"""simple docstring"""





import cva
import numpy as np
class  _snake_case     :






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			float							,					UpperCAmelCase      :			int    ):

													if k in (0.0_4, 0.0_6):
																				__lowerCamelCase					:		Any		   =     k
																				__lowerCamelCase					:		str		   =     window_size
													else:
																				raise ValueError("invalid k value"    )






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







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

																											__lowerCamelCase					:		Any		   =     (wxx * wyy) - (wxy**2)
																											__lowerCamelCase					:		Tuple		   =     wxx + wyy
																											__lowerCamelCase					:		List[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)							,					255    )
													return color_img, corner_list


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





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)