[Enhancement]Add out_file in add_datasample to directly save image (#2090)

* [Enhancement]Add `out_file` in add_datasample to for save vis image directly

* comments

* ut
This commit is contained in:
Miao Zheng 2022-09-20 15:23:13 +08:00 committed by GitHub
parent 230246f557
commit 2a183283f5
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3 changed files with 30 additions and 24 deletions

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@ -199,9 +199,8 @@ def show_result_pyplot(model: BaseSegmentor,
draw_gt=draw_gt,
draw_pred=draw_pred,
wait_time=wait_time,
out_file=out_file,
show=show)
vis_img = visualizer.get_image()
if out_file is not None:
mmcv.imwrite(vis_img, out_file)
return vis_img

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@ -1,6 +1,7 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Tuple
import mmcv
import numpy as np
from mmengine.dist import master_only
from mmengine.structures import PixelData
@ -99,15 +100,18 @@ class SegLocalVisualizer(Visualizer):
return self.get_image()
@master_only
def add_datasample(self,
name: str,
image: np.ndarray,
data_sample: Optional[SegDataSample] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: float = 0,
step: int = 0) -> None:
def add_datasample(
self,
name: str,
image: np.ndarray,
data_sample: Optional[SegDataSample] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: float = 0,
# TODO: Supported in mmengine's Viusalizer.
out_file: Optional[str] = None,
step: int = 0) -> None:
"""Draw datasample and save to all backends.
- If GT and prediction are plotted at the same time, they are
@ -115,6 +119,9 @@ class SegLocalVisualizer(Visualizer):
ground truth and the right image is the prediction.
- If ``show`` is True, all storage backends are ignored, and
the images will be displayed in a local window.
- If ``out_file`` is specified, the drawn image will be
saved to ``out_file``. it is usually used when the display
is not available.
Args:
name (str): The image identifier.
@ -128,6 +135,7 @@ class SegLocalVisualizer(Visualizer):
Defaults to True.
show (bool): Whether to display the drawn image. Default to False.
wait_time (float): The interval of show (s). Defaults to 0.
out_file (str): Path to output file. Defaults to None.
step (int): Global step value to record. Defaults to 0.
"""
classes = self.dataset_meta.get('classes', None)
@ -166,5 +174,8 @@ class SegLocalVisualizer(Visualizer):
if show:
self.show(drawn_img, win_name=name, wait_time=wait_time)
if out_file is not None:
mmcv.imwrite(drawn_img, out_file)
else:
self.add_image(name, drawn_img, step)

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@ -118,19 +118,14 @@ class TestSegLocalVisualizer(TestCase):
[255, 0, 0], [0, 0, 142], [0, 0, 70],
[0, 60, 100], [0, 80, 100], [0, 0, 230],
[119, 11, 32]])
seg_local_visualizer.add_datasample(out_file, image,
data_sample)
# 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)
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(
@ -139,12 +134,13 @@ class TestSegLocalVisualizer(TestCase):
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(