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)