


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

import cva
import numpy as np

# Parrameters
UpperCAmelCase_   :				Optional[int]						      =		(720, 1280)  # Height, Width
UpperCAmelCase_   :				int						      =		(0.4, 0.6)  # if height or width lower than this scale, drop it.
UpperCAmelCase_   :				int						      =		1 / 100
UpperCAmelCase_   :				Any						      =		''
UpperCAmelCase_   :				Any						      =		''
UpperCAmelCase_   :				Union[str, Any]						      =		''
UpperCAmelCase_   :				List[Any]						      =		250







def  SCREAMING_SNAKE_CASE_		(    )			->	None:

       """simple docstring"""

       a_      :	Any							=						get_dataset(__A						,					__A       )
       for index in range(__A       ):
              a_      :	Tuple							=						random.sample(range(len(__A       )       )						,					4       )
              a_      :	Optional[int]							=						update_image_and_anno(
                  __A						,					__A						,					__A						,					__A						,					__A						,					filter_scale=__A						,					)

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a_      :	Dict							=						random_chars(32       )
              a_      :	Any							=						path.split(os.sep       )[-1].rsplit('.'						,					1       )[0]
              a_      :	Tuple							=						F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(F"""{file_root}.jpg"""						,					__A						,					[cva.IMWRITE_JPEG_QUALITY, 85]       )
              print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""       )
              a_      :	Tuple							=						[]
              for anno in new_annos:
                     a_      :	Optional[Any]							=						anno[3] - anno[1]
                     a_      :	Optional[Any]							=						anno[4] - anno[2]
                     a_      :	List[str]							=						anno[1] + width / 2
                     a_      :	List[Any]							=						anno[2] + height / 2
                     a_      :	List[Any]							=						F"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__A       )
              with open(F"""{file_root}.txt"""						,					'w'       ) as outfile:
                     outfile.write('\n'.join(line for line in annos_list       )       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	tuple[list, list]:

       """simple docstring"""

       a_      :	Union[str, Any]							=						[]
       a_      :	str							=						[]
       for label_file in glob.glob(os.path.join(__A						,					'*.txt'       )       ):
              a_      :	Any							=						label_file.split(os.sep       )[-1].rsplit('.'						,					1       )[0]
              with open(__A       ) as in_file:
                     a_      :	Dict							=						in_file.readlines()
              a_      :	int							=						os.path.join(__A						,					F"""{label_name}.jpg"""       )

              a_      :	Dict							=						[]
              for obj_list in obj_lists:
                     a_      :	List[Any]							=						obj_list.rstrip('\n'       ).split(' '       )
                     a_      :	Optional[Any]							=						float(obj[1]       ) - float(obj[3]       ) / 2
                     a_      :	List[Any]							=						float(obj[2]       ) - float(obj[4]       ) / 2
                     a_      :	Dict							=						float(obj[1]       ) + float(obj[3]       ) / 2
                     a_      :	int							=						float(obj[2]       ) + float(obj[4]       ) / 2

                     boxes.append([int(obj[0]       ), xmin, ymin, xmax, ymax]       )
              if not boxes:
                     continue
              img_paths.append(__A       )
              labels.append(__A       )
       return img_paths, labels







def  SCREAMING_SNAKE_CASE_		(    __A				:    list						,					__A				:    list						,					__A				:    list[int]						,					__A				:    tuple[int, int]						,					__A				:    tuple[float, float]						,					__A				:    float = 0.0						,					)			->	tuple[list, list, str]:

       """simple docstring"""

       a_      :	Dict							=						np.zeros([output_size[0], output_size[1], 3]						,					dtype=np.uinta       )
       a_      :	List[str]							=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       a_      :	Dict							=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       a_      :	List[str]							=						int(scale_x * output_size[1]       )
       a_      :	Optional[int]							=						int(scale_y * output_size[0]       )

       a_      :	Dict							=						[]
       a_      :	int							=						[]
       for i, index in enumerate(__A       ):
              a_      :	Optional[int]							=						all_img_list[index]
              path_list.append(__A       )
              a_      :	Dict							=						all_annos[index]
              a_      :	List[Any]							=						cva.imread(__A       )
              if i == 0:  # top-left
                     a_      :	Union[str, Any]							=						cva.resize(__A						,					(divid_point_x, divid_point_y)       )
                     a_      :	Dict							=						img
                     for bbox in img_annos:
                            a_      :	Optional[Any]							=						bbox[1] * scale_x
                            a_      :	Optional[int]							=						bbox[2] * scale_y
                            a_      :	List[str]							=						bbox[3] * scale_x
                            a_      :	List[Any]							=						bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
              elif i == 1:  # top-right
                     a_      :	Tuple							=						cva.resize(__A						,					(output_size[1] - divid_point_x, divid_point_y)       )
                     a_      :	List[str]							=						img
                     for bbox in img_annos:
                            a_      :	List[Any]							=						scale_x + bbox[1] * (1 - scale_x)
                            a_      :	Dict							=						bbox[2] * scale_y
                            a_      :	int							=						scale_x + bbox[3] * (1 - scale_x)
                            a_      :	Optional[int]							=						bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
              elif i == 2:  # bottom-left
                     a_      :	Dict							=						cva.resize(__A						,					(divid_point_x, output_size[0] - divid_point_y)       )
                     a_      :	str							=						img
                     for bbox in img_annos:
                            a_      :	List[Any]							=						bbox[1] * scale_x
                            a_      :	Optional[Any]							=						scale_y + bbox[2] * (1 - scale_y)
                            a_      :	Optional[int]							=						bbox[3] * scale_x
                            a_      :	int							=						scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]       )
              else:  # bottom-right
                     a_      :	List[str]							=						cva.resize(
                         __A						,					(output_size[1] - divid_point_x, output_size[0] - divid_point_y)       )
                     a_      :	List[Any]							=						img
                     for bbox in img_annos:
                            a_      :	Optional[Any]							=						scale_x + bbox[1] * (1 - scale_x)
                            a_      :	List[str]							=						scale_y + bbox[2] * (1 - scale_y)
                            a_      :	Optional[int]							=						scale_x + bbox[3] * (1 - scale_x)
                            a_      :	Union[str, Any]							=						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_      :	Optional[Any]							=						[
                  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  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	str:

       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       a_      :	List[str]							=						ascii_lowercase + digits
       return "".join(random.choice(__A       ) for _ in range(__A       )       )


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






def  SCREAMING_SNAKE_CASE_		(    __A				:    list       )			->	list:

       """simple docstring"""

       if len(__A       ) <= 1:
              return [tuple(__A       )]

       a_      :	Optional[Any]							=						[]

       def generate(__A				:    int						,					__A				:    list       ):
              a_      :	str							=						[0] * n
              res.append(tuple(__A       )       )

              a_      :	Optional[int]							=						0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   a_							,	a_      :	Dict							=						arr[i], arr[0]
                            else:
                                   a_							,	a_      :	Union[str, Any]							=						arr[i], arr[c[i]]
                            res.append(tuple(__A       )       )
                            c[i] += 1
                            a_      :	Dict							=						0
                     else:
                            a_      :	Optional[Any]							=						0
                            i += 1

       generate(len(__A       )						,					__A       )
       return res


if __name__ == "__main__":
   UpperCAmelCase_   :				Union[str, Any]						      = input('Enter numbers separated by a comma:\n').strip()
   UpperCAmelCase_   :				Optional[Any]						      = [int(item) for item in user_input.split(',')]
   print(heaps(arr))


