def visualization(self, image_index, save_dir):
'''
visualize the images loaded into the dataset
'''
image_path = self.image_info[image_index]['path']
_, filename, _ = fileparts(image_path)
image = self.load_image(image_index)
masks, class_ids = self.load_mask(image_index)
bbox = bboxes_from_mask(masks) # TLBR format, N x 4
fig, _ = visualize_image_with_bbox_mask(image,
boxes=bbox,
masks=masks,
class_ids=class_ids,
class_names=['BG'] +
cityscape_class_names)
save_path_tmp = os.path.join(save_dir, filename + '.jpg')
save_vis_close_helper(fig=fig,
transparent=False,
save_path=save_path_tmp)
pin_memory=True)
count = 1
vis_dir = os.path.join(dataset_dir, gttype, 'vis_%s' % split)
mkdir_if_missing(vis_dir)
for images, image_metas, rpn_match, rpn_bbox, gt_class_ids, gt_boxes, gt_masks, image_index, filename in train_generator:
print('Cityscapes Dataset Visualization Processing: %d' % count)
# dataset_cityscape.visualization(image_index.item(), save_dir=vis_dir)
image_path = dataset_cityscape.image_info[image_index.item()]['path']
_, filename, _ = fileparts(image_path)
images = images.numpy()[0].transpose(1, 2, 0)
images = unmold_image(images, config)
gt_masks, gt_boxes, gt_class_ids = gt_masks.numpy()[0], gt_boxes.numpy(
)[0].astype('int64'), gt_class_ids.numpy()[0]
gt_masks = gt_masks.transpose(1, 2, 0)
gt_masks = expand_mask(gt_boxes, gt_masks, images.shape)
gt_boxes[:, [0, 1]] = gt_boxes[:, [1, 0]]
gt_boxes[:, [2, 3]] = gt_boxes[:, [3, 2]]
fig, _ = visualize_image_with_bbox_mask(images,
boxes=gt_boxes,
masks=gt_masks,
class_ids=gt_class_ids,
class_names=['BG'] +
cityscape_class_names)
save_path_tmp = os.path.join(vis_dir, filename + '.jpg')
save_vis_close_helper(fig=fig, transparent=False, save_path=save_path_tmp)
count += 1