text: # 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")
  
          print('\n', folder_scene, 'done...')
  
  
  if __name__== "__main__":
      main()
  