



"""simple docstring"""


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    x - x % self.config.vae_scale_factor for x in (width, height)
												)  # resize to integer multiple of vae_scale_factor
												UpperCAmelCase			 =					image.resize((width, height)  ,    resample=PIL_INTERPOLATION[self.config.resample]   )
												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		if self.config.do_convert_rgb:
																								UpperCAmelCase			 =					[self.convert_to_rgb(lowercase_   ) for i in image]
																		if self.config.do_resize:
																								UpperCAmelCase			 =					[self.resize(lowercase_  ,    lowercase_  ,    lowercase_   ) for i in image]
																		UpperCAmelCase			 =					self.pil_to_numpy(lowercase_   )  # to np
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )  # to pt

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape
																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

												elif isinstance(image[0]  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape

																		# don't need any preprocess if the image is latents
																		if channel == 4:
																								return image

																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

        # expected range [0,1], normalize to [-1,1]
												UpperCAmelCase			 =					self.config.do_normalize
												if image.min() < 0:
																		warnings.warn(
																		    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																		    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												if output_type == "pt":
																		return image

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		if image.shape[-1] == 6:
																								UpperCAmelCase			 =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]  ,    axis=0   )
																		else:
																								UpperCAmelCase			 =					image[:, :, :, 3:]
																		return image[:, :, :, :3], image_depth

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   ), self.numpy_to_depth(lowercase_   )
												else:
																		raise Exception(f"""This type {output_type} is not supported"""   )







'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)