



"""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        snake_case_  (      ):


       '''simple docstring'''





       _lowerCamelCase			,    _lowerCamelCase						:				Optional[Any]    			= get_dataset(A__,				A__						)
       for index in range(A__						):
              _lowerCamelCase						:				Optional[Any]    			= random.sample(range(len(A__						)						),				4						)
              _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				Any    			= update_image_and_anno(
                  A__,				A__,				A__,				A__,				A__,				filter_scale=A__,				)

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _lowerCamelCase						:				str    			= random_chars(32						)
              _lowerCamelCase						:				Union[str, Any]    			= path.split(os.sep						)[-1].rsplit('''.''',				1						)[0]
              _lowerCamelCase						:				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}'''						)
              _lowerCamelCase						:				Optional[Any]    			= []
              for anno in new_annos:
                     _lowerCamelCase						:				Optional[int]    			= anno[3] - anno[1]
                     _lowerCamelCase						:				List[str]    			= anno[4] - anno[2]
                     _lowerCamelCase						:				int    			= anno[1] + width / 2
                     _lowerCamelCase						:				Dict    			= anno[2] + height / 2
                     _lowerCamelCase						:				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        snake_case_  (      A_					:		List[Any],				A_					:		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				str    			= []
       _lowerCamelCase						:				Optional[Any]    			= []
       for label_file in glob.glob(os.path.join(A__,				'''*.txt'''						)						):
              _lowerCamelCase						:				List[str]    			= label_file.split(os.sep						)[-1].rsplit('''.''',				1						)[0]
              with open(A__						) as in_file:
                     _lowerCamelCase						:				List[str]    			= in_file.readlines()
              _lowerCamelCase						:				Optional[Any]    			= os.path.join(A__,				F'''{label_name}.jpg'''						)

              _lowerCamelCase						:				Tuple    			= []
              for obj_list in obj_lists:
                     _lowerCamelCase						:				int    			= obj_list.rstrip('''\n'''						).split(''' '''						)
                     _lowerCamelCase						:				Optional[int]    			= float(obj[1]						) - float(obj[3]						) / 2
                     _lowerCamelCase						:				str    			= float(obj[2]						) - float(obj[4]						) / 2
                     _lowerCamelCase						:				Dict    			= float(obj[1]						) + float(obj[3]						) / 2
                     _lowerCamelCase						:				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        snake_case_  (      A_					:		Optional[Any],				A_					:		Dict,				A_					:		Optional[int],				A_					:		Optional[Any],				A_					:		Tuple,				A_					:		int = 0.0,				):


       '''simple docstring'''





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

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


       '''simple docstring'''





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


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







'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

