#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun 19 11:47:42 2019 @author: jbuisine """ # main imports import sys, os, argparse import numpy as np # images processing imports from PIL import Image from ipfml.processing.segmentation import divide_in_blocks # modules imports sys.path.insert(0, '') # trick to enable import of main folder module import custom_config as cfg from modules.utils.data import get_scene_image_quality from modules.classes.Transformation import Transformation # getting configuration information zone_folder = cfg.zone_folder min_max_filename = cfg.min_max_filename_extension # define all scenes values scenes_list = cfg.scenes_names scenes_indices = cfg.scenes_indices path = cfg.dataset_path zones = cfg.zones_indices seuil_expe_filename = cfg.seuil_expe_filename features_choices = cfg.features_choices_labels output_data_folder = cfg.output_data_folder generic_output_file_svd = '_random.csv' def generate_data(transformation, _scenes): """ @brief Method which generates all .csv files from scenes @return nothing """ scenes = os.listdir(path) # remove min max file from scenes folder scenes = [s for s in scenes if min_max_filename not in s] # go ahead each scenes for id_scene, folder_scene in enumerate(scenes): if folder_scene in _scenes: print(folder_scene) scene_path = os.path.join(path, folder_scene) # construct each zones folder name zones_folder = [] features_folder = [] zones_threshold = [] # get zones list info for index in zones: index_str = str(index) if len(index_str) < 2: index_str = "0" + index_str current_zone = "zone"+index_str zones_folder.append(current_zone) zone_path = os.path.join(scene_path, current_zone) with open(os.path.join(zone_path, cfg.seuil_expe_filename)) as f: zones_threshold.append(int(f.readline())) # custom path for feature feature_path = os.path.join(zone_path, transformation.getName()) if not os.path.exists(feature_path): os.makedirs(feature_path) # custom path for interval of reconstruction and feature feature_interval_path = os.path.join(zone_path, transformation.getTransformationPath()) features_folder.append(feature_interval_path) if not os.path.exists(feature_interval_path): os.makedirs(feature_interval_path) # create for each zone the labels folder labels = [cfg.not_noisy_folder, cfg.noisy_folder] for label in labels: label_folder = os.path.join(feature_interval_path, label) if not os.path.exists(label_folder): os.makedirs(label_folder) # get all images of folder scene_images = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img]) number_scene_image = len(scene_images) # for each images for id_img, img_path in enumerate(scene_images): current_img = Image.open(img_path) img_blocks = divide_in_blocks(current_img, cfg.sub_image_size) current_quality_index = int(get_scene_image_quality(img_path)) for id_block, block in enumerate(img_blocks): ########################## # Image computation part # ########################## # pass block to grey level output_block = transformation.getTransformedImage(block) output_block = np.array(output_block, 'uint8') # current output image output_block_img = Image.fromarray(output_block) label_path = features_folder[id_block] # get label folder for block if current_quality_index > zones_threshold[id_block]: label_path = os.path.join(label_path, cfg.not_noisy_folder) else: label_path = os.path.join(label_path, cfg.noisy_folder) # Data augmentation! rotations = [0, 90, 180, 270] img_flip_labels = ['original', 'horizontal', 'vertical', 'both'] horizontal_img = output_block_img.transpose(Image.FLIP_LEFT_RIGHT) vertical_img = output_block_img.transpose(Image.FLIP_TOP_BOTTOM) both_img = output_block_img.transpose(Image.TRANSPOSE) flip_images = [output_block_img, horizontal_img, vertical_img, both_img] # rotate and flip image to increase dataset size for id, flip in enumerate(flip_images): for rotation in rotations: rotated_output_img = flip.rotate(rotation) output_reconstructed_filename = img_path.split('/')[-1].replace('.png', '') + '_' + zones_folder[id_block] + cfg.post_image_name_separator output_reconstructed_filename = output_reconstructed_filename + img_flip_labels[id] + '_' + str(rotation) + '.png' output_reconstructed_path = os.path.join(label_path, output_reconstructed_filename) rotated_output_img.save(output_reconstructed_path) print(transformation.getName() + "_" + folder_scene + " - " + "{0:.2f}".format(((id_img + 1) / number_scene_image)* 100.) + "%") sys.stdout.write("\033[F") print('\n') print("%s_%s : end of data generation\n" % (transformation.getName(), transformation.getParam())) def main(): parser = argparse.ArgumentParser(description="Compute and prepare data of feature of all scenes using specific interval if necessary") parser.add_argument('--features', type=str, help="list of features choice in order to compute data", default='svd_reconstruction, ipca_reconstruction', required=True) parser.add_argument('--params', type=str, help="list of specific param for each feature choice (See README.md for further information in 3D mode)", default='100, 200 :: 50, 25', required=True) parser.add_argument('--size', type=str, help="specific size of image", default='100, 100', required=True) parser.add_argument('--scenes', type=str, help='List of scenes to use for training data') args = parser.parse_args() p_features = list(map(str.strip, args.features.split(','))) p_params = list(map(str.strip, args.params.split('::'))) p_size = args.size p_scenes = args.scenes.split(',') # getting scenes from indexes user selection scenes_selected = [] for scene_id in p_scenes: index = scenes_indices.index(scene_id.strip()) scenes_selected.append(scenes_list[index]) # list of transformations transformations = [] for id, feature in enumerate(p_features): if feature not in features_choices or feature == 'static': raise ValueError("Unknown feature, please select a correct feature (`static` excluded) : ", features_choices) transformations.append(Transformation(feature, p_params[id], p_size)) print("Scenes used", scenes_selected) # generate all or specific feature data for transformation in transformations: generate_data(transformation, scenes_selected) if __name__== "__main__": main()