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2305 lines
79 KiB
C++
2305 lines
79 KiB
C++
/*
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* cvoneway.cpp
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* one_way_sample
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*
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* Created by Victor Eruhimov on 3/23/10.
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* Copyright 2010 Argus Corp. All rights reserved.
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*
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*/
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#include "precomp.hpp"
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_HIGHGUI
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# include "opencv2/highgui.hpp"
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#endif
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#include <stdio.h>
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namespace cv{
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inline int round(float value)
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{
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if(value > 0)
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{
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return int(value + 0.5f);
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}
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else
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{
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return int(value - 0.5f);
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}
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}
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inline CvRect resize_rect(CvRect rect, float alpha)
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{
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return cvRect(rect.x + round((float)(0.5*(1 - alpha)*rect.width)), rect.y + round((float)(0.5*(1 - alpha)*rect.height)),
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round(rect.width*alpha), round(rect.height*alpha));
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}
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CvMat* ConvertImageToMatrix(IplImage* patch);
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class CvCameraPose
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{
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public:
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CvCameraPose()
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{
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m_rotation = cvCreateMat(1, 3, CV_32FC1);
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m_translation = cvCreateMat(1, 3, CV_32FC1);
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};
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~CvCameraPose()
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{
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cvReleaseMat(&m_rotation);
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cvReleaseMat(&m_translation);
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};
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void SetPose(CvMat* rotation, CvMat* translation)
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{
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cvCopy(rotation, m_rotation);
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cvCopy(translation, m_translation);
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};
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CvMat* GetRotation() {return m_rotation;};
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CvMat* GetTranslation() {return m_translation;};
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protected:
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CvMat* m_rotation;
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CvMat* m_translation;
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};
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// AffineTransformPatch: generates an affine transformed image patch.
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// - src: source image (roi is supported)
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// - dst: output image. ROI of dst image should be 2 times smaller than ROI of src.
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// - pose: parameters of an affine transformation
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void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose);
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// GenerateAffineTransformFromPose: generates an affine transformation matrix from CvAffinePose instance
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// - size: the size of image patch
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// - pose: affine transformation
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// - transform: 2x3 transformation matrix
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void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform);
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// Generates a random affine pose
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CvAffinePose GenRandomAffinePose();
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const static int num_mean_components = 500;
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const static float noise_intensity = 0.15f;
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static inline CvPoint rect_center(CvRect rect)
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{
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return cvPoint(rect.x + rect.width/2, rect.y + rect.height/2);
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}
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// static void homography_transform(IplImage* frontal, IplImage* result, CvMat* homography)
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// {
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// cvWarpPerspective(frontal, result, homography);
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// }
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static CvAffinePose perturbate_pose(CvAffinePose pose, float noise)
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{
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// perturbate the matrix
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float noise_mult_factor = 1 + (0.5f - float(rand())/RAND_MAX)*noise;
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float noise_add_factor = noise_mult_factor - 1;
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CvAffinePose pose_pert = pose;
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pose_pert.phi += noise_add_factor;
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pose_pert.theta += noise_mult_factor;
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pose_pert.lambda1 *= noise_mult_factor;
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pose_pert.lambda2 *= noise_mult_factor;
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return pose_pert;
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}
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static void generate_mean_patch(IplImage* frontal, IplImage* result, CvAffinePose pose, int pose_count, float noise)
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{
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IplImage* sum = cvCreateImage(cvSize(result->width, result->height), IPL_DEPTH_32F, 1);
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IplImage* workspace = cvCloneImage(result);
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IplImage* workspace_float = cvCloneImage(sum);
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cvSetZero(sum);
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for(int i = 0; i < pose_count; i++)
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{
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CvAffinePose pose_pert = perturbate_pose(pose, noise);
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AffineTransformPatch(frontal, workspace, pose_pert);
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cvConvertScale(workspace, workspace_float);
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cvAdd(sum, workspace_float, sum);
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}
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cvConvertScale(sum, result, 1.0f/pose_count);
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cvReleaseImage(&workspace);
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cvReleaseImage(&sum);
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cvReleaseImage(&workspace_float);
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}
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// static void generate_mean_patch_fast(IplImage* /*frontal*/, IplImage* /*result*/, CvAffinePose /*pose*/,
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// CvMat* /*pca_hr_avg*/, CvMat* /*pca_hr_eigenvectors*/, const OneWayDescriptor* /*pca_descriptors*/)
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// {
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// /*for(int i = 0; i < pca_hr_eigenvectors->cols; i++)
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// {
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// }*/
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// }
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void readPCAFeatures(const char *filename, CvMat** avg, CvMat** eigenvectors, const char *postfix = "");
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void readPCAFeatures(const FileNode &fn, CvMat** avg, CvMat** eigenvectors, const char* postfix = "");
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void savePCAFeatures(FileStorage &fs, const char* postfix, CvMat* avg, CvMat* eigenvectors);
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void calcPCAFeatures(std::vector<IplImage*>& patches, FileStorage &fs, const char* postfix, CvMat** avg,
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CvMat** eigenvectors);
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void loadPCAFeatures(const char* path, const char* images_list, std::vector<IplImage*>& patches, CvSize patch_size);
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void generatePCAFeatures(const char* path, const char* img_filename, FileStorage& fs, const char* postfix,
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CvSize patch_size, CvMat** avg, CvMat** eigenvectors);
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void eigenvector2image(CvMat* eigenvector, IplImage* img);
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void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int& desc_idx, int& pose_idx, float& distance,
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CvMat* avg = 0, CvMat* eigenvalues = 0);
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void FindOneWayDescriptor(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch, int n,
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std::vector<int>& desc_idxs, std::vector<int>& pose_idxs, std::vector<float>& distances,
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CvMat* avg = 0, CvMat* eigenvalues = 0);
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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,
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CvMat* avg = 0, CvMat* eigenvalues = 0);
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void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
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float scale_min, float scale_max, float scale_step,
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int& desc_idx, int& pose_idx, float& distance, float& scale,
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CvMat* avg, CvMat* eigenvectors);
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void FindOneWayDescriptorEx(int desc_count, const OneWayDescriptor* descriptors, IplImage* patch,
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float scale_min, float scale_max, float scale_step,
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int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
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std::vector<float>& distances, std::vector<float>& scales,
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CvMat* avg, CvMat* eigenvectors);
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void FindOneWayDescriptorEx(cv::flann::Index* m_pca_descriptors_tree, CvSize patch_size, int m_pca_dim_low, int m_pose_count, IplImage* patch,
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float scale_min, float scale_max, float scale_step,
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int& desc_idx, int& pose_idx, float& distance, float& scale,
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CvMat* avg, CvMat* eigenvectors);
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inline CvRect fit_rect_roi_fixedsize(CvRect rect, CvRect roi)
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{
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CvRect fit = rect;
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fit.x = MAX(fit.x, roi.x);
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fit.y = MAX(fit.y, roi.y);
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fit.x = MIN(fit.x, roi.x + roi.width - fit.width - 1);
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fit.y = MIN(fit.y, roi.y + roi.height - fit.height - 1);
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return(fit);
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}
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inline CvRect fit_rect_fixedsize(CvRect rect, IplImage* img)
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{
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CvRect roi = cvGetImageROI(img);
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return fit_rect_roi_fixedsize(rect, roi);
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}
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OneWayDescriptor::OneWayDescriptor()
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{
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m_pose_count = 0;
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m_samples = 0;
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m_input_patch = 0;
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m_train_patch = 0;
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m_pca_coeffs = 0;
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m_affine_poses = 0;
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m_transforms = 0;
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m_pca_dim_low = 100;
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m_pca_dim_high = 100;
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}
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OneWayDescriptor::~OneWayDescriptor()
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{
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if(m_pose_count)
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{
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for(int i = 0; i < m_pose_count; i++)
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{
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cvReleaseImage(&m_samples[i]);
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cvReleaseMat(&m_pca_coeffs[i]);
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}
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cvReleaseImage(&m_input_patch);
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cvReleaseImage(&m_train_patch);
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delete []m_samples;
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delete []m_pca_coeffs;
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if(!m_transforms)
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{
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delete []m_affine_poses;
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}
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}
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}
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void OneWayDescriptor::Allocate(int pose_count, CvSize size, int nChannels)
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{
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m_pose_count = pose_count;
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m_samples = new IplImage* [m_pose_count];
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m_pca_coeffs = new CvMat* [m_pose_count];
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m_patch_size = cvSize(size.width/2, size.height/2);
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if(!m_transforms)
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{
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m_affine_poses = new CvAffinePose[m_pose_count];
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}
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int length = m_pca_dim_low;//roi.width*roi.height;
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for(int i = 0; i < m_pose_count; i++)
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{
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m_samples[i] = cvCreateImage(cvSize(size.width/2, size.height/2), IPL_DEPTH_32F, nChannels);
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m_pca_coeffs[i] = cvCreateMat(1, length, CV_32FC1);
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}
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m_input_patch = cvCreateImage(GetPatchSize(), IPL_DEPTH_8U, 1);
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m_train_patch = cvCreateImage(GetInputPatchSize(), IPL_DEPTH_8U, 1);
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}
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// static void cvmSet2DPoint(CvMat* matrix, int row, int col, CvPoint2D32f point)
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// {
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// cvmSet(matrix, row, col, point.x);
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// cvmSet(matrix, row, col + 1, point.y);
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// }
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// static void cvmSet3DPoint(CvMat* matrix, int row, int col, CvPoint3D32f point)
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// {
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// cvmSet(matrix, row, col, point.x);
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// cvmSet(matrix, row, col + 1, point.y);
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// cvmSet(matrix, row, col + 2, point.z);
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// }
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CvAffinePose GenRandomAffinePose()
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{
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const float scale_min = 0.8f;
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const float scale_max = 1.2f;
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CvAffinePose pose;
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pose.theta = float(rand())/RAND_MAX*120 - 60;
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pose.phi = float(rand())/RAND_MAX*360;
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pose.lambda1 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min);
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pose.lambda2 = scale_min + float(rand())/RAND_MAX*(scale_max - scale_min);
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return pose;
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}
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void GenerateAffineTransformFromPose(CvSize size, CvAffinePose pose, CvMat* transform)
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{
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CvMat* temp = cvCreateMat(3, 3, CV_32FC1);
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CvMat* final = cvCreateMat(3, 3, CV_32FC1);
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cvmSet(temp, 2, 0, 0.0f);
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cvmSet(temp, 2, 1, 0.0f);
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cvmSet(temp, 2, 2, 1.0f);
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CvMat rotation;
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cvGetSubRect(temp, &rotation, cvRect(0, 0, 3, 2));
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cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.phi, 1.0, &rotation);
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cvCopy(temp, final);
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cvmSet(temp, 0, 0, pose.lambda1);
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cvmSet(temp, 0, 1, 0.0f);
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cvmSet(temp, 1, 0, 0.0f);
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cvmSet(temp, 1, 1, pose.lambda2);
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cvmSet(temp, 0, 2, size.width/2*(1 - pose.lambda1));
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cvmSet(temp, 1, 2, size.height/2*(1 - pose.lambda2));
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cvMatMul(temp, final, final);
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cv2DRotationMatrix(cvPoint2D32f(size.width/2, size.height/2), pose.theta - pose.phi, 1.0, &rotation);
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cvMatMul(temp, final, final);
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cvGetSubRect(final, &rotation, cvRect(0, 0, 3, 2));
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cvCopy(&rotation, transform);
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cvReleaseMat(&temp);
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cvReleaseMat(&final);
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}
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void AffineTransformPatch(IplImage* src, IplImage* dst, CvAffinePose pose)
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{
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CvRect src_large_roi = cvGetImageROI(src);
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IplImage* temp = cvCreateImage(cvSize(src_large_roi.width, src_large_roi.height), IPL_DEPTH_32F, src->nChannels);
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cvSetZero(temp);
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IplImage* temp2 = cvCloneImage(temp);
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CvMat* rotation_phi = cvCreateMat(2, 3, CV_32FC1);
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CvSize new_size = cvSize(cvRound(temp->width*pose.lambda1), cvRound(temp->height*pose.lambda2));
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IplImage* temp3 = cvCreateImage(new_size, IPL_DEPTH_32F, src->nChannels);
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cvConvertScale(src, temp);
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cvResetImageROI(temp);
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cv2DRotationMatrix(cvPoint2D32f(temp->width/2, temp->height/2), pose.phi, 1.0, rotation_phi);
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cvWarpAffine(temp, temp2, rotation_phi);
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cvSetZero(temp);
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cvResize(temp2, temp3);
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cv2DRotationMatrix(cvPoint2D32f(temp3->width/2, temp3->height/2), pose.theta - pose.phi, 1.0, rotation_phi);
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cvWarpAffine(temp3, temp, rotation_phi);
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cvSetImageROI(temp, cvRect(temp->width/2 - src_large_roi.width/4, temp->height/2 - src_large_roi.height/4,
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src_large_roi.width/2, src_large_roi.height/2));
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cvConvertScale(temp, dst);
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cvReleaseMat(&rotation_phi);
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cvReleaseImage(&temp3);
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cvReleaseImage(&temp2);
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cvReleaseImage(&temp);
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}
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void OneWayDescriptor::GenerateSamples(int pose_count, IplImage* frontal, int norm)
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{
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/* if(m_transforms)
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{
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GenerateSamplesWithTransforms(pose_count, frontal);
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return;
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}
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*/
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CvRect roi = cvGetImageROI(frontal);
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IplImage* patch_8u = cvCreateImage(cvSize(roi.width/2, roi.height/2), frontal->depth, frontal->nChannels);
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for(int i = 0; i < pose_count; i++)
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{
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if(!m_transforms)
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{
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m_affine_poses[i] = GenRandomAffinePose();
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}
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//AffineTransformPatch(frontal, patch_8u, m_affine_poses[i]);
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generate_mean_patch(frontal, patch_8u, m_affine_poses[i], num_mean_components, noise_intensity);
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double scale = 1.0f;
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if(norm)
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{
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double sum = cvSum(patch_8u).val[0];
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scale = 1/sum;
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}
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cvConvertScale(patch_8u, m_samples[i], scale);
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#if 0
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double maxval;
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cvMinMaxLoc(m_samples[i], 0, &maxval);
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IplImage* test = cvCreateImage(cvSize(roi.width/2, roi.height/2), IPL_DEPTH_8U, 1);
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cvConvertScale(m_samples[i], test, 255.0/maxval);
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cvNamedWindow("1", 1);
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cvShowImage("1", test);
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cvWaitKey(0);
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#endif
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}
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cvReleaseImage(&patch_8u);
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}
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void OneWayDescriptor::GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
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CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors)
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{
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CvRect roi = cvGetImageROI(frontal);
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if(roi.width != GetInputPatchSize().width || roi.height != GetInputPatchSize().height)
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{
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cvResize(frontal, m_train_patch);
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frontal = m_train_patch;
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}
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CvMat* pca_coeffs = cvCreateMat(1, pca_hr_eigenvectors->cols, CV_32FC1);
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double maxval;
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cvMinMaxLoc(frontal, 0, &maxval);
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CvMat* frontal_data = ConvertImageToMatrix(frontal);
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double sum = cvSum(frontal_data).val[0];
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cvConvertScale(frontal_data, frontal_data, 1.0f/sum);
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cvProjectPCA(frontal_data, pca_hr_avg, pca_hr_eigenvectors, pca_coeffs);
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for(int i = 0; i < m_pose_count; i++)
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{
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cvSetZero(m_samples[i]);
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for(int j = 0; j < m_pca_dim_high; j++)
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{
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double coeff = cvmGet(pca_coeffs, 0, j);
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IplImage* patch = pca_descriptors[j + 1].GetPatch(i);
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cvAddWeighted(m_samples[i], 1.0, patch, coeff, 0, m_samples[i]);
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#if 0
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printf("coeff%d = %f\n", j, coeff);
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IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
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double maxval;
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cvMinMaxLoc(patch, 0, &maxval);
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cvConvertScale(patch, test, 255.0/maxval);
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cvNamedWindow("1", 1);
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cvShowImage("1", test);
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cvWaitKey(0);
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#endif
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}
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cvAdd(pca_descriptors[0].GetPatch(i), m_samples[i], m_samples[i]);
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double sm = cvSum(m_samples[i]).val[0];
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cvConvertScale(m_samples[i], m_samples[i], 1.0/sm);
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#if 0
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IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
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/* IplImage* temp1 = cvCreateImage(cvSize(12, 12), IPL_DEPTH_32F, 1);
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eigenvector2image(pca_hr_avg, temp1);
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IplImage* test = cvCreateImage(cvSize(12, 12), IPL_DEPTH_8U, 1);
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cvAdd(m_samples[i], temp1, temp1);
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cvMinMaxLoc(temp1, 0, &maxval);
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cvConvertScale(temp1, test, 255.0/maxval);*/
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cvMinMaxLoc(m_samples[i], 0, &maxval);
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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 = 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 = 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;
|
|
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.png", 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);
|
|
|
|
#ifdef HAVE_OPENCV_HIGHGUI
|
|
cv::imwrite(buf, cv::cvarrToMat(patch));
|
|
#else
|
|
CV_Error(CV_StsNotImplemented, "OpenCV has been compiled without image I/O support");
|
|
#endif
|
|
|
|
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))
|
|
{
|
|
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(std::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);
|
|
}
|
|
|
|
static void extractPatches (IplImage *img, std::vector<IplImage*>& patches, CvSize patch_size)
|
|
{
|
|
std::vector<KeyPoint> features;
|
|
Ptr<FeatureDetector> surf_extractor = FeatureDetector::create("SURF");
|
|
if( surf_extractor.empty() )
|
|
CV_Error(CV_StsNotImplemented, "OpenCV was built without SURF support");
|
|
surf_extractor->set("hessianThreshold", 1.0);
|
|
//printf("Extracting SURF features...");
|
|
surf_extractor->detect(cv::cvarrToMat(img), 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, std::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, std::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;
|
|
#ifdef HAVE_OPENCV_HIGHGUI
|
|
Mat img2 = cv::imread(filename, IMREAD_GRAYSCALE);
|
|
img = img2;
|
|
#else
|
|
CV_Error(CV_StsNotImplemented, "OpenCV has been compiled without image I/O support");
|
|
#endif
|
|
//printf("done\n");
|
|
|
|
extractPatches (&img, patches, patch_size);
|
|
}
|
|
fclose(pFile);
|
|
}
|
|
|
|
void generatePCAFeatures(const char* path, const char* img_filename, FileStorage& fs, const char* postfix,
|
|
CvSize patch_size, CvMat** avg, CvMat** eigenvectors)
|
|
{
|
|
std::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)
|
|
{
|
|
std::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 std::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 std::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;
|
|
}
|
|
|
|
std::vector<KeyPoint> OneWayDescriptorObject::_GetLabeledFeatures() const
|
|
{
|
|
std::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)
|
|
{
|
|
String str = String ("avg") + postfix;
|
|
CvMat* _avg = reinterpret_cast<CvMat*> (fn[str].readObj());
|
|
if (_avg != 0)
|
|
{
|
|
*avg = cvCloneMat(_avg);
|
|
cvReleaseMat(&_avg);
|
|
}
|
|
|
|
str = String ("eigenvectors") + postfix;
|
|
CvMat* _eigenvectors = reinterpret_cast<CvMat*> (fn[str].readObj());
|
|
if (_eigenvectors != 0)
|
|
{
|
|
*eigenvectors = cvCloneMat(_eigenvectors);
|
|
cvReleaseMat(&_eigenvectors);
|
|
}
|
|
}
|
|
|
|
/****************************************************************************************\
|
|
* OneWayDescriptorMatcher *
|
|
\****************************************************************************************/
|
|
|
|
OneWayDescriptorMatcher::Params::Params( int _poseCount, Size _patchSize, String _pcaFilename,
|
|
String _trainPath, String _trainImagesList,
|
|
float _minScale, float _maxScale, float _stepScale ) :
|
|
poseCount(_poseCount), patchSize(_patchSize), pcaFilename(_pcaFilename),
|
|
trainPath(_trainPath), trainImagesList(_trainImagesList),
|
|
minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale)
|
|
{}
|
|
|
|
|
|
OneWayDescriptorMatcher::OneWayDescriptorMatcher( const Params& _params)
|
|
{
|
|
initialize(_params);
|
|
}
|
|
|
|
OneWayDescriptorMatcher::~OneWayDescriptorMatcher()
|
|
{}
|
|
|
|
void OneWayDescriptorMatcher::initialize( const Params& _params, const Ptr<OneWayDescriptorBase>& _base )
|
|
{
|
|
clear();
|
|
|
|
if( _base.empty() )
|
|
base = _base;
|
|
|
|
params = _params;
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::clear()
|
|
{
|
|
GenericDescriptorMatcher::clear();
|
|
|
|
prevTrainCount = 0;
|
|
if( !base.empty() )
|
|
base->clear();
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::train()
|
|
{
|
|
if( base.empty() || prevTrainCount < (int)trainPointCollection.keypointCount() )
|
|
{
|
|
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
|
|
params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale );
|
|
|
|
base->Allocate( (int)trainPointCollection.keypointCount() );
|
|
prevTrainCount = (int)trainPointCollection.keypointCount();
|
|
|
|
const std::vector<std::vector<KeyPoint> >& points = trainPointCollection.getKeypoints();
|
|
int count = 0;
|
|
for( size_t i = 0; i < points.size(); i++ )
|
|
{
|
|
IplImage _image = trainPointCollection.getImage((int)i);
|
|
for( size_t j = 0; j < points[i].size(); j++ )
|
|
base->InitializeDescriptor( count++, &_image, points[i][j], "" );
|
|
}
|
|
|
|
#if defined(_KDTREE)
|
|
base->ConvertDescriptorsArrayToTree();
|
|
#endif
|
|
}
|
|
}
|
|
|
|
bool OneWayDescriptorMatcher::isMaskSupported()
|
|
{
|
|
return false;
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::knnMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
|
|
std::vector<std::vector<DMatch> >& matches, int knn,
|
|
const std::vector<Mat>& /*masks*/, bool /*compactResult*/ )
|
|
{
|
|
train();
|
|
|
|
CV_Assert( knn == 1 ); // knn > 1 unsupported because of bug in OneWayDescriptorBase for this case
|
|
|
|
matches.resize( queryKeypoints.size() );
|
|
IplImage _qimage = queryImage;
|
|
for( size_t i = 0; i < queryKeypoints.size(); i++ )
|
|
{
|
|
int descIdx = -1, poseIdx = -1;
|
|
float distance;
|
|
base->FindDescriptor( &_qimage, queryKeypoints[i].pt, descIdx, poseIdx, distance );
|
|
matches[i].push_back( DMatch((int)i, descIdx, distance) );
|
|
}
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::radiusMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
|
|
std::vector<std::vector<DMatch> >& matches, float maxDistance,
|
|
const std::vector<Mat>& /*masks*/, bool /*compactResult*/ )
|
|
{
|
|
train();
|
|
|
|
matches.resize( queryKeypoints.size() );
|
|
IplImage _qimage = queryImage;
|
|
for( size_t i = 0; i < queryKeypoints.size(); i++ )
|
|
{
|
|
int descIdx = -1, poseIdx = -1;
|
|
float distance;
|
|
base->FindDescriptor( &_qimage, queryKeypoints[i].pt, descIdx, poseIdx, distance );
|
|
if( distance < maxDistance )
|
|
matches[i].push_back( DMatch((int)i, descIdx, distance) );
|
|
}
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::read( const FileNode &fn )
|
|
{
|
|
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, String (), String (), String (),
|
|
params.minScale, params.maxScale, params.stepScale );
|
|
base->Read (fn);
|
|
}
|
|
|
|
void OneWayDescriptorMatcher::write( FileStorage& fs ) const
|
|
{
|
|
base->Write (fs);
|
|
}
|
|
|
|
bool OneWayDescriptorMatcher::empty() const
|
|
{
|
|
return base.empty() || base->empty();
|
|
}
|
|
|
|
Ptr<GenericDescriptorMatcher> OneWayDescriptorMatcher::clone( bool emptyTrainData ) const
|
|
{
|
|
OneWayDescriptorMatcher* matcher = new OneWayDescriptorMatcher( params );
|
|
|
|
if( !emptyTrainData )
|
|
{
|
|
CV_Error( CV_StsNotImplemented, "deep clone functionality is not implemented, because "
|
|
"OneWayDescriptorBase has not copy constructor or clone method ");
|
|
|
|
//matcher->base;
|
|
matcher->params = params;
|
|
matcher->prevTrainCount = prevTrainCount;
|
|
matcher->trainPointCollection = trainPointCollection;
|
|
}
|
|
return matcher;
|
|
}
|
|
}
|