





'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       __SCREAMING_SNAKE_CASE	(						UpperCamelCase__			):



     '''simple docstring'''






     UpperCAmelCase__       :						Dict      =   42
     UpperCAmelCase__       :						str      =   42

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






     @torch.no_grad()
     def __call__(					self						,   lowerCamelCase = 1						,   lowerCamelCase = 2_000						,   lowerCamelCase = None						,   lowerCamelCase = "pil"						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       _snake_case     		=				self.unet.config.sample_size
       _snake_case     		=				(batch_size, 3, img_size, img_size)

       _snake_case     		=				self.unet

       _snake_case     		=				randn_tensor(_a						,   generator=_a	) * self.scheduler.init_noise_sigma
       _snake_case     		=				sample.to(self.device	)

       self.scheduler.set_timesteps(_a	)
       self.scheduler.set_sigmas(_a	)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
         _snake_case     		=				self.scheduler.sigmas[i] * torch.ones(shape[0]						,   device=self.device	)

         # correction step
         for _ in range(self.scheduler.config.correct_steps	):
           _snake_case     		=				self.unet(_a						,   _a	).sample
           _snake_case     		=				self.scheduler.step_correct(_a						,   _a						,   generator=_a	).prev_sample

         # prediction step
         _snake_case     		=				model(_a						,   _a	).sample
         _snake_case     		=				self.scheduler.step_pred(_a						,   _a						,   _a						,   generator=_a	)

         _snake_case     		=				output.prev_sample, output.prev_sample_mean

       _snake_case     		=				sample_mean.clamp(0						,   1	)
       _snake_case     		=				sample.cpu().permute(0						,   2						,   3						,   1	).numpy()
       if output_type == "pil":
         _snake_case     		=				self.numpy_to_pil(_a	)

       if not return_dict:
         return (sample,)

       return ImagePipelineOutput(images=_a	)









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

       if k in (0.04, 0.06):
         _snake_case     		=				k
         _snake_case     		=				window_size
       else:
         raise ValueError("invalid k value"	)

     def __str__(					self	):
       return str(self.k	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				cva.imread(lowerCamelCase						,   0	)
       _snake_case		,		_snake_case     		=				img.shape
       _snake_case     		=				[]
       _snake_case     		=				img.copy()
       _snake_case     		=				cva.cvtColor(lowerCamelCase						,   cva.COLOR_GRAY2RGB	)
       _snake_case		,		_snake_case     		=				np.gradient(lowerCamelCase	)
       _snake_case     		=				dx**2
       _snake_case     		=				dy**2
       _snake_case     		=				dx * dy
       _snake_case     		=				0.04
       _snake_case     		=				self.window_size // 2
       for y in range(lowerCamelCase						,   h - offset	):
         for x in range(lowerCamelCase						,   w - offset	):
           _snake_case     		=				ixx[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				iyy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				ixy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _snake_case     		=				det - k * (trace**2)
           # Can change the value
           if r > 0.5:
             corner_list.append([x, y, r]	)
             color_img.itemset((y, x, 0)						,   0	)
             color_img.itemset((y, x, 1)						,   0	)
             color_img.itemset((y, x, 2)						,   255	)
       return color_img, corner_list


if __name__ == "__main__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


