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algorithmparameters.h
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algorithmparameters.h
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#pragma once
#include "managed.h"
//cost function
enum { PM_COST = 0, CENSUS_TRANSFORM = 1, ADAPTIVE_CENSUS = 2, CENSUS_SELFSIMILARITY = 3, PM_SELFSIMILARITY = 4, ADCENSUS = 5, ADCENSUS_SELFSIMILARITY = 6, SPARSE_CENSUS = 7 };
//cost combination
enum { COMB_ALL = 0, COMB_BEST_N = 1, COMB_ANGLE = 2, COMB_GOOD = 3};
class AlgorithmParameters : public Managed{
public:
int algorithm; // algorithm cost type
float max_disparity; // maximal disparity value CUDA
float min_disparity; // minimum disparity value (default 0) CUDA
int box_hsize; // filter kernel width CUDA
int box_vsize; // filter kernel height CUDA
float tau_color; // PM_COST max. threshold for color CUDA
float tau_gradient; // PM_COST max. threshold for gradient CUDA
float alpha; // PM_COST weighting between color and gradient CUDA
float gamma; // parameter for weight function (used e.g. in PM_COST) CUDA
int border_value; // what value should pixel at extended border get (constant or replicate -1)
int iterations; // number of iterations
bool color_processing; // use color processing or not (otherwise just grayscale processing)
float dispTol; //PM Stereo: 1, PM Huber: 0.5
float normTol; // 0.1 ... about 5.7 degrees
float census_epsilon; //for census transform
int self_similarity_n; // number of pixels considered for self similarity
float cam_scale; //used to rescale K in case of rescaled image size
int num_img_processed; //number of images that are processed as reference images
float costThresh; // threshold to decide whether disparity/depth is valid or not
float good_factor; // for cost aggregation/combination good: factor for truncation CUDA
int n_best; // CUDA
int cost_comb; // CUDA
bool viewSelection;
float depthMin; // CUDA
float depthMax; // CUDA
// hack XXX
int cols;
int rows;
// fuse
bool storePlyFiles;
bool gt_compare;
bool gt_normal_compare;
bool remove_black_background;
//threshold for consistency check
float depthThresh;
float normalThresh ;
//how many views need to be consistent? (for update: numConsistentThresh+1)
int numConsistentThresh;
bool saveTexture;
AlgorithmParameters(){
algorithm = 0; // algorithm cost type
max_disparity = 256.0f; // maximal disparity value CUDA
min_disparity = 0.0f; // minimum disparity value (default 0) CUDA
box_hsize = 15; // filter kernel width CUDA
box_vsize = 15; // filter kernel height CUDA
tau_color = 10; // PM_COST max. threshold for color CUDA
tau_gradient = 2.0f; // PM_COST max. threshold for gradient CUDA
alpha = 0.9f; // PM_COST weighting between color and gradient CUDA
gamma = 10.0f; // parameter for weight function (used e.g. in PM_COST) CUDA
border_value = -1; // what value should pixel at extended border get (constant or replicate -1)
iterations = 8; // number of iterations
color_processing = true; // use color processing or not (otherwise just grayscale processing)
dispTol = 1.0; //PM Stereo: 1, PM Huber: 0.5
normTol = 0.1f; // 0.1 ... about 5.7 degrees
census_epsilon = 2.5f; //for census transform
self_similarity_n = 50; // number of pixels considered for self similarity
cam_scale = 1.0f; //used to rescale K in case of rescaled image size
num_img_processed = 1; //number of images that are processed as reference images
costThresh = 40.0f; // threshold to decide whether disparity/depth is valid or not
good_factor = 1.5f; // for cost aggregation/combination good: factor for truncation CUDA
n_best = 2; // CUDA
cost_comb = 1; // CUDA
viewSelection = false;
depthMin = 2.0f; // CUDA
depthMax = 20.0f; // CUDA
remove_black_background = false; // CUDA
storePlyFiles = true;
gt_compare = false;
gt_normal_compare = false;
//threshold for consistency check
depthThresh = 0.5f;
normalThresh = 0.52f;
//how many views need to be consistent? (for update: numConsistentThresh+1)
numConsistentThresh = 2;
saveTexture = true;
}
};