






import cva
import numpy as np



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Any							,		__A					:  float							,		__A					:  int	):

						if k in (0.0_4, 0.0_6):
							snake_case__			:				Optional[int]       =  k
							snake_case__			:				Union[str, Any]       =  window_size
						else:
							raise ValueError("invalid k value"	)



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






					def 	_lowercase					( self					:  int							,		__A					:  str	):
						snake_case__			:				List[Any]       =  cva.imread(__lowerCamelCase							,		0	)
						snake_case__			:				Union[str, Any]       =  img.shape
						snake_case__			:				list[list[int]]       =  []
						snake_case__			:				Optional[Any]       =  img.copy()
						snake_case__			:				Optional[Any]       =  cva.cvtColor(__lowerCamelCase							,		cva.COLOR_GRAY2RGB	)
						snake_case__			:				List[Any]       =  np.gradient(__lowerCamelCase	)
						snake_case__			:				Union[str, Any]       =  dx**2
						snake_case__			:				str       =  dy**2
						snake_case__			:				List[Any]       =  dx * dy
						snake_case__			:				str       =  0.0_4
						snake_case__			:				Union[str, Any]       =  self.window_size // 2
						for y in range(__lowerCamelCase							,		h - offset	):
							for x in range(__lowerCamelCase							,		w - offset	):
								snake_case__			:				Optional[int]       =  ixx[
								    y - offset : y + offset + 1, x - offset : x + offset + 1
								].sum()
								snake_case__			:				Any       =  iyy[
								    y - offset : y + offset + 1, x - offset : x + offset + 1
								].sum()
								snake_case__			:				Optional[int]       =  ixy[
								    y - offset : y + offset + 1, x - offset : x + offset + 1
								].sum()

								snake_case__			:				Tuple       =  (wxx * wyy) - (wxy**2)
								snake_case__			:				Optional[int]       =  wxx + wyy
								snake_case__			:				Union[str, Any]       =  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)							,		2_5_5	)
						return color_img, corner_list


if __name__ == "__main__":
	__lowerCamelCase :      Optional[int]										=				HarrisCorner(0.04, 3)
	__lowerCamelCase :      Optional[Any]										=				edge_detect.detect("""path_to_image""")
	cva.imwrite("""detect.png""", color_img)











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



