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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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from transformers.models.superpoint.modeling_superpoint import SuperPointKeypointDescriptionOutput
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if is_vision_available():
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from transformers import SuperPointImageProcessor
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if is_torchvision_available():
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from transformers import SuperPointImageProcessorFast
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class SuperPointImageProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_grayscale=True,
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):
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size = size if size is not None else {"height": 480, "width": 640}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_grayscale = do_grayscale
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_grayscale": self.do_grayscale,
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}
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def expected_output_image_shape(self, images):
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return self.num_channels, self.size["height"], self.size["width"]
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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def prepare_keypoint_detection_output(self, pixel_values):
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max_number_keypoints = 50
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batch_size = len(pixel_values)
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mask = torch.zeros((batch_size, max_number_keypoints))
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keypoints = torch.zeros((batch_size, max_number_keypoints, 2))
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scores = torch.zeros((batch_size, max_number_keypoints))
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descriptors = torch.zeros((batch_size, max_number_keypoints, 16))
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for i in range(batch_size):
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random_number_keypoints = np.random.randint(0, max_number_keypoints)
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mask[i, :random_number_keypoints] = 1
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keypoints[i, :random_number_keypoints] = torch.rand((random_number_keypoints, 2))
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scores[i, :random_number_keypoints] = torch.rand((random_number_keypoints,))
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descriptors[i, :random_number_keypoints] = torch.rand((random_number_keypoints, 16))
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return SuperPointKeypointDescriptionOutput(
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loss=None, keypoints=keypoints, scores=scores, descriptors=descriptors, mask=mask, hidden_states=None
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)
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@require_torch
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@require_vision
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class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = SuperPointImageProcessor if is_vision_available() else None
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fast_image_processing_class = SuperPointImageProcessorFast if is_torchvision_available() else None
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def setUp(self) -> None:
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super().setUp()
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self.image_processor_tester = SuperPointImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processing(self):
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for image_processing_class in self.image_processor_list:
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image_processing = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_grayscale"))
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def test_image_processor_from_dict_with_kwargs(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 480, "width": 640})
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image_processor = self.image_processing_class.from_dict(
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self.image_processor_dict, size={"height": 42, "width": 42}
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)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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@unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image")
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def test_call_numpy_4_channels(self):
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pass
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def test_input_image_properly_converted_to_grayscale(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs)
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for image in pre_processed_images["pixel_values"]:
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if isinstance(image, torch.Tensor):
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self.assertTrue(
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torch.all(image[0, ...] == image[1, ...]).item()
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and torch.all(image[1, ...] == image[2, ...]).item()
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)
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else:
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self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))
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@require_torch
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def test_post_processing_keypoint_detection(self):
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def check_post_processed_output(post_processed_output, image_size):
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for post_processed_output, image_size in zip(post_processed_output, image_size):
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self.assertTrue("keypoints" in post_processed_output)
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self.assertTrue("descriptors" in post_processed_output)
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self.assertTrue("scores" in post_processed_output)
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keypoints = post_processed_output["keypoints"]
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all_below_image_size = torch.all(keypoints[:, 0] <= image_size[1]) and torch.all(
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keypoints[:, 1] <= image_size[0]
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)
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all_above_zero = torch.all(keypoints[:, 0] >= 0) and torch.all(keypoints[:, 1] >= 0)
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self.assertTrue(all_below_image_size)
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self.assertTrue(all_above_zero)
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
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outputs = self.image_processor_tester.prepare_keypoint_detection_output(**pre_processed_images)
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tuple_image_sizes = [(image.size[0], image.size[1]) for image in image_inputs]
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tuple_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tuple_image_sizes)
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check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
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tensor_image_sizes = torch.tensor([image.size for image in image_inputs]).flip(1)
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tensor_post_processed_outputs = image_processor.post_process_keypoint_detection(
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outputs, tensor_image_sizes
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)
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check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)
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