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https://github.com/facebookresearch/pytorch3d.git
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4300030d7a
Summary: Implements several changes needed for the CO3Dv2 release: - FrameData contains crop_bbox_xywh which defines the outline of the image crop corresponding to the image-shaped tensors in FrameData - revised the definition of a bounding box inside JsonDatasetIndex: bbox_xyxy is [xmin, ymin, xmax, ymax], where xmax, ymax are not inclusive; bbox_xywh = [xmin, ymain, xmax-xmin, ymax-ymin] - is_filtered for detecting whether the entries of the dataset were somehow filtered - seq_frame_index_to_dataset_index allows to skip entries that are not present in the dataset Reviewed By: shapovalov Differential Revision: D37687547 fbshipit-source-id: 7842756b0517878cc0964fc0935d3c0769454d78
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import numpy as np
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import torch
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from pytorch3d.implicitron.dataset.json_index_dataset import (
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_bbox_xywh_to_xyxy,
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_bbox_xyxy_to_xywh,
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_get_bbox_from_mask,
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)
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from tests.common_testing import TestCaseMixin
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class TestBBox(TestCaseMixin, unittest.TestCase):
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def setUp(self):
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torch.manual_seed(42)
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def test_bbox_conversion(self):
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bbox_xywh_list = torch.LongTensor(
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[
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[0, 0, 10, 20],
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[10, 20, 5, 1],
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[10, 20, 1, 1],
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[5, 4, 0, 1],
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]
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)
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for bbox_xywh in bbox_xywh_list:
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bbox_xyxy = _bbox_xywh_to_xyxy(bbox_xywh)
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bbox_xywh_ = _bbox_xyxy_to_xywh(bbox_xyxy)
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bbox_xyxy_ = _bbox_xywh_to_xyxy(bbox_xywh_)
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self.assertClose(bbox_xywh_, bbox_xywh)
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self.assertClose(bbox_xyxy, bbox_xyxy_)
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def test_compare_to_expected(self):
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bbox_xywh_to_xyxy_expected = torch.LongTensor(
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[
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[[0, 0, 10, 20], [0, 0, 10, 20]],
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[[10, 20, 5, 1], [10, 20, 15, 21]],
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[[10, 20, 1, 1], [10, 20, 11, 21]],
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[[5, 4, 0, 1], [5, 4, 5, 5]],
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]
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)
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for bbox_xywh, bbox_xyxy_expected in bbox_xywh_to_xyxy_expected:
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self.assertClose(_bbox_xywh_to_xyxy(bbox_xywh), bbox_xyxy_expected)
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self.assertClose(_bbox_xyxy_to_xywh(bbox_xyxy_expected), bbox_xywh)
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clamp_amnt = 3
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bbox_xywh_to_xyxy_clamped_expected = torch.LongTensor(
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[
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[[0, 0, 10, 20], [0, 0, 10, 20]],
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[[10, 20, 5, 1], [10, 20, 15, 20 + clamp_amnt]],
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[[10, 20, 1, 1], [10, 20, 10 + clamp_amnt, 20 + clamp_amnt]],
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[[5, 4, 0, 1], [5, 4, 5 + clamp_amnt, 4 + clamp_amnt]],
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]
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)
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for bbox_xywh, bbox_xyxy_expected in bbox_xywh_to_xyxy_clamped_expected:
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self.assertClose(
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_bbox_xywh_to_xyxy(bbox_xywh, clamp_size=clamp_amnt),
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bbox_xyxy_expected,
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)
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def test_mask_to_bbox(self):
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mask = np.array(
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[
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[0, 0, 0, 0, 0, 0],
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[0, 0, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0],
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]
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).astype(np.float32)
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expected_bbox_xywh = [2, 1, 2, 1]
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bbox_xywh = _get_bbox_from_mask(mask, 0.5)
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self.assertClose(bbox_xywh, expected_bbox_xywh)
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