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import os
import os.path as osp
import tempfile
from unittest import TestCase
import cv2
import mmcv
import numpy as np
import torch
from mmengine.structures import PixelData
from mmseg.structures import SegDataSample
from mmseg.visualization import SegLocalVisualizer
class TestSegLocalVisualizer(TestCase):
def test_add_datasample(self):
h = 10
w = 12
num_class = 2
out_file = 'out_file'
image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
# test gt_sem_seg
gt_sem_seg_data = dict(data=torch.randint(0, num_class, (1, h, w)))
gt_sem_seg = PixelData(**gt_sem_seg_data)
def test_add_datasample_forward(gt_sem_seg):
data_sample = SegDataSample()
data_sample.gt_sem_seg = gt_sem_seg
with tempfile.TemporaryDirectory() as tmp_dir:
seg_local_visualizer = SegLocalVisualizer(
vis_backends=[dict(type='LocalVisBackend')],
save_dir=tmp_dir)
seg_local_visualizer.dataset_meta = dict(
classes=('background', 'foreground'),
palette=[[120, 120, 120], [6, 230, 230]])
# test out_file
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
assert os.path.exists(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'))
drawn_img = cv2.imread(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'))
assert drawn_img.shape == (h, w, 3)
# test gt_instances and pred_instances
pred_sem_seg_data = dict(
data=torch.randint(0, num_class, (1, h, w)))
pred_sem_seg = PixelData(**pred_sem_seg_data)
data_sample.pred_sem_seg = pred_sem_seg
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h, w * 2, 3))
seg_local_visualizer.add_datasample(
out_file, image, data_sample, draw_gt=False)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h, w, 3))
if torch.cuda.is_available():
test_add_datasample_forward(gt_sem_seg.cuda())
test_add_datasample_forward(gt_sem_seg)
def test_cityscapes_add_datasample(self):
h = 128
w = 256
num_class = 19
out_file = 'out_file_cityscapes'
image = mmcv.imread(
osp.join(
osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png' # noqa
),
'color')
sem_seg = mmcv.imread(
osp.join(
osp.dirname(__file__),
'../data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png' # noqa
),
'unchanged')
sem_seg = torch.unsqueeze(torch.from_numpy(sem_seg), 0)
gt_sem_seg_data = dict(data=sem_seg)
gt_sem_seg = PixelData(**gt_sem_seg_data)
def test_cityscapes_add_datasample_forward(gt_sem_seg):
data_sample = SegDataSample()
data_sample.gt_sem_seg = gt_sem_seg
with tempfile.TemporaryDirectory() as tmp_dir:
seg_local_visualizer = SegLocalVisualizer(
vis_backends=[dict(type='LocalVisBackend')],
save_dir=tmp_dir)
seg_local_visualizer.dataset_meta = dict(
classes=('road', 'sidewalk', 'building', 'wall', 'fence',
'pole', 'traffic light', 'traffic sign',
'vegetation', 'terrain', 'sky', 'person', 'rider',
'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle'),
palette=[[128, 64, 128], [244, 35, 232], [70, 70, 70],
[102, 102, 156], [190, 153, 153], [153, 153, 153],
[250, 170, 30], [220, 220, 0], [107, 142, 35],
[152, 251, 152], [70, 130, 180], [220, 20, 60],
[255, 0, 0], [0, 0, 142], [0, 0, 70],
[0, 60, 100], [0, 80, 100], [0, 0, 230],
[119, 11, 32]])
# test out_file
seg_local_visualizer.add_datasample(
out_file,
image,
data_sample,
out_file=osp.join(tmp_dir, 'test.png'))
self._assert_image_and_shape(
osp.join(tmp_dir, 'test.png'), (h, w, 3))
# test gt_instances and pred_instances
pred_sem_seg_data = dict(
data=torch.randint(0, num_class, (1, h, w)))
pred_sem_seg = PixelData(**pred_sem_seg_data)
data_sample.pred_sem_seg = pred_sem_seg
# test draw prediction with gt
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h, w * 2, 3))
# test draw prediction without gt
seg_local_visualizer.add_datasample(
out_file, image, data_sample, draw_gt=False)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h, w, 3))
if torch.cuda.is_available():
test_cityscapes_add_datasample_forward(gt_sem_seg.cuda())
test_cityscapes_add_datasample_forward(gt_sem_seg)
def _assert_image_and_shape(self, out_file, out_shape):
assert os.path.exists(out_file)
drawn_img = cv2.imread(out_file)
assert drawn_img.shape == out_shape
def test_add_datasample_depth(self):
h = 10
w = 12
out_file = 'out_file'
image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
# test gt_depth_map
gt_depth_map = PixelData(data=torch.rand(1, h, w))
def test_add_datasample_forward_depth(gt_depth_map):
data_sample = SegDataSample()
data_sample.gt_depth_map = gt_depth_map
with tempfile.TemporaryDirectory() as tmp_dir:
seg_local_visualizer = SegLocalVisualizer(
vis_backends=[dict(type='LocalVisBackend')],
save_dir=tmp_dir)
seg_local_visualizer.dataset_meta = dict(
classes=('background', 'foreground'),
palette=[[120, 120, 120], [6, 230, 230]])
# test out_file
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
assert os.path.exists(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'))
drawn_img = cv2.imread(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'))
assert drawn_img.shape == (h * 2, w, 3)
# test gt_instances and pred_instances
pred_depth_map = PixelData(data=torch.rand(1, h, w))
data_sample.pred_depth_map = pred_depth_map
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h * 2, w * 2, 3))
seg_local_visualizer.add_datasample(
out_file, image, data_sample, draw_gt=False)
self._assert_image_and_shape(
osp.join(tmp_dir, 'vis_data', 'vis_image',
out_file + '_0.png'), (h * 2, w, 3))
if torch.cuda.is_available():
test_add_datasample_forward_depth(gt_depth_map.cuda())
test_add_datasample_forward_depth(gt_depth_map)
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