



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   str     )     ->					Union[str, Any]:
   UpperCamelCase   :  Union[str, Any]						     =     k_size // 2
   UpperCamelCase     ,					UpperCamelCase   :  Any						     =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   UpperCamelCase   :  int						     =     1 / (2 * pi * sigma) * exp(-(square(snake_case__     ) + square(snake_case__     )) / (2 * square(snake_case__     ))     )
   return g
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Any	,					snake_case__	:   int     )     ->					int:
   UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     image.shape[0], image.shape[1]
   # dst image height and width
   UpperCamelCase   :  str						     =     height - k_size + 1
   UpperCamelCase   :  List[Any]						     =     width - k_size + 1

   # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
   UpperCamelCase   :  List[str]						     =     zeros((dst_height * dst_width, k_size * k_size)     )
   UpperCamelCase   :  int						     =     0
   for i, j in product(range(snake_case__     )	,					range(snake_case__     )     ):
      UpperCamelCase   :  str						     =     ravel(image[i : i + k_size, j : j + k_size]     )
      UpperCamelCase   :  List[str]						     =     window
      row += 1

   #  turn the kernel into shape(k*k, 1)
   UpperCamelCase   :  List[str]						     =     gen_gaussian_kernel(snake_case__	,					snake_case__     )
   UpperCamelCase   :  List[str]						     =     ravel(snake_case__     )

   # reshape and get the dst image
   UpperCamelCase   :  Optional[int]						     =     dot(snake_case__	,					snake_case__     ).reshape(snake_case__	,					snake_case__     ).astype(snake_case__     )

   return dst


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

    # get values with two different mask size
    __UpperCAmelCase	       =	gaussian_filter(gray, 3, sigma=1)
    __UpperCAmelCase	       =	gaussian_filter(gray, 5, sigma=0.8)

    # show result images
    imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
    imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
    waitKey()








import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:



    class     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     depth_estimator(
              [
                  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(
              [
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          # This is highly irregular to have no small tests.
          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'	)



