def initTestFromCfg(cfg_file):
'''
@brief: initialize all parameter from the cfg file.
'''
# Load cfg parameter from yaml file
cfg = utl.cfgFromFile(cfg_file)
# Fist load the dataset name
dataset = cfg.DATASET
# Set default values
use_mask = cfg[dataset].USE_MASK
use_perspective = cfg[dataset].USE_PERSPECTIVE
# Mask pattern ending
mask_file = cfg[dataset].MASK_FILE
# Img patterns ending
dot_ending = cfg[dataset].DOT_ENDING
# Test vars
test_names_file = cfg[dataset].TEST_LIST
# Im folder
im_folder = cfg[dataset].IM_FOLDER
# Results output foder
results_file = cfg[dataset].RESULTS_OUTPUT
# Resize image
resize_im = cfg[dataset].RESIZE
# Patch parameters
pw = cfg[dataset].PW # Patch with
sigmadots = cfg[dataset].SIG # Densities sigma
n_scales = cfg[dataset].N_SCALES # Escales to extract
perspective_path = cfg[dataset].PERSPECTIVE_MAP
is_colored = cfg[dataset].COLOR
return (dataset, use_mask, mask_file, test_names_file, im_folder,
dot_ending, pw, sigmadots, n_scales, perspective_path,
use_perspective, is_colored, results_file, resize_im)
def initTestFromCfg(cfg_file):
'''
@brief: initialize all parameter from the cfg file.
'''
# Load cfg parameter from yaml file
cfg = utl.cfgFromFile(cfg_file)
# Fist load the dataset name
dataset = cfg.DATASET
# Set default values
use_mask = cfg[dataset].USE_MASK
use_perspective = cfg[dataset].USE_PERSPECTIVE
# Mask pattern ending
mask_file = cfg[dataset].MASK_FILE
# Img patterns ending
dot_ending = cfg[dataset].DOT_ENDING
# Test vars
test_names_file = cfg[dataset].TEST_LIST
# Im folder
im_folder = cfg[dataset].IM_FOLDER
# Results output foder
results_file = cfg[dataset].RESULTS_OUTPUT
# Resize image
resize_im = cfg[dataset].RESIZE
# Patch parameters
pw = cfg[dataset].PW # Patch with
sigmadots = cfg[dataset].SIG # Densities sigma
n_scales = cfg[dataset].N_SCALES # Escales to extract
perspective_path = cfg[dataset].PERSPECTIVE_MAP
is_colored = cfg[dataset].COLOR
return (dataset, use_mask, mask_file, test_names_file, im_folder,
dot_ending, pw, sigmadots, n_scales, perspective_path,
use_perspective, is_colored, results_file, resize_im)
def initGenFeatFromCfg(cfg_file):
'''
@brief: initialize all parameter from the cfg file.
'''
# Load cfg parameter from yaml file
cfg = utl.cfgFromFile(cfg_file)
# Fist load the dataset name
dataset = cfg.DATASET
# Set values
im_folder = cfg[dataset].IM_FOLDER
im_list_file = cfg[dataset].TRAINVAL_LIST
output_file = cfg[dataset].TRAIN_FEAT
feature_file_path = cfg[dataset].TRAIN_FEAT_LIST
# Img patterns
dot_ending = cfg[dataset].DOT_ENDING
# Features extraction vars
pw_base = cfg[dataset].PW # Patch width
pw_norm = cfg[dataset].CNN_PW_IN # Patch width
pw_dens = cfg[dataset].CNN_PW_OUT # Patch width
sigmadots = cfg[dataset].SIG # Densities sigma
Nr = cfg[
dataset].NR # Number of patches extracted from the compute_mean images
n_scales = cfg[dataset].N_SCALES # Number of scales
# Others
split_size = cfg[dataset].SPLIT
do_flip = cfg[dataset].FLIP
use_perspective = cfg[dataset].USE_PERSPECTIVE
perspective_path = cfg[dataset].PERSPECTIVE_MAP
is_colored = cfg[dataset].COLOR
resize_im = cfg[dataset].RESIZE
return (dataset, im_folder, im_list_file, output_file, feature_file_path,
dot_ending, pw_base, pw_norm, pw_dens, sigmadots, Nr, n_scales,
split_size, do_flip, perspective_path, use_perspective, is_colored,
resize_im)
def init_parameters_from_config(cfg_file):
cfg = utl.cfgFromFile(cfg_file)
dataset = cfg.DATASET
use_mask = cfg[dataset].USE_MASK
use_perspective = cfg[dataset].USE_PERSPECTIVE
mask_file = cfg[dataset].MASK_FILE
dot_ending = cfg[dataset].DOT_ENDING
test_names_file = cfg[dataset].TEST_LIST
im_folder = cfg[dataset].IM_FOLDER
results_file = cfg[dataset].RESULTS_OUTPUT
resize_im = cfg[dataset].RESIZE
pw = cfg[dataset].PW
sigmadots = cfg[dataset].SIG
n_scales = cfg[dataset].N_SCALES
perspective_path = cfg[dataset].PERSPECTIVE_MAP
is_colored = cfg[dataset].COLOR
return (dataset, use_mask, mask_file, test_names_file, im_folder,
dot_ending, pw, sigmadots, n_scales, perspective_path,
use_perspective, is_colored, results_file, resize_im)
def initGenFeatFromCfg(cfg_file):
'''
@brief: initialize all parameter from the cfg file.
'''
# Load cfg parameter from yaml file
cfg = utl.cfgFromFile(cfg_file)
# Fist load the dataset name
dataset = cfg.DATASET
# Set values
im_folder = cfg[dataset].IM_FOLDER
im_list_file = cfg[dataset].TRAINVAL_LIST
output_file = cfg[dataset].TRAIN_FEAT
feature_file_path = cfg[dataset].TRAIN_FEAT_LIST
# Img patterns
dot_ending = cfg[dataset].DOT_ENDING
# Features extraction vars
pw_base = cfg[dataset].PW # Patch width
pw_norm = cfg[dataset].CNN_PW_IN # Patch width
pw_dens = cfg[dataset].CNN_PW_OUT # Patch width
sigmadots = cfg[dataset].SIG # Densities sigma
Nr = cfg[dataset].NR # Number of patches extracted from the compute_mean images
n_scales = cfg[dataset].N_SCALES # Number of scales
# Others
split_size = cfg[dataset].SPLIT
do_flip = cfg[dataset].FLIP
use_perspective = cfg[dataset].USE_PERSPECTIVE
perspective_path = cfg[dataset].PERSPECTIVE_MAP
is_colored = cfg[dataset].COLOR
resize_im = cfg[dataset].RESIZE
return (dataset, im_folder, im_list_file, output_file, feature_file_path,
dot_ending, pw_base, pw_norm, pw_dens, sigmadots, Nr, n_scales, split_size,
do_flip, perspective_path, use_perspective, is_colored, resize_im)