






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class     UpperCamelCase_ (__A       ,		__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       StableDiffusionInstructPixaPixPipeline
	__magic_name__			   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
	__magic_name__			   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__magic_name__			   =       IMAGE_TO_IMAGE_IMAGE_PARAMS
	__magic_name__			   =       IMAGE_TO_IMAGE_IMAGE_PARAMS





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				List[str]      =      UNetaDConditionModel(
				    block_out_channels=(32, 64)			,			layers_per_block=2			,			sample_size=32			,			in_channels=8			,			out_channels=4			,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,			cross_attention_dim=32			,			)
				UpperCAmelCase_  :				int      =      PNDMScheduler(skip_prk_steps=lowerCAmelCase_		)
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Optional[int]      =      AutoencoderKL(
				    block_out_channels=[32, 64]			,			in_channels=3			,			out_channels=3			,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,			latent_channels=4			,			)
				torch.manual_seed(0		)
				UpperCAmelCase_  :				str      =      CLIPTextConfig(
				    bos_token_id=0			,			eos_token_id=2			,			hidden_size=32			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_000			,			)
				UpperCAmelCase_  :				Optional[int]      =      CLIPTextModel(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				UpperCAmelCase_  :				Dict      =      {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=0		)		->    str:
				UpperCAmelCase_  :				List[str]      =      floats_tensor((1, 3, 32, 32)			,			rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      image.cpu().permute(0			,			2			,			3			,			1		)[0]
				UpperCAmelCase_  :				str      =      Image.fromarray(np.uinta(lowerCAmelCase_		)		).convert("RGB"		)
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				int      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				str      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      {
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "image_guidance_scale": 1,
				    "output_type": "numpy",
				}
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				int      =      "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_  :				str      =      self.get_dummy_components()
				UpperCAmelCase_  :				str      =      StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      sd_pipe.to(lowerCAmelCase_		)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[Any]      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      sd_pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				Optional[Any]      =      image[0, -3:, -3:, -1]
				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_  :				Optional[Any]      =      np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    str:
				UpperCAmelCase_  :				Any      =      "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_  :				List[Any]      =      self.get_dummy_components()
				UpperCAmelCase_  :				str      =      StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      sd_pipe.to(lowerCAmelCase_		)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				str      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      "french fries"
				UpperCAmelCase_  :				Optional[int]      =      sd_pipe(**lowerCAmelCase_			,			negative_prompt=lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      output.images
				UpperCAmelCase_  :				Tuple      =      image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_  :				Tuple      =      np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_  :				List[Any]      =      self.get_dummy_components()
				UpperCAmelCase_  :				Optional[Any]      =      StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      sd_pipe.to(lowerCAmelCase_		)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      [inputs["prompt"]] * 2

				UpperCAmelCase_  :				str      =      np.array(inputs["image"]		).astype(np.floataa		) / 2_5_5.0
				UpperCAmelCase_  :				List[Any]      =      torch.from_numpy(lowerCAmelCase_		).unsqueeze(0		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      image / 2 + 0.5
				UpperCAmelCase_  :				Dict      =      image.permute(0			,			3			,			1			,			2		)
				UpperCAmelCase_  :				Any      =      image.repeat(2			,			1			,			1			,			1		)

				UpperCAmelCase_  :				int      =      sd_pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				List[Any]      =      image[-1, -3:, -3:, -1]

				assert image.shape == (2, 32, 32, 3)
				UpperCAmelCase_  :				List[Any]      =      np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				UpperCAmelCase_  :				Union[str, Any]      =      "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_dummy_components()
				UpperCAmelCase_  :				str      =      EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			beta_schedule="scaled_linear"		)
				UpperCAmelCase_  :				Optional[Any]      =      StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      sd_pipe.to(lowerCAmelCase_		)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      sd_pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				int      =      image[0, -3:, -3:, -1]

				UpperCAmelCase_  :				Dict      =      [round(lowerCAmelCase_			,			4		) for x in image_slice.flatten().tolist()]
				print(",".join([str(lowerCAmelCase_		) for x in slice]		)		)

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_  :				Any      =      np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				super().test_inference_batch_single_identical(expected_max_diff=3e-3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[int]:
				UpperCAmelCase_  :				Any      =      self.get_dummy_components()
				UpperCAmelCase_  :				Union[str, Any]      =      StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      VaeImageProcessor(do_resize=lowerCAmelCase_			,			do_normalize=lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_			,			input_image_type="pt"		)		)[0]

				UpperCAmelCase_  :				Union[str, Any]      =      components["vae"]
				UpperCAmelCase_  :				int      =      self.get_dummy_inputs_by_type(lowerCAmelCase_			,			input_image_type="pt"		)

				for image_param in self.image_latents_params:
							if image_param in inputs.keys():
										UpperCAmelCase_  :				Dict      =      vae.encode(inputs[image_param]		).latent_dist.mode()

				UpperCAmelCase_  :				Union[str, Any]      =      pipe(**lowerCAmelCase_		)[0]

				UpperCAmelCase_  :				Tuple      =      np.abs(out - out_latents_inputs		).max()
				self.assertLess(lowerCAmelCase_			,			1e-4			,			"passing latents as image input generate different result from passing image"		)







@slow
@require_torch_gpu
class     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				List[str]=0		)		->    Any:
				UpperCAmelCase_  :				Optional[int]      =      torch.manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"		)
				UpperCAmelCase_  :				List[Any]      =      {
				    "prompt": "turn him into a cyborg",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "image_guidance_scale": 1.0,
				    "output_type": "numpy",
				}
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    str:
				UpperCAmelCase_  :				str      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"			,			safety_checker=lowerCAmelCase_		)
				pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing()

				UpperCAmelCase_  :				Tuple      =      self.get_inputs()
				UpperCAmelCase_  :				List[str]      =      pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				List[str]      =      image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				UpperCAmelCase_  :				Tuple      =      np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Any:
				UpperCAmelCase_  :				int      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"			,			safety_checker=lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
				pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing()

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_inputs()
				UpperCAmelCase_  :				Any      =      pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				List[Any]      =      image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				UpperCAmelCase_  :				List[Any]      =      np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				UpperCAmelCase_  :				str      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"			,			safety_checker=lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      DDIMScheduler.from_config(pipe.scheduler.config		)
				pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing()

				UpperCAmelCase_  :				Tuple      =      self.get_inputs()
				UpperCAmelCase_  :				str      =      pipe(**lowerCAmelCase_		).images
				UpperCAmelCase_  :				Optional[Any]      =      image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				UpperCAmelCase_  :				Optional[int]      =      np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[Any]      =      0

				def callback_fn(lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				torch.FloatTensor		) -> None:
							UpperCAmelCase_  :				int      =      True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
										UpperCAmelCase_  :				Tuple      =      latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										UpperCAmelCase_  :				List[str]      =      latents[0, -3:, -3:, -1]
										UpperCAmelCase_  :				List[str]      =      np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
							elif step == 2:
										UpperCAmelCase_  :				List[str]      =      latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										UpperCAmelCase_  :				Union[str, Any]      =      latents[0, -3:, -3:, -1]
										UpperCAmelCase_  :				Optional[Any]      =      np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

				UpperCAmelCase_  :				List[Any]      =      False

				UpperCAmelCase_  :				Optional[int]      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"			,			safety_checker=lowerCAmelCase_			,			torch_dtype=torch.floataa		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing()

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_inputs()
				pipe(**lowerCAmelCase_			,			callback=lowerCAmelCase_			,			callback_steps=1		)
				assert callback_fn.has_been_called
				assert number_of_steps == 3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				UpperCAmelCase_  :				List[Any]      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix"			,			safety_checker=lowerCAmelCase_			,			torch_dtype=torch.floataa		)
				UpperCAmelCase_  :				List[str]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing(1		)
				pipe.enable_sequential_cpu_offload()

				UpperCAmelCase_  :				str      =      self.get_inputs()
				UpperCAmelCase_  :				Dict      =      pipe(**lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      torch.cuda.max_memory_allocated()
				# make sure that less than 2.2 GB is allocated
				assert mem_bytes < 2.2 * 10**9





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      self.get_inputs()
				# resize to resolution that is divisible by 8 but not 16 or 32
				UpperCAmelCase_  :				Optional[int]      =      inputs["image"].resize((504, 504)		)

				UpperCAmelCase_  :				str      =      "timbrooks/instruct-pix2pix"
				UpperCAmelCase_  :				List[Any]      =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    lowerCAmelCase_			,			safety_checker=lowerCAmelCase_			,			)
				pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				pipe.enable_attention_slicing()

				UpperCAmelCase_  :				Dict      =      pipe(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      output.images[0]

				UpperCAmelCase_  :				List[str]      =      image[255:258, 383:386, -1]

				assert image.shape == (504, 504, 3)
				UpperCAmelCase_  :				str      =      np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



