def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None):
mmcv.mkdir_or_exist(out_dir)
for mAP_info in mAPs:
index, mAP = mAP_info
data_info = dataset.prepare_train_img(index)
# calc save file path
filename = data_info['filename']
if data_info['img_prefix'] is not None:
filename = osp.join(data_info['img_prefix'], filename)
else:
filename = data_info['filename']
fname, name = osp.splitext(osp.basename(filename))
save_filename = fname + '_' + str(round(mAP, 3)) + name
out_file = osp.join(out_dir, save_filename)
imshow_gt_det_bboxes(data_info['img'],
data_info,
results[index],
dataset.CLASSES,
gt_bbox_color=dataset.PALETTE,
gt_text_color=(200, 200, 200),
gt_mask_color=dataset.PALETTE,
det_bbox_color=dataset.PALETTE,
det_text_color=(200, 200, 200),
det_mask_color=dataset.PALETTE,
show=self.show,
score_thr=self.score_thr,
wait_time=self.wait_time,
out_file=out_file)
def test_imshow_gt_det_bboxes():
tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image',
'image.jpg')
image = np.ones((10, 10, 3), np.uint8)
bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
label = np.array([0, 1])
annotation = dict(gt_bboxes=bbox, gt_labels=label)
det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]])
result = [det_result]
out_image = vis.imshow_gt_det_bboxes(image,
annotation,
result,
out_file=tmp_filename,
show=False)
assert osp.isfile(tmp_filename)
assert image.shape == out_image.shape
assert not np.allclose(image, out_image)
os.remove(tmp_filename)
# test grayscale images
image = np.ones((10, 10), np.uint8)
bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
label = np.array([0, 1])
annotation = dict(gt_bboxes=bbox, gt_labels=label)
det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]])
result = [det_result]
vis.imshow_gt_det_bboxes(image,
annotation,
result,
out_file=tmp_filename,
show=False)
assert osp.isfile(tmp_filename)
os.remove(tmp_filename)
# test numpy mask
gt_mask = np.ones((2, 10, 10))
annotation['gt_masks'] = gt_mask
vis.imshow_gt_det_bboxes(image,
annotation,
result,
out_file=tmp_filename,
show=False)
assert osp.isfile(tmp_filename)
os.remove(tmp_filename)
# test tensor mask
gt_mask = torch.ones((2, 10, 10))
annotation['gt_masks'] = gt_mask
vis.imshow_gt_det_bboxes(image,
annotation,
result,
out_file=tmp_filename,
show=False)
assert osp.isfile(tmp_filename)
os.remove(tmp_filename)
# test unsupported type
annotation['gt_masks'] = []
with pytest.raises(TypeError):
vis.imshow_gt_det_bboxes(image, annotation, result, show=False)
def _save_image_gts_results(self,
dataset,
results,
performances,
out_dir=None):
"""Display or save image with groung truths and predictions from a
model.
Args:
dataset (Dataset): A PyTorch dataset.
results (list): Object detection or panoptic segmentation
results from test results pkl file.
performances (dict): A dict contains samples's indices
in dataset and model's performance on them.
out_dir (str, optional): The filename to write the image.
Defaults: None.
"""
mmcv.mkdir_or_exist(out_dir)
for performance_info in performances:
index, performance = performance_info
data_info = dataset.prepare_train_img(index)
# calc save file path
filename = data_info['filename']
if data_info['img_prefix'] is not None:
filename = osp.join(data_info['img_prefix'], filename)
else:
filename = data_info['filename']
fname, name = osp.splitext(osp.basename(filename))
save_filename = fname + '_' + str(round(performance, 3)) + name
out_file = osp.join(out_dir, save_filename)
imshow_gt_det_bboxes(data_info['img'],
data_info,
results[index],
dataset.CLASSES,
gt_bbox_color=dataset.PALETTE,
gt_text_color=(200, 200, 200),
gt_mask_color=dataset.PALETTE,
det_bbox_color=dataset.PALETTE,
det_text_color=(200, 200, 200),
det_mask_color=dataset.PALETTE,
show=self.show,
score_thr=self.score_thr,
wait_time=self.wait_time,
out_file=out_file,
overlay_gt_pred=self.overlay_gt_pred)
