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| #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <string.h>
#include <time.h>
#include "GCoptimization.h"
#include "opencv/cv.h"
#include "opencv/highgui.h"
#include "opencv/ml.h"
using namespace cv;
#include <vector>
#include <iostream>
#include <limits>
using namespace std;
#define __PI 3.14159265
void graphcut(Mat& featureVec, Mat& im, int num_lables) {
Mat lables, centers;
cout << "Kmeans: #centers = " << num_lables << ",#tries = 2...";
kmeans(featureVec,
num_lables,
lables,
TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 200, 0.0001),
2,
KMEANS_PP_CENTERS,centers);
cout << "Done" << endl;
for (int i = 0; i<num_lables; i++) {
cout << "center " << i << ": ";
for (int j = 0; j<centers.cols; j++) cout << centers.at<float>(i, j) << ",";
cout << endl;
}
#ifdef BTM_DEBUG
{
Mat _tmp = lables.reshape(1, im.rows);
Mat _tmpUC;
_tmp.convertTo(_tmpUC, CV_8UC1, 255.0 / (double)num_lables);
imshow("tmp", _tmpUC);
waitKey();
}
#endif
GCoptimizationGridGraph *gc = new GCoptimizationGridGraph(im.cols, im.rows, num_lables);
Mat _d(featureVec.size(), CV_32FC1);
for (int center = 0; center < num_lables; center++) {
_d.setTo(Scalar(0));
int count = 0;
for (int i = 0, ii = 0; i<featureVec.rows; i++) {
if (((int*)lables.data)[i] == center) {
float* dptr = (float*)(_d.data + _d.step * (ii++));
float* fptr = (float*)(featureVec.data + featureVec.step * i);
for (int j = 0; j<featureVec.cols; j++) {
dptr[j] = fptr[j];
}
count++;
}
}
Mat d = _d(Rect(0, 0, _d.cols, count));
Mat covar;
calcCovarMatrix(d, covar, Mat(), CV_COVAR_NORMAL + CV_COVAR_ROWS, CV_32F);
Mat icv = covar.inv();
Mat centerRepeat;
repeat(centers(Rect(0, center, centers.cols, 1)), featureVec.rows, 1, centerRepeat);
Mat diff = featureVec - centerRepeat; //difference between each pixel's value and it's center's value
Mat A = (diff*icv).mul(diff) * 0.5f;
for (int i = 0; i<A.rows; i++) {
float* _ptr = (float*)(A.data + A.step * i);
float cost = 0;
for (int j = 0; j<A.cols; j++) {
cost += _ptr[j];
}
int icost = MAX(0, (int)floor(-log(cost)));
gc->setDataCost(i, center + 1, icost);
}
}
//int *smooth = new int[num_lables*num_lables];
//int cost;
//for ( int l1 = 0; l1 < num_lables; l1++ )
// for (int l2 = 0; l2 < num_lables; l2++ ){
// cost = (l1-l2)*(l1-l2) <= 4 ? (l1-l2)*(l1-l2):4;
// //Mat _a = centers(Rect(0,l1,centers.cols,1)) - centers(Rect(0,l2,centers.cols,1));
// //float n = (float)norm(_a);
// //if(n==0) cost = 0;
// //else cost = (int)ceil(-log(n));
// smooth[l1+l2*num_lables] = cost;
// }
//Mat gk = getGaussianKernel(3,-1,CV_32F);
//gk = gk * gk.t();
//cv::Sobel(gk,gk,-1,1,0);
Mat gray; cvtColor(im, gray, CV_RGB2GRAY);
//imshow("tmp",gray); waitKey();
gray.convertTo(gray, CV_32FC1, 1.0f / 255.0f);
//imshow("tmp",gray); waitKey();
Mat grayInt, grayInt1;
{
Mat _tmp;
//filter2D(gray,_tmp,CV_32F,gk);
Sobel(gray, _tmp, -1, 1, 0); //sobel for dx
//Canny(gray,_tmp,50.0,150.0);
_tmp = abs(_tmp);
#ifdef BTM_DEBUG
imshow("tmp", _tmp); waitKey();
#endif
double maxVal, minVal;
minMaxLoc(_tmp, &minVal, &maxVal);
cv::log((_tmp - minVal) / (maxVal - minVal), _tmp);
_tmp = -_tmp * 0.35;
_tmp.convertTo(grayInt, CV_32SC1);
//grayInt = grayInt * 5;
//filter2D(gray,_tmp,CV_32F,gk.t());
Sobel(gray, _tmp, -1, 0, 1); //sobel for dy
//Canny(gray,_tmp,50.0,150.0);
_tmp = abs(_tmp);
#ifdef BTM_DEBUG
imshow("tmp", _tmp); waitKey();
#endif
minMaxLoc(_tmp, &minVal, &maxVal);
cv::log((_tmp - minVal) / (maxVal - minVal), _tmp);
_tmp = -_tmp * 0.35;
_tmp.convertTo(grayInt1, CV_32SC1);
//grayInt1 = grayInt1 * 5;
}
//// next set up spatially varying arrays V and H
//int *V = new int[featureVec.rows];
//int *H = new int[featureVec.rows];
//
//for ( int i = 0; i < featureVec.rows; i++ ){
// //H[i] = i+(i+1)%im.rows;
// //V[i] = i*(i+im.cols)%im.cols;
// H[i] = 1;
// V[i] = 1;
//}
Mat Sc = 10 * (Mat::ones(num_lables, num_lables, CV_32SC1) - Mat::eye(num_lables, num_lables, CV_32SC1));
gc->setSmoothCostVH((double*)(Sc.data), (double*)grayInt.data, (double*)grayInt1.data);
//gc->setSmoothCost((int*)(Sc.data));
while (true) {
printf("\nBefore optimization energy is %d", gc->compute_energy());
gc->expansion(1);// run expansion for 2 iterations. For swap use gc->swap(num_iterations);
printf("\nAfter optimization energy is %d", gc->compute_energy());
for (int i = 0; i < featureVec.rows; i++)
((int*)(lables.data + lables.step * i))[0] = gc->whatLabel(i);
{
Mat _tmp = lables.reshape(1, im.rows);
#ifdef BTM_DEBUG
{
Mat _tmpUC;
_tmp.convertTo(_tmpUC, CV_8UC1, 255.0 / (double)num_lables);
vector<Mat> chns; split(im, chns);
for (unsigned int ch = 0; ch<chns.size(); ch++)
{
chns[ch] = /*chns[ch] + */(_tmp == ch)/**0.5*/;
}
cv::merge(chns, _tmpUC);
imshow("tmp", _tmpUC);
int c = waitKey();
if (c == 'q') break;
}
#endif
}
}
delete gc;
//delete[] smooth;
//delete[] V;
//delete[] H;
//printf("%d\n",reshaped.rows);
}
void graphcut1(Mat& im, Mat& probs, Mat& dx, Mat& dy, int num_lables, Mat& lables = Mat()) {
GCoptimizationGridGraph gc(im.cols, im.rows, num_lables);
int N = im.cols*im.rows;
//probs = probs.reshape(1,N);
double log2 = log(1.3);
for (int i = 0; i<N; i++) {
double* ppt = probs.ptr<double>(i);
for (int l = 0; l<num_lables; l++) {
int icost = MAX(0, (int)floor(-log(ppt[l]) / log2));
gc.setDataCost(i, l, icost);
}
}
Mat Sc = 5 * (Mat::ones(num_lables, num_lables, CV_32SC1) - Mat::eye(num_lables, num_lables, CV_32SC1));
//int score[9] = {0,50,50,
// 50,0,50,
// 50,50,0};
gc.setSmoothCostVH((double*)(/*score*/Sc.data), (double*)dx.data, (double*)dy.data);
lables.create(N, 1, CV_8UC1);
while (true) {
printf("\nBefore optimization energy is %d\n", gc.compute_energy());
gc.expansion(1);// run expansion for 2 iterations. For swap use gc->swap(num_iterations);
printf("\nAfter optimization energy is %d\n", gc.compute_energy());
for (int i = 0; i < N; i++)
((uchar*)(lables.data + lables.step * i))[0] = gc.whatLabel(i);
{
Mat _tmp = lables.reshape(1, im.rows);
{
vector<Mat> chns(3);
for (unsigned int ch = 0; ch<chns.size(); ch++) chns[ch] = (_tmp == ch);
Mat _tmpUC; cv::merge(chns, _tmpUC);
imshow("tmp", _tmpUC);
cout << "Press 'q' to finish GC iterations" << endl;
int c = waitKey();
if (c == 'q') break;
}
}
}
}
int selectedRange = 0;
Range rs[6] = { Range(40, 164), Range(56, 149), Range(192, 241), Range(32, 185), Range(16, 169), Range(163, 206) };
bool draw = false;
void on_mouse(int event, int x, int y, int flags, void* param)
{
Mat* pm = (Mat*)param;
switch (event)
{
case CV_EVENT_LBUTTONDOWN:
{
rs[selectedRange * 2].start = min(max(0, y), pm->cols - 1);
rs[selectedRange * 2 + 1].start = min(max(0, x), pm->rows - 1);
cout << "Begin " << x << "," << y << endl;
draw = true;
}
break;
case CV_EVENT_RBUTTONDOWN:
break;
case CV_EVENT_LBUTTONUP:
{
rs[selectedRange * 2].end = min(max(0, y), pm->cols - 1);
rs[selectedRange * 2 + 1].end = min(max(0, x), pm->rows - 1);
cout << "End " << x << "," << y << endl;
draw = false;
}
break;
case CV_EVENT_RBUTTONUP:
break;
case CV_EVENT_MOUSEMOVE:
if (draw) {
Mat _tmp; (*((Mat*)param)).copyTo(_tmp);
if (selectedRange != 0)
rectangle(_tmp, Point(rs[1].start, rs[0].start), Point(rs[1].end, rs[0].end), Scalar(255, 0, 0), 2);
else
rectangle(_tmp, Point(rs[1].start, rs[0].start), Point(x, y), Scalar(255, 0, 0), 2);
if (selectedRange != 1)
rectangle(_tmp, Point(rs[3].start, rs[2].start), Point(rs[3].end, rs[2].end), Scalar(0, 255, 0), 2);
else
rectangle(_tmp, Point(rs[3].start, rs[2].start), Point(x, y), Scalar(0, 255, 0), 2);
imshow("tmp1", _tmp);
}
break;
}
}
void getEdges(Mat& gray, Mat& grayInt, Mat& grayInt1) {
Mat _tmp, _tmp1, gray32f, res;
gray.convertTo(gray32f, CV_32FC1, 1.0 / 255.0);
GaussianBlur(gray32f, gray32f, Size(11, 11), 0.75);
Sobel(gray32f, _tmp, -1, 1, 0, 3); //sobel for dx
//Sobel(gray32f,_tmp1,-1,1,0,3,-1.0); //sobel for -dx
//_tmp = abs(_tmp) + abs(_tmp1);
//_tmp.copyTo(_tmp,(_tmp > 0.0));
//_tmp1.copyTo(_tmp1,(_tmp1 > 0.0));
_tmp1 = abs(_tmp); // + (_tmp1 == 0.0);
_tmp1.copyTo(res, (_tmp1 > 0.2));
//res = -res + 1.0;
imshow("tmp", res);
double maxVal, minVal;
minMaxLoc(_tmp, &minVal, &maxVal);
cv::log(/*(_tmp - minVal) / (maxVal - minVal)*/res, _tmp);
_tmp = -_tmp * 0.17;
_tmp.convertTo(grayInt1, CV_32SC1);
Sobel(gray32f, _tmp, -1, 0, 1, 3); //sobel for dy
//Sobel(gray32f,_tmp1,-1,0,2,3,-1.0); //sobel for -dy
//_tmp = abs(_tmp) + abs(_tmp1);
//_tmp = (_tmp + _tmp1 + 2.0) / 4.0;
_tmp1 = abs(_tmp);
res.setTo(Scalar(0));
_tmp1.copyTo(res, (_tmp1 > 0.2));
//res = -res+1.0;
imshow("tmp1", res); waitKey();
minMaxLoc(_tmp, &minVal, &maxVal);
cv::log(/*(_tmp - minVal) / (maxVal - minVal)*/res, _tmp);
_tmp = -_tmp * 0.17;
_tmp.convertTo(grayInt, CV_32SC1);
}
void doEM3D(Mat& _im, Mat& probs, int num_models = 2, int num_gaussians = 3, bool useRanges = true) {
Mat im = _im;
vector<CvEM> model(num_models);
double CvEMParams; double ps(num_gaussians);
imshow("tmp1", im);
vector<Mat> samples(num_models);
while (true) {
if (useRanges) {
cout << "Define ranges (press <space> to continue)" << endl;
while (true) {
int c = waitKey();
if (c == ' ') break;
else selectedRange = c - '1';
cout << "Selected range: " << selectedRange << endl;
}
vector<Mat> splitted;
for (int i = 0; i<num_models; i++) {
Mat _tmp; im(rs[i * 2], rs[i * 2 + 1]).copyTo(_tmp);
_tmp.reshape(1, _tmp.rows*_tmp.cols).convertTo(samples[i], CV_32FC1, 1.0 / 255.0);
}
}
for (int i = 0; i<num_models; i++) {
model[i].clear();
model[i].train(samples[i], Mat(), ps, NULL);
cout << "Model " << i << " means: ";
const CvMat* m = model[i].get_means();
for (int g = 0; g<num_gaussians; g++) {
for (int c = 0; c<3; c++) cout << m->data.db[g * 3 + c] << ", ";
cout << endl;
}
cout << endl;
}
Mat out_lables(im.size(), CV_32FC1);
float _s[3];
Mat sample(1, 3, CV_32FC1, &_s);
probs = Mat(im.rows*im.cols, num_models, CV_64FC1);
{
Mat _tmp(1, 2, CV_64FC1);
for (int y = 0; y<im.rows; y++) {
uchar* imp = im.ptr<uchar>(y);
float* outl_ptr = out_lables.ptr<float>(y);
int probs_mult = y*im.cols;
for (int x = 0; x<im.cols; x++) {
float _label, maxv = std::numeric_limits<float>::min();
double p;
int probs_mult1 = x*num_models;
for (int i = 0; i<num_models; i++) {
float ps[3];
for (int c = 0; c<3; c++) {
_s[c] = ((float)imp[c + x * 3]) / 255.0f;
}
Mat X; sample.copyTo(X);
Mat xprob;
model[i].predict(X, &xprob);
double* w = model[i].get_weights()->data.db;
p = std::numeric_limits<double>::min();
for (int g = 0; g<num_gaussians; g++) p = max(p, w[g] * (double)(((float*)xprob.data)[g]));
probs.at<double>(probs_mult + x, i) = p;
if (p>maxv) { maxv = p; _label = (float)i; }
}
outl_ptr[x] = _label;
}
}
}
{
Mat _tmp(im.size(), CV_8UC1);
out_lables.convertTo(_tmp, CV_8UC1);
vector<Mat> vm(3); for (int i = 0; i<3; i++) vm[i] = (_tmp == i);
Mat out; merge(vm, out);
imshow("tmp", out);
if (num_models == 3)
imshow("tmp2", probs.reshape(num_models, im.rows));
}
cout << "Press any key for another round or 'q' to finish" << endl;
int c = waitKey();
if (c == 'q') break;
}
}
/**
* if useRanges is true, use the mouse to define ranges, else use the labeling from lables
* to train the GMM
*/
void doEM1D(Mat& _im, Mat& probs, int num_models = 2, bool useRanges = true, Mat& lables = Mat()) {
//Mat im; cvtColor(_im,im,CV_RGB2HSV);
Mat im = _im;
vector<vector<CvEM>> model(num_models);
for (int i = 0; i<num_models; i++) {
model[i] = vector<CvEM>(3);
}
double CvEMParams;
double ps(1);
imshow("tmp1", im);
vector<vector<Mat>> samples(num_models);
for (int i = 0; i<num_models; i++) samples[i] = vector<Mat>(3);
while (true) {
if (useRanges) {
cout << "Define ranges (press <space> to continue)" << endl;
while (true) {
int c = waitKey();
if (c == ' ') break;
else selectedRange = c - '1';
cout << "Selected range: " << selectedRange << endl;
}
vector<Mat> splitted;
for (int i = 0; i<num_models; i++) {
int cnt = 0;
Mat _tmp; im(rs[i * 2], rs[i * 2 + 1]).copyTo(_tmp);
//Mat __tmp = _tmp.reshape(1,_tmp.rows*_tmp.cols);
split(_tmp, splitted);
for (int c = 0; c<3; c++)
splitted[c].reshape(1, _tmp.rows*_tmp.cols).convertTo(samples[i][c], CV_32FC1, 1.0 / 255.0);
}
}
for (int i = 0; i<num_models; i++) {
for (int c = 0; c<3; c++) {
model[i][c].clear();
model[i][c].train(samples[i][c], Mat(), ps, NULL);
//m_probs[i] = model[i].get_probs();
}
cout << "Model " << i << " means: ";
for (int c = 0; c<3; c++) cout << model[i][c].get_means()->data.db[0] << ", ";
cout << endl;
}
Mat out_lables(im.size(), CV_32FC1);
float _s;
Mat sample(1, 1, CV_32FC1, &_s);
probs = Mat(im.rows*im.cols, num_models, CV_64FC1);
{
Mat _tmp(1, 2, CV_64FC1);
for (int y = 0; y<im.rows; y++) {
uchar* imp = im.ptr<uchar>(y);
float* outl_ptr = out_lables.ptr<float>(y);
int probs_mult = y*im.cols;
for (int x = 0; x<im.cols; x++) {
float _label, p, maxv = std::numeric_limits<float>::min();
int probs_mult1 = x*num_models;
for (int i = 0; i<num_models; i++) {
float ps[3];
for (int c = 0; c<3; c++) {
_s = ((float)imp[c + x * 3]) / 255.0f;
//model[i][c].predict(sample,&_tmp);
double mu = model[i][c].get_means()->data.db[0];
double x = (double)_s;
double sigma_sq = model[i][c].get_covs()[0]->data.db[0];
double _p = (1 / sqrt(2 * __PI*sigma_sq))*exp(-((x - mu)*(x - mu)) / (2 * sigma_sq));
ps[c] = (float)_p; //((double*)_tmp.data)[0];
}
p = ps[0] * ps[1] * ps[2];
//((double*)probs.data + probs_mult + probs_mult1 + i)[0] = p;
probs.at<double>(probs_mult + x, i) = p;
if (p>maxv) { maxv = p; _label = (float)i; }
}
outl_ptr[x] = _label;
}
}
}
{
Mat _tmp(im.size(), CV_8UC1);
out_lables.convertTo(_tmp, CV_8UC1);
vector<Mat> vm(3); for (int i = 0; i<3; i++) vm[i] = (_tmp == i);
Mat out; merge(vm, out);
imshow("tmp", out);
if (num_models == 3)
imshow("tmp2", probs.reshape(num_models, im.rows));
}
cout << "Press any key for another round or 'q' to finish" << endl;
int c = waitKey();
if (c == 'q') break;
}
}
int main(int argc, char** argv) {
Mat _im = imread("40406598_fd4e74d51c_d.jpg");
Mat im;
resize(_im, im, Size(_im.cols / 2, _im.rows / 2));
rs[4] = Range(0, im.rows - 1);
rs[5] = Range(0, im.cols - 1);
namedWindow("tmp");
namedWindow("tmp1");
cvSetMouseCallback("tmp1", on_mouse, &im);
namedWindow("tmp2");
Mat gray;
cvtColor(im, gray, CV_RGB2GRAY);
Mat probs;
Mat hsv_im; cvtColor(im, hsv_im, CV_BGR2HSV);
vector<Mat> v; split(hsv_im, v);
{
Mat gray32f; gray.convertTo(gray32f, CV_32FC1, 1.0 / 255.0);
Mat _tmp; Sobel(gray32f, _tmp, -1, 1, 1, 3);
Mat _tmp1; Sobel(gray32f, _tmp1, -1, 1, 1, 3, -1.0);
Mat(abs(_tmp) + abs(_tmp1)).convertTo(v[2], CV_8UC1, 255.0);
}
Mat combined; cv::merge(v, combined);
doEM3D(combined, probs, 3);
Mat dx, dy;
getEdges(gray, dx, dy);
Mat lables;
graphcut1(im, probs, dx, dy, 3, lables);
char* winnm[3] = { "tmp", "tmp1", "tmp2" };
for (int i = 0; i<3; i++)
{
Mat _tmp; im.copyTo(_tmp, lables == i);
imshow(string(winnm[i]), _tmp);
}
waitKey();
{
Mat _tmpLabels = lables.reshape(1, im.rows);
//find connected components in hair and face masks
vector<vector<Point>> contours;
for (int itr = 0; itr<2; itr++) {
Mat mask = (_tmpLabels == itr);
contours.clear();
cv::findContours(mask, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
//compute areas
vector<double> areas(contours.size());
for (unsigned int ai = 0; ai<contours.size(); ai++) {
Mat _pts(contours[ai]);
Scalar mp = mean(_pts);
//bias score according to distance from center face
areas[ai] = contourArea(Mat(contours[ai]))/* * bias.at<double>(mp.val[1],mp.val[0])*/;
}
//find largest connected component
double max; Point maxLoc;
minMaxLoc(Mat(areas), 0, &max, 0, &maxLoc);
//draw back on mask
_tmpLabels.setTo(Scalar(3), mask); //all unassigned pixels will have value of 3, later we'll use it
mask.setTo(Scalar(0)); //clear...
drawContours(mask, contours, maxLoc.y, Scalar(255), CV_FILLED);
_tmpLabels.setTo(Scalar(itr), mask);
}
lables.setTo(Scalar(2), lables == 3); //all 3's should be the "other label"
}
for (int i = 0; i<3; i++)
{
Mat _tmp; im.copyTo(_tmp, lables == i);
imshow(string(winnm[i]), _tmp);
}
waitKey();
return 0;
} |
OpenCV - erreurs de compilations
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