






'''simple docstring'''



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

import cva
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						''
SCREAMING_SNAKE_CASE							=						1  # (0 is vertical, 1 is horizontal)


def 				lowercase_					(     )							->   None:



 """simple docstring"""




 lowercase	,		lowercase       : int						=get_dataset(__A       ,   __A      )
 print('''Processing...'''      )
 lowercase	,		lowercase	,		lowercase       : int						=update_image_and_anno(__A       ,   __A       ,   __A      )

 for index, image in enumerate(__A      ):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  lowercase       : Any						=random_chars(3_2      )
  lowercase       : Optional[int]						=paths[index].split(os.sep      )[-1].rsplit('''.'''       ,   1      )[0]
  lowercase       : Optional[Any]						=F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
  cva.imwrite(F'/{file_root}.jpg'       ,   __A       ,   [cva.IMWRITE_JPEG_QUALITY, 8_5]      )
  print(F'Success {index+1}/{len(__A      )} with {file_name}'      )
  lowercase       : Optional[Any]						=[]
  for anno in new_annos[index]:
   lowercase       : Tuple						=F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
   annos_list.append(__A      )
  with open(F'/{file_root}.txt'       ,   '''w'''      ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list      )      )


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



 """simple docstring"""




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

  lowercase       : Optional[Any]						=[]
  for obj_list in obj_lists:
   lowercase       : Dict						=obj_list.rstrip('''\n'''      ).split(''' '''      )
   boxes.append(
       [
           int(obj[0]      ),
           float(obj[1]      ),
           float(obj[2]      ),
           float(obj[3]      ),
           float(obj[4]      ),
       ]      )
  if not boxes:
   continue
  img_paths.append(__A      )
  labels.append(__A      )
 return img_paths, labels


def 				lowercase_					(     __A			:							list       ,   __A			:							list       ,   __A			:							int = 1      )							->   tuple[list, list, list]:



 """simple docstring"""




 lowercase       : Optional[int]						=[]
 lowercase       : Optional[Any]						=[]
 lowercase       : List[str]						=[]
 for idx in range(len(__A      )      ):
  lowercase       : Optional[Any]						=[]
  lowercase       : Optional[Any]						=img_list[idx]
  path_list.append(__A      )
  lowercase       : Any						=anno_list[idx]
  lowercase       : Any						=cva.imread(__A      )
  if flip_type == 1:
   lowercase       : Dict						=cva.flip(__A       ,   __A      )
   for bbox in img_annos:
    lowercase       : Any						=1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]      )
  elif flip_type == 0:
   lowercase       : Dict						=cva.flip(__A       ,   __A      )
   for bbox in img_annos:
    lowercase       : Optional[int]						=1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]      )
  new_annos_lists.append(__A      )
  new_imgs_list.append(__A      )
 return new_imgs_list, new_annos_lists, path_list


def 				lowercase_					(     __A			:							int = 3_2      )							->   str:



 """simple docstring"""




 assert number_char > 1, "The number of character should greater than 1"
 lowercase       : Tuple						=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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Tuple      )							->   List[List[ImageInput]]:



 """simple docstring"""




 if isinstance(__A       ,   (list, tuple)      ) and isinstance(videos[0]       ,   (list, tuple)      ) and is_valid_image(videos[0][0]      ):
  return videos

 elif isinstance(__A       ,   (list, tuple)      ) and is_valid_image(videos[0]      ):
  return [videos]

 elif is_valid_image(__A      ):
  return [[videos]]

 raise ValueError(F'Could not make batched video from {videos}'      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''pixel_values''']






      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Union[int, float] = 1 / 255  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase       : Tuple						=size if size is not None else {'''shortest_edge''': 224}
       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : List[str]						=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase       : List[Any]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

       lowercase       : Tuple						=do_resize
       lowercase       : Any						=size
       lowercase       : Optional[Any]						=do_center_crop
       lowercase       : str						=crop_size
       lowercase       : Any						=resample
       lowercase       : List[Any]						=do_rescale
       lowercase       : Dict						=rescale_factor
       lowercase       : List[str]						=do_normalize
       lowercase       : Optional[int]						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase       : List[Any]						=image_std if image_std is not None else IMAGENET_STANDARD_STD






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		List[str]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       if "shortest_edge" in size:
        lowercase       : int						=get_resize_output_image_size(UpperCAmelCase  ,   size['''shortest_edge''']  ,   default_to_square=UpperCAmelCase		)
       elif "height" in size and "width" in size:
        lowercase       : str						=(size['''height'''], size['''width'''])
       else:
        raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
       return resize(UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Optional[Any]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : List[str]						=get_size_dict(UpperCAmelCase		)
       if "height" not in size or "width" not in size:
        raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
       return center_crop(UpperCAmelCase  ,   size=(size['''height'''], size['''width'''])  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[int, float]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	List[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		int  ,   )	->	np.ndarray:


       '''simple docstring'''

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






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[ChannelDimension] = ChannelDimension.FIRST  ,   )	->	np.ndarray:


       '''simple docstring'''

       if do_resize and size is None or resample is None:
        raise ValueError('''Size and resample 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.'''		)

       # All transformations expect numpy arrays.
       lowercase       : Dict						=to_numpy_array(UpperCAmelCase		)

       if do_resize:
        lowercase       : Union[str, Any]						=self.resize(image=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase		)

       if do_center_crop:
        lowercase       : Optional[Any]						=self.center_crop(UpperCAmelCase  ,   size=UpperCAmelCase		)

       if do_rescale:
        lowercase       : Dict						=self.rescale(image=UpperCAmelCase  ,   scale=UpperCAmelCase		)

       if do_normalize:
        lowercase       : int						=self.normalize(image=UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase		)

       lowercase       : Optional[Any]						=to_channel_dimension_format(UpperCAmelCase  ,   UpperCAmelCase		)
       return image






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[str, TensorType]] = None  ,   UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST  ,   **UpperCAmelCase      :		Optional[int]  ,   )	->	PIL.Image.Image:


       '''simple docstring'''

       lowercase       : Any						=do_resize if do_resize is not None else self.do_resize
       lowercase       : Union[str, Any]						=resample if resample is not None else self.resample
       lowercase       : Tuple						=do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase       : List[Any]						=do_rescale if do_rescale is not None else self.do_rescale
       lowercase       : str						=rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase       : List[str]						=do_normalize if do_normalize is not None else self.do_normalize
       lowercase       : List[str]						=image_mean if image_mean is not None else self.image_mean
       lowercase       : Optional[int]						=image_std if image_std is not None else self.image_std

       lowercase       : Optional[Any]						=size if size is not None else self.size
       lowercase       : Any						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : Union[str, Any]						=crop_size if crop_size is not None else self.crop_size
       lowercase       : Optional[int]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

       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.'''		)

       lowercase       : List[str]						=make_batched(UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[
           [
               self._preprocess_image(
                   image=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   do_center_crop=UpperCAmelCase  ,   crop_size=UpperCAmelCase  ,   do_rescale=UpperCAmelCase  ,   rescale_factor=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   image_mean=UpperCAmelCase  ,   image_std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   )
               for img in video
           ]
           for video in videos
       ]

       lowercase       : Dict						={'''pixel_values''': videos}
       return BatchFeature(data=UpperCAmelCase  ,   tensor_type=UpperCAmelCase		)



