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https://github.com/open-mmlab/mmsegmentation.git
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743171ddef
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Support inference and visualization of VPD ## Modification 1. add a new VPD model that does not generate black border in predictions 2. update `SegLocalVisualizer` to support depth visualization 3. update `MMSegInferencer` to support save predictions of depth estimation in method `postprocess` ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) Run inference with VPD using the this command ```sh python demo/image_demo_with_inferencer.py demo/classroom__rgb_00283.jpg vpd_depth --out-dir vis_results ``` The following image will be saved under `vis_results/vis`  ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 4. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 5. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 6. The documentation has been modified accordingly, like docstring or example tutorials.
214 lines
8.7 KiB
Python
214 lines
8.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import os.path as osp
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import tempfile
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from unittest import TestCase
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import cv2
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import mmcv
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import numpy as np
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import torch
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from mmengine.structures import PixelData
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from mmseg.structures import SegDataSample
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from mmseg.visualization import SegLocalVisualizer
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class TestSegLocalVisualizer(TestCase):
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def test_add_datasample(self):
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h = 10
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w = 12
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num_class = 2
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out_file = 'out_file'
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image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
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# test gt_sem_seg
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gt_sem_seg_data = dict(data=torch.randint(0, num_class, (1, h, w)))
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gt_sem_seg = PixelData(**gt_sem_seg_data)
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def test_add_datasample_forward(gt_sem_seg):
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data_sample = SegDataSample()
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data_sample.gt_sem_seg = gt_sem_seg
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with tempfile.TemporaryDirectory() as tmp_dir:
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seg_local_visualizer = SegLocalVisualizer(
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vis_backends=[dict(type='LocalVisBackend')],
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save_dir=tmp_dir)
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seg_local_visualizer.dataset_meta = dict(
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classes=('background', 'foreground'),
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palette=[[120, 120, 120], [6, 230, 230]])
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# test out_file
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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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assert os.path.exists(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'))
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drawn_img = cv2.imread(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'))
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assert drawn_img.shape == (h, w, 3)
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# test gt_instances and pred_instances
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pred_sem_seg_data = dict(
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data=torch.randint(0, num_class, (1, h, w)))
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pred_sem_seg = PixelData(**pred_sem_seg_data)
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data_sample.pred_sem_seg = pred_sem_seg
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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h, w * 2, 3))
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seg_local_visualizer.add_datasample(
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out_file, image, data_sample, draw_gt=False)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h, w, 3))
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if torch.cuda.is_available():
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test_add_datasample_forward(gt_sem_seg.cuda())
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test_add_datasample_forward(gt_sem_seg)
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def test_cityscapes_add_datasample(self):
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h = 128
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w = 256
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num_class = 19
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out_file = 'out_file_cityscapes'
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image = mmcv.imread(
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osp.join(
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osp.dirname(__file__),
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'../data/pseudo_cityscapes_dataset/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png' # noqa
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),
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'color')
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sem_seg = mmcv.imread(
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osp.join(
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osp.dirname(__file__),
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'../data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png' # noqa
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),
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'unchanged')
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sem_seg = torch.unsqueeze(torch.from_numpy(sem_seg), 0)
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gt_sem_seg_data = dict(data=sem_seg)
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gt_sem_seg = PixelData(**gt_sem_seg_data)
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def test_cityscapes_add_datasample_forward(gt_sem_seg):
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data_sample = SegDataSample()
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data_sample.gt_sem_seg = gt_sem_seg
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with tempfile.TemporaryDirectory() as tmp_dir:
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seg_local_visualizer = SegLocalVisualizer(
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vis_backends=[dict(type='LocalVisBackend')],
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save_dir=tmp_dir)
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seg_local_visualizer.dataset_meta = dict(
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classes=('road', 'sidewalk', 'building', 'wall', 'fence',
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'pole', 'traffic light', 'traffic sign',
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'vegetation', 'terrain', 'sky', 'person', 'rider',
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'car', 'truck', 'bus', 'train', 'motorcycle',
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'bicycle'),
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palette=[[128, 64, 128], [244, 35, 232], [70, 70, 70],
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[102, 102, 156], [190, 153, 153], [153, 153, 153],
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[250, 170, 30], [220, 220, 0], [107, 142, 35],
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[152, 251, 152], [70, 130, 180], [220, 20, 60],
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[255, 0, 0], [0, 0, 142], [0, 0, 70],
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[0, 60, 100], [0, 80, 100], [0, 0, 230],
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[119, 11, 32]])
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# test out_file
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seg_local_visualizer.add_datasample(
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out_file,
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image,
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data_sample,
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out_file=osp.join(tmp_dir, 'test.png'))
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'test.png'), (h, w, 3))
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# test gt_instances and pred_instances
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pred_sem_seg_data = dict(
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data=torch.randint(0, num_class, (1, h, w)))
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pred_sem_seg = PixelData(**pred_sem_seg_data)
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data_sample.pred_sem_seg = pred_sem_seg
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# test draw prediction with gt
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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h, w * 2, 3))
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# test draw prediction without gt
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seg_local_visualizer.add_datasample(
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out_file, image, data_sample, draw_gt=False)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h, w, 3))
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if torch.cuda.is_available():
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test_cityscapes_add_datasample_forward(gt_sem_seg.cuda())
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test_cityscapes_add_datasample_forward(gt_sem_seg)
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def _assert_image_and_shape(self, out_file, out_shape):
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assert os.path.exists(out_file)
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drawn_img = cv2.imread(out_file)
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assert drawn_img.shape == out_shape
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def test_add_datasample_depth(self):
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h = 10
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w = 12
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out_file = 'out_file'
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image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
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# test gt_depth_map
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gt_depth_map = PixelData(data=torch.rand(1, h, w))
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def test_add_datasample_forward_depth(gt_depth_map):
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data_sample = SegDataSample()
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data_sample.gt_depth_map = gt_depth_map
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with tempfile.TemporaryDirectory() as tmp_dir:
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seg_local_visualizer = SegLocalVisualizer(
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vis_backends=[dict(type='LocalVisBackend')],
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save_dir=tmp_dir)
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seg_local_visualizer.dataset_meta = dict(
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classes=('background', 'foreground'),
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palette=[[120, 120, 120], [6, 230, 230]])
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# test out_file
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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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assert os.path.exists(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'))
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drawn_img = cv2.imread(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'))
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assert drawn_img.shape == (h * 2, w, 3)
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# test gt_instances and pred_instances
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pred_depth_map = PixelData(data=torch.rand(1, h, w))
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data_sample.pred_depth_map = pred_depth_map
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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h * 2, w * 2, 3))
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seg_local_visualizer.add_datasample(
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out_file, image, data_sample, draw_gt=False)
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self._assert_image_and_shape(
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osp.join(tmp_dir, 'vis_data', 'vis_image',
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out_file + '_0.png'), (h * 2, w, 3))
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if torch.cuda.is_available():
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test_add_datasample_forward_depth(gt_depth_map.cuda())
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test_add_datasample_forward_depth(gt_depth_map)
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