


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


_UpperCAmelCase      :	Any  =logging.get_logger(__name__)







class 					snake_case__(				lowerCamelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Dict       =				["""pixel_values"""]




						def __init__(					self ,    __lowercase = True ,    __lowercase = None ,    __lowercase = PIL.Image.NEAREST ,    __lowercase = False ,    __lowercase = None ,    __lowercase = 1 / 2_5_5 ,    __lowercase = False ,    __lowercase = True ,    __lowercase = True ,    __lowercase = None ,    __lowercase = None ,    __lowercase = True ,    **__lowercase ,    )       ->     None:
										super().__init__(**__lowercase				)
										lowerCAmelCase_			:   Any     					=					size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
										lowerCAmelCase_			:   int     					=					get_size_dict(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
										lowerCAmelCase_			:   Optional[Any]     					=					get_size_dict(__lowercase ,    param_name='''crop_size'''				)

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




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = PIL.Image.NEAREST ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										lowerCAmelCase_			:   Optional[Any]     					=					get_size_dict(__lowercase				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""				)
										return resize(
										    __lowercase ,    size=(size['''height'''], size['''width''']) ,    resample=__lowercase ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										lowerCAmelCase_			:   Union[str, Any]     					=					get_size_dict(__lowercase				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""				)
										return center_crop(__lowercase ,    size=(size['''height'''], size['''width''']) ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = True ,    __lowercase = None ,    **__lowercase ,    )       ->     Any:





										if offset:
														lowerCAmelCase_			:   List[str]     					=					(image - 1_27.5) * scale
														if data_format is not None:
																		lowerCAmelCase_			:   str     					=					to_channel_dimension_format(__lowercase ,    __lowercase				)
														lowerCAmelCase_			:   Optional[int]     					=					rescaled_image.astype(np.floataa				)
										else:
														lowerCAmelCase_			:   str     					=					rescale(__lowercase ,    scale=__lowercase ,    data_format=__lowercase ,    **__lowercase				)
										return rescaled_image




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase = None ,    **__lowercase ,    )       ->     np.ndarray:
										return normalize(__lowercase ,    mean=__lowercase ,    std=__lowercase ,    data_format=__lowercase ,    **__lowercase				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase = None ,    __lowercase = None ,    __lowercase=None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = None ,    __lowercase = ChannelDimension.FIRST ,    **__lowercase ,    )       ->     PIL.Image.Image:
										lowerCAmelCase_			:   Any     					=					do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_			:   Optional[Any]     					=					resample if resample is not None else self.resample
										lowerCAmelCase_			:   Optional[Any]     					=					do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCAmelCase_			:   Optional[int]     					=					do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_			:   Tuple     					=					rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_			:   Dict     					=					rescale_offset if rescale_offset is not None else self.rescale_offset
										lowerCAmelCase_			:   Union[str, Any]     					=					do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_			:   Tuple     					=					image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_			:   Dict     					=					image_std if image_std is not None else self.image_std
										lowerCAmelCase_			:   Union[str, Any]     					=					include_top if include_top is not None else self.include_top

										lowerCAmelCase_			:   Any     					=					size if size is not None else self.size
										lowerCAmelCase_			:   List[str]     					=					get_size_dict(__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					crop_size if crop_size is not None else self.crop_size
										lowerCAmelCase_			:   Dict     					=					get_size_dict(__lowercase ,    param_name='''crop_size'''				)

										lowerCAmelCase_			:   Optional[Any]     					=					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 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.
										lowerCAmelCase_			:   Union[str, Any]     					=					[to_numpy_array(__lowercase				) for image in images]

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

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

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

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

										if include_top:
														lowerCAmelCase_			:   Optional[Any]     					=					[self.normalize(image=__lowercase ,    mean=[0, 0, 0] ,    std=__lowercase				) for image in images]

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

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


"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
