
'''simple docstring'''





import cva
import numpy as np

class 						a   :




				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				float,    SCREAMING_SNAKE_CASE_	:				int      ):

										if k in (0.04, 0.06):
																snake_case       :     Tuple =    k
																snake_case       :     List[Any] =    window_size
										else:
																raise ValueError('''invalid k value'''      )




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




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     str =    cva.imread(SCREAMING_SNAKE_CASE_,    0      )
										snake_case,					snake_case       :     List[Any] =    img.shape
										snake_case       :     list[list[int]] =    []
										snake_case       :     Optional[Any] =    img.copy()
										snake_case       :     Optional[Any] =    cva.cvtColor(SCREAMING_SNAKE_CASE_,    cva.COLOR_GRAY2RGB      )
										snake_case,					snake_case       :     int =    np.gradient(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    dx**2
										snake_case       :     Dict =    dy**2
										snake_case       :     int =    dx * dy
										snake_case       :     Any =    0.04
										snake_case       :     str =    self.window_size // 2
										for y in range(SCREAMING_SNAKE_CASE_,    h - offset      ):
																for x in range(SCREAMING_SNAKE_CASE_,    w - offset      ):
																						snake_case       :     List[str] =    ixx[
																						    y - offset : y + offset + 1, x - offset : x + offset + 1
																						].sum()
																						snake_case       :     List[str] =    iyy[
																						    y - offset : y + offset + 1, x - offset : x + offset + 1
																						].sum()
																						snake_case       :     Any =    ixy[
																						    y - offset : y + offset + 1, x - offset : x + offset + 1
																						].sum()

																						snake_case       :     Optional[int] =    (wxx * wyy) - (wxy**2)
																						snake_case       :     Any =    wxx + wyy
																						snake_case       :     Optional[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_55      )
										return color_img, corner_list


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



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

