




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

import cva
import numpy as np

# Parrameters
lowerCamelCase			:			Optional[Any]								=       (7_2_0, 1_2_8_0)  # Height, Width
lowerCamelCase			:			Any								=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase			:			Union[str, Any]								=       1 / 1_0_0
lowerCamelCase			:			str								=       ''
lowerCamelCase			:			Union[str, Any]								=       ''
lowerCamelCase			:			str								=       ''
lowerCamelCase			:			Union[str, Any]								=       2_5_0




def 		SCREAMING_SNAKE_CASE__   (       )    ->  None:
   snake_case :							Tuple           =			get_dataset(snake_case_		,snake_case_	)
   for index in range(snake_case_	):
      snake_case :							List[Any]           =			random.sample(range(len(snake_case_	)	)		,4	)
      snake_case :							Tuple           =			update_image_and_anno(
          snake_case_		,snake_case_		,snake_case_		,snake_case_		,snake_case_		,filter_scale=snake_case_		,)

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




def 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase	)    ->  tuple[list, list]:
   snake_case :							List[Any]           =			[]
   snake_case :							int           =			[]
   for label_file in glob.glob(os.path.join(snake_case_		,"""*.txt"""	)	):
      snake_case :							Any           =			label_file.split(os.sep	)[-1].rsplit("""."""		,1	)[0]
      with open(snake_case_	) as in_file:
         snake_case :							int           =			in_file.readlines()
      snake_case :							Any           =			os.path.join(snake_case_		,f"""{label_name}.jpg"""	)

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

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




def 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase = 0.0		,)    ->  tuple[list, list, str]:
   snake_case :							List[str]           =			np.zeros([output_size[0], output_size[1], 3]		,dtype=np.uinta	)
   snake_case :							str           =			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   snake_case :							List[Any]           =			scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   snake_case :							int           =			int(scale_x * output_size[1]	)
   snake_case :							Any           =			int(scale_y * output_size[0]	)

   snake_case :							Tuple           =			[]
   snake_case :							str           =			[]
   for i, index in enumerate(snake_case_	):
      snake_case :							Optional[int]           =			all_img_list[index]
      path_list.append(snake_case_	)
      snake_case :							List[Any]           =			all_annos[index]
      snake_case :							str           =			cva.imread(snake_case_	)
      if i == 0:  # top-left
         snake_case :							List[str]           =			cva.resize(snake_case_		,(divid_point_x, divid_point_y)	)
         snake_case :							Union[str, Any]           =			img
         for bbox in img_annos:
            snake_case :							Tuple           =			bbox[1] * scale_x
            snake_case :							Tuple           =			bbox[2] * scale_y
            snake_case :							List[str]           =			bbox[3] * scale_x
            snake_case :							Optional[int]           =			bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
      elif i == 1:  # top-right
         snake_case :							str           =			cva.resize(snake_case_		,(output_size[1] - divid_point_x, divid_point_y)	)
         snake_case :							Tuple           =			img
         for bbox in img_annos:
            snake_case :							List[Any]           =			scale_x + bbox[1] * (1 - scale_x)
            snake_case :							Tuple           =			bbox[2] * scale_y
            snake_case :							Optional[Any]           =			scale_x + bbox[3] * (1 - scale_x)
            snake_case :							str           =			bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
      elif i == 2:  # bottom-left
         snake_case :							Optional[Any]           =			cva.resize(snake_case_		,(divid_point_x, output_size[0] - divid_point_y)	)
         snake_case :							Optional[int]           =			img
         for bbox in img_annos:
            snake_case :							int           =			bbox[1] * scale_x
            snake_case :							int           =			scale_y + bbox[2] * (1 - scale_y)
            snake_case :							List[str]           =			bbox[3] * scale_x
            snake_case :							List[str]           =			scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
      else:  # bottom-right
         snake_case :							List[Any]           =			cva.resize(
             snake_case_		,(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
         snake_case :							List[str]           =			img
         for bbox in img_annos:
            snake_case :							Dict           =			scale_x + bbox[1] * (1 - scale_x)
            snake_case :							Dict           =			scale_y + bbox[2] * (1 - scale_y)
            snake_case :							int           =			scale_x + bbox[3] * (1 - scale_x)
            snake_case :							List[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:
      snake_case :							List[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__   (       lowercase	)    ->  str:
   assert number_char > 1, "The number of character should greater than 1"
   snake_case :							Optional[Any]           =			ascii_lowercase + digits
   return "".join(random.choice(snake_case_	) for _ in range(snake_case_	)	)


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












"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->float:



    '''simple docstring'''







    __A       :		Tuple							=	0
    while len(snake_case_		) > 1:
        __A       :		List[Any]							=	0
        # Consider two files with minimum cost to be merged
        for _ in range(2		):
            __A       :		Dict							=	files.index(min(snake_case_		)		)
            temp += files[min_index]
            files.pop(snake_case_		)
        files.append(snake_case_		)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
       import doctest

       doctest.testmod()




