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[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
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@ -199,9 +199,8 @@ def show_result_pyplot(model: BaseSegmentor,
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draw_gt=draw_gt,
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draw_pred=draw_pred,
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wait_time=wait_time,
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out_file=out_file,
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show=show)
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vis_img = visualizer.get_image()
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if out_file is not None:
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mmcv.imwrite(vis_img, out_file)
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return vis_img
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@ -1,6 +1,7 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Dict, List, Optional, Tuple
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import mmcv
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import numpy as np
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from mmengine.dist import master_only
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from mmengine.structures import PixelData
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@ -99,15 +100,18 @@ class SegLocalVisualizer(Visualizer):
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return self.get_image()
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@master_only
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def add_datasample(self,
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name: str,
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image: np.ndarray,
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data_sample: Optional[SegDataSample] = None,
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draw_gt: bool = True,
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draw_pred: bool = True,
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show: bool = False,
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wait_time: float = 0,
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step: int = 0) -> None:
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def add_datasample(
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self,
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name: str,
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image: np.ndarray,
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data_sample: Optional[SegDataSample] = None,
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draw_gt: bool = True,
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draw_pred: bool = True,
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show: bool = False,
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wait_time: float = 0,
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# TODO: Supported in mmengine's Viusalizer.
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out_file: Optional[str] = None,
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step: int = 0) -> None:
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"""Draw datasample and save to all backends.
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- If GT and prediction are plotted at the same time, they are
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@ -115,6 +119,9 @@ class SegLocalVisualizer(Visualizer):
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ground truth and the right image is the prediction.
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- If ``show`` is True, all storage backends are ignored, and
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the images will be displayed in a local window.
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- If ``out_file`` is specified, the drawn image will be
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saved to ``out_file``. it is usually used when the display
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is not available.
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Args:
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name (str): The image identifier.
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@ -128,6 +135,7 @@ class SegLocalVisualizer(Visualizer):
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Defaults to True.
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show (bool): Whether to display the drawn image. Default to False.
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wait_time (float): The interval of show (s). Defaults to 0.
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out_file (str): Path to output file. Defaults to None.
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step (int): Global step value to record. Defaults to 0.
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"""
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classes = self.dataset_meta.get('classes', None)
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@ -166,5 +174,8 @@ class SegLocalVisualizer(Visualizer):
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if show:
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self.show(drawn_img, win_name=name, wait_time=wait_time)
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if out_file is not None:
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mmcv.imwrite(drawn_img, out_file)
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else:
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self.add_image(name, drawn_img, step)
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@ -118,19 +118,14 @@ class TestSegLocalVisualizer(TestCase):
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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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seg_local_visualizer.add_datasample(out_file, image,
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data_sample)
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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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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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@ -139,12 +134,13 @@ class TestSegLocalVisualizer(TestCase):
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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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