


'''simple docstring'''



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:



      class 			a       :


           """simple docstring"""






           @staticmethod
           def 				lowerCamelCase__				(       *snake_case  :	str							,				**snake_case  :	str    )		->							List[Any]:
                pass



@is_pipeline_test
@require_vision
@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[Any]							     =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	Optional[Any]    )		->							int:
          __UpperCAmelCase			:				Optional[Any]													=    pipeline(
              '''zero-shot-object-detection'''							,				model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

          __UpperCAmelCase			:				int													=    [
              {
                  '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                  '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
              }
          ]
          return object_detector, examples

     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	Any							,				snake_case  :	Dict    )		->							str:
          __UpperCAmelCase			:				List[str]													=    object_detector(examples[0]							,				threshold=0.0    )

          __UpperCAmelCase			:				Optional[int]													=    len(_UpperCAmelCase    )
          self.assertGreater(_UpperCAmelCase							,				0    )
          self.assertEqual(
              _UpperCAmelCase							,				[
                  {
                      '''score''': ANY(_UpperCAmelCase    ),
                      '''label''': ANY(_UpperCAmelCase    ),
                      '''box''': {'''xmin''': ANY(_UpperCAmelCase    ), '''ymin''': ANY(_UpperCAmelCase    ), '''xmax''': ANY(_UpperCAmelCase    ), '''ymax''': ANY(_UpperCAmelCase    )},
                  }
                  for i in range(_UpperCAmelCase    )
              ]							,				)

     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''    )
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Dict:
          pass

     @require_torch
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Tuple:
          __UpperCAmelCase			:				str													=    pipeline(
              '''zero-shot-object-detection'''							,				model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

          __UpperCAmelCase			:				Optional[int]													=    object_detector(
              '''./tests/fixtures/tests_samples/COCO/000000039769.png'''							,				candidate_labels=['''cat''', '''remote''', '''couch''']							,				threshold=0.64							,				)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  {'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                  {'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                  {'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                  {'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                  {'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                  {'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                  {'''score''': 0.6_456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                  {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                  {'''score''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
              ]							,				)

          __UpperCAmelCase			:				Optional[int]													=    object_detector(
              [
                  {
                      '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                      '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                  }
              ]							,				threshold=0.64							,				)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  [
                      {'''score''': 0.7_235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                      {'''score''': 0.7_218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                      {'''score''': 0.7_184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                      {'''score''': 0.6_748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                      {'''score''': 0.6_656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                      {'''score''': 0.6_614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                      {'''score''': 0.6_456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                      {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                      {'''score''': 0.6_419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                  ]
              ]							,				)

     @require_torch
     @slow
     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[int]:
          __UpperCAmelCase			:				Tuple													=    pipeline('''zero-shot-object-detection'''    )

          __UpperCAmelCase			:				Dict													=    object_detector(
              '''http://images.cocodataset.org/val2017/000000039769.jpg'''							,				candidate_labels=['''cat''', '''remote''', '''couch''']							,				)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  {'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                  {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                  {'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                  {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                  {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
              ]							,				)

          __UpperCAmelCase			:				Dict													=    object_detector(
              [
                  {
                      '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                      '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                  },
                  {
                      '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                      '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                  },
              ]							,				)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  [
                      {'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                      {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                      {'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                      {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                      {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                  ],
                  [
                      {'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                      {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                      {'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                      {'''score''': 0.1_474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                      {'''score''': 0.1_208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                  ],
              ]							,				)

     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''    )
     def 				lowerCamelCase__				(       self  :	int    )		->							List[str]:
          pass

     @require_torch
     @slow
     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          __UpperCAmelCase			:				int													=    0.2
          __UpperCAmelCase			:				List[str]													=    pipeline('''zero-shot-object-detection'''    )

          __UpperCAmelCase			:				Any													=    object_detector(
              '''http://images.cocodataset.org/val2017/000000039769.jpg'''							,				candidate_labels=['''cat''', '''remote''', '''couch''']							,				threshold=_UpperCAmelCase							,				)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  {'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                  {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                  {'''score''': 0.2_537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
              ]							,				)






     @require_torch
     @slow
     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          __UpperCAmelCase			:				Dict													=    2
          __UpperCAmelCase			:				str													=    pipeline('''zero-shot-object-detection'''    )

          __UpperCAmelCase			:				List[Any]													=    object_detector(
              '''http://images.cocodataset.org/val2017/000000039769.jpg'''							,				candidate_labels=['''cat''', '''remote''', '''couch''']							,				top_k=_UpperCAmelCase							,				)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase							,				decimals=4    )							,				[
                  {'''score''': 0.2_868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                  {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
              ]							,				)



'''simple docstring'''



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 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

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

     return dst


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

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[Any]				       =     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()