
"""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       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=7       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=18       ,       SCREAMING_SNAKE_CASE_=30       ,       SCREAMING_SNAKE_CASE_=4_00       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = size if size is not None else {'''shortest_edge''': 18}
									SCREAMING_SNAKE_CASE_						 = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									SCREAMING_SNAKE_CASE_						 = parent
									SCREAMING_SNAKE_CASE_						 = batch_size
									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = image_size
									SCREAMING_SNAKE_CASE_						 = min_resolution
									SCREAMING_SNAKE_CASE_						 = max_resolution
									SCREAMING_SNAKE_CASE_						 = do_resize
									SCREAMING_SNAKE_CASE_						 = size
									SCREAMING_SNAKE_CASE_						 = do_center_crop
									SCREAMING_SNAKE_CASE_						 = crop_size
									SCREAMING_SNAKE_CASE_						 = do_normalize
									SCREAMING_SNAKE_CASE_						 = image_mean
									SCREAMING_SNAKE_CASE_						 = image_std







							def _lowercase					(self    ):

									"""simple docstring"""







									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     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      LevitImageProcessor if is_vision_available() else None

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = LevitImageProcessingTester(self    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''image_mean'''    )    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''image_std'''    )    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''do_normalize'''    )    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''do_resize'''    )    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''do_center_crop'''    )    )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''size'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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}    )

									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									SCREAMING_SNAKE_CASE_						 = prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       Image.Image    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_						 = prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=SCREAMING_SNAKE_CASE_       ,       numpify=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       np.ndarray    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_						 = prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=SCREAMING_SNAKE_CASE_       ,       torchify=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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'''],
									    )       ,       )

"""simple docstring"""




import cva
import numpy as np





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def __str__(self    ):

									"""simple docstring"""







									return str(self.k    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

													SCREAMING_SNAKE_CASE_						 = (wxx * wyy) - (wxy**2)
													SCREAMING_SNAKE_CASE_						 = wxx + wyy
													SCREAMING_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)       ,       2_55    )
									return color_img, corner_list


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