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- # main imports
- import sys, os, argparse
- import numpy as np
- import time
- import random
- import math
- # image processing imports
- from PIL import Image
- from ipfml.processing import transform, segmentation
- from ipfml import utils
- # modules imports
- sys.path.insert(0, '') # trick to enable import of main folder module
- import custom_config as cfg
- from modules.utils import data as dt
- # 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_indexes = cfg.scenes_indices
- choices = cfg.normalization_choices
- 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
- image_scene_size = (800, 800)
- image_zone_size = (200, 200)
- possible_point_zone = tuple(np.asarray(image_scene_size) - np.array(image_zone_size))
- data_augmented_filename = cfg.data_augmented_filename
- def main():
-
- parser = argparse.ArgumentParser(description="Compute and prepare data augmentation of scenes")
- parser.add_argument('--output', type=str, help="output folder expected", required=True)
- parser.add_argument('--number', type=int, help="number of images for each sample of scene", required=True)
- parser.add_argument('--rotation', type=bool, help="", required=True, default=False)
- args = parser.parse_args()
- p_output = args.output
- p_number = args.number
- p_rotation = args.rotation
- scenes = os.listdir(path)
- # remove min max file from scenes folder
- scenes = [s for s in scenes if min_max_filename not in s]
- # getting image zone size and usefull information
- zone_width, zone_height = image_zone_size
- scene_width, scene_height = image_scene_size
- nb_x_parts = math.floor(scene_width / zone_width)
- output_dataset_filename_path = os.path.join(p_output, data_augmented_filename)
- # go ahead each scenes
- for folder_scene in scenes:
- scene_path = os.path.join(path, folder_scene)
- # build output scene path
- output_scene_path = os.path.join(p_output, folder_scene)
- if not os.path.exists(output_scene_path):
- os.makedirs(output_scene_path)
- # construct each zones folder name
- zones_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, seuil_expe_filename)) as f:
- zones_threshold.append(int(f.readline()))
- possible_x, possible_y = possible_point_zone
- # 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 id_img, img_path in enumerate(scene_images):
-
- current_img = Image.open(img_path)
- img = np.array(current_img)
- for generation in range(p_number):
- p_x, p_y = (random.randrange(possible_x), random.randrange(possible_y))
- # extract random zone into scene image
- extracted_img = img[p_y:(p_y + zone_height), p_x:(p_x + zone_width)]
-
- extracted_img.shape
- pil_extracted_img = Image.fromarray(extracted_img)
- # coordinate of specific zone, hence use threshold of zone
- if p_x % zone_width == 0 and p_y % zone_height == 0:
-
- zone_index = math.floor(p_x / zone_width) + math.floor(p_y / zone_height) * nb_x_parts
- final_threshold = int(zones_threshold[zone_index])
- else:
- # get zone identifiers of this new zones (from endpoints)
- p_top_left = (p_x, p_y)
- p_top_right = (p_x + zone_width, p_y)
- p_bottom_right = (p_x + zone_width, p_y + zone_height)
- p_bottom_left = (p_x, p_y + zone_height)
- points = [p_top_left, p_top_right, p_bottom_right, p_bottom_left]
- p_zones_indices = []
- # for each points get threshold information
- for p in points:
- x, y = p
- zone_index = math.floor(x / zone_width) + math.floor(y / zone_height) * nb_x_parts
- p_zones_indices.append(zone_index)
- p_thresholds = np.array(zones_threshold)[p_zones_indices]
- # get proportions of pixels of img into each zone
- overlaps = []
- p_x_max = p_x + zone_width
- p_y_max = p_y + zone_height
- for index, zone_index in enumerate(p_zones_indices):
- x_zone = (zone_index % nb_x_parts) * zone_width
- y_zone = (math.floor(zone_index / nb_x_parts)) * zone_height
- x_max_zone = x_zone + zone_width
- y_max_zone = y_zone + zone_height
- # computation of overlap
- # x_overlap = max(0, min(rect1.right, rect2.right) - max(rect1.left, rect2.left))
- # y_overlap = max(0, min(rect1.bottom, rect2.bottom) - max(rect1.top, rect2.top))
- x_overlap = max(0, min(x_max_zone, p_x_max) - max(x_zone, p_x))
- y_overlap = max(0, min(y_max_zone, p_y_max) - max(y_zone, p_y))
- overlapArea = x_overlap * y_overlap
- overlaps.append(overlapArea)
- overlapSum = sum(overlaps)
- proportions = [item / overlapSum for item in overlaps]
-
- final_threshold = 0
- for index, proportion in enumerate(proportions):
- final_threshold += proportion * p_thresholds[index]
-
- final_threshold = int(final_threshold)
- # save image into new scene folder
- current_image_postfix = dt.get_scene_image_postfix(img_path)
- # prepare output img name
- label_img = (int(current_image_postfix) < final_threshold)
- extracted_image_name = dt.get_scene_image_prefix(img_path) + '_' + str(generation) + '_x' + str(p_x) + '_y' + str(p_y) + '_label' + str(int(label_img))
- # if wished add of rotations images with same final threshold (increase data)
- # write new line into global .csv ('threshold', 'filepath')
- if p_rotation:
- # do rotations and save
- rotations = [0, 90, 180, 270]
- for rotation in rotations:
- rotated_img_name = extracted_image_name + 'rot' + str(rotation) + '_' + current_image_postfix + cfg.scene_image_extension
- rotated_img_path = os.path.join(output_scene_path, rotated_img_name)
- saved_rotated_img_path = os.path.join(folder_scene, rotated_img_name)
- rotated_img = pil_extracted_img.rotate(rotation)
- rotated_img.save(rotated_img_path)
- csv_line = folder_scene + ';' + str(final_threshold) + ';' + str(int(current_image_postfix)) + ';' + str(int(label_img)) + ';' + saved_rotated_img_path + '\n'
- with open(output_dataset_filename_path, 'a') as f:
- f.write(csv_line)
- else:
- extracted_image_name += current_image_postfix + cfg.scene_image_extension
- extracted_image_path = os.path.join(output_scene_path, extracted_image_name)
- saved_extracted_image_path = os.path.join(output_scene_path, extracted_image_name)
-
- pil_extracted_img.save(extracted_image_path)
- csv_line = folder_scene + ';' + str(final_threshold) + ';' + str(int(current_image_postfix)) + ';' + str(int(label_img)) + ';' + saved_extracted_image_path + '\n'
- with open(output_dataset_filename_path, 'a') as f:
- f.write(csv_line)
- print(folder_scene + " - " + "{0:.2f}".format(((id_img * p_number + generation) + 1) / (p_number * number_scene_image) * 100.) + "%")
- sys.stdout.write("\033[F")
- if __name__== "__main__":
- main()
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