def savefig(): visualize.display_instances(image, gt_bbox, gt_mask, gt_class_ids, [categories.category2name(i) for i in range(categories.cate_cnt)], savefilename=os.path.join(save_visual_path, '%05d_gt.jpg' % i)) visualize.display_instances(image, bbox, mask, class_ids, [categories.category2name(i) for i in range(categories.cate_cnt)], savefilename=os.path.join(save_visual_path, '%05d_pred.jpg' % i))
def load_s3d(self, subset): assert subset in ['train', 'test', 'val'] file_dict = dataset_tool.load_fileinfo() for i in range(1, categories.cate_cnt): self.add_class("s3d", i, categories.category2name(i)) if subset == 'train': limit = [0, 3000] elif subset == 'val': limit = [3000, 3250] else: limit = [3250, 3500] count = 0 for scene in file_dict: if not (scene >= limit[0] and scene < limit[1]): continue for room in file_dict[scene]: for position in file_dict[scene][room]: count += 1 full_path = os.path.join(dataset_path, 'scene_%05d' % scene, '2D_rendering', str(room), 'perspective', 'full', str(position)) self.add_image(source="s3d", image_id=count, path=self.full_path(scene, room, position), width=IMAGE_WIDTH, height=IMAGE_HEIGHT, scene=scene, room=room, position=position)