분석 예정~^^;
Warped Patch에 대해 현재 헷갈리고 있는데..ㅠㅠ
관련 자료를 찾다가 다음 소스를 찾았다..그냥 AffineTransformPatch이름이라는 이유로 구글링해서..ㅎ
그런데 이 소스가 범상찾아서 찾아보니..
"Real-Time Learning of Accurate Patch Rectification"
의 논문을 OpenCV에서 구현해놓은것이다.
일단 논문과 다음 소스가 어떻게 적용되고 있는지 그리고 내가 원하는것인지 참고하려고 한다.
void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose)
{
CvRect src_large_roi = cvGetImageROI(src);
IplImage* temp = cvCreateImage(cvSize(src_large_roi.width, src_large_roi.height), IPL_DEPTH_32F, src->nChannels);
cvSetZero(temp);
IplImage* temp2 = cvCloneImage(temp);
CvMat* rotation_phi = cvCreateMat(2, 3, CV_32FC1);
CvSize new_size = cvSize(cvRound(temp->width*pose.lambda1), cvRound(temp->height*pose.lambda2));
IplImage* temp3 = cvCreateImage(new_size, IPL_DEPTH_32F, src->nChannels);
cvConvertScale(src, temp);
cvResetImageROI(temp);
cv2DRotationMatrix(cvPoint2D32f(temp->width/2, temp->height/2), pose.phi, 1.0, rotation_phi);
cvWarpAffine(temp, temp2, rotation_phi);
cvSetZero(temp);
cvResize(temp2, temp3);
cv2DRotationMatrix(cvPoint2D32f(temp3->width/2, temp3->height/2), pose.theta - pose.phi, 1.0, rotation_phi);
cvWarpAffine(temp3, temp, rotation_phi);
cvSetImageROI(temp, cvRect(temp->width/2 - src_large_roi.width/4, temp->height/2 - src_large_roi.height/4,
src_large_roi.width/2, src_large_roi.height/2));
cvConvertScale(temp, dst);
cvReleaseMat(&rotation_phi);
cvReleaseImage(&temp3);
cvReleaseImage(&temp2);
cvReleaseImage(&temp);
}
--------------------------------------------------------------------------------
http://build.shr-project.org/sources/svn/code.ros.org/svn/opencv/branches/2.2/opencv/modules/features2d/src/oneway.cpp
/*
* cvoneway.cpp
* one_way_sample
*
* Created by Victor Eruhimov on 3/23/10.
* Copyright 2010 Argus Corp. All rights reserved.
*
*/
#include "precomp.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdio.h>
namespace cv{
inline int round(float value)
{
if(value > 0)
{
return int(value + 0.5f);
}
else
{
return int(value - 0.5f);
}
}
inline CvRect resize_rect(CvRect rect, float alpha)
{
return cvRect(rect.x + round((float)(0.5*(1 - alpha)*rect.width)), rect.y + round((float)(0.5*(1 - alpha)*rect.height)),
round(rect.width*alpha), round(rect.height*alpha));
}
CvMat* ConvertImageToMatrix(IplImage* patch);
class CvCameraPose
{
public:
CvCameraPose()
{
m_rotation = cvCreateMat(1, 3, CV_32FC1);
m_translation = cvCreateMat(1, 3, CV_32FC1);
};
~CvCameraPose()
{
cvReleaseMat(&m_rotation);
cvReleaseMat(&m_translation);
};
void SetPose(CvMat* rotation, CvMat* translation)
{
cvCopy(rotation, m_rotation);
cvCopy(translation, m_translation);
};
CvMat* GetRotation() {return m_rotation;};
CvMat* GetTranslation() {return m_translation;};
protected:
CvMat* m_rotation;
CvMat* m_translation;
};
// AffineTransformPatch: generates an affine transformed image patch.
// - src: source image (roi is supported)
// - dst: output image. ROI of dst image should be 2 times smaller than ROI of src.
// - pose: parameters of an affine transformation
void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose);
// GenerateAffineTransformFromPose: generates an affine transformation matrix from CvAffinePose instance
// - size: the size of image patch
// - pose: affine transformation
// - transform: 2x3 transformation matrix
void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform);
// Generates a random affine pose
CvAffinePose GenRandomAffinePose();
const static int num_mean_components = 500;
const static float noise_intensity = 0.15f;
static inline CvPoint rect_center(CvRect rect)
{
return cvPoint(rect.x + rect.width/2, rect.y + rect.height/2);
}
void homography_transform(IplImage* frontal, IplImage* result, CvMat* homography)
{
cvWarpPerspective(frontal, result, homography);
}
CvAffinePose perturbate_pose(CvAffinePose pose, float noise)
{
// perturbate the matrix
float noise_mult_factor = 1 + (0.5f - float(rand())/RAND_MAX)*noise;
float noise_add_factor = noise_mult_factor - 1;
CvAffinePose pose_pert = pose;
pose_pert.phi += noise_add_factor;
pose_pert.theta += noise_mult_factor;
pose_pert.lambda1 *= noise_mult_factor;
pose_pert.lambda2 *= noise_mult_factor;
return pose_pert;
}
void generate_mean_patch(IplImage* frontal, IplImage* result, CvAffinePose pose, int pose_count, float noise)
{
IplImage* sum = cvCreateImage(cvSize(result->width, result->height), IPL_DEPTH_32F, 1);
IplImage* workspace = cvCloneImage(result);
IplImage* workspace_float = cvCloneImage(sum);
cvSetZero(sum);
for(int i = 0; i < pose_count; i++)
{
CvAffinePose pose_pert = perturbate_pose(pose, noise);
AffineTransformPatch(frontal, workspace, pose_pert);
cvConvertScale(workspace, workspace_float);
cvAdd(sum, workspace_float, sum);
}
cvConvertScale(sum, result, 1.0f/pose_count);
cvReleaseImage(&workspace);
cvReleaseImage(&sum);
cvReleaseImage(&workspace_float);
}
void generate_mean_patch_fast(IplImage* /*frontal*/, IplImage* /*result*/, CvAffinePose /*pose*/,
CvMat* /*pca_hr_avg*/, CvMat* /*pca_hr_eigenvectors*/, const OneWayDescriptor* /*pca_descriptors*/)
{
/*for(int i = 0; i < pca_hr_eigenvectors->cols; i++)
{
}*/
}
void readPCAFeatures(const char *filename, CvMat** avg, CvMat** eigenvectors, const char *postfix = "");
void readPCAFeatures(const FileNode &fn, CvMat** avg, CvMat** eigenvectors, const char* postfix = "");
void savePCAFeatures(FileStorage &fs, const char* postfix, CvMat* avg, CvMat* eigenvectors);
void calcPCAFeatures(vector<IplImage*>& patches, FileStorage &fs, const char* postfix, CvMat** avg,
CvMat** eigenvectors);
void loadPCAFeatures(const char* path, const char* images_list, vector<IplImage*>& patches, CvSize patch_size);
void generatePCAFeatures(const char* path, const char* img_filename, FileStorage& fs, const char* postfix,
CvSize patch_size, CvMat** avg, CvMat** eigenvectors);
void eigenvector2image(CvMat* eigenvector, IplImage* img);
void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int& desc_idx, int& pose_idx, float& distance,
CvMat* avg = 0, CvMat* eigenvalues = 0);
void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int n,
std::vector<int>& desc_idxs, std::vector<int>& pose_idxs, std::vector<float>& distances,
CvMat* avg = 0, CvMat* eigenvalues = 0);
void FindOneWayDescriptor(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch, int& desc_idx, int& pose_idx, float& distance,
CvMat* avg = 0, CvMat* eigenvalues = 0);
void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int& desc_idx, int& pose_idx, float& distance, float& scale,
CvMat* avg, CvMat* eigenvectors);
void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& scales,
CvMat* avg, CvMat* eigenvectors);
void FindOneWayDescriptorEx(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int& desc_idx, int& pose_idx, float& distance, float& scale,
CvMat* avg, CvMat* eigenvectors);
inline CvRect fit_rect_roi_fixedsize(CvRect rect, CvRect roi)
{
CvRect fit = rect;
fit.x = MAX(fit.x, roi.x);
fit.y = MAX(fit.y, roi.y);
fit.x = MIN(fit.x, roi.x + roi.width - fit.width - 1);
fit.y = MIN(fit.y, roi.y + roi.height - fit.height - 1);
return(fit);
}
inline CvRect fit_rect_fixedsize(CvRect rect, IplImage* img)
{
CvRect roi = cvGetImageROI(img);
return fit_rect_roi_fixedsize(rect, roi);
}
OneWayDescriptor::OneWayDescriptor()
{
m_pose_count = 0;
m_samples = 0;
m_input_patch = 0;
m_train_patch = 0;
m_pca_coeffs = 0;
m_affine_poses = 0;
m_transforms = 0;
m_pca_dim_low = 100;
m_pca_dim_high = 100;
}
OneWayDescriptor::~OneWayDescriptor()
{
if(m_pose_count)
{
for(int i = 0; i < m_pose_count; i++)
{
cvReleaseImage(&m_samples[i]);
cvReleaseMat(&m_pca_coeffs[i]);
}
cvReleaseImage(&m_input_patch);
cvReleaseImage(&m_train_patch);
delete []m_samples;
delete []m_pca_coeffs;
if(!m_transforms)
{
delete []m_affine_poses;
}
}
}
void OneWayDescriptor::Allocate(int pose_count, CvSize size, int nChannels)
{
m_pose_count = pose_count;
m_samples = new IplImage* [m_pose_count];
m_pca_coeffs = new CvMat* [m_pose_count];
m_patch_size = cvSize(size.width/2, size.height/2);
if(!m_transforms)
{
m_affine_poses = new CvAffinePose[m_pose_count];
}
int length = m_pca_dim_low;//roi.width*roi.height;
for(int i = 0; i < m_pose_count; i++)
{
m_samples[i] = cvCreateImage(cvSize(size.width/2, size.height/2), IPL_DEPTH_32F, nChannels);
m_pca_coeffs[i] = cvCreateMat(1, length, CV_32FC1);
}
m_input_patch = cvCreateImage(GetPatchSize(), IPL_DEPTH_8U, 1);
m_train_patch = cvCreateImage(GetInputPatchSize(), IPL_DEPTH_8U, 1);
}
void cvmSet2DPoint(CvMat* matrix, int row, int col, CvPoint2D32f point)
{
cvmSet(matrix, row, col, point.x);
cvmSet(matrix, row, col + 1, point.y);
}
void cvmSet3DPoint(CvMat* matrix, int row, int col, CvPoint3D32f point)
{
cvmSet(matrix, row, col, point.x);
cvmSet(matrix, row, col + 1, point.y);
cvmSet(matrix, row, col + 2, point.z);
}
CvAffinePose GenRandomAffinePose()
{
const float scale_min = 0.8f;
const float scale_max = 1.2f;
CvAffinePose pose;
pose.theta = float(rand())/RAND_MAX*120 - 60;
pose.phi = float(rand())/RAND_MAX*360;
pose.lambda1 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min);
pose.lambda2 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min);
return pose;
}
void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform)
{
CvMat* temp = cvCreateMat(3, 3, CV_32FC1);
CvMat* final = cvCreateMat(3, 3, CV_32FC1);
cvmSet(temp, 2, 0, 0.0f);
cvmSet(temp, 2, 1, 0.0f);
cvmSet(temp, 2, 2, 1.0f);
CvMat rotation;
cvGetSubRect(temp, &rotation, cvRect(0, 0, 3, 2));
cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.phi, 1.0, &rotation);
cvCopy(temp, final);
cvmSet(temp, 0, 0, pose.lambda1);
cvmSet(temp, 0, 1, 0.0f);
cvmSet(temp, 1, 0, 0.0f);
cvmSet(temp, 1, 1, pose.lambda2);
cvmSet(temp, 0, 2, size.width/2*(1 - pose.lambda1));
cvmSet(temp, 1, 2, size.height/2*(1 - pose.lambda2));
cvMatMul(temp, final, final);
cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.theta - pose.phi, 1.0, &rotation);
cvMatMul(temp, final, final);
cvGetSubRect(final, &rotation, cvRect(0, 0, 3, 2));
cvCopy(&rotation, transform);
cvReleaseMat(&temp);
cvReleaseMat(&final);
}
void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose)
{
CvRect src_large_roi = cvGetImageROI(src);
IplImage* temp = cvCreateImage(cvSize(src_large_roi.width, src_large_roi.height), IPL_DEPTH_32F, src->nChannels);
cvSetZero(temp);
IplImage* temp2 = cvCloneImage(temp);
CvMat* rotation_phi = cvCreateMat(2, 3, CV_32FC1);
CvSize new_size = cvSize(cvRound(temp->width*pose.lambda1), cvRound(temp->height*pose.lambda2));
IplImage* temp3 = cvCreateImage(new_size, IPL_DEPTH_32F, src->nChannels);
cvConvertScale(src, temp);
cvResetImageROI(temp);
cv2DRotationMatrix(cvPoint2D32f(temp->width/2, temp->height/2), pose.phi, 1.0, rotation_phi);
cvWarpAffine(temp, temp2, rotation_phi);
cvSetZero(temp);
cvResize(temp2, temp3);
cv2DRotationMatrix(cvPoint2D32f(temp3->width/2, temp3->height/2), pose.theta - pose.phi, 1.0, rotation_phi);
cvWarpAffine(temp3, temp, rotation_phi);
cvSetImageROI(temp, cvRect(temp->width/2 - src_large_roi.width/4, temp->height/2 - src_large_roi.height/4,
src_large_roi.width/2, src_large_roi.height/2));
cvConvertScale(temp, dst);
cvReleaseMat(&rotation_phi);
cvReleaseImage(&temp3);
cvReleaseImage(&temp2);
cvReleaseImage(&temp);
}
void OneWayDescriptor::GenerateSamples(int pose_count, IplImage* frontal, int norm)
{
/* if(m_transforms)
{
GenerateSamplesWithTransforms(pose_count, frontal);
return;
}
*/
CvRect roi = cvGetImageROI(frontal);
IplImage* patch_8u = cvCreateImage(cvSize(roi.width/2, roi.height/2), frontal->depth, frontal->nChannels);
for(int i = 0; i < pose_count; i++)
{
if(!m_transforms)
{
m_affine_poses[i] = GenRandomAffinePose();
}
//AffineTransformPatch(frontal, patch_8u, m_affine_poses[i]);
generate_mean_patch(frontal, patch_8u, m_affine_poses[i], num_mean_components, noise_intensity);
double scale = 1.0f;
if(norm)
{
double sum = cvSum(patch_8u).val[0];
scale = 1/sum;
}
cvConvertScale(patch_8u, m_samples[i], scale);
#if 0
double maxval;
cvMinMaxLoc(m_samples[i], 0, &maxval);
IplImage* test = cvCreateImage(cvSize(roi.width/2, roi.height/2), IPL_DEPTH_8U, 1);
cvConvertScale(m_samples[i], test, 255.0/maxval);
cvNamedWindow("1", 1);
cvShowImage("1", test);
cvWaitKey(0);
#endif
}
cvReleaseImage(&patch_8u);
}
void OneWayDescriptor::GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors)
{
CvRect roi = cvGetImageROI(frontal);
if(roi.width != GetInputPatchSize().width || roi.height != GetInputPatchSize().height)
{
cvResize(frontal, m_train_patch);
frontal = m_train_patch;
}
CvMat* pca_coeffs = cvCreateMat(1, pca_hr_eigenvectors->cols, CV_32FC1);
double maxval;
cvMinMaxLoc(frontal, 0, &maxval);
CvMat* frontal_data = ConvertImageToMatrix(frontal);
double sum = cvSum(frontal_data).val[0];
cvConvertScale(frontal_data, frontal_data, 1.0f/sum);
cvProjectPCA(frontal_data, pca_hr_avg, pca_hr_eigenvectors, pca_coeffs);
for(int i = 0; i < m_pose_count; i++)
{
cvSetZero(m_samples[i]);
for(int j = 0; j < m_pca_dim_high; j++)
{
double coeff = cvmGet(pca_coeffs, 0, j);
IplImage* patch = pca_descriptors[j + 1].GetPatch(i);
cvAddWeighted(m_samples[i], 1.0, patch, coeff, 0, m_samples[i]);
#if 0
printf("coeff%d = %f\n", j, coeff);
IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
double maxval;
cvMinMaxLoc(patch, 0, &maxval);
cvConvertScale(patch, test, 255.0/maxval);
cvNamedWindow("1", 1);
cvShowImage("1", test);
cvWaitKey(0);
#endif
}
cvAdd(pca_descriptors[0].GetPatch(i), m_samples[i], m_samples[i]);
double sum = cvSum(m_samples[i]).val[0];
cvConvertScale(m_samples[i], m_samples[i], 1.0/sum);
#if 0
IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
/* IplImage* temp1 = cvCreateImage(cvSize(12, 12), IPL_DEPTH_32F, 1);
eigenvector2image(pca_hr_avg, temp1);
IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
cvAdd(m_samples[i], temp1, temp1);
cvMinMaxLoc(temp1, 0, &maxval);
cvConvertScale(temp1, test, 255.0/maxval);*/
cvMinMaxLoc(m_samples[i], 0, &maxval);
cvConvertScale(m_samples[i], test, 255.0/maxval);
cvNamedWindow("1", 1);
cvShowImage("1", frontal);
cvNamedWindow("2", 1);
cvShowImage("2", test);
cvWaitKey(0);
#endif
}
cvReleaseMat(&pca_coeffs);
cvReleaseMat(&frontal_data);
}
void OneWayDescriptor::SetTransforms(CvAffinePose* poses, CvMat** transforms)
{
if(m_affine_poses)
{
delete []m_affine_poses;
}
m_affine_poses = poses;
m_transforms = transforms;
}
void OneWayDescriptor::Initialize(int pose_count, IplImage* frontal, const char* feature_name, int norm)
{
m_feature_name = std::string(feature_name);
CvRect roi = cvGetImageROI(frontal);
m_center = rect_center(roi);
Allocate(pose_count, cvSize(roi.width, roi.height), frontal->nChannels);
GenerateSamples(pose_count, frontal, norm);
}
void OneWayDescriptor::InitializeFast(int pose_count, IplImage* frontal, const char* feature_name,
CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors)
{
if(pca_hr_avg == 0)
{
Initialize(pose_count, frontal, feature_name, 1);
return;
}
m_feature_name = std::string(feature_name);
CvRect roi = cvGetImageROI(frontal);
m_center = rect_center(roi);
Allocate(pose_count, cvSize(roi.width, roi.height), frontal->nChannels);
GenerateSamplesFast(frontal, pca_hr_avg, pca_hr_eigenvectors, pca_descriptors);
}
void OneWayDescriptor::InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors)
{
for(int i = 0; i < m_pose_count; i++)
{
ProjectPCASample(m_samples[i], avg, eigenvectors, m_pca_coeffs[i]);
}
}
void OneWayDescriptor::ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const
{
CvMat* patch_mat = ConvertImageToMatrix(patch);
// CvMat eigenvectorsr;
// cvGetSubRect(eigenvectors, &eigenvectorsr, cvRect(0, 0, eigenvectors->cols, pca_coeffs->cols));
CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1);
cvProjectPCA(patch_mat, avg, eigenvectors, temp);
CvMat temp1;
cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1));
cvCopy(&temp1, pca_coeffs);
cvReleaseMat(&temp);
cvReleaseMat(&patch_mat);
}
void OneWayDescriptor::EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvectors) const
{
if(avg == 0)
{
// do not use pca
if (!CV_IS_MAT(patch))
{
EstimatePose((IplImage*)patch, pose_idx, distance);
}
else
{
}
return;
}
CvRect roi={0,0,0,0};
if (!CV_IS_MAT(patch))
{
roi = cvGetImageROI((IplImage*)patch);
if(roi.width != GetPatchSize().width || roi.height != GetPatchSize().height)
{
cvResize(patch, m_input_patch);
patch = m_input_patch;
roi = cvGetImageROI((IplImage*)patch);
}
}
CvMat* pca_coeffs = cvCreateMat(1, m_pca_dim_low, CV_32FC1);
if (CV_IS_MAT(patch))
{
cvCopy((CvMat*)patch, pca_coeffs);
}
else
{
IplImage* patch_32f = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_32F, 1);
double sum = cvSum(patch).val[0];
cvConvertScale(patch, patch_32f, 1.0f/sum);
ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs);
cvReleaseImage(&patch_32f);
}
distance = 1e10;
pose_idx = -1;
for(int i = 0; i < m_pose_count; i++)
{
double dist = cvNorm(m_pca_coeffs[i], pca_coeffs);
// float dist = 0;
// float data1, data2;
// //CvMat* pose_pca_coeffs = m_pca_coeffs[i];
// for (int x=0; x < pca_coeffs->width; x++)
// for (int y =0 ; y < pca_coeffs->height; y++)
// {
// data1 = ((float*)(pca_coeffs->data.ptr + pca_coeffs->step*x))[y];
// data2 = ((float*)(m_pca_coeffs[i]->data.ptr + m_pca_coeffs[i]->step*x))[y];
// dist+=(data1-data2)*(data1-data2);
// }
////#if 1
// for (int j = 0; j < m_pca_dim_low; j++)
// {
// dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])*(pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]);
// }
//#else
// for (int j = 0; j <= m_pca_dim_low - 4; j += 4)
// {
// dist += (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j])*
// (pose_pca_coeffs->data.fl[j]- pca_coeffs->data.fl[j]);
// dist += (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1])*
// (pose_pca_coeffs->data.fl[j+1]- pca_coeffs->data.fl[j+1]);
// dist += (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2])*
// (pose_pca_coeffs->data.fl[j+2]- pca_coeffs->data.fl[j+2]);
// dist += (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3])*
// (pose_pca_coeffs->data.fl[j+3]- pca_coeffs->data.fl[j+3]);
// }
//#endif
if(dist < distance)
{
distance = (float)dist;
pose_idx = i;
}
}
cvReleaseMat(&pca_coeffs);
}
void OneWayDescriptor::EstimatePose(IplImage* patch, int& pose_idx, float& distance) const
{
distance = 1e10;
pose_idx = -1;
CvRect roi = cvGetImageROI(patch);
IplImage* patch_32f = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_32F, patch->nChannels);
double sum = cvSum(patch).val[0];
cvConvertScale(patch, patch_32f, 1/sum);
for(int i = 0; i < m_pose_count; i++)
{
if(m_samples[i]->width != patch_32f->width || m_samples[i]->height != patch_32f->height)
{
continue;
}
double dist = cvNorm(m_samples[i], patch_32f);
//float dist = 0.0f;
//float i1,i2;
//for (int y = 0; y<patch_32f->height; y++)
// for (int x = 0; x< patch_32f->width; x++)
// {
// i1 = ((float*)(m_samples[i]->imageData + m_samples[i]->widthStep*y))[x];
// i2 = ((float*)(patch_32f->imageData + patch_32f->widthStep*y))[x];
// dist+= (i1-i2)*(i1-i2);
// }
if(dist < distance)
{
distance = (float)dist;
pose_idx = i;
}
#if 0
IplImage* img1 = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_8U, 1);
IplImage* img2 = cvCreateImage(cvSize(roi.width, roi.height), IPL_DEPTH_8U, 1);
double maxval;
cvMinMaxLoc(m_samples[i], 0, &maxval);
cvConvertScale(m_samples[i], img1, 255.0/maxval);
cvMinMaxLoc(patch_32f, 0, &maxval);
cvConvertScale(patch_32f, img2, 255.0/maxval);
cvNamedWindow("1", 1);
cvShowImage("1", img1);
cvNamedWindow("2", 1);
cvShowImage("2", img2);
printf("Distance = %f\n", dist);
cvWaitKey(0);
#endif
}
cvReleaseImage(&patch_32f);
}
void OneWayDescriptor::Save(const char* path)
{
for(int i = 0; i < m_pose_count; i++)
{
char buf[1024];
sprintf(buf, "%s/patch_%04d.jpg", path, i);
IplImage* patch = cvCreateImage(cvSize(m_samples[i]->width, m_samples[i]->height), IPL_DEPTH_8U, m_samples[i]->nChannels);
double maxval;
cvMinMaxLoc(m_samples[i], 0, &maxval);
cvConvertScale(m_samples[i], patch, 255/maxval);
cvSaveImage(buf, patch);
cvReleaseImage(&patch);
}
}
void OneWayDescriptor::Write(CvFileStorage* fs, const char* name)
{
CvMat* mat = cvCreateMat(m_pose_count, m_samples[0]->width*m_samples[0]->height, CV_32FC1);
// prepare data to write as a single matrix
for(int i = 0; i < m_pose_count; i++)
{
for(int y = 0; y < m_samples[i]->height; y++)
{
for(int x = 0; x < m_samples[i]->width; x++)
{
float val = *((float*)(m_samples[i]->imageData + m_samples[i]->widthStep*y) + x);
cvmSet(mat, i, y*m_samples[i]->width + x, val);
}
}
}
cvWrite(fs, name, mat);
cvReleaseMat(&mat);
}
int OneWayDescriptor::ReadByName(const FileNode &parent, const char* name)
{
CvMat* mat = reinterpret_cast<CvMat*> (parent[name].readObj ());
if(!mat)
{
return 0;
}
for(int i = 0; i < m_pose_count; i++)
{
for(int y = 0; y < m_samples[i]->height; y++)
{
for(int x = 0; x < m_samples[i]->width; x++)
{
float val = (float)cvmGet(mat, i, y*m_samples[i]->width + x);
*((float*)(m_samples[i]->imageData + y*m_samples[i]->widthStep) + x) = val;
}
}
}
cvReleaseMat(&mat);
return 1;
}
int OneWayDescriptor::ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name)
{
return ReadByName (FileNode (fs, parent), name);
}
IplImage* OneWayDescriptor::GetPatch(int index)
{
return m_samples[index];
}
CvAffinePose OneWayDescriptor::GetPose(int index) const
{
return m_affine_poses[index];
}
void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int& desc_idx, int& pose_idx, float& distance,
CvMat* avg, CvMat* eigenvectors)
{
desc_idx = -1;
pose_idx = -1;
distance = 1e10;
//--------
//PCA_coeffs precalculating
int m_pca_dim_low = descriptors[0].GetPCADimLow();
CvMat* pca_coeffs = cvCreateMat(1, m_pca_dim_low, CV_32FC1);
int patch_width = descriptors[0].GetPatchSize().width;
int patch_height = descriptors[0].GetPatchSize().height;
if (avg)
{
CvRect _roi = cvGetImageROI((IplImage*)patch);
IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1);
if(_roi.width != patch_width|| _roi.height != patch_height)
{
cvResize(patch, test_img);
_roi = cvGetImageROI(test_img);
}
else
{
cvCopy(patch,test_img);
}
IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1);
double sum = cvSum(test_img).val[0];
cvConvertScale(test_img, patch_32f, 1.0f/sum);
//ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs);
//Projecting PCA
CvMat* patch_mat = ConvertImageToMatrix(patch_32f);
CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1);
cvProjectPCA(patch_mat, avg, eigenvectors, temp);
CvMat temp1;
cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1));
cvCopy(&temp1, pca_coeffs);
cvReleaseMat(&temp);
cvReleaseMat(&patch_mat);
//End of projecting
cvReleaseImage(&patch_32f);
cvReleaseImage(&test_img);
}
//--------
for(int i = 0; i < desc_count; i++)
{
int _pose_idx = -1;
float _distance = 0;
#if 0
descriptors[i].EstimatePose(patch, _pose_idx, _distance);
#else
if (!avg)
{
descriptors[i].EstimatePosePCA(patch, _pose_idx, _distance, avg, eigenvectors);
}
else
{
descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors);
}
#endif
if(_distance < distance)
{
desc_idx = i;
pose_idx = _pose_idx;
distance = _distance;
}
}
cvReleaseMat(&pca_coeffs);
}
#if defined(_KDTREE)
void FindOneWayDescriptor(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch, int& desc_idx, int& pose_idx, float& distance,
CvMat* avg, CvMat* eigenvectors)
{
desc_idx = -1;
pose_idx = -1;
distance = 1e10;
//--------
//PCA_coeffs precalculating
CvMat* pca_coeffs = cvCreateMat(1, m_pca_dim_low, CV_32FC1);
int patch_width = patch_size.width;
int patch_height = patch_size.height;
//if (avg)
//{
CvRect _roi = cvGetImageROI((IplImage*)patch);
IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1);
if(_roi.width != patch_width|| _roi.height != patch_height)
{
cvResize(patch, test_img);
_roi = cvGetImageROI(test_img);
}
else
{
cvCopy(patch,test_img);
}
IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1);
float sum = cvSum(test_img).val[0];
cvConvertScale(test_img, patch_32f, 1.0f/sum);
//ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs);
//Projecting PCA
CvMat* patch_mat = ConvertImageToMatrix(patch_32f);
CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1);
cvProjectPCA(patch_mat, avg, eigenvectors, temp);
CvMat temp1;
cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1));
cvCopy(&temp1, pca_coeffs);
cvReleaseMat(&temp);
cvReleaseMat(&patch_mat);
//End of projecting
cvReleaseImage(&patch_32f);
cvReleaseImage(&test_img);
// }
//--------
//float* target = new float[m_pca_dim_low];
//::cvflann::KNNResultSet res(1,pca_coeffs->data.fl,m_pca_dim_low);
//::cvflann::SearchParams params;
//params.checks = -1;
//int maxDepth = 1000000;
//int neighbors_count = 1;
//int* neighborsIdx = new int[neighbors_count];
//float* distances = new float[neighbors_count];
//if (m_pca_descriptors_tree->findNearest(pca_coeffs->data.fl,neighbors_count,maxDepth,neighborsIdx,0,distances) > 0)
//{
// desc_idx = neighborsIdx[0] / m_pose_count;
// pose_idx = neighborsIdx[0] % m_pose_count;
// distance = distances[0];
//}
//delete[] neighborsIdx;
//delete[] distances;
cv::Mat m_object(1, m_pca_dim_low, CV_32F);
cv::Mat m_indices(1, 1, CV_32S);
cv::Mat m_dists(1, 1, CV_32F);
float* object_ptr = m_object.ptr<float>(0);
for (int i=0;i<m_pca_dim_low;i++)
{
object_ptr[i] = pca_coeffs->data.fl[i];
}
m_pca_descriptors_tree->knnSearch(m_object, m_indices, m_dists, 1, cv::flann::SearchParams(-1) );
desc_idx = ((int*)(m_indices.ptr<int>(0)))[0] / m_pose_count;
pose_idx = ((int*)(m_indices.ptr<int>(0)))[0] % m_pose_count;
distance = ((float*)(m_dists.ptr<float>(0)))[0];
// delete[] target;
// for(int i = 0; i < desc_count; i++)
// {
// int _pose_idx = -1;
// float _distance = 0;
//
//#if 0
// descriptors[i].EstimatePose(patch, _pose_idx, _distance);
//#else
// if (!avg)
// {
// descriptors[i].EstimatePosePCA(patch, _pose_idx, _distance, avg, eigenvectors);
// }
// else
// {
// descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors);
// }
//#endif
//
// if(_distance < distance)
// {
// desc_idx = i;
// pose_idx = _pose_idx;
// distance = _distance;
// }
// }
cvReleaseMat(&pca_coeffs);
}
#endif
//**
void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int n,
std::vector<int>& desc_idxs, std::vector<int>& pose_idxs, std::vector<float>& distances,
CvMat* avg, CvMat* eigenvectors)
{
for (int i=0;i<n;i++)
{
desc_idxs[i] = -1;
pose_idxs[i] = -1;
distances[i] = 1e10;
}
//--------
//PCA_coeffs precalculating
int m_pca_dim_low = descriptors[0].GetPCADimLow();
CvMat* pca_coeffs = cvCreateMat(1, m_pca_dim_low, CV_32FC1);
int patch_width = descriptors[0].GetPatchSize().width;
int patch_height = descriptors[0].GetPatchSize().height;
if (avg)
{
CvRect _roi = cvGetImageROI((IplImage*)patch);
IplImage* test_img = cvCreateImage(cvSize(patch_width,patch_height), IPL_DEPTH_8U, 1);
if(_roi.width != patch_width|| _roi.height != patch_height)
{
cvResize(patch, test_img);
_roi = cvGetImageROI(test_img);
}
else
{
cvCopy(patch,test_img);
}
IplImage* patch_32f = cvCreateImage(cvSize(_roi.width, _roi.height), IPL_DEPTH_32F, 1);
double sum = cvSum(test_img).val[0];
cvConvertScale(test_img, patch_32f, 1.0f/sum);
//ProjectPCASample(patch_32f, avg, eigenvectors, pca_coeffs);
//Projecting PCA
CvMat* patch_mat = ConvertImageToMatrix(patch_32f);
CvMat* temp = cvCreateMat(1, eigenvectors->cols, CV_32FC1);
cvProjectPCA(patch_mat, avg, eigenvectors, temp);
CvMat temp1;
cvGetSubRect(temp, &temp1, cvRect(0, 0, pca_coeffs->cols, 1));
cvCopy(&temp1, pca_coeffs);
cvReleaseMat(&temp);
cvReleaseMat(&patch_mat);
//End of projecting
cvReleaseImage(&patch_32f);
cvReleaseImage(&test_img);
}
//--------
for(int i = 0; i < desc_count; i++)
{
int _pose_idx = -1;
float _distance = 0;
#if 0
descriptors[i].EstimatePose(patch, _pose_idx, _distance);
#else
if (!avg)
{
descriptors[i].EstimatePosePCA(patch, _pose_idx, _distance, avg, eigenvectors);
}
else
{
descriptors[i].EstimatePosePCA(pca_coeffs, _pose_idx, _distance, avg, eigenvectors);
}
#endif
for (int j=0;j<n;j++)
{
if(_distance < distances[j])
{
for (int k=(n-1);k > j;k--)
{
desc_idxs[k] = desc_idxs[k-1];
pose_idxs[k] = pose_idxs[k-1];
distances[k] = distances[k-1];
}
desc_idxs[j] = i;
pose_idxs[j] = _pose_idx;
distances[j] = _distance;
break;
}
}
}
cvReleaseMat(&pca_coeffs);
}
void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int& desc_idx, int& pose_idx, float& distance, float& scale,
CvMat* avg, CvMat* eigenvectors)
{
CvSize patch_size = descriptors[0].GetPatchSize();
IplImage* input_patch;
CvRect roi;
input_patch= cvCreateImage(patch_size, IPL_DEPTH_8U, 1);
roi = cvGetImageROI((IplImage*)patch);
int _desc_idx, _pose_idx;
float _distance;
distance = 1e10;
for(float cur_scale = scale_min; cur_scale < scale_max; cur_scale *= scale_step)
{
// printf("Scale = %f\n", cur_scale);
CvRect roi_scaled = resize_rect(roi, cur_scale);
cvSetImageROI(patch, roi_scaled);
cvResize(patch, input_patch);
#if 0
if(roi.x > 244 && roi.y < 200)
{
cvNamedWindow("1", 1);
cvShowImage("1", input_patch);
cvWaitKey(0);
}
#endif
FindOneWayDescriptor(desc_count, descriptors, input_patch, _desc_idx, _pose_idx, _distance, avg, eigenvectors);
if(_distance < distance)
{
distance = _distance;
desc_idx = _desc_idx;
pose_idx = _pose_idx;
scale = cur_scale;
}
}
cvSetImageROI((IplImage*)patch, roi);
cvReleaseImage(&input_patch);
}
void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& scales,
CvMat* avg, CvMat* eigenvectors)
{
CvSize patch_size = descriptors[0].GetPatchSize();
IplImage* input_patch;
CvRect roi;
input_patch= cvCreateImage(patch_size, IPL_DEPTH_8U, 1);
roi = cvGetImageROI((IplImage*)patch);
// float min_distance = 1e10;
std::vector<int> _desc_idxs;
_desc_idxs.resize(n);
std::vector<int> _pose_idxs;
_pose_idxs.resize(n);
std::vector<float> _distances;
_distances.resize(n);
for (int i=0;i<n;i++)
{
distances[i] = 1e10;
}
for(float cur_scale = scale_min; cur_scale < scale_max; cur_scale *= scale_step)
{
CvRect roi_scaled = resize_rect(roi, cur_scale);
cvSetImageROI(patch, roi_scaled);
cvResize(patch, input_patch);
FindOneWayDescriptor(desc_count, descriptors, input_patch, n,_desc_idxs, _pose_idxs, _distances, avg, eigenvectors);
for (int i=0;i<n;i++)
{
if(_distances[i] < distances[i])
{
distances[i] = _distances[i];
desc_idxs[i] = _desc_idxs[i];
pose_idxs[i] = _pose_idxs[i];
scales[i] = cur_scale;
}
}
}
cvSetImageROI((IplImage*)patch, roi);
cvReleaseImage(&input_patch);
}
#if defined(_KDTREE)
void FindOneWayDescriptorEx(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low,
int m_pose_count, IplImage* patch,
float scale_min, float scale_max, float scale_step,
int& desc_idx, int& pose_idx, float& distance, float& scale,
CvMat* avg, CvMat* eigenvectors)
{
IplImage* input_patch;
CvRect roi;
input_patch= cvCreateImage(patch_size, IPL_DEPTH_8U, 1);
roi = cvGetImageROI((IplImage*)patch);
int _desc_idx, _pose_idx;
float _distance;
distance = 1e10;
for(float cur_scale = scale_min; cur_scale < scale_max; cur_scale *= scale_step)
{
// printf("Scale = %f\n", cur_scale);
CvRect roi_scaled = resize_rect(roi, cur_scale);
cvSetImageROI(patch, roi_scaled);
cvResize(patch, input_patch);
FindOneWayDescriptor(m_pca_descriptors_tree, patch_size, m_pca_dim_low, m_pose_count, input_patch, _desc_idx, _pose_idx, _distance, avg, eigenvectors);
if(_distance < distance)
{
distance = _distance;
desc_idx = _desc_idx;
pose_idx = _pose_idx;
scale = cur_scale;
}
}
cvSetImageROI((IplImage*)patch, roi);
cvReleaseImage(&input_patch);
}
#endif
const char* OneWayDescriptor::GetFeatureName() const
{
return m_feature_name.c_str();
}
CvPoint OneWayDescriptor::GetCenter() const
{
return m_center;
}
int OneWayDescriptor::GetPCADimLow() const
{
return m_pca_dim_low;
}
int OneWayDescriptor::GetPCADimHigh() const
{
return m_pca_dim_high;
}
CvMat* ConvertImageToMatrix(IplImage* patch)
{
CvRect roi = cvGetImageROI(patch);
CvMat* mat = cvCreateMat(1, roi.width*roi.height, CV_32FC1);
if(patch->depth == 32)
{
for(int y = 0; y < roi.height; y++)
{
for(int x = 0; x < roi.width; x++)
{
mat->data.fl[y*roi.width + x] = *((float*)(patch->imageData + (y + roi.y)*patch->widthStep) + x + roi.x);
}
}
}
else if(patch->depth == 8)
{
for(int y = 0; y < roi.height; y++)
{
for(int x = 0; x < roi.width; x++)
{
mat->data.fl[y*roi.width + x] = (float)(unsigned char)patch->imageData[(y + roi.y)*patch->widthStep + x + roi.x];
}
}
}
else
{
printf("Image depth %d is not supported\n", patch->depth);
return 0;
}
return mat;
}
OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path,
const char* pca_config, const char* pca_hr_config,
const char* pca_desc_config, int pyr_levels,
int pca_dim_high, int pca_dim_low)
: m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low), scale_min (0.7f), scale_max(1.5f), scale_step (1.2f)
{
#if defined(_KDTREE)
m_pca_descriptors_matrix = 0;
m_pca_descriptors_tree = 0;
#endif
// m_pca_descriptors_matrix = 0;
m_patch_size = patch_size;
m_pose_count = pose_count;
m_pyr_levels = pyr_levels;
m_poses = 0;
m_transforms = 0;
m_pca_avg = 0;
m_pca_eigenvectors = 0;
m_pca_hr_avg = 0;
m_pca_hr_eigenvectors = 0;
m_pca_descriptors = 0;
m_descriptors = 0;
if(train_path == 0 || strlen(train_path) == 0)
{
// skip pca loading
return;
}
char pca_config_filename[1024];
sprintf(pca_config_filename, "%s/%s", train_path, pca_config);
readPCAFeatures(pca_config_filename, &m_pca_avg, &m_pca_eigenvectors);
if(pca_hr_config && strlen(pca_hr_config) > 0)
{
char pca_hr_config_filename[1024];
sprintf(pca_hr_config_filename, "%s/%s", train_path, pca_hr_config);
readPCAFeatures(pca_hr_config_filename, &m_pca_hr_avg, &m_pca_hr_eigenvectors);
}
m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1];
#if !defined(_GH_REGIONS)
if(pca_desc_config && strlen(pca_desc_config) > 0)
// if(0)
{
//printf("Loading the descriptors...");
char pca_desc_config_filename[1024];
sprintf(pca_desc_config_filename, "%s/%s", train_path, pca_desc_config);
LoadPCADescriptors(pca_desc_config_filename);
//printf("done.\n");
}
else
{
printf("Initializing the descriptors...\n");
InitializePoseTransforms();
CreatePCADescriptors();
SavePCADescriptors("pca_descriptors.yml");
}
#endif //_GH_REGIONS
// SavePCADescriptors("./pca_descriptors.yml");
}
OneWayDescriptorBase::OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename,
const string &train_path, const string &images_list, float _scale_min, float _scale_max,
float _scale_step, int pyr_levels,
int pca_dim_high, int pca_dim_low)
: m_pca_dim_high(pca_dim_high), m_pca_dim_low(pca_dim_low), scale_min(_scale_min), scale_max(_scale_max), scale_step(_scale_step)
{
#if defined(_KDTREE)
m_pca_descriptors_matrix = 0;
m_pca_descriptors_tree = 0;
#endif
m_patch_size = patch_size;
m_pose_count = pose_count;
m_pyr_levels = pyr_levels;
m_poses = 0;
m_transforms = 0;
m_pca_avg = 0;
m_pca_eigenvectors = 0;
m_pca_hr_avg = 0;
m_pca_hr_eigenvectors = 0;
m_pca_descriptors = 0;
m_descriptors = 0;
if (pca_filename.length() == 0)
{
return;
}
CvFileStorage* fs = cvOpenFileStorage(pca_filename.c_str(), NULL, CV_STORAGE_READ);
if (fs != 0)
{
cvReleaseFileStorage(&fs);
readPCAFeatures(pca_filename.c_str(), &m_pca_avg, &m_pca_eigenvectors, "_lr");
readPCAFeatures(pca_filename.c_str(), &m_pca_hr_avg, &m_pca_hr_eigenvectors, "_hr");
m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1];
#if !defined(_GH_REGIONS)
LoadPCADescriptors(pca_filename.c_str());
#endif //_GH_REGIONS
}
else
{
GeneratePCA(train_path.c_str(), images_list.c_str());
m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1];
char pca_default_filename[1024];
sprintf(pca_default_filename, "%s/%s", train_path.c_str(), GetPCAFilename().c_str());
LoadPCADescriptors(pca_default_filename);
}
}
void OneWayDescriptorBase::Read (const FileNode &fn)
{
clear ();
m_pose_count = fn["poseCount"];
int patch_width = fn["patchWidth"];
int patch_height = fn["patchHeight"];
m_patch_size = cvSize (patch_width, patch_height);
m_pyr_levels = fn["pyrLevels"];
m_pca_dim_high = fn["pcaDimHigh"];
m_pca_dim_low = fn["pcaDimLow"];
scale_min = fn["minScale"];
scale_max = fn["maxScale"];
scale_step = fn["stepScale"];
LoadPCAall (fn);
}
void OneWayDescriptorBase::LoadPCAall (const FileNode &fn)
{
readPCAFeatures(fn, &m_pca_avg, &m_pca_eigenvectors, "_lr");
readPCAFeatures(fn, &m_pca_hr_avg, &m_pca_hr_eigenvectors, "_hr");
m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1];
#if !defined(_GH_REGIONS)
LoadPCADescriptors(fn);
#endif //_GH_REGIONS
}
OneWayDescriptorBase::~OneWayDescriptorBase()
{
cvReleaseMat(&m_pca_avg);
cvReleaseMat(&m_pca_eigenvectors);
if(m_pca_hr_eigenvectors)
{
delete[] m_pca_descriptors;
cvReleaseMat(&m_pca_hr_avg);
cvReleaseMat(&m_pca_hr_eigenvectors);
}
if(m_descriptors)
delete []m_descriptors;
if(m_poses)
delete []m_poses;
if (m_transforms)
{
for(int i = 0; i < m_pose_count; i++)
{
cvReleaseMat(&m_transforms[i]);
}
delete []m_transforms;
}
#if defined(_KDTREE)
if (m_pca_descriptors_matrix)
{
cvReleaseMat(&m_pca_descriptors_matrix);
}
if (m_pca_descriptors_tree)
{
delete m_pca_descriptors_tree;
}
#endif
}
void OneWayDescriptorBase::clear(){
if (m_descriptors)
{
delete []m_descriptors;
m_descriptors = 0;
}
#if defined(_KDTREE)
if (m_pca_descriptors_matrix)
{
cvReleaseMat(&m_pca_descriptors_matrix);
m_pca_descriptors_matrix = 0;
}
if (m_pca_descriptors_tree)
{
delete m_pca_descriptors_tree;
m_pca_descriptors_tree = 0;
}
#endif
}
void OneWayDescriptorBase::InitializePoses()
{
m_poses = new CvAffinePose[m_pose_count];
for(int i = 0; i < m_pose_count; i++)
{
m_poses[i] = GenRandomAffinePose();
}
}
void OneWayDescriptorBase::InitializeTransformsFromPoses()
{
m_transforms = new CvMat*[m_pose_count];
for(int i = 0; i < m_pose_count; i++)
{
m_transforms[i] = cvCreateMat(2, 3, CV_32FC1);
GenerateAffineTransformFromPose(cvSize(m_patch_size.width*2, m_patch_size.height*2), m_poses[i], m_transforms[i]);
}
}
void OneWayDescriptorBase::InitializePoseTransforms()
{
InitializePoses();
InitializeTransformsFromPoses();
}
void OneWayDescriptorBase::InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label)
{
// TBD add support for octave != 0
CvPoint center = keypoint.pt;
CvRect roi = cvRect(center.x - m_patch_size.width/2, center.y - m_patch_size.height/2, m_patch_size.width, m_patch_size.height);
cvResetImageROI(train_image);
roi = fit_rect_fixedsize(roi, train_image);
cvSetImageROI(train_image, roi);
if(roi.width != m_patch_size.width || roi.height != m_patch_size.height)
{
return;
}
InitializeDescriptor(desc_idx, train_image, feature_label);
cvResetImageROI(train_image);
}
void OneWayDescriptorBase::InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label)
{
m_descriptors[desc_idx].SetPCADimHigh(m_pca_dim_high);
m_descriptors[desc_idx].SetPCADimLow(m_pca_dim_low);
m_descriptors[desc_idx].SetTransforms(m_poses, m_transforms);
if(!m_pca_hr_eigenvectors)
{
m_descriptors[desc_idx].Initialize(m_pose_count, train_image, feature_label);
}
else
{
m_descriptors[desc_idx].InitializeFast(m_pose_count, train_image, feature_label,
m_pca_hr_avg, m_pca_hr_eigenvectors, m_pca_descriptors);
}
if(m_pca_avg)
{
m_descriptors[desc_idx].InitializePCACoeffs(m_pca_avg, m_pca_eigenvectors);
}
}
void OneWayDescriptorBase::FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const
{
CvRect roi = cvRect(cvRound(pt.x - m_patch_size.width/4),
cvRound(pt.y - m_patch_size.height/4),
m_patch_size.width/2, m_patch_size.height/2);
cvSetImageROI(src, roi);
FindDescriptor(src, desc_idx, pose_idx, distance);
cvResetImageROI(src);
}
void OneWayDescriptorBase::FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale, float* scale_ranges) const
{
#if 0
::FindOneWayDescriptor(m_train_feature_count, m_descriptors, patch, desc_idx, pose_idx, distance, m_pca_avg, m_pca_eigenvectors);
#else
float min = scale_min;
float max = scale_max;
float step = scale_step;
if (scale_ranges)
{
min = scale_ranges[0];
max = scale_ranges[1];
}
float scale = 1.0f;
#if !defined(_KDTREE)
cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch,
min, max, step, desc_idx, pose_idx, distance, scale,
m_pca_avg, m_pca_eigenvectors);
#else
cv::FindOneWayDescriptorEx(m_pca_descriptors_tree, m_descriptors[0].GetPatchSize(), m_descriptors[0].GetPCADimLow(), m_pose_count, patch,
min, max, step, desc_idx, pose_idx, distance, scale,
m_pca_avg, m_pca_eigenvectors);
#endif
if (_scale)
*_scale = scale;
#endif
}
void OneWayDescriptorBase::FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges) const
{
float min = scale_min;
float max = scale_max;
float step = scale_step;
if (scale_ranges)
{
min = scale_ranges[0];
max = scale_ranges[1];
}
distances.resize(n);
_scales.resize(n);
desc_idxs.resize(n);
pose_idxs.resize(n);
/*float scales = 1.0f;*/
cv::FindOneWayDescriptorEx(m_train_feature_count, m_descriptors, patch,
min, max, step ,n, desc_idxs, pose_idxs, distances, _scales,
m_pca_avg, m_pca_eigenvectors);
}
void OneWayDescriptorBase::SetPCAHigh(CvMat* avg, CvMat* eigenvectors)
{
m_pca_hr_avg = cvCloneMat(avg);
m_pca_hr_eigenvectors = cvCloneMat(eigenvectors);
}
void OneWayDescriptorBase::SetPCALow(CvMat* avg, CvMat* eigenvectors)
{
m_pca_avg = cvCloneMat(avg);
m_pca_eigenvectors = cvCloneMat(eigenvectors);
}
void OneWayDescriptorBase::AllocatePCADescriptors()
{
m_pca_descriptors = new OneWayDescriptor[m_pca_dim_high + 1];
for(int i = 0; i < m_pca_dim_high + 1; i++)
{
m_pca_descriptors[i].SetPCADimHigh(m_pca_dim_high);
m_pca_descriptors[i].SetPCADimLow(m_pca_dim_low);
}
}
void OneWayDescriptorBase::CreatePCADescriptors()
{
if(m_pca_descriptors == 0)
{
AllocatePCADescriptors();
}
IplImage* frontal = cvCreateImage(m_patch_size, IPL_DEPTH_32F, 1);
eigenvector2image(m_pca_hr_avg, frontal);
m_pca_descriptors[0].SetTransforms(m_poses, m_transforms);
m_pca_descriptors[0].Initialize(m_pose_count, frontal, "", 0);
for(int j = 0; j < m_pca_dim_high; j++)
{
CvMat eigenvector;
cvGetSubRect(m_pca_hr_eigenvectors, &eigenvector, cvRect(0, j, m_pca_hr_eigenvectors->cols, 1));
eigenvector2image(&eigenvector, frontal);
m_pca_descriptors[j + 1].SetTransforms(m_poses, m_transforms);
m_pca_descriptors[j + 1].Initialize(m_pose_count, frontal, "", 0);
printf("Created descriptor for PCA component %d\n", j);
}
cvReleaseImage(&frontal);
}
int OneWayDescriptorBase::LoadPCADescriptors(const char* filename)
{
FileStorage fs = FileStorage (filename, FileStorage::READ);
if(!fs.isOpened ())
{
printf("File %s not found...\n", filename);
return 0;
}
LoadPCADescriptors (fs.root ());
printf("Successfully read %d pca components\n", m_pca_dim_high);
fs.release ();
return 1;
}
int OneWayDescriptorBase::LoadPCADescriptors(const FileNode &fn)
{
// read affine poses
// FileNode* node = cvGetFileNodeByName(fs, 0, "affine poses");
CvMat* poses = reinterpret_cast<CvMat*> (fn["affine_poses"].readObj ());
if (poses == 0)
{
poses = reinterpret_cast<CvMat*> (fn["affine poses"].readObj ());
if (poses == 0)
return 0;
}
if(m_poses)
{
delete m_poses;
}
m_poses = new CvAffinePose[m_pose_count];
for(int i = 0; i < m_pose_count; i++)
{
m_poses[i].phi = (float)cvmGet(poses, i, 0);
m_poses[i].theta = (float)cvmGet(poses, i, 1);
m_poses[i].lambda1 = (float)cvmGet(poses, i, 2);
m_poses[i].lambda2 = (float)cvmGet(poses, i, 3);
}
cvReleaseMat(&poses);
// now initialize pose transforms
InitializeTransformsFromPoses();
m_pca_dim_high = (int) fn["pca_components_number"];
if (m_pca_dim_high == 0)
{
m_pca_dim_high = (int) fn["pca components number"];
}
if(m_pca_descriptors)
{
delete []m_pca_descriptors;
}
AllocatePCADescriptors();
for(int i = 0; i < m_pca_dim_high + 1; i++)
{
m_pca_descriptors[i].Allocate(m_pose_count, m_patch_size, 1);
m_pca_descriptors[i].SetTransforms(m_poses, m_transforms);
char buf[1024];
sprintf(buf, "descriptor_for_pca_component_%d", i);
if (! m_pca_descriptors[i].ReadByName(fn, buf))
{
char buf[1024];
sprintf(buf, "descriptor for pca component %d", i);
m_pca_descriptors[i].ReadByName(fn, buf);
}
}
return 1;
}
void savePCAFeatures(FileStorage &fs, const char* postfix, CvMat* avg, CvMat* eigenvectors)
{
char buf[1024];
sprintf(buf, "avg_%s", postfix);
fs.writeObj(buf, avg);
sprintf(buf, "eigenvectors_%s", postfix);
fs.writeObj(buf, eigenvectors);
}
void calcPCAFeatures(vector<IplImage*>& patches, FileStorage &fs, const char* postfix, CvMat** avg,
CvMat** eigenvectors)
{
int width = patches[0]->width;
int height = patches[0]->height;
int length = width * height;
int patch_count = (int)patches.size();
CvMat* data = cvCreateMat(patch_count, length, CV_32FC1);
*avg = cvCreateMat(1, length, CV_32FC1);
CvMat* eigenvalues = cvCreateMat(1, length, CV_32FC1);
*eigenvectors = cvCreateMat(length, length, CV_32FC1);
for (int i = 0; i < patch_count; i++)
{
float nf = (float)(1./cvSum(patches[i]).val[0]);
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
*((float*)(data->data.ptr + data->step * i) + y * width + x)
= (unsigned char)patches[i]->imageData[y * patches[i]->widthStep + x] * nf;
}
}
}
//printf("Calculating PCA...");
cvCalcPCA(data, *avg, eigenvalues, *eigenvectors, CV_PCA_DATA_AS_ROW);
//printf("done\n");
// save pca data
savePCAFeatures(fs, postfix, *avg, *eigenvectors);
cvReleaseMat(&data);
cvReleaseMat(&eigenvalues);
}
void extractPatches (IplImage *img, vector<IplImage*>& patches, CvSize patch_size)
{
vector<KeyPoint> features;
SURF surf_extractor(1.0f);
//printf("Extracting SURF features...");
surf_extractor(img, Mat(), features);
//printf("done\n");
for (int j = 0; j < (int)features.size(); j++)
{
int patch_width = patch_size.width;
int patch_height = patch_size.height;
CvPoint center = features[j].pt;
CvRect roi = cvRect(center.x - patch_width / 2, center.y - patch_height / 2, patch_width, patch_height);
cvSetImageROI(img, roi);
roi = cvGetImageROI(img);
if (roi.width != patch_width || roi.height != patch_height)
{
continue;
}
IplImage* patch = cvCreateImage(cvSize(patch_width, patch_height), IPL_DEPTH_8U, 1);
cvCopy(img, patch);
patches.push_back(patch);
cvResetImageROI(img);
}
//printf("Completed file, extracted %d features\n", (int)features.size());
}
/*
void loadPCAFeatures(const FileNode &fn, vector<IplImage*>& patches, CvSize patch_size)
{
FileNodeIterator begin = fn.begin();
for (FileNodeIterator i = fn.begin(); i != fn.end(); i++)
{
IplImage *img = reinterpret_cast<IplImage*> ((*i).readObj());
extractPatches (img, patches, patch_size);
cvReleaseImage(&img);
}
}
*/
void loadPCAFeatures(const char* path, const char* images_list, vector<IplImage*>& patches, CvSize patch_size)
{
char images_filename[1024];
sprintf(images_filename, "%s/%s", path, images_list);
FILE *pFile = fopen(images_filename, "r");
if (pFile == 0)
{
printf("Cannot open images list file %s\n", images_filename);
return;
}
while (!feof(pFile))
{
char imagename[1024];
if (fscanf(pFile, "%s", imagename) <= 0)
{
break;
}
char filename[1024];
sprintf(filename, "%s/%s", path, imagename);
//printf("Reading image %s...", filename);
IplImage* img = cvLoadImage(filename, CV_LOAD_IMAGE_GRAYSCALE);
//printf("done\n");
extractPatches (img, patches, patch_size);
cvReleaseImage(&img);
}
fclose(pFile);
}
void generatePCAFeatures(const char* path, const char* img_filename, FileStorage& fs, const char* postfix,
CvSize patch_size, CvMat** avg, CvMat** eigenvectors)
{
vector<IplImage*> patches;
loadPCAFeatures(path, img_filename, patches, patch_size);
calcPCAFeatures(patches, fs, postfix, avg, eigenvectors);
}
/*
void generatePCAFeatures(const FileNode &fn, const char* postfix,
CvSize patch_size, CvMat** avg, CvMat** eigenvectors)
{
vector<IplImage*> patches;
loadPCAFeatures(fn, patches, patch_size);
calcPCAFeatures(patches, fs, postfix, avg, eigenvectors);
}
void OneWayDescriptorBase::GeneratePCA(const FileNode &fn, int pose_count)
{
generatePCAFeatures(fn, "hr", m_patch_size, &m_pca_hr_avg, &m_pca_hr_eigenvectors);
generatePCAFeatures(fn, "lr", cvSize(m_patch_size.width / 2, m_patch_size.height / 2),
&m_pca_avg, &m_pca_eigenvectors);
OneWayDescriptorBase descriptors(m_patch_size, pose_count);
descriptors.SetPCAHigh(m_pca_hr_avg, m_pca_hr_eigenvectors);
descriptors.SetPCALow(m_pca_avg, m_pca_eigenvectors);
printf("Calculating %d PCA descriptors (you can grab a coffee, this will take a while)...\n",
descriptors.GetPCADimHigh());
descriptors.InitializePoseTransforms();
descriptors.CreatePCADescriptors();
descriptors.SavePCADescriptors(*fs);
}
*/
void OneWayDescriptorBase::GeneratePCA(const char* img_path, const char* images_list, int pose_count)
{
char pca_filename[1024];
sprintf(pca_filename, "%s/%s", img_path, GetPCAFilename().c_str());
FileStorage fs = FileStorage(pca_filename, FileStorage::WRITE);
generatePCAFeatures(img_path, images_list, fs, "hr", m_patch_size, &m_pca_hr_avg, &m_pca_hr_eigenvectors);
generatePCAFeatures(img_path, images_list, fs, "lr", cvSize(m_patch_size.width / 2, m_patch_size.height / 2),
&m_pca_avg, &m_pca_eigenvectors);
OneWayDescriptorBase descriptors(m_patch_size, pose_count);
descriptors.SetPCAHigh(m_pca_hr_avg, m_pca_hr_eigenvectors);
descriptors.SetPCALow(m_pca_avg, m_pca_eigenvectors);
printf("Calculating %d PCA descriptors (you can grab a coffee, this will take a while)...\n",
descriptors.GetPCADimHigh());
descriptors.InitializePoseTransforms();
descriptors.CreatePCADescriptors();
descriptors.SavePCADescriptors(*fs);
fs.release();
}
void OneWayDescriptorBase::Write (FileStorage &fs) const
{
fs << "poseCount" << m_pose_count;
fs << "patchWidth" << m_patch_size.width;
fs << "patchHeight" << m_patch_size.height;
fs << "minScale" << scale_min;
fs << "maxScale" << scale_max;
fs << "stepScale" << scale_step;
fs << "pyrLevels" << m_pyr_levels;
fs << "pcaDimHigh" << m_pca_dim_high;
fs << "pcaDimLow" << m_pca_dim_low;
SavePCAall (fs);
}
void OneWayDescriptorBase::SavePCAall (FileStorage &fs) const
{
savePCAFeatures(fs, "hr", m_pca_hr_avg, m_pca_hr_eigenvectors);
savePCAFeatures(fs, "lr", m_pca_avg, m_pca_eigenvectors);
SavePCADescriptors(*fs);
}
void OneWayDescriptorBase::SavePCADescriptors(const char* filename)
{
CvMemStorage* storage = cvCreateMemStorage();
CvFileStorage* fs = cvOpenFileStorage(filename, storage, CV_STORAGE_WRITE);
SavePCADescriptors (fs);
cvReleaseMemStorage(&storage);
cvReleaseFileStorage(&fs);
}
void OneWayDescriptorBase::SavePCADescriptors(CvFileStorage *fs) const
{
cvWriteInt(fs, "pca_components_number", m_pca_dim_high);
cvWriteComment(
fs,
"The first component is the average Vector, so the total number of components is <pca components number> + 1",
0);
cvWriteInt(fs, "patch_width", m_patch_size.width);
cvWriteInt(fs, "patch_height", m_patch_size.height);
// pack the affine transforms into a single CvMat and write them
CvMat* poses = cvCreateMat(m_pose_count, 4, CV_32FC1);
for (int i = 0; i < m_pose_count; i++)
{
cvmSet(poses, i, 0, m_poses[i].phi);
cvmSet(poses, i, 1, m_poses[i].theta);
cvmSet(poses, i, 2, m_poses[i].lambda1);
cvmSet(poses, i, 3, m_poses[i].lambda2);
}
cvWrite(fs, "affine_poses", poses);
cvReleaseMat(&poses);
for (int i = 0; i < m_pca_dim_high + 1; i++)
{
char buf[1024];
sprintf(buf, "descriptor_for_pca_component_%d", i);
m_pca_descriptors[i].Write(fs, buf);
}
}
void OneWayDescriptorBase::Allocate(int train_feature_count)
{
m_train_feature_count = train_feature_count;
m_descriptors = new OneWayDescriptor[m_train_feature_count];
for(int i = 0; i < m_train_feature_count; i++)
{
m_descriptors[i].SetPCADimHigh(m_pca_dim_high);
m_descriptors[i].SetPCADimLow(m_pca_dim_low);
}
}
void OneWayDescriptorBase::InitializeDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
const char* feature_label, int desc_start_idx)
{
for(int i = 0; i < (int)features.size(); i++)
{
InitializeDescriptor(desc_start_idx + i, train_image, features[i], feature_label);
}
cvResetImageROI(train_image);
#if defined(_KDTREE)
ConvertDescriptorsArrayToTree();
#endif
}
void OneWayDescriptorBase::CreateDescriptorsFromImage(IplImage* src, const std::vector<KeyPoint>& features)
{
m_train_feature_count = (int)features.size();
m_descriptors = new OneWayDescriptor[m_train_feature_count];
InitializeDescriptors(src, features);
}
#if defined(_KDTREE)
void OneWayDescriptorBase::ConvertDescriptorsArrayToTree()
{
int n = this->GetDescriptorCount();
if (n<1)
return;
int pca_dim_low = this->GetDescriptor(0)->GetPCADimLow();
//if (!m_pca_descriptors_matrix)
// m_pca_descriptors_matrix = new ::cvflann::Matrix<float>(n*m_pose_count,pca_dim_low);
//else
//{
// if ((m_pca_descriptors_matrix->cols != pca_dim_low)&&(m_pca_descriptors_matrix->rows != n*m_pose_count))
// {
// delete m_pca_descriptors_matrix;
// m_pca_descriptors_matrix = new ::cvflann::Matrix<float>(n*m_pose_count,pca_dim_low);
// }
//}
m_pca_descriptors_matrix = cvCreateMat(n*m_pose_count,pca_dim_low,CV_32FC1);
for (int i=0;i<n;i++)
{
CvMat** pca_coeffs = m_descriptors[i].GetPCACoeffs();
for (int j = 0;j<m_pose_count;j++)
{
for (int k=0;k<pca_dim_low;k++)
{
m_pca_descriptors_matrix->data.fl[(i*m_pose_count+j)*m_pca_dim_low + k] = pca_coeffs[j]->data.fl[k];
}
}
}
cv::Mat pca_descriptors_mat(m_pca_descriptors_matrix,false);
//::cvflann::KDTreeIndexParams params;
//params.trees = 1;
//m_pca_descriptors_tree = new KDTree(pca_descriptors_mat);
m_pca_descriptors_tree = new cv::flann::Index(pca_descriptors_mat,cv::flann::KDTreeIndexParams(1));
//cvReleaseMat(&m_pca_descriptors_matrix);
//m_pca_descriptors_tree->buildIndex();
}
#endif
void OneWayDescriptorObject::Allocate(int train_feature_count, int object_feature_count)
{
OneWayDescriptorBase::Allocate(train_feature_count);
m_object_feature_count = object_feature_count;
m_part_id = new int[m_object_feature_count];
}
void OneWayDescriptorObject::InitializeObjectDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
const char* feature_label, int desc_start_idx, float scale, int is_background)
{
InitializeDescriptors(train_image, features, feature_label, desc_start_idx);
for(int i = 0; i < (int)features.size(); i++)
{
CvPoint center = features[i].pt;
if(!is_background)
{
// remember descriptor part id
CvPoint center_scaled = cvPoint(round(center.x*scale), round(center.y*scale));
m_part_id[i + desc_start_idx] = MatchPointToPart(center_scaled);
}
}
cvResetImageROI(train_image);
}
int OneWayDescriptorObject::IsDescriptorObject(int desc_idx) const
{
return desc_idx < m_object_feature_count ? 1 : 0;
}
int OneWayDescriptorObject::MatchPointToPart(CvPoint pt) const
{
int idx = -1;
const int max_dist = 10;
for(int i = 0; i < (int)m_train_features.size(); i++)
{
if(norm(Point2f(pt) - m_train_features[i].pt) < max_dist)
{
idx = i;
break;
}
}
return idx;
}
int OneWayDescriptorObject::GetDescriptorPart(int desc_idx) const
{
// return MatchPointToPart(GetDescriptor(desc_idx)->GetCenter());
return desc_idx < m_object_feature_count ? m_part_id[desc_idx] : -1;
}
OneWayDescriptorObject::OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path,
const char* pca_config, const char* pca_hr_config, const char* pca_desc_config, int pyr_levels) :
OneWayDescriptorBase(patch_size, pose_count, train_path, pca_config, pca_hr_config, pca_desc_config, pyr_levels)
{
m_part_id = 0;
}
OneWayDescriptorObject::OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
const string &train_path, const string &images_list, float _scale_min, float _scale_max, float _scale_step, int pyr_levels) :
OneWayDescriptorBase(patch_size, pose_count, pca_filename, train_path, images_list, _scale_min, _scale_max, _scale_step, pyr_levels)
{
m_part_id = 0;
}
OneWayDescriptorObject::~OneWayDescriptorObject()
{
if (m_part_id)
delete []m_part_id;
}
vector<KeyPoint> OneWayDescriptorObject::_GetLabeledFeatures() const
{
vector<KeyPoint> features;
for(size_t i = 0; i < m_train_features.size(); i++)
{
features.push_back(m_train_features[i]);
}
return features;
}
void eigenvector2image(CvMat* eigenvector, IplImage* img)
{
CvRect roi = cvGetImageROI(img);
if(img->depth == 32)
{
for(int y = 0; y < roi.height; y++)
{
for(int x = 0; x < roi.width; x++)
{
float val = (float)cvmGet(eigenvector, 0, roi.width*y + x);
*((float*)(img->imageData + (roi.y + y)*img->widthStep) + roi.x + x) = val;
}
}
}
else
{
for(int y = 0; y < roi.height; y++)
{
for(int x = 0; x < roi.width; x++)
{
float val = (float)cvmGet(eigenvector, 0, roi.width*y + x);
img->imageData[(roi.y + y)*img->widthStep + roi.x + x] = (unsigned char)val;
}
}
}
}
void readPCAFeatures(const char* filename, CvMat** avg, CvMat** eigenvectors, const char* postfix)
{
FileStorage fs = FileStorage(filename, FileStorage::READ);
if (!fs.isOpened ())
{
printf("Cannot open file %s! Exiting!", filename);
}
readPCAFeatures (fs.root (), avg, eigenvectors, postfix);
fs.release ();
}
void readPCAFeatures(const FileNode &fn, CvMat** avg, CvMat** eigenvectors, const char* postfix)
{
std::string str = std::string ("avg") + postfix;
CvMat* _avg = reinterpret_cast<CvMat*> (fn[str].readObj());
if (_avg != 0)
{
*avg = cvCloneMat(_avg);
cvReleaseMat(&_avg);
}
str = std::string ("eigenvectors") + postfix;
CvMat* _eigenvectors = reinterpret_cast<CvMat*> (fn[str].readObj());
if (_eigenvectors != 0)
{
*eigenvectors = cvCloneMat(_eigenvectors);
cvReleaseMat(&_eigenvectors);
}
}
}
