




"""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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BlipImageProcessor
class 			lowerCamelCase__    (  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=400				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				, SCREAMING_SNAKE_CASE=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				, SCREAMING_SNAKE_CASE=True				, ):



     """simple docstring"""







     snake_case		:						Optional[Any]								=				size if size is not None else {"height": 20, "width": 20}
     snake_case		:						Tuple								=				parent
     snake_case		:						List[Any]								=				batch_size
     snake_case		:						Tuple								=				num_channels
     snake_case		:						Union[str, Any]								=				image_size
     snake_case		:						Tuple								=				min_resolution
     snake_case		:						str								=				max_resolution
     snake_case		:						Any								=				do_resize
     snake_case		:						Optional[Any]								=				size
     snake_case		:						int								=				do_normalize
     snake_case		:						str								=				image_mean
     snake_case		:						Optional[Any]								=				image_std
     snake_case		:						int								=				do_pad
     snake_case		:						int								=				do_convert_rgb


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
         "do_pad": self.do_pad,
     }



   def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False							):



     """simple docstring"""







     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       snake_case		:						Dict								=				[]
       for i in range(self.batch_size							):
         image_inputs.append(
             np.random.randint(
                 255				, size=(self.num_channels, self.max_resolution, self.max_resolution)				, dtype=np.uinta							)							)
     else:
       snake_case		:						Optional[Any]								=				[]
       for i in range(self.batch_size							):
         snake_case		:						Any								=				np.random.choice(np.arange(self.min_resolution				, self.max_resolution							)				, 2							)
         image_inputs.append(np.random.randint(255				, size=(self.num_channels, width, height)				, dtype=np.uinta							)							)

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       snake_case		:						Optional[int]								=				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				, 0				, -1							)							) for x in image_inputs]

     if torchify:
       snake_case		:						Optional[Any]								=				[torch.from_numpy(SCREAMING_SNAKE_CASE							) for x in image_inputs]

     return image_inputs







@require_torch
@require_vision
class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
   a__ :      Dict								=     BlipImageProcessor if is_vision_available() else None


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						str								=				BlipImageProcessingTester(self							)


   @property
   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						Optional[int]								=				self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     pass


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						Any								=				self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)

     # Test not batched input
     snake_case		:						Union[str, Any]								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape				, (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )				, )

     # Test batched
     snake_case		:						Dict								=				image_processor(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.size["height"],
             self.image_processor_tester.size["width"],
         )				, )


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     snake_case		:						Dict								=				self.image_processor_tester.prepare_inputs(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
     snake_case		:						Any								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape				, (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )				, )

     # Test batched
     snake_case		:						List[str]								=				image_processor(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.size["height"],
             self.image_processor_tester.size["width"],
         )				, )



   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						int								=				self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     snake_case		:						Union[str, Any]								=				self.image_processor_tester.prepare_inputs(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
     snake_case		:						str								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape				, (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )				, )

     # Test batched
     snake_case		:						List[Any]								=				image_processor(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.size["height"],
             self.image_processor_tester.size["width"],
         )				, )







@require_torch
@require_vision
class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
   a__ :      Union[str, Any]								=     BlipImageProcessor if is_vision_available() else None


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						Dict								=				BlipImageProcessingTester(self				, num_channels=4							)
     snake_case		:						Optional[int]								=				3


   @property
   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						Tuple								=				self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)


   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     pass



   def 							lowerCamelCase_				(     self							):



     """simple docstring"""







     snake_case		:						List[Any]								=				self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)

     # Test not batched input
     snake_case		:						Any								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape				, (
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )				, )

     # Test batched
     snake_case		:						List[str]								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape				, (
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )				, )







"""simple docstring"""

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						int								=				0
					# max greyscale value for #FFFFFF
					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)

					if not self.min_threshold < threshold < self.max_threshold:
							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
							raise ValueError(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				input_img
					snake_case		:						Optional[Any]								=				threshold
					snake_case			,				snake_case		:						List[str]								=				self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					snake_case		:						Union[str, Any]								=				[
					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
					]
					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255


			@classmethod
			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for y in range(self.height							):
							for x in range(self.width							):
									snake_case		:						Dict								=				int(self.get_greyscale(*self.input_img[y][x]							)							)
									if self.threshold > greyscale + self.error_table[y][x]:
											snake_case		:						List[str]								=				(0, 0, 0)
											snake_case		:						List[Any]								=				greyscale + self.error_table[x][y]
									else:
											snake_case		:						int								=				(255, 255, 255)
											snake_case		:						Tuple								=				greyscale + self.error_table[x][y] - 255
									self.error_table[y][x + 1] += int(8 / 32 * current_error							)
									self.error_table[y][x + 2] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x] += int(8 / 32 * current_error							)
									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error							)
									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error							)


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							__A 		=			[
							    Burkes(imread("image_data/lena.jpg", 1), threshold)
							    for threshold in (1, 126, 130, 140)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f'Original image with dithering threshold: {burkes.threshold}',
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

