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- #!/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
- config_filename = cfg.config_filename
- zone_folder = cfg.zone_folder
- min_max_filename = cfg.min_max_filename_extension
- # define all scenes values
- scenes_list = cfg.scenes_names
- scenes_indexes = 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):
- """
- @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):
- print(folder_scene)
- scene_path = os.path.join(path, folder_scene)
- config_file_path = os.path.join(scene_path, config_filename)
- with open(config_file_path, "r") as config_file:
- last_image_name = config_file.readline().strip()
- prefix_image_name = config_file.readline().strip()
- start_index_image = config_file.readline().strip()
- end_index_image = config_file.readline().strip()
- step_counter = int(config_file.readline().strip())
- # 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.keras_img_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)
- args = parser.parse_args()
- p_features = list(map(str.strip, args.features.split(',')))
- p_params = list(map(str.strip, args.params.split('::')))
- transformations = []
- for id, feature in enumerate(p_features):
- if feature not in features_choices:
- raise ValueError("Unknown feature, please select a correct feature : ", features_choices)
- transformations.append(Transformation(feature, p_params[id]))
- # generate all or specific feature data
- for transformation in transformations:
- generate_data(transformation)
- if __name__== "__main__":
- main()
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