codingutf8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license initialize imageprocessing create random numpy tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by sizedivisibility initialize imageprocessing create random pytorch tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by sizedivisibility initialize imageprocessing create random pil images test not batched input image processor does not support batched inputs verify that width and height can be divided by sizedivisibility test that can process images which have an arbitrary number of channels initialize imageprocessing create random numpy tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by sizedivisibility coding utf 8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license initialize image_processing create random numpy tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by size_divisibility initialize image_processing create random pytorch tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by size_divisibility initialize image_processing create random pil images test not batched input image processor does not support batched inputs verify that width and height can be divided by size_divisibility test that can process images which have an arbitrary number of channels initialize image_processing create random numpy tensors test not batched input image processor does not support batched inputs verify that width and height can be divided by size_divisibility
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 ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import VitMatteImageProcessor


class VitMatteImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_rescale=True,
        rescale_factor=0.5,
        do_pad=True,
        size_divisibility=10,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_pad = do_pad
        self.size_divisibility = size_divisibility
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std

    def prepare_image_processor_dict(self):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "size_divisibility": self.size_divisibility,
        }

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class VitMatteImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = VitMatteImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = VitMatteImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "do_rescale"))
        self.assertTrue(hasattr(image_processing, "rescale_factor"))
        self.assertTrue(hasattr(image_processing, "do_pad"))
        self.assertTrue(hasattr(image_processing, "size_divisibility"))

    def test_call_numpy(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)

        
        image = image_inputs[0]
        trimap = np.random.randint(0, 3, size=image.shape[:2])
        encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values

        
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)

    def test_call_pytorch(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)

        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        
        image = image_inputs[0]
        trimap = np.random.randint(0, 3, size=image.shape[:2])
        encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values

        
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)

    def test_call_pil(self):
        
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, Image.Image)

        
        image = image_inputs[0]
        trimap = np.random.randint(0, 3, size=image.size[::-1])
        encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values

        
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)

    def test_call_numpy_4_channels(self):
        
        
        image_processor = self.image_processing_class(**self.image_processor_dict)

        
        self.image_processor_tester.num_channels = 4
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)

        
        image = image_inputs[0]
        trimap = np.random.randint(0, 3, size=image.shape[:2])
        encoded_images = image_processor(
            images=image,
            trimaps=trimap,
            input_data_format="channels_first",
            image_mean=0,
            image_std=1,
            return_tensors="pt",
        ).pixel_values

        
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)

    def test_padding(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        image = np.random.randn(3, 249, 491)
        images = image_processing.pad_image(image)
        assert images.shape == (3, 256, 512)