

'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__lowerCamelCase						    =     (720, 1280)  # Height, Width
__lowerCamelCase						    =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase						    =     1 / 100
__lowerCamelCase						    =     ''''''
__lowerCamelCase						    =     ''''''
__lowerCamelCase						    =     ''''''
__lowerCamelCase						    =     250
def  UpperCAmelCase__      ( )     ->     None:
       A_   ,     A_		   = get_dataset(__lowerCamelCase,    __lowerCamelCase			)
       for index in range(__lowerCamelCase			):
              A_		   = random.sample(range(len(__lowerCamelCase			)			),    4			)
              A_   ,     A_   ,     A_		   = update_image_and_anno(
                  __lowerCamelCase,    __lowerCamelCase,    __lowerCamelCase,    __lowerCamelCase,    __lowerCamelCase,    filter_scale=__lowerCamelCase,    )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A_		   = random_chars(32			)
              A_		   = path.split(os.sep			)[-1].rsplit(""".""",    1			)[0]
              A_		   = F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
              cva.imwrite(F'''{file_root}.jpg''',    __lowerCamelCase,    [cva.IMWRITE_JPEG_QUALITY, 85]			)
              print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''			)
              A_		   = []
              for anno in new_annos:
                     A_		   = anno[3] - anno[1]
                     A_		   = anno[4] - anno[2]
                     A_		   = anno[1] + width / 2
                     A_		   = anno[2] + height / 2
                     A_		   = F'''{anno[0]} {x_center} {y_center} {width} {height}'''
                     annos_list.append(__lowerCamelCase			)
              with open(F'''{file_root}.txt''',    """w"""			) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list			)			)
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     tuple[list, list]:
       A_		   = []
       A_		   = []
       for label_file in glob.glob(os.path.join(__lowerCamelCase,    """*.txt"""			)			):
              A_		   = label_file.split(os.sep			)[-1].rsplit(""".""",    1			)[0]
              with open(__lowerCamelCase			) as in_file:
                     A_		   = in_file.readlines()
              A_		   = os.path.join(__lowerCamelCase,    F'''{label_name}.jpg'''			)

              A_		   = []
              for obj_list in obj_lists:
                     A_		   = obj_list.rstrip("""\n"""			).split(""" """			)
                     A_		   = float(obj[1]			) - float(obj[3]			) / 2
                     A_		   = float(obj[2]			) - float(obj[4]			) / 2
                     A_		   = float(obj[1]			) + float(obj[3]			) / 2
                     A_		   = float(obj[2]			) + float(obj[4]			) / 2

                     boxes.append([int(obj[0]			), xmin, ymin, xmax, ymax]			)
              if not boxes:
                     continue
              img_paths.append(__lowerCamelCase			)
              labels.append(__lowerCamelCase			)
       return img_paths, labels
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = 0.0,    )     ->     tuple[list, list, str]:
       A_		   = np.zeros([output_size[0], output_size[1], 3],    dtype=np.uinta			)
       A_		   = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A_		   = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A_		   = int(scale_x * output_size[1]			)
       A_		   = int(scale_y * output_size[0]			)

       A_		   = []
       A_		   = []
       for i, index in enumerate(__lowerCamelCase			):
              A_		   = all_img_list[index]
              path_list.append(__lowerCamelCase			)
              A_		   = all_annos[index]
              A_		   = cva.imread(__lowerCamelCase			)
              if i == 0:  # top-left
                     A_		   = cva.resize(__lowerCamelCase,    (divid_point_x, divid_point_y)			)
                     A_		   = img
                     for bbox in img_annos:
                            A_		   = bbox[1] * scale_x
                            A_		   = bbox[2] * scale_y
                            A_		   = bbox[3] * scale_x
                            A_		   = bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]			)
              elif i == 1:  # top-right
                     A_		   = cva.resize(__lowerCamelCase,    (output_size[1] - divid_point_x, divid_point_y)			)
                     A_		   = img
                     for bbox in img_annos:
                            A_		   = scale_x + bbox[1] * (1 - scale_x)
                            A_		   = bbox[2] * scale_y
                            A_		   = scale_x + bbox[3] * (1 - scale_x)
                            A_		   = bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]			)
              elif i == 2:  # bottom-left
                     A_		   = cva.resize(__lowerCamelCase,    (divid_point_x, output_size[0] - divid_point_y)			)
                     A_		   = img
                     for bbox in img_annos:
                            A_		   = bbox[1] * scale_x
                            A_		   = scale_y + bbox[2] * (1 - scale_y)
                            A_		   = bbox[3] * scale_x
                            A_		   = scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]			)
              else:  # bottom-right
                     A_		   = cva.resize(
                         __lowerCamelCase,    (output_size[1] - divid_point_x, output_size[0] - divid_point_y)			)
                     A_		   = img
                     for bbox in img_annos:
                            A_		   = scale_x + bbox[1] * (1 - scale_x)
                            A_		   = scale_y + bbox[2] * (1 - scale_y)
                            A_		   = scale_x + bbox[3] * (1 - scale_x)
                            A_		   = scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]			)

              # Remove bounding box small than scale of filter
       if filter_scale > 0:
              A_		   = [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       assert number_char > 1, "The number of character should greater than 1"
       A_		   = ascii_lowercase + digits
       return "".join(random.choice(__lowerCamelCase			) for _ in range(__lowerCamelCase			)			)


if __name__ == "__main__":
      main()
      print('''DONE ✅''')



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 10_00			)     ->     int:
       return sum(2 * a * ((a - 1) // 2) for a in range(3,    n + 1			)			)


if __name__ == "__main__":
      print(solution())
