import cva
import numpy as np


class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Any:

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



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







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   tuple[cva.Mat, list[list[int]]]:
								__a		 =       cva.imread(lowerCAmelCase_				,						0   )
								__a ,				__a		 =       img.shape
								__a		 =       []
								__a		 =       img.copy()
								__a		 =       cva.cvtColor(lowerCAmelCase_				,						cva.COLOR_GRAY2RGB   )
								__a ,				__a		 =       np.gradient(lowerCAmelCase_   )
								__a		 =       dx**2
								__a		 =       dy**2
								__a		 =       dx * dy
								__a		 =       0.04
								__a		 =       self.window_size // 2
								for y in range(lowerCAmelCase_				,						h - offset   ):
													for x in range(lowerCAmelCase_				,						w - offset   ):
																		__a		 =       ixx[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()
																		__a		 =       iyy[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()
																		__a		 =       ixy[
																		    y - offset : y + offset + 1, x - offset : x + offset + 1
																		].sum()

																		__a		 =       (wxx * wyy) - (wxy**2)
																		__a		 =       wxx + wyy
																		__a		 =       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_    :		int							=	HarrisCorner(0.0_4, 3)
	lowerCamelCase_						,  lowerCamelCase_    :		Optional[Any]							=	edge_detect.detect("""path_to_image""")
	cva.imwrite("""detect.png""", color_img)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




