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main.cpp
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main.cpp
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#include "main.h"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <ctime>
#include <stdexcept>
#include <vector>
#include <string>
#include <iostream>
// Includes CUDA
#include <cuda_runtime.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <cuda_texture_types.h>
#include <vector_types.h>
// CUDA helper functions
#include "helper_cuda.h" // helper functions for CUDA error check
#include <sys/stat.h> // mkdir
#include <sys/types.h> // mkdir
#ifdef WIN32
#include <win32_dirent.h> // opendir()
#else
#include <dirent.h> // opendir()
#endif
#include "algorithmparameters.h"
#include "globalstate.h"
#include "gipuma.h"
#include "fileIoUtils.h"
#include "cameraGeometryUtils.h"
#include "mathUtils.h"
#include "displayUtils.h"
#include "groundTruthUtils.h"
// file format from https://www.cs.cornell.edu/~snavely/bundler/bundler-v0.4-manual.html#S6
static void parse_bundler_3d_points ( const char *filename, std::vector<Vec3f> &point_list)
{
unsigned int num_cameras, num_points;
ifstream myfile;
myfile.open(filename, ifstream::in);
char line[512];
if(myfile.peek()=='#')
myfile.getline(line,512); // skip comment
myfile.getline(line,512); // <num_cameras> <num_points> [two integers]
sscanf ( line, "%u %u", &num_cameras, &num_points);
for (size_t i=0; i< num_cameras; i++)
{
myfile.getline(line,512); // <f> <k1> <k2> [the focal length, followed by two radial distortion coeffs]
myfile.getline(line,512); // <R> [a 3x3 matrix representing the camera rotation]
myfile.getline(line,512); // <R> [a 3x3 matrix representing the camera rotation]
myfile.getline(line,512); // <R> [a 3x3 matrix representing the camera rotation]
myfile.getline(line,512); // <t> [a 3-vector describing the camera translation]
}
// Now parse point list
/*
* Each point entry has the form:
*
*
* <position> [a 3-vector describing the 3D position of the point]
* <color> [a 3-vector describing the RGB color of the point]
* <view list> [a list of views the point is visible in]
*/
point_list.resize (num_points);
for (size_t i=0; i< num_points; i++)
{
Vec3f X;
myfile.getline(line, 512); // <position> [a 3-vector describing the 3D position of the point]
sscanf ( line, "%f %f %f", &X[0], &X[1], &X[2]);
myfile.getline(line, 512); // <color> [a 3-vector describing the RGB color of the point]
myfile.getline(line, 512); // <view list> [a list of views the point is visible in]
//if (i<10)
//printf("3d point is %f %f %f\n", X[0], X[1], X[2]);
point_list[i] = X;
}
}
static void from_bundler_get_range (CameraParameters &cameraParams,
AlgorithmParameters &algParams,
const char *filename)
{
std::vector<Vec3f> point_list;
vector<Camera> &cameras = cameraParams.cameras;
parse_bundler_3d_points(filename, point_list);
float min_depth = 9999;
float max_depth = 0;
// For each camera
for ( size_t i = 1; i < cameras.size (); i++ ) {
// For each point
for (auto X : point_list)
{
// Compute euclidean distance to camera center
float depth = static_cast<float>(cv::norm(X - cameras[i].C));
min_depth = std::min(depth, min_depth);
max_depth = std::max(depth, max_depth);
}
}
if (algParams.depthMin == -1)
algParams.depthMin = min_depth - min_depth*0.4f;
if (algParams.depthMax == -1)
algParams.depthMax = max_depth + max_depth*0.2f;
// For each camera compute minimum and maximum depth
}
static void print_help (char **argv)
{
printf ( "\nUsage: %s <im1> <im2> ... [--parameter=<parameter>]\n", argv[0] );
}
static void get_directory_entries(
const char *dirname,
vector<string> &directory_entries)
{
DIR *dir;
struct dirent *ent;
// Open directory stream
dir = opendir (dirname);
if (dir != NULL) {
//cout << "Dirname is " << dirname << endl;
//cout << "Dirname type is " << ent->d_type << endl;
//cout << "Dirname type DT_DIR " << DT_DIR << endl;
// Print all files and directories within the directory
while ((ent = readdir (dir)) != NULL) {
//cout << "INSIDE" << endl;
//if(ent->d_type == DT_DIR)
{
char* name = ent->d_name;
if(strcmp(name,".") == 0 || strcmp(ent->d_name,"..") == 0)
continue;
//printf ("dir %s/\n", name);
directory_entries.push_back(string(name));
}
}
closedir (dir);
} else {
// Could not open directory
printf ("Cannot open directory %s\n", dirname);
exit (EXIT_FAILURE);
}
sort ( directory_entries.begin (), directory_entries.end () );
}
/* process command line arguments
* Input: argc, argv - command line arguments
* Output: inputFiles, outputFiles, parameters, gt_parameters, - algorithm parameters
*/
static int getParametersFromCommandLine ( int argc,
char** argv,
InputFiles &inputFiles,
OutputFiles &outputFiles,
AlgorithmParameters &algParams,
GTcheckParameters >_parameters
)
{
int camera_idx = 0;
const char* algorithm_opt = "--algorithm=";
const char* maxdisp_opt = "--max-disparity=";
const char* blocksize_opt = "--blocksize=";
const char* cost_tau_color_opt = "--cost_tau_color=";
const char* cost_tau_gradient_opt = "--cost_tau_gradient=";
const char* cost_alpha_opt = "--cost_alpha=";
const char* cost_gamma_opt = "--cost_gamma=";
const char* disparity_tolerance_opt = "--disp_tol=";
const char* normal_tolerance_opt = "--norm_tol=";
const char* border_value = "--border_value=";
const char* gtDepth_divFactor_opt = "--gtDepth_divisionFactor=";
const char* gtDepth_tolerance_opt = "--gtDepth_tolerance=";
const char* gtDepth_tolerance2_opt = "--gtDepth_tolerance2=";
const char* colorProc_opt = "-color_processing";
const char* num_iterations_opt = "--iterations=";
const char* self_similariy_n_opt = "--ss_n=";
const char* ct_epsilon_opt = "--ct_eps=";
const char* cam_scale_opt = "--cam_scale=";
const char* num_img_processed_opt = "--num_img_processed=";
const char* n_best_opt = "--n_best=";
const char* cost_comb_opt = "--cost_comb=";
const char* cost_good_factor_opt = "--good_factor=";
const char* depth_min_opt = "--depth_min=";
const char* depth_max_opt = "--depth_max=";
// const char* scale_opt = "--scale=";
const char* outputPath_opt = "-output_folder";
const char* calib_opt = "-calib_file";
const char* gt_opt = "-gt";
const char* gt_nocc_opt = "-gt_nocc";
const char* occl_mask_opt = "-occl_mask";
const char* gt_normal_opt = "-gt_normal";
const char* images_input_folder_opt = "-images_folder";
const char* p_input_folder_opt = "-p_folder";
const char* krt_file_opt = "-krt_file";
const char* camera_input_folder_opt = "-camera_folder";
const char* bounding_folder_opt = "-bounding_folder";
const char* viewSelection_opt = "-view_selection";
const char* initial_seed_opt = "--initial_seed";
const char* min_angle_opt = "--min_angle=";
const char* max_angle_opt = "--max_angle=";
const char* no_texture_sim_opt = "--no_texture_sim";
const char* no_texture_per_opt = "--no_texture_per";
const char* max_views_opt = "--max_views=";
const char* pmvs_folder_opt = "--pmvs_folder";
const char* camera_idx_opt = "--camera_idx=";
//read in arguments
for ( int i = 1; i < argc; i++ ) {
if ( argv[i][0] != '-' )
{
inputFiles.img_filenames.push_back ( argv[i] );
}
else if ( strncmp ( argv[i], algorithm_opt, strlen ( algorithm_opt ) ) == 0 )
{
char* _alg = argv[i] + strlen ( algorithm_opt );
algParams.algorithm = strcmp ( _alg, "pm" ) == 0 ? PM_COST :
strcmp ( _alg, "ct" ) == 0 ? CENSUS_TRANSFORM :
strcmp ( _alg, "sct" ) == 0 ? SPARSE_CENSUS :
strcmp ( _alg, "ct_ss" ) == 0 ? CENSUS_SELFSIMILARITY :
strcmp ( _alg, "adct" ) == 0 ? ADCENSUS :
strcmp ( _alg, "adct_ss" ) == 0 ? ADCENSUS_SELFSIMILARITY :
strcmp ( _alg, "pm_ss" ) == 0 ? PM_SELFSIMILARITY : -1;
if ( algParams.algorithm < 0 )
{
printf ( "Command-line parameter error: Unknown stereo algorithm\n\n" );
print_help (argv);
return -1;
}
}
else if ( strncmp ( argv[i], cost_comb_opt, strlen ( cost_comb_opt ) ) == 0 )
{
char* _alg = argv[i] + strlen ( algorithm_opt );
algParams.cost_comb = strcmp ( _alg, "all" ) == 0 ? COMB_ALL :
strcmp ( _alg, "best_n" ) == 0 ? COMB_BEST_N :
strcmp ( _alg, "angle" ) == 0 ? COMB_ANGLE :
strcmp ( _alg, "good" ) == 0 ? COMB_GOOD : -1;
if ( algParams.cost_comb < 0 )
{
printf ( "Command-line parameter error: Unknown cost combination method\n\n" );
print_help (argv);
return -1;
}
}
else if ( strncmp ( argv[i], maxdisp_opt, strlen ( maxdisp_opt ) ) == 0 )
{
if ( sscanf ( argv[i] + strlen ( maxdisp_opt ), "%f", &algParams.max_disparity ) != 1 ||
algParams.max_disparity < 1 )
{
printf ( "Command-line parameter error: The max disparity (--maxdisparity=<...>) must be a positive integer \n" );
print_help (argv);
return -1;
}
}
else if ( strncmp ( argv[i], blocksize_opt, strlen ( blocksize_opt ) ) == 0 )
{
int k_size;
if ( sscanf ( argv[i] + strlen ( blocksize_opt ), "%d", &k_size ) != 1 ||
k_size < 1 || k_size % 2 != 1 )
{
printf ( "Command-line parameter error: The block size (--blocksize=<...>) must be a positive odd number\n" );
return -1;
}
algParams.box_hsize = k_size;
algParams.box_vsize = k_size;
}
else if ( strncmp ( argv[i], cost_good_factor_opt, strlen ( cost_good_factor_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cost_good_factor_opt ), "%f", &algParams.good_factor );
}
else if ( strncmp ( argv[i], cost_tau_color_opt, strlen ( cost_tau_color_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cost_tau_color_opt ), "%f", &algParams.tau_color );
}
else if ( strncmp ( argv[i], cost_tau_gradient_opt, strlen ( cost_tau_gradient_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cost_tau_gradient_opt ), "%f", &algParams.tau_gradient );
}
else if ( strncmp ( argv[i], cost_alpha_opt, strlen ( cost_alpha_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cost_alpha_opt ), "%f", &algParams.alpha );
}
else if ( strncmp ( argv[i], cost_gamma_opt, strlen ( cost_gamma_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cost_gamma_opt ), "%f", &algParams.gamma );
}
else if ( strncmp ( argv[i], border_value, strlen ( border_value ) ) == 0 )
{
sscanf ( argv[i] + strlen ( border_value ), "%d", &algParams.border_value );
}
else if ( strncmp ( argv[i], num_iterations_opt, strlen ( num_iterations_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( num_iterations_opt ), "%d", &algParams.iterations );
}
else if ( strncmp ( argv[i], disparity_tolerance_opt, strlen ( disparity_tolerance_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( disparity_tolerance_opt ), "%f", &algParams.dispTol );
}
else if ( strncmp ( argv[i], normal_tolerance_opt, strlen ( normal_tolerance_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( normal_tolerance_opt ), "%f", &algParams.normTol );
}
else if ( strncmp ( argv[i], self_similariy_n_opt, strlen ( self_similariy_n_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( self_similariy_n_opt ), "%d", &algParams.self_similarity_n );
}
else if ( strncmp ( argv[i], ct_epsilon_opt, strlen ( ct_epsilon_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( ct_epsilon_opt ), "%f", &algParams.census_epsilon );
}
else if ( strncmp ( argv[i], cam_scale_opt, strlen ( cam_scale_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( cam_scale_opt ), "%f", &algParams.cam_scale );
}
else if ( strncmp ( argv[i], num_img_processed_opt, strlen ( num_img_processed_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( num_img_processed_opt ), "%d", &algParams.num_img_processed );
}
else if ( strncmp ( argv[i], n_best_opt, strlen ( n_best_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( n_best_opt ), "%d", &algParams.n_best );
}
else if ( strncmp ( argv[i], gtDepth_divFactor_opt, strlen ( gtDepth_divFactor_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( gtDepth_divFactor_opt ), "%f", >_parameters.divFactor );
}
else if ( strncmp ( argv[i], gtDepth_tolerance_opt, strlen ( gtDepth_tolerance_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( gtDepth_tolerance_opt ), "%f", >_parameters.dispTolGT );
}
else if ( strncmp ( argv[i], gtDepth_tolerance2_opt, strlen ( gtDepth_tolerance2_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( gtDepth_tolerance2_opt ), "%f", >_parameters.dispTolGT2 );
}
else if ( strncmp ( argv[i], depth_min_opt, strlen ( depth_min_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( depth_min_opt ), "%f", &algParams.depthMin );
}
else if ( strncmp ( argv[i], depth_max_opt, strlen ( depth_max_opt ) ) == 0 )
{
sscanf ( argv[i] + strlen ( depth_max_opt ), "%f", &algParams.depthMax );
}
else if ( strncmp ( argv[i], min_angle_opt, strlen ( min_angle_opt ) ) == 0 )
sscanf ( argv[i] + strlen ( min_angle_opt ), "%f", &algParams.min_angle );
else if ( strncmp ( argv[i], max_angle_opt, strlen ( max_angle_opt ) ) == 0 ) {
sscanf ( argv[i] + strlen ( max_angle_opt ), "%f", &algParams.max_angle );
}
else if ( strncmp ( argv[i], pmvs_folder_opt, strlen ( pmvs_folder_opt ) ) == 0 ) {
inputFiles.pmvs_folder = argv[++i];
}
else if ( strncmp ( argv[i], max_views_opt, strlen ( max_views_opt ) ) == 0 )
sscanf ( argv[i] + strlen ( max_views_opt ), "%u", &algParams.max_views );
else if ( strncmp ( argv[i], no_texture_sim_opt, strlen ( no_texture_sim_opt ) ) == 0 )
sscanf ( argv[i] + strlen ( no_texture_sim_opt ), "%f", &algParams.no_texture_sim );
else if ( strncmp ( argv[i], no_texture_per_opt, strlen ( no_texture_per_opt ) ) == 0 )
sscanf ( argv[i] + strlen ( no_texture_per_opt ), "%f", &algParams.no_texture_per );
else if ( strcmp ( argv[i], viewSelection_opt ) == 0 )
algParams.viewSelection = true;
else if ( strcmp ( argv[i], colorProc_opt ) == 0 )
algParams.color_processing = true;
else if ( strcmp ( argv[i], "-o" ) == 0 )
outputFiles.disparity_filename = argv[++i];
else if ( strcmp ( argv[i], outputPath_opt ) == 0 )
outputFiles.parentFolder = argv[++i];
else if ( strcmp ( argv[i], calib_opt ) == 0 )
inputFiles.calib_filename = argv[++i];
else if ( strcmp ( argv[i], gt_opt ) == 0 )
inputFiles.gt_filename = argv[++i];
else if ( strcmp ( argv[i], gt_nocc_opt ) == 0 )
inputFiles.gt_nocc_filename = argv[++i];
else if ( strcmp ( argv[i], occl_mask_opt ) == 0 )
inputFiles.occ_filename = argv[++i];
else if ( strcmp ( argv[i], gt_normal_opt ) == 0 )
inputFiles.gt_normal_filename = argv[++i];
else if ( strcmp ( argv[i], images_input_folder_opt ) == 0 )
inputFiles.images_folder = argv[++i];
else if ( strcmp ( argv[i], p_input_folder_opt ) == 0 )
inputFiles.p_folder = argv[++i];
else if ( strcmp ( argv[i], krt_file_opt ) == 0 )
inputFiles.krt_file = argv[++i];
else if ( strcmp ( argv[i], camera_input_folder_opt ) == 0 )
inputFiles.camera_folder = argv[++i];
else if ( strcmp ( argv[i], initial_seed_opt ) == 0 )
inputFiles.seed_file = argv[++i];
else if ( strcmp ( argv[i], bounding_folder_opt ) == 0 )
inputFiles.bounding_folder = argv[++i];
else if ( strncmp ( argv[i], camera_idx_opt, strlen( camera_idx_opt) ) == 0 ){
sscanf ( argv[i] + strlen ( camera_idx_opt ), "%d", &camera_idx);
}
else
{
printf ( "Command-line parameter warning: unknown option %s\n", argv[i] );
//return -1;
}
}
//cout << "Seed file is " << inputFiles.seed_file << endl;
//cout << "Min angle is " << algParams.min_angle << endl;
if (inputFiles.pmvs_folder.size()>0)
{
cout << "Using pmvs information inside directory " << inputFiles.pmvs_folder << endl;
inputFiles.images_folder = inputFiles.pmvs_folder + "/visualize/";
inputFiles.img_filenames.clear();
get_directory_entries(inputFiles.images_folder.c_str(), inputFiles.img_filenames);
inputFiles.p_folder = inputFiles.pmvs_folder + "/txt/";
cout << "Using image " << inputFiles.img_filenames[camera_idx] << " as reference camera" << endl;
std::swap( inputFiles.img_filenames[0], inputFiles.img_filenames[camera_idx]);
}
cout << "Input files are: ";
for (const auto i: inputFiles.img_filenames)
cout << i << " ";
cout << endl;
return 0;
}
static void selectViews (CameraParameters &cameraParams, int imgWidth, int imgHeight, AlgorithmParameters &algParams ) {
vector<Camera> &cameras = cameraParams.cameras;
Camera ref = cameras[cameraParams.idRef];
int x = imgWidth / 2;
int y = imgHeight / 2;
cameraParams.viewSelectionSubset.clear ();
Vec3f viewVectorRef = getViewVector ( ref, x, y);
// TODO hardcoded value makes it a parameter
float minimum_angle_degree = algParams.min_angle;
float maximum_angle_degree = algParams.max_angle;
unsigned int maximum_view = algParams.max_views;
float minimum_angle_radians = minimum_angle_degree * M_PI / 180.0f;
float maximum_angle_radians = maximum_angle_degree * M_PI / 180.0f;
float min_depth = 9999;
float max_depth = 0;
if ( algParams.viewSelection )
printf("Accepting intersection angle of central rays from %f to %f degrees, use --min_angle=<angle> and --max_angle=<angle> to modify them\n", minimum_angle_degree, maximum_angle_degree);
for ( size_t i = 1; i < cameras.size (); i++ ) {
//if ( !algParams.viewSelection ) { //select all views, dont perform selection
//cameraParams.viewSelectionSubset.push_back ( i );
//continue;
//}
Vec3f vec = getViewVector ( cameras[i], x, y);
float baseline = static_cast<float>(norm (cameras[0].C, cameras[i].C));
float angle = getAngle ( viewVectorRef, vec );
if ( angle > minimum_angle_radians &&
angle < maximum_angle_radians ) //0.6 select if angle between 5.7 and 34.8 (0.6) degrees (10 and 30 degrees suggested by some paper)
{
if ( algParams.viewSelection ) {
cameraParams.viewSelectionSubset.push_back ( static_cast<int>(i) );
//printf("\taccepting camera %ld with angle\t %f degree (%f radians) and baseline %f\n", i, angle*180.0f/M_PI, angle, baseline);
}
float min_range = (baseline/2.0f) / sin(maximum_angle_radians/2.0f);
float max_range = (baseline/2.0f) / sin(minimum_angle_radians/2.0f);
min_depth = std::min(min_range, min_depth);
max_depth = std::max(max_range, max_depth);
//printf("Min max ranges are %f %f\n", min_range, max_range);
//printf("Min max depth are %f %f\n", min_depth, max_depth);
}
//else
//printf("Discarding camera %ld with angle\t %f degree (%f radians) and baseline, %f\n", i, angle*180.0f/M_PI, angle, baseline);
}
if (algParams.depthMin == -1)
algParams.depthMin = min_depth;
if (algParams.depthMax == -1)
algParams.depthMax = max_depth;
if (!algParams.viewSelection) {
cameraParams.viewSelectionSubset.clear();
for ( size_t i = 1; i < cameras.size (); i++ )
cameraParams.viewSelectionSubset.push_back ( static_cast<int>(i) );
return;
}
if (cameraParams.viewSelectionSubset.size() >= maximum_view) {
printf("Too many camera, randomly selecting only %d of them (modify with --max_views=<number>)\n", maximum_view);
std::srand ( unsigned ( std::time(0) ) );
std::random_shuffle( cameraParams.viewSelectionSubset.begin(), cameraParams.viewSelectionSubset.end() ); // shuffle elements of v
cameraParams.viewSelectionSubset.erase (cameraParams.viewSelectionSubset.begin()+maximum_view,cameraParams.viewSelectionSubset.end());
}
//for (auto i : cameraParams.viewSelectionSubset )
//printf("\taccepting camera %d\n", i);
}
static void delTexture (int num, cudaTextureObject_t texs[], cudaArray *cuArray[])
{
for (int i=0; i<num; i++) {
cudaFreeArray(cuArray[i]);
cudaDestroyTextureObject(texs[i]);
}
}
static void addImageToTextureUint (vector<Mat_<uint8_t> > &imgs, cudaTextureObject_t texs[], cudaArray *cuArray[])
{
for (size_t i=0; i<imgs.size(); i++)
{
int rows = imgs[i].rows;
int cols = imgs[i].cols;
// Create channel with uint8_t point type
cudaChannelFormatDesc channelDesc =
//cudaCreateChannelDesc (8,
//0,
//0,
//0,
//cudaChannelFormatKindUnsigned);
cudaCreateChannelDesc<char>();
// Allocate array with correct size and number of channels
checkCudaErrors(cudaMallocArray(&cuArray[i],
&channelDesc,
cols,
rows));
checkCudaErrors (cudaMemcpy2DToArray (cuArray[i],
0,
0,
imgs[i].ptr<uint8_t>(),
imgs[i].step[0],
cols*sizeof(uint8_t),
rows,
cudaMemcpyHostToDevice));
// Specify texture
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
resDesc.res.array.array = cuArray[i];
// Specify texture object parameters
struct cudaTextureDesc texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = cudaAddressModeWrap;
texDesc.addressMode[1] = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModePoint;
texDesc.readMode = cudaReadModeElementType;
texDesc.normalizedCoords = 0;
// Create texture object
//cudaTextureObject_t &texObj = texs[i];
checkCudaErrors(cudaCreateTextureObject(&(texs[i]), &resDesc, &texDesc, NULL));
//texs[i] = texObj;
}
return;
}
static void addImageToTextureFloatColor (vector<Mat > &imgs, cudaTextureObject_t texs[], cudaArray *cuArray[])
{
for (size_t i=0; i<imgs.size(); i++)
{
int rows = imgs[i].rows;
int cols = imgs[i].cols;
// Create channel with floating point type
cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc<float4>();
// Allocate array with correct size and number of channels
//cudaArray *cuArray;
checkCudaErrors(cudaMallocArray(&cuArray[i],
&channelDesc,
cols,
rows));
checkCudaErrors (cudaMemcpy2DToArray (cuArray[i],
0,
0,
imgs[i].ptr<float>(),
imgs[i].step[0],
cols*sizeof(float)*4,
rows,
cudaMemcpyHostToDevice));
// Specify texture
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
resDesc.res.array.array = cuArray[i];
// Specify texture object parameters
struct cudaTextureDesc texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = cudaAddressModeWrap;
texDesc.addressMode[1] = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModeLinear;
texDesc.readMode = cudaReadModeElementType;
texDesc.normalizedCoords = 0;
// Create texture object
//cudaTextureObject_t &texObj = texs[i];
checkCudaErrors(cudaCreateTextureObject(&(texs[i]), &resDesc, &texDesc, NULL));
}
return;
}
static void addImageToTextureFloatGray (vector<Mat > &imgs, cudaTextureObject_t texs[], cudaArray *cuArray[])
{
for (size_t i=0; i<imgs.size(); i++)
{
int rows = imgs[i].rows;
int cols = imgs[i].cols;
// Create channel with floating point type
cudaChannelFormatDesc channelDesc =
cudaCreateChannelDesc (32,
0,
0,
0,
cudaChannelFormatKindFloat);
// Allocate array with correct size and number of channels
checkCudaErrors(cudaMallocArray(&cuArray[i],
&channelDesc,
cols,
rows));
checkCudaErrors (cudaMemcpy2DToArray (cuArray[i],
0,
0,
imgs[i].ptr<float>(),
imgs[i].step[0],
cols*sizeof(float),
rows,
cudaMemcpyHostToDevice));
// Specify texture
struct cudaResourceDesc resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = cudaResourceTypeArray;
resDesc.res.array.array = cuArray[i];
// Specify texture object parameters
struct cudaTextureDesc texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = cudaAddressModeWrap;
texDesc.addressMode[1] = cudaAddressModeWrap;
texDesc.filterMode = cudaFilterModeLinear;
texDesc.readMode = cudaReadModeElementType;
texDesc.normalizedCoords = 0;
// Create texture object
//cudaTextureObject_t &texObj = texs[i];
checkCudaErrors(cudaCreateTextureObject(&(texs[i]), &resDesc, &texDesc, NULL));
//texs[i] = texObj;
}
return;
}
static void selectCudaDevice ()
{
int deviceCount = 0;
checkCudaErrors(cudaGetDeviceCount(&deviceCount));
if (deviceCount == 0) {
fprintf(stderr, "There is no cuda capable device!\n");
exit(EXIT_FAILURE);
}
cout << "Detected " << deviceCount << " devices!" << endl;
std::vector<int> usableDevices;
std::vector<std::string> usableDeviceNames;
for (int i = 0; i < deviceCount; i++) {
cudaDeviceProp prop;
if (cudaGetDeviceProperties(&prop, i) == cudaSuccess) {
if (prop.major >= 3 && prop.minor >= 0) {
usableDevices.push_back(i);
usableDeviceNames.push_back(string(prop.name));
} else {
cout << "CUDA capable device " << string(prop.name)
<< " is only compute cabability " << prop.major << '.'
<< prop.minor << endl;
}
} else {
cout << "Could not check device properties for one of the cuda "
"devices!" << endl;
}
}
if(usableDevices.empty()) {
fprintf(stderr, "There is no cuda device supporting gipuma!\n");
exit(EXIT_FAILURE);
}
cout << "Detected gipuma compatible device: " << usableDeviceNames[0] << endl;;
checkCudaErrors(cudaSetDevice(usableDevices[0]));
cudaDeviceSetLimit(cudaLimitPrintfFifoSize, 1024*128);
}
static int runGipuma ( InputFiles &inputFiles,
OutputFiles &outputFiles,
AlgorithmParameters &algParams,
GTcheckParameters >Parameters,
Results &results
)
{
// create folder to store result images
time_t timeObj;
time ( &timeObj );
tm *pTime = localtime ( &timeObj );
#if defined(_WIN32)
_mkdir ( outputFiles.parentFolder );
#else
mkdir ( outputFiles.parentFolder, 0777 );
#endif
char outputFolder[256];
if(inputFiles.img_filenames.empty())
{
throw std::runtime_error("There was a problem finding the input files!");
}
string ref_name = inputFiles.img_filenames[0].substr ( 0, inputFiles.img_filenames[0].length() - 4 );
sprintf ( outputFolder, "%s/%04d%02d%02d_%02d%02d%02d_%s", outputFiles.parentFolder, pTime->tm_year + 1900, pTime->tm_mon + 1, pTime->tm_mday, pTime->tm_hour, pTime->tm_min, pTime->tm_sec, ref_name.c_str () );
#if defined(_WIN32)
_mkdir ( outputFolder );
#else
mkdir ( outputFolder, 0777 );
#endif
// store results to file
char resultsFile[256];
sprintf ( resultsFile, "%s/results.txt", outputFolder );
// load images
if ( inputFiles.img_filenames.size () < 2 )
{
printf ( "Command-line parameter error: at least 2 images must be specified\n" );
return -1;
}
size_t numImages = inputFiles.img_filenames.size ();
algParams.num_img_processed = min ( ( int ) numImages, algParams.num_img_processed );
vector<Mat_<Vec3b> > img_color(numImages); // imgLeft_color, imgRight_color;
vector<Mat_<uint8_t> > img_grayscale(numImages);
for ( size_t i = 0; i < numImages; i++ ) {
img_grayscale[i] = imread ( ( inputFiles.images_folder + inputFiles.img_filenames[i] ), IMREAD_GRAYSCALE );
if ( algParams.color_processing ) {
img_color[i] = imread ( ( inputFiles.images_folder + inputFiles.img_filenames[i] ), IMREAD_COLOR );
}
if ( img_grayscale[i].rows == 0 ) {
printf ( "Image seems to be invalid\n" );
return -1;
}
}
uint32_t rows = img_grayscale[0].rows;
uint32_t cols = img_grayscale[0].cols;
uint32_t numPixels = rows * cols;
Mat_<float> groundTruthDisp;
Mat_<float> groundTruthDispNocc;
Mat_<Vec3f> groundTruthNormals;
if ( !inputFiles.gt_filename.empty () ) {
gtParameters.gtCheck = true;
printf ( "Opening GT image %s\n", inputFiles.gt_filename.c_str () );
string ext = inputFiles.gt_filename.substr ( inputFiles.gt_filename.find_last_of ( "." ) + 1 );
if ( ext.compare ( "pfm" ) == 0 ) {
long nx, ny;
readPfm ( inputFiles.gt_filename.c_str (), groundTruthDisp, &nx, &ny );
} else if ( ext.compare ( "dmb" ) == 0 ) {
readDmb ( inputFiles.gt_filename.c_str (), groundTruthDisp );
} else {
Mat gtImg = imread ( inputFiles.gt_filename, -1 );
gtImg.convertTo ( groundTruthDisp, CV_32F );
}
cout << "gt: " << groundTruthDisp.rows << " " << groundTruthDisp.cols << " " << groundTruthDisp.channels () << " " << groundTruthDisp.depth () << endl;
double minVal, maxVal;
minMaxLoc ( groundTruthDisp, &minVal, &maxVal );
//cout << "depth min max: " << minVal << " " << maxVal << endl;
}
if ( !inputFiles.gt_nocc_filename.empty () ) {
if ( !gtParameters.gtCheck ) {
printf ( "Command-line parameter error: Ground truth image (-gt) must be specified for use of nocc GT\n" );
return -1;
}
gtParameters.noccCheck = true;
printf ( "Opening nocc GT image %s\n", inputFiles.gt_nocc_filename.c_str () );
Mat gtImg = imread ( inputFiles.gt_nocc_filename, -1 );
gtImg.convertTo ( groundTruthDispNocc, CV_32F );
} else if ( !inputFiles.occ_filename.empty () ) {
if ( !gtParameters.gtCheck ) {
printf ( "Command-line parameter error: Ground truth image (-gt) must be specified for use of occlusion mask\n" );
return -1;
}
//Mat occlusionImg; //(imgLeft.rows, imgLeft.cols, CV_16UC1,Scalar(255));
printf ( "Opening Occlusion image %s\n", inputFiles.occ_filename.c_str () );
Mat occlusionImg = imread ( inputFiles.occ_filename, IMREAD_GRAYSCALE );
getNoccGTimg ( groundTruthDisp, occlusionImg, groundTruthDispNocc );
} else {
groundTruthDispNocc = groundTruthDisp;
}
if ( !inputFiles.gt_normal_filename.empty () ) {
cout << inputFiles.gt_normal_filename << endl;
Mat gtNormImg = imread ( inputFiles.gt_normal_filename, -1 );
cvtColor ( gtNormImg, gtNormImg, COLOR_BGR2RGB );
//gtNormImg.convertTo ( groundTruthNormals, CV_32FC3 );
groundTruthNormals = Mat::zeros(gtNormImg.rows,gtNormImg.cols, CV_32FC3);
for(int y = 0; y < gtNormImg.rows; y++) {
for(int x = 0; x < gtNormImg.cols; x++) {
Vec3i gtN_int = (Vec3i) gtNormImg.at<Vec<uint16_t, 3> >(y,x);
gtN_int = gtN_int - Vec3i(32767,32767,32767);
if(gtN_int(0) == gtN_int(1) && gtN_int(0) == gtN_int(2) && gtN_int(0) == 0){
continue;
//gtN_int = Vec3i(0,0,0);
}
groundTruthNormals(y,x) = normalize((Vec3f)gtN_int); // get rid of scaling
}
}
}
// Read initial seeds from disk if available
if (!inputFiles.seed_file.empty())
{
// TODO
}
size_t avail;
size_t used;
size_t total;
GlobalState *gs = new GlobalState;
//cudaMemGetInfo( &avail, &total );
//used = total - avail;
//printf("Device memory used after GlobalState allocation: %fMB\n", used/1000000.0f);
CameraParameters cameraParams = getCameraParameters ( *(gs->cameras), inputFiles, algParams.cam_scale);
writeParametersToFile ( resultsFile, inputFiles, algParams, gtParameters, numPixels );
//allocation for disparity and normal stores
vector<Mat_<float> > disp ( algParams.num_img_processed );
vector<Mat_<uchar> > validCost ( algParams.num_img_processed );
for ( int i = 0; i < algParams.num_img_processed; i++ ) {
disp[i] = Mat::zeros ( img_grayscale[0].rows, img_grayscale[0].cols, CV_32F );
validCost[i] = Mat::zeros ( img_grayscale[0].rows, img_grayscale[0].cols, CV_8U );
}
Mat testImg_display;
//visualize normals on a halfsphere (just for comparision with normal result image)
{
int sizeN = cols/8;
float halfSize = (float)sizeN/2.0f;
Mat_<Vec3f> normalTestImg = Mat::zeros(sizeN, sizeN, CV_32FC3);
for(int i=0;i<sizeN;i++){
for(int j=0;j<sizeN;j++){
float y = (float)i/halfSize-1.0f;
float x = (float)j/halfSize-1.0f;
float xy = pow(x,2)+pow(y,2);
if(xy <= 1.0f){
float z = sqrt(1.0f-xy);
normalTestImg(sizeN-1-i,sizeN-1-j) = Vec3f(-x,-y,-z);
}
}
}
normalTestImg.convertTo(testImg_display,CV_16U,32767,32767);
cvtColor(testImg_display,testImg_display,COLOR_RGB2BGR);
//char outputPathTest[256];
//sprintf(outputPathTest, "%s/normalsTestImg.png", outputFolder);
//imwrite(outputPathTest,testImg_display);
}
selectViews ( cameraParams, cols, rows, algParams);
if (inputFiles.pmvs_folder.size()>0) {
cout << "Using bundler file " << inputFiles.pmvs_folder + "/bundle.rd.out" << " to obtain depth range" << endl;
from_bundler_get_range (cameraParams, algParams, (inputFiles.pmvs_folder + "/bundle.rd.out").c_str());
}
//cout << "Range of Minimum/Maximum depth is: " << algParams.depthMin << " " << algParams.depthMax << endl;
size_t numSelViews = cameraParams.viewSelectionSubset.size ();
cout << "Total number of images used: " << numSelViews << endl;
ofstream myfile;
myfile.open ( resultsFile, ios::out | ios::app );
myfile << "\nNumber of selected views: " << numSelViews << endl;
myfile << "Selected views: ";
cout << "Selected views: ";
for ( int i = 0; i < numSelViews; i++ ) {
myfile << cameraParams.viewSelectionSubset[i] << ", ";
cout << cameraParams.viewSelectionSubset[i] << ", ";
gs->cameras->viewSelectionSubset[i] = cameraParams.viewSelectionSubset[i];
}
cout << endl;
myfile << "\n\n";
myfile.close ();
for ( int i = 0; i < algParams.num_img_processed; i++ ) {
cameraParams.cameras[i].depthMin = algParams.depthMin;
cameraParams.cameras[i].depthMax = algParams.depthMax;
gs->cameras->cameras[i].depthMin = algParams.depthMin;
gs->cameras->cameras[i].depthMax = algParams.depthMax;
algParams.min_disparity = disparityDepthConversion ( cameraParams.f, cameraParams.cameras[i].baseline, cameraParams.cameras[i].depthMax );
algParams.max_disparity = disparityDepthConversion ( cameraParams.f, cameraParams.cameras[i].baseline, cameraParams.cameras[i].depthMin );
double minVal, maxVal;
minMaxLoc ( disp[i], &minVal, &maxVal );
}
cout << "Range of Minimum/Maximum depth is: " << algParams.min_disparity << " " << algParams.max_disparity << ", change it with --depth_min=<value> and --depth_max=<value>" <<endl;
// run gpu run
// Init parameters
gs->params = &algParams;
gs->cameras->viewSelectionSubsetNumber = static_cast<int>(numSelViews);
// Init ImageInfo
gs->cameras->cols = cols;
gs->cameras->rows = rows;
gs->params->cols = cols;
gs->params->rows = rows;
// Resize lines
{
gs->lines->n = rows * cols;
gs->lines->resize(rows * cols);
//gs->lines.s = img_grayscale[0].step[0];
gs->lines->s = cols;
gs->lines->l = cols;
}
vector<Mat > img_grayscale_float(numImages);
vector<Mat > img_color_float(numImages);
vector<Mat > img_color_float_alpha(numImages);
vector<Mat_<uint16_t> > img_grayscale_uint(numImages);
for (size_t i = 0; i<numImages; i++)
{
img_grayscale[i].convertTo(img_grayscale_float[i], CV_32FC1); // or CV_32F works (too)
img_grayscale[i].convertTo(img_grayscale_uint[i], CV_16UC1); // or CV_32F works (too)
if(algParams.color_processing) {
vector<Mat_<float> > rgbChannels ( 3 );
img_color_float_alpha[i] = Mat::zeros ( img_grayscale[0].rows, img_grayscale[0].cols, CV_32FC4 );
img_color[i].convertTo (img_color_float[i], CV_32FC3); // or CV_32F works (too)
Mat alpha( img_grayscale[0].rows, img_grayscale[0].cols, CV_32FC1 );
//Mat out[] = { img_color_float[i], alpha };
//// add alpha channel
//int from_to[] = { 0,0, 1,1, 2,2, 3,3 };
//mixChannels( &img_color_float_alpha[i], 1, out, 2, from_to, 4 );
split (img_color_float[i], rgbChannels);
rgbChannels.push_back( alpha);
merge (rgbChannels, img_color_float_alpha[i]);
}
}
int64_t t = getTickCount ();
cudaMemGetInfo( &avail, &total );
used = total - avail;
//printf("Device memory used: %fMB\n", used/1000000.0f);
// Copy images to texture memory
//addImageToTextureUint (img_grayscale, gs->imgs);
if (algParams.color_processing)
addImageToTextureFloatColor (img_color_float_alpha, gs->imgs, gs->cuArray);
else
addImageToTextureFloatGray (img_grayscale_float, gs->imgs, gs->cuArray);
cudaMemGetInfo( &avail, &total );
used = total - avail;
//printf("Device memory used: %fMB\n", used/1000000.0f);
runcuda(*gs);
Mat_<Vec3f> norm0 = Mat::zeros ( img_grayscale[0].rows, img_grayscale[0].cols, CV_32FC3 );
Mat_<float> cudadisp = Mat::zeros ( img_grayscale[0].rows, img_grayscale[0].cols, CV_32FC1 );
for( int i = 0; i < img_grayscale[0].cols; i++ )
for( int j = 0; j < img_grayscale[0].rows; j++ )
{
int center = i+img_grayscale[0].cols*j;
float4 n = gs->lines->norm4[center];
norm0 (j, i) = Vec3f ( n.x,
n.y,
n.z);
cudadisp (j, i) = gs->lines->norm4[i+img_grayscale[0].cols*j].w;
}
Mat_<Vec3f> norm0disp = norm0.clone ();
Mat planes_display, planescalib_display, planescalib_display2;
getNormalsForDisplay ( norm0disp, planes_display );
testImg_display.copyTo(planes_display(Rect(cols-testImg_display.cols, 0, testImg_display.cols, testImg_display.rows)));
writeImageToFile ( "./", "normals", planes_display );
writeImageToFile ( outputFolder, "normals", planes_display );
planes_display.release ();
Mat cost_display;
normalize ( cudadisp, cost_display, 0, 65535, NORM_MINMAX, CV_16U );
writeImageToFile ( "./", "cudacost", cost_display );
Mat_<float> disp0 = cudadisp.clone ();