


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]							  =							1_80







def   lowerCamelCase(      a__		,a__):
				_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(a__) + np.square(a__)) / (2 * np.square(a__)))
				)
				return g







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =np.zeros(a__)

				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   lowerCamelCase(      a__		,a__		,a__		,a__		,a__		,a__):
				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   lowerCamelCase(      a__		,a__		,a__		,a__):
				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   lowerCamelCase(      a__		,a__=15		,a__=30		,a__=128		,a__=255):
				# gaussian_filter
				_SCREAMING_SNAKE_CASE	     =img_convolve(a__		,gen_gaussian_kernel(9		,sigma=1.4))
				# get the gradient and degree by sobel_filter
				_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =sobel_filter(a__)
				_SCREAMING_SNAKE_CASE	     =PI + np.radadeg(a__)

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

				detect_high_low_threshold(
				    image.shape		,a__		,a__		,a__		,a__		,a__)

				track_edge(image.shape		,a__		,a__		,a__)

				return destination


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



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

										# set step values
										self.scheduler.set_timesteps(_a     )

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

										_SCREAMING_SNAKE_CASE	     =(image / 2 + 0.5).clamp(0 ,			1     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     ).numpy()
										if output_type == "pil":
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pil(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"