(Result.cols() - 1).setTo(new Scalar(0));

Python

我們需要訪問多行和多列，這可以透過向當前中心(i,j)新增或減去1來實現。然後我們應用求和並將新值放入Result矩陣中。

    for j in range(1, height - 1)
        for i in range(1, width - 1)
            if is_grayscale(my_image)
sum_value = 5 * my_image[j, i] - my_image[j + 1, i] - my_image[j - 1, i] \
- my_image[j, i + 1] - my_image[j, i - 1]
result[j, i] = saturated(sum_value)
            else:
                for k in range(0, n_channels)
sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k] \
- my_image[j - 1, i, k] - my_image[j, i + 1, k]\
- my_image[j, i - 1, k]
result[j, i, k] = saturated(sum_value)

filter2D 函式

這類濾波器在影像處理中非常常見，以至於OpenCV中有一個函式可以處理掩模（在某些地方也稱為核）的應用。為此，您首先需要定義一個持有掩模的物件。

C++

    Mat kernel = (Mat_<char>(3,3) << 0, -1, 0,
                                   -1,  5, -1,
                                    0, -1,  0);

Java

Mat kern = new Mat(3, 3, CvType.CV_8S);
        int row = 0, col = 0;
kern.put(row, col, 0, -1, 0, -1, 5, -1, 0, -1, 0);

Python

kernel = np.array([[0, -1, 0],
                       [-1, 5, -1],
[0, -1, 0]], np.float32) # kernel should be floating point type

然後呼叫filter2D()函式，指定輸入影像、輸出影像和要使用的核。

C++

    filter2D( src, dst1, src.depth(), kernel );

Java

Imgproc.filter2D(src, dst1, src.depth(), kern);

Python

dst1 = cv.filter2D(src, -1, kernel)
    # ddepth = -1, means destination image has depth same as input image

該函式甚至還有一個可選的第五個引數用於指定核的中心，第六個引數用於在將過濾後的畫素儲存到K中之前向其新增一個可選值，以及第七個引數用於確定在操作未定義的區域（邊界）中如何處理。

這個函式更短，更簡潔，而且由於存在一些最佳化，它通常比手動編寫的方法更快。例如，在我的測試中，第二個方法只用了13毫秒，而第一個方法用了大約31毫秒。差異相當大。

例如

C++

在我們的YouTube頻道上檢視執行該程式的一個例項。