



"""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 OwlViTImageProcessor







class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      def __init__( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=7       ,     SCREAMING_SNAKE_CASE_							: List[str]=3       ,     SCREAMING_SNAKE_CASE_							: List[Any]=1_8       ,     SCREAMING_SNAKE_CASE_							: Any=3_0       ,     SCREAMING_SNAKE_CASE_							: str=4_0_0       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE_							: Dict=None       ,     SCREAMING_SNAKE_CASE_							: int=True       ,     SCREAMING_SNAKE_CASE_							: str=None       ,     SCREAMING_SNAKE_CASE_							: Dict=True       ,     SCREAMING_SNAKE_CASE_							: str=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,     SCREAMING_SNAKE_CASE_							: Dict=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,     SCREAMING_SNAKE_CASE_							: Any=True       ,     ):
        lowerCAmelCase_  :							int								=					parent
        lowerCAmelCase_  :							str								=					batch_size
        lowerCAmelCase_  :							List[str]								=					num_channels
        lowerCAmelCase_  :							int								=					image_size
        lowerCAmelCase_  :							Union[str, Any]								=					min_resolution
        lowerCAmelCase_  :							Optional[int]								=					max_resolution
        lowerCAmelCase_  :							Dict								=					do_resize
        lowerCAmelCase_  :							Tuple								=					size if size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_  :							str								=					do_center_crop
        lowerCAmelCase_  :							str								=					crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_  :							Optional[int]								=					do_normalize
        lowerCAmelCase_  :							Optional[int]								=					image_mean
        lowerCAmelCase_  :							List[str]								=					image_std
        lowerCAmelCase_  :							List[Any]								=					do_convert_rgb





      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }







@require_torch
@require_vision
class  UpperCamelCase__  (      lowercase_,   unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	OwlViTImageProcessor if is_vision_available() else None



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        lowerCAmelCase_  :							int								=					OwlViTImageProcessingTester(self			)



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        return self.image_processor_tester.prepare_image_processor_dict()



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							List[str]								=					self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'do_resize'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'size'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'do_center_crop'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'center_crop'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'do_normalize'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'image_mean'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'image_std'			)			)
        self.assertTrue(hasattr(UpperCAmelCase__       ,     'do_convert_rgb'			)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        lowerCAmelCase_  :							Optional[Any]								=					self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size       ,     {'height': 1_8, 'width': 1_8}			)
        self.assertEqual(image_processor.crop_size       ,     {'height': 1_8, 'width': 1_8}			)

        lowerCAmelCase_  :							Optional[Any]								=					self.image_processing_class.from_dict(self.image_processor_dict       ,     size=4_2       ,     crop_size=8_4			)
        self.assertEqual(image_processor.size       ,     {'height': 4_2, 'width': 4_2}			)
        self.assertEqual(image_processor.crop_size       ,     {'height': 8_4, 'width': 8_4}			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        # Initialize image_processing
        lowerCAmelCase_  :							Tuple								=					self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowerCAmelCase_  :							Optional[Any]								=					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_  :							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_  :							Any								=					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 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        # Initialize image_processing
        lowerCAmelCase_  :							List[str]								=					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_  :							str								=					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 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        # Initialize image_processing
        lowerCAmelCase_  :							Union[str, Any]								=					self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowerCAmelCase_  :							List[str]								=					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_  :							Optional[Any]								=					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'],
            )       ,     )








"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


if __name__ == "__main__":
   # read original image
   _a					 =				imread('../image_data/lena.jpg')
   # turn image in gray scale value
   _a					 =				cvtColor(img, COLOR_BGR2GRAY)

   _a ,		_a					 =				sobel_filter(gray)

   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)




