






"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     UpperCAmelCase					=	gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row, col - 1]
                            UpperCAmelCase					=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row + 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col]
                            UpperCAmelCase					=	sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   UpperCAmelCase					=	sobel_grad[row, col]

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0]						):
              for col in range(1		,					image_shape[1]						):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowerCAmelCase_		:			Optional[Any]				   =					cva.imread(R'''../image_data/lena.jpg''', 0)
 # canny edge detection
 lowerCAmelCase_		:			Dict				   =					canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:

					class  a     :


											@staticmethod
											def 				_UpperCAmelCase (				*A_  ,    **A_		):







															'''simple docstring'''


															pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class  a     (		unittest.TestCase ):
						_lowercase			  =	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						_lowercase			  =	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			ImageClassificationPipeline(model=A_  ,    image_processor=A_  ,    top_k=2		)
										_UpperCAmelCase				:						Optional[Any]       						=			[
										    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
										    "http://images.cocodataset.org/val2017/000000039769.jpg",
										]
										return image_classifier, examples


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

										self.assertEqual(
										    A_  ,    [
										        {"score": ANY(A_		), "label": ANY(A_		)},
										        {"score": ANY(A_		), "label": ANY(A_		)},
										    ]  ,    )

										import datasets

										_UpperCAmelCase				:						int       						=			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,    "image"  ,    split="test"		)

										# Accepts URL + PIL.Image + lists
										_UpperCAmelCase				:						Any       						=			image_classifier(
										    [
										        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        # RGBA
										        dataset[0]["file"],
										        # LA
										        dataset[1]["file"],
										        # L
										        dataset[2]["file"],
										    ]		)
										self.assertEqual(
										    A_  ,    [
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										    ]  ,    )


						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_		)

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						List[str]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						@require_tf
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_  ,    framework="tf"		)

										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			PreTrainedTokenizer()

										# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
										_UpperCAmelCase				:						int       						=			pipeline(
										    "image-classification"  ,    model="hf-internal-testing/tiny-random-vit"  ,    tokenizer=A_		)

										self.assertIs(image_classifier.tokenizer  ,    A_		)





						@slow
						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-conv"		)
										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.43_85, "label": "tabby, tabby cat"},
										        {"score": 0.3_21, "label": "tiger cat"},
										        {"score": 0.05_02, "label": "Egyptian cat"},
										        {"score": 0.01_37, "label": "crib, cot"},
										        {"score": 0.0_07, "label": "radiator"},
										    ]  ,    )

										_UpperCAmelCase				:						str       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-fourier"		)
										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.56_58, "label": "tabby, tabby cat"},
										        {"score": 0.13_09, "label": "tiger cat"},
										        {"score": 0.07_22, "label": "Egyptian cat"},
										        {"score": 0.07_07, "label": "remote control, remote"},
										        {"score": 0.00_82, "label": "computer keyboard, keypad"},
										    ]  ,    )

										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-learned"		)
										_UpperCAmelCase				:						str       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.30_22, "label": "tabby, tabby cat"},
										        {"score": 0.23_62, "label": "Egyptian cat"},
										        {"score": 0.18_56, "label": "tiger cat"},
										        {"score": 0.03_24, "label": "remote control, remote"},
										        {"score": 0.00_96, "label": "quilt, comforter, comfort, puff"},
										    ]  ,    )


