


'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case							   =  (720, 1280)  # Height, Width
__snake_case							   =  (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case							   =  1 / 100
__snake_case							   =  ''''''
__snake_case							   =  ''''''
__snake_case							   =  ''''''
__snake_case							   =  250


def     a   (    )						->			int:



					'''simple docstring'''






					UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				get_dataset(__a				,      __a     )
					for index in range(__a     ):
										UpperCamelCase__     :Union[str, Any]							      =				random.sample(range(len(__a     )     )				,      4     )
										UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				update_image_and_anno(
										    __a				,      __a				,      __a				,      __a				,      __a				,      filter_scale=__a				,      )

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



					'''simple docstring'''






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

										UpperCamelCase__     :List[str]							      =				[]
										for obj_list in obj_lists:
															UpperCamelCase__     :Any							      =				obj_list.rstrip('''\n'''     ).split(''' '''     )
															UpperCamelCase__     :Optional[int]							      =				float(obj[1]     ) - float(obj[3]     ) / 2
															UpperCamelCase__     :str							      =				float(obj[2]     ) - float(obj[4]     ) / 2
															UpperCamelCase__     :Any							      =				float(obj[1]     ) + float(obj[3]     ) / 2
															UpperCamelCase__     :List[str]							      =				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     a   (    __a				,      __a				,      __a				,      __a				,      __a				,      __a = 0.0				,      )						->			Tuple:



					'''simple docstring'''






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

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



					'''simple docstring'''






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


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



'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__snake_case							   =  logging.get_logger(__name__)


if is_vision_available():
		import PIL
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




		def __init__(	self					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = True					,     UpperCamelCase_ = 1 / 255					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = True					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				size if size is not None else {'''shortest_edge''': 224}
							UpperCamelCase__     :Optional[int]							      =				get_size_dict(UpperCamelCase_					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :str							      =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							UpperCamelCase__     :Union[str, Any]							      =				get_size_dict(UpperCamelCase_					,     default_to_square=UpperCamelCase_					,     param_name='''crop_size'''							)

							UpperCamelCase__     :Any							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :Any							      =				resample
							UpperCamelCase__     :Optional[Any]							      =				do_center_crop
							UpperCamelCase__     :List[str]							      =				crop_size
							UpperCamelCase__     :Optional[int]							      =				do_rescale
							UpperCamelCase__     :Optional[Any]							      =				rescale_factor
							UpperCamelCase__     :Any							      =				do_normalize
							UpperCamelCase__     :int							      =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else OPENAI_CLIP_STD
							UpperCamelCase__     :Union[str, Any]							      =				do_convert_rgb




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				get_size_dict(UpperCamelCase_					,     default_to_square=UpperCamelCase_							)
							if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
							UpperCamelCase__     :str							      =				get_resize_output_image_size(UpperCamelCase_					,     size=size['''shortest_edge''']					,     default_to_square=UpperCamelCase_							)
							return resize(UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				get_size_dict(UpperCamelCase_							)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
							return center_crop(UpperCamelCase_					,     size=(size['''height'''], size['''width'''])					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return rescale(UpperCamelCase_					,     scale=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return normalize(UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = ChannelDimension.FIRST					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Optional[Any]							      =				size if size is not None else self.size
							UpperCamelCase__     :Optional[int]							      =				get_size_dict(UpperCamelCase_					,     param_name='''size'''					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :int							      =				do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase__     :Any							      =				crop_size if crop_size is not None else self.crop_size
							UpperCamelCase__     :Any							      =				get_size_dict(UpperCamelCase_					,     param_name='''crop_size'''					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :List[Any]							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[int]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Tuple							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :str							      =				image_std if image_std is not None else self.image_std
							UpperCamelCase__     :Optional[Any]							      =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCamelCase__     :str							      =				make_list_of_images(UpperCamelCase_							)

							if not valid_images(UpperCamelCase_							):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

							if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''							)

							if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

							if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
												UpperCamelCase__     :Any							      =				[convert_to_rgb(UpperCamelCase_							) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase__     :str							      =				[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__     :Optional[Any]							      =				[self.resize(image=UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_							) for image in images]

							if do_center_crop:
												UpperCamelCase__     :Dict							      =				[self.center_crop(image=UpperCamelCase_					,     size=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__     :Optional[Any]							      =				[self.rescale(image=UpperCamelCase_					,     scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__     :Tuple							      =				[self.normalize(image=UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :Optional[Any]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)