C++-图像修复-Criminisi算法

2021/5/19 20:55:50
编程Tag： 算法 const C++ include Mat point cv Criminisi contours
本文主要是介绍C++-图像修复-Criminisi算法，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！
原理概述

代码

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/core.hpp"
#include <assert.h>
#include <stdio.h>
#include <algorithm>
#include <vector>
#include <string>
#include <iostream>
#include <cmath>
#include <iostream>
#include <string>
#include <opencv2\imgproc\types_c.h>

using namespace cv;
using namespace std;

typedef std::vector<std::vector<cv::Point>> contours_t;
typedef std::vector<cv::Vec4i> hierarchy_t;
typedef std::vector<cv::Point> contour_t;


// Patch raduius
#define RADIUS 4
// The maximum number of pixels around a specified point on the target outline
#define BORDER_RADIUS 4

int mod(int a, int b);

void loadInpaintingImages(
    const std::string& colorFilename,
    const std::string& maskFilename,
    cv::Mat& colorMat,
    cv::Mat& maskMat,
    cv::Mat& grayMat);

void getContours(const cv::Mat& mask, contours_t& contours, hierarchy_t& hierarchy);

double computeConfidence(const cv::Mat& confidencePatch);

cv::Mat getPatch(const cv::Mat& image, const cv::Point& p);

void getDerivatives(const cv::Mat& grayMat, cv::Mat& dx, cv::Mat& dy);

cv::Point2f getNormal(const contour_t& contour, const cv::Point& point);

void computePriority(const contours_t& contours, const cv::Mat& grayMat, const cv::Mat& confidenceMat, cv::Mat& priorityMat);

void transferPatch(const cv::Point& psiHatQ, const cv::Point& psiHatP, cv::Mat& mat, const cv::Mat& maskMat);

cv::Mat computeSSD(const cv::Mat& tmplate, const cv::Mat& source, const cv::Mat& tmplateMask);

#ifndef DEBUG
    #define DEBUG 0
#endif

/*
 * Return a % b where % is the mathematical modulus operator.
 */
int mod(int a, int b) 
{
    return ((a % b) + b) % b;
}


/*
 * Load the color, mask, grayscale images with a border of size
 * radius around every image to prevent boundary collisions when taking patches
 */
void loadInpaintingImages(
    const std::string& colorFilename,
    const std::string& maskFilename,
    cv::Mat& colorMat,
    cv::Mat& maskMat,
    cv::Mat& grayMat)
{
    assert(colorFilename.length() && maskFilename.length());

    colorMat = cv::imread(colorFilename, 1); // color
    maskMat = cv::imread(maskFilename, 0);  // grayscale

    assert(colorMat.size() == maskMat.size());
    assert(!colorMat.empty() && !maskMat.empty());

    // convert colorMat to depth CV_32F for colorspace conversions
    colorMat.convertTo(colorMat, CV_32F);
    colorMat /= 255.0f;

    // add border around colorMat
    cv::copyMakeBorder(
        colorMat,
        colorMat,
        RADIUS,
        RADIUS,
        RADIUS,
        RADIUS,
        cv::BORDER_CONSTANT,
        cv::Scalar_<float>(0, 0, 0)
    );

    cv::cvtColor(colorMat, grayMat, CV_BGR2GRAY);
}

/*
 * Extract closed boundary from mask.
 */
void getContours(const cv::Mat& mask,
    contours_t& contours,
    hierarchy_t& hierarchy
)
{
    assert(mask.type() == CV_8UC1);
    cv::findContours(mask, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
}


/*
 * Get a patch of size RAIDUS around point p in mat.
 */
cv::Mat getPatch(const cv::Mat& mat, const cv::Point& p)
{
    assert(RADIUS <= p.x && p.x < mat.cols - RADIUS && RADIUS <= p.y && p.y < mat.rows - RADIUS);
    return  mat(
        cv::Range(p.y - RADIUS, p.y + RADIUS + 1),
        cv::Range(p.x - RADIUS, p.x + RADIUS + 1)
    );
}


// get the x and y derivatives of a patch centered at patchCenter in image
// computed using a 3x3 Scharr filter
void getDerivatives(const cv::Mat& grayMat, cv::Mat& dx, cv::Mat& dy)
{
    assert(grayMat.type() == CV_32FC1);

    cv::Sobel(grayMat, dx, -1, 1, 0, -1);
    cv::Sobel(grayMat, dy, -1, 0, 1, -1);
}


/*
 * Get the unit normal of a dense list of boundary points centered around point p.
 */
cv::Point2f getNormal(const contour_t& contour, const cv::Point& point)
{
    int sz = (int)contour.size();

    assert(sz != 0);

    int pointIndex = (int)(std::find(contour.begin(), contour.end(), point) - contour.begin());

    assert(pointIndex != contour.size());

    if (sz == 1)
    {
        return cv::Point2f(1.0f, 0.0f);
    }
    else if (sz < 2 * BORDER_RADIUS + 1)
    {
        // Too few points in contour to use LSTSQ regression
        // return the normal with respect to adjacent neigbourhood
        cv::Point adj = contour[(pointIndex + 1) % sz] - contour[pointIndex];
        return cv::Point2f(adj.y, -adj.x) / cv::norm(adj);
    }

    // Use least square regression
    // create X and Y mat to SVD
    cv::Mat X(cv::Size(2, 2 * BORDER_RADIUS + 1), CV_32F);
    cv::Mat Y(cv::Size(1, 2 * BORDER_RADIUS + 1), CV_32F);

    assert(X.rows == Y.rows && X.cols == 2 && Y.cols == 1 && X.type() == Y.type()
        && Y.type() == CV_32F);

    int i = mod((pointIndex - BORDER_RADIUS), sz);

    float* Xrow;
    float* Yrow;

    int count = 0;
    int countXequal = 0;
    while (count < 2 * BORDER_RADIUS + 1)
    {
        Xrow = X.ptr<float>(count);
        Xrow[0] = contour[i].x;
        Xrow[1] = 1.0f;

        Yrow = Y.ptr<float>(count);
        Yrow[0] = contour[i].y;

        if (Xrow[0] == contour[pointIndex].x)
        {
            ++countXequal;
        }

        i = mod(i + 1, sz);
        ++count;
    }

    if (countXequal == count)
    {
        return cv::Point2f(1.0f, 0.0f);
    }
    // to find the line of best fit
    cv::Mat sol;
    cv::solve(X, Y, sol, cv::DECOMP_SVD);

    assert(sol.type() == CV_32F);

    float slope = sol.ptr<float>(0)[0];
    cv::Point2f normal(-slope, 1);

    return normal / cv::norm(normal);
}


/*
 * Return the confidence of confidencePatch
 */
double computeConfidence(const cv::Mat& confidencePatch)
{
    return cv::sum(confidencePatch)[0] / (double)confidencePatch.total();
}


/*
 * Iterate over every contour point in contours and compute the
 * priority of path centered at point using grayMat and confidenceMat
 */
void computePriority(const contours_t& contours, const cv::Mat& grayMat, const cv::Mat& confidenceMat, cv::Mat& priorityMat)
{
    assert(grayMat.type() == CV_32FC1 &&
        priorityMat.type() == CV_32FC1 &&
        confidenceMat.type() == CV_32FC1
    );

    // define some patches
    cv::Mat confidencePatch;
    cv::Mat magnitudePatch;

    cv::Point2f normal;
    cv::Point maxPoint;
    cv::Point2f gradient;

    double confidence;

    // get the derivatives and magnitude of the greyscale image
    cv::Mat dx, dy, magnitude;
    getDerivatives(grayMat, dx, dy);
    cv::magnitude(dx, dy, magnitude);

    // mask the magnitude
    cv::Mat maskedMagnitude(magnitude.size(), magnitude.type(), cv::Scalar_<float>(0));
    magnitude.copyTo(maskedMagnitude, (confidenceMat != 0.0f));
    cv::erode(maskedMagnitude, maskedMagnitude, cv::Mat());

    assert(maskedMagnitude.type() == CV_32FC1);

    // for each point in contour
    cv::Point point;

    for (int i = 0; i < contours.size(); ++i)
    {
        contour_t contour = contours[i];

        for (int j = 0; j < contour.size(); ++j)
        {

            point = contour[j];

            confidencePatch = getPatch(confidenceMat, point);

            // get confidence of patch
            confidence = cv::sum(confidencePatch)[0] / (double)confidencePatch.total();
            assert(0 <= confidence && confidence <= 1.0f);

            // get the normal to the border around point
            normal = getNormal(contour, point);

            // get the maximum gradient in source around patch
            magnitudePatch = getPatch(maskedMagnitude, point);
            cv::minMaxLoc(magnitudePatch, NULL, NULL, NULL, &maxPoint);
            gradient = cv::Point2f(
                -getPatch(dy, point).ptr<float>(maxPoint.y)[maxPoint.x],
                getPatch(dx, point).ptr<float>(maxPoint.y)[maxPoint.x]
            );

            // set the priority in priorityMat
            priorityMat.ptr<float>(point.y)[point.x] = std::abs((float)confidence * gradient.dot(normal));
            assert(priorityMat.ptr<float>(point.y)[point.x] >= 0);
        }
    }
}


/*
 * Transfer the values from patch centered at psiHatQ to patch centered at psiHatP in
 * mat according to maskMat.
 */
void transferPatch(const cv::Point& psiHatQ, const cv::Point& psiHatP, cv::Mat& mat, const cv::Mat& maskMat)
{
    assert(maskMat.type() == CV_8U);
    assert(mat.size() == maskMat.size());
    assert(RADIUS <= psiHatQ.x && psiHatQ.x < mat.cols - RADIUS && RADIUS <= psiHatQ.y && psiHatQ.y < mat.rows - RADIUS);
    assert(RADIUS <= psiHatP.x && psiHatP.x < mat.cols - RADIUS && RADIUS <= psiHatP.y && psiHatP.y < mat.rows - RADIUS);

    // copy contents of psiHatQ to psiHatP with mask
    getPatch(mat, psiHatQ).copyTo(getPatch(mat, psiHatP), getPatch(maskMat, psiHatP));
}

/*
 * Runs template matching with tmplate and mask tmplateMask on source.
 * Resulting Mat is stored in result.
 *
 */
cv::Mat computeSSD(const cv::Mat& tmplate, const cv::Mat& source, const cv::Mat& tmplateMask)
{
    assert(tmplate.type() == CV_32FC3 && source.type() == CV_32FC3);
    assert(tmplate.rows <= source.rows && tmplate.cols <= source.cols);
    assert(tmplateMask.size() == tmplate.size() && tmplate.type() == tmplateMask.type());

    cv::Mat result(source.rows - tmplate.rows + 1, source.cols - tmplate.cols + 1, CV_32F, 0.0f);

    cv::matchTemplate(source,
        tmplate,
        result,
        CV_TM_SQDIFF,
        tmplateMask
    );
    cv::normalize(result, result, 0, 1, cv::NORM_MINMAX);
    cv::copyMakeBorder(result, result, RADIUS, RADIUS, RADIUS, RADIUS, cv::BORDER_CONSTANT, 0.1f);

    return result;
}

int main (int argc, char** argv) {
    // --------------- read filename strings ------------------
    std::string colorFilename, maskFilename;
    
    colorFilename = "criminisi1.bmp";
    maskFilename = "criminisi1_mask.bmp";

    // ---------------- read the images ------------------------
    // colorMat     - color picture + border
    // maskMat      - mask picture + border
    // grayMat      - gray picture + border
    cv::Mat colorMat, maskMat, grayMat;
    loadInpaintingImages(
                         colorFilename,
                         maskFilename,
                         colorMat,
                         maskMat,
                         grayMat
                         );
    
    // confidenceMat - confidence picture + border
    cv::Mat confidenceMat;
    maskMat.convertTo(confidenceMat, CV_32F);
    confidenceMat /= 255.0f;
    
    // add borders around maskMat and confidenceMat
    cv::copyMakeBorder(maskMat, maskMat,
                       RADIUS, RADIUS, RADIUS, RADIUS,
                       cv::BORDER_CONSTANT, 255);
    cv::copyMakeBorder(confidenceMat, confidenceMat,
                       RADIUS, RADIUS, RADIUS, RADIUS,
                       cv::BORDER_CONSTANT, 0.0001f);
    
    // ---------------- start the algorithm -----------------
    
    contours_t contours;            // mask contours
    hierarchy_t hierarchy;          // contours hierarchy
    
    
    // priorityMat - priority values for all contour points + border
    cv::Mat priorityMat(
                        confidenceMat.size(),
                        CV_32FC1
                        );  // priority value matrix for each contour point
    
    assert(
           colorMat.size() == grayMat.size() &&
           colorMat.size() == confidenceMat.size() &&
           colorMat.size() == maskMat.size()
           );
    
    cv::Point psiHatP;          // psiHatP - point of highest confidence
    
    cv::Mat psiHatPColor;       // color patch around psiHatP
    
    cv::Mat psiHatPConfidence;  // confidence patch around psiHatP
    double confidence;          // confidence of psiHatPConfidence
    
    cv::Point psiHatQ;          // psiHatQ - point of closest patch
    
    cv::Mat result;             // holds result from template matching
    cv::Mat erodedMask;         // eroded mask
    
    cv::Mat templateMask;       // mask for template match (3 channel)
    
    // eroded mask is used to ensure that psiHatQ is not overlapping with target
    cv::erode(maskMat, erodedMask, cv::Mat(), cv::Point(-1, -1), RADIUS);
    
    cv::Mat drawMat;
    
    
    // main loop
    const size_t area = maskMat.total();
    
    while (cv::countNonZero(maskMat) != area)   // end when target is filled
    {
        // set priority matrix to -.1, lower than 0 so that border area is never selected
        priorityMat.setTo(-0.1f);
        
        // get the contours of mask
        getContours((maskMat == 0), contours, hierarchy);
        
        if (DEBUG) {
            drawMat = colorMat.clone();
        }
        
        // compute the priority for all contour points
        computePriority(contours, grayMat, confidenceMat, priorityMat);
        
        // get the patch with the greatest priority
        cv::minMaxLoc(priorityMat, NULL, NULL, NULL, &psiHatP);
        psiHatPColor = getPatch(colorMat, psiHatP);
        psiHatPConfidence = getPatch(confidenceMat, psiHatP);
        
        cv::Mat confInv = (psiHatPConfidence != 0.0f);
        confInv.convertTo(confInv, CV_32F);
        confInv /= 255.0f;
        // get the patch in source with least distance to psiHatPColor wrt source of psiHatP
        cv::Mat mergeArrays[3] = {confInv, confInv, confInv};
        cv::merge(mergeArrays, 3, templateMask);
        result = computeSSD(psiHatPColor, colorMat, templateMask);
        
        // set all target regions to 1.1, which is over the maximum value possilbe
        // from SSD
        result.setTo(1.1f, erodedMask == 0);
        // get minimum point of SSD between psiHatPColor and colorMat
        cv::minMaxLoc(result, NULL, NULL, &psiHatQ);
        
        assert(psiHatQ != psiHatP);
        
        if (DEBUG) {
        cv::rectangle(drawMat, psiHatP - cv::Point(RADIUS, RADIUS), psiHatP + cv::Point(RADIUS+1, RADIUS+1), cv::Scalar(255, 0, 0));
        cv::rectangle(drawMat, psiHatQ - cv::Point(RADIUS, RADIUS), psiHatQ + cv::Point(RADIUS+1, RADIUS+1), cv::Scalar(0, 0, 255));
        imshow("red - psiHatQ", drawMat);
        }
        // updates
        // copy from psiHatQ to psiHatP for each colorspace
        transferPatch(psiHatQ, psiHatP, grayMat, (maskMat == 0));
        transferPatch(psiHatQ, psiHatP, colorMat, (maskMat == 0));
        
        // fill in confidenceMat with confidences C(pixel) = C(psiHatP)
        confidence = computeConfidence(psiHatPConfidence);
        assert(0 <= confidence && confidence <= 1.0f);
        // update confidence
        psiHatPConfidence.setTo(confidence, (psiHatPConfidence == 0.0f));
        // update maskMat
        maskMat = (confidenceMat != 0.0f);
    }
    
    //showMat("final result", colorMat, 0);

    Mat imageROI, image_src;//ROI区域
    int x_begin, y_begin, width, height;		//裁取区域的坐标及大小
    int srcWidth, srcHeight;					//存储原图宽、高
    srcWidth = colorMat.cols;	//获取原图宽、高
    srcHeight = colorMat.rows;
    x_begin = 4;
    y_begin = 4;

    width = srcWidth - 8;
    height = srcHeight - 8;
    image_src = colorMat.clone();			//备份原图
    imageROI = image_src(Rect(x_begin, y_begin, width, height));	//设置待裁取ROI
    imshow("final result", imageROI);
    normalize(imageROI, imageROI, 0, 255, NORM_MINMAX, CV_8U);
    imwrite("result.jpg", imageROI);
    waitKey(0);
    return 0;
}