




"""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


A_						=				logging.get_logger(__name__)


if is_vision_available():
     import PIL






class 					lowercase(     UpperCAmelCase_ ):







 '''simple docstring'''





 lowercase__ 					=					["""pixel_values"""]


 def __init__(				self:		int,      a_:		Dict = True,      a_:		int = None,      a_:		Optional[int] = PILImageResampling.BICUBIC,      a_:		List[Any] = True,      a_:		Dict = None,      a_:		List[str] = True,      a_:		Any = 1 / 255,      a_:		int = True,      a_:		int = None,      a_:		Any = None,      a_:		Tuple = True,      **a_:		List[str],      ):




   '''simple docstring'''



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

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


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		List[Any],      a_:		Optional[Any] = PILImageResampling.BICUBIC,      a_:		List[str] = None,      **a_:		str,      ):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Optional[Any],      a_:		Union[str, Any],      a_:		Dict = None,      **a_:		List[str],      ):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  get_size_dict(__lowercase		)
   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(__lowercase,      size=(size["""height"""], size["""width"""]),      data_format=__lowercase,      **__lowercase		)


 def  UpperCamelCase_   (				self:		Dict,      a_:		Optional[Any],      a_:		List[str],      a_:		Union[str, Any] = None,      **a_:		Any,      ):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Any,      a_:		List[Any],      a_:		List[str],      a_:		Optional[Any],      a_:		Dict = None,      **a_:		str,      ):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Any,      a_:		Any,      a_:		Optional[int] = None,      a_:		Union[str, Any] = None,      a_:		str = None,      a_:		List[str] = None,      a_:		Union[str, Any] = None,      a_:		Optional[Any] = None,      a_:		Optional[int] = None,      a_:		Union[str, Any] = None,      a_:		Optional[Any] = None,      a_:		Optional[Any] = None,      a_:		Union[str, Any] = None,      a_:		Any = None,      a_:		Optional[Any] = ChannelDimension.FIRST,      **a_:		Union[str, Any],      ):




   '''simple docstring'''



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

   _snake_case							:		List[str]								=  make_list_of_images(__lowercase		)

   if not valid_images(__lowercase		):
     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:
     _snake_case							:		Optional[int]								=  [convert_to_rgb(__lowercase		) for image in images]

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

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

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

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

   if do_normalize:
     _snake_case							:		List[Any]								=  [self.normalize(image=__lowercase,      mean=__lowercase,      std=__lowercase		) for image in images]

   _snake_case							:		Optional[Any]								=  [to_channel_dimension_format(__lowercase,      __lowercase		) for image in images]

   _snake_case							:		List[str]								=  {'''pixel_values''': images}
   return BatchFeature(data=__lowercase,      tensor_type=__lowercase		)



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

import cva
import numpy as np

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



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		update_image_and_anno(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			filter_scale=SCREAMING_SNAKE_CASE  ,			)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		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(SCREAMING_SNAKE_CASE	)
        labels.append(SCREAMING_SNAKE_CASE	)
    return img_paths, labels



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







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

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







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


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





