

"""simple docstring"""


import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    List[str]=7					,  UpperCamelCase__							:    Union[str, Any]=3					,  UpperCamelCase__							:    Union[str, Any]=1_8					,  UpperCamelCase__							:    Optional[int]=3_0					,  UpperCamelCase__							:    int=4_0_0					,  UpperCamelCase__							:    List[Any]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Union[str, Any]=[0.5, 0.5, 0.5]					,  UpperCamelCase__							:    List[Any]=[0.5, 0.5, 0.5]					,  )->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      size if size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	List[Any]   				=      parent
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Optional[int]   				=      num_channels
						__lowerCAmelCase:	Optional[int]   				=      image_size
						__lowerCAmelCase:	List[Any]   				=      min_resolution
						__lowerCAmelCase:	List[Any]   				=      max_resolution
						__lowerCAmelCase:	str   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	Tuple   				=      do_normalize
						__lowerCAmelCase:	int   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


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





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  DPTImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      DPTImageProcessingTester(self)

				@property
				def 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Tuple)->       Tuple:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Any)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2)
						self.assertEqual(image_processor.size					,  {"height": 4_2, "width": 4_2})

				def 							lowercase_					(     self							:    str)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Tuple   				=      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:	Tuple   				=      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.size["height"],
						        self.image_processor_tester.size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	str   				=      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.size["height"],
						        self.image_processor_tester.size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    Tuple)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      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:	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.size["height"],
						        self.image_processor_tester.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.size["height"],
						        self.image_processor_tester.size["width"],
						    )					,  )




				def 							lowercase_					(     self							:    Optional[int])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Optional[Any]   				=      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:	int   				=      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.size["height"],
						        self.image_processor_tester.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.size["height"],
						        self.image_processor_tester.size["width"],
						    )					,  )





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


