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import os |
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import json |
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import numpy as np |
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from glob import glob |
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from tqdm import tqdm |
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from .dataset import Dataset |
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from .video import Video |
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class VOTVideo(Video): |
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""" |
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Args: |
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name: video name |
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root: dataset root |
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video_dir: video directory |
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init_rect: init rectangle |
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img_names: image names |
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gt_rect: groundtruth rectangle |
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camera_motion: camera motion tag |
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illum_change: illum change tag |
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motion_change: motion change tag |
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size_change: size change |
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occlusion: occlusion |
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""" |
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def __init__(self, name, root, video_dir, init_rect, img_names, gt_rect, |
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camera_motion, illum_change, motion_change, size_change, occlusion, width, height): |
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super(VOTVideo, self).__init__(name, root, video_dir, init_rect, img_names, gt_rect, None) |
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self.tags= {'all': [1] * len(gt_rect)} |
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self.tags['camera_motion'] = camera_motion |
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self.tags['illum_change'] = illum_change |
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self.tags['motion_change'] = motion_change |
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self.tags['size_change'] = size_change |
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self.tags['occlusion'] = occlusion |
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self.width = width |
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self.height = height |
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all_tag = [v for k, v in self.tags.items() if len(v) > 0 ] |
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self.tags['empty'] = np.all(1 - np.array(all_tag), axis=1).astype(np.int32).tolist() |
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self.tag_names = list(self.tags.keys()) |
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def select_tag(self, tag, start=0, end=0): |
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if tag == 'empty': |
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return self.tags[tag] |
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return self.tags[tag][start:end] |
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def load_tracker(self, path, tracker_names=None, store=True): |
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""" |
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Args: |
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path(str): path to result |
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tracker_name(list): name of tracker |
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""" |
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if not tracker_names: |
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tracker_names = [x.split('/')[-1] for x in glob(path) |
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if os.path.isdir(x)] |
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if isinstance(tracker_names, str): |
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tracker_names = [tracker_names] |
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for name in tracker_names: |
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traj_files = glob(os.path.join(path, name, 'baseline', self.name, '*0*.txt')) |
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if len(traj_files) == 15: |
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traj_files = traj_files |
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else: |
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traj_files = traj_files[0:1] |
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pred_traj = [] |
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for traj_file in traj_files: |
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with open(traj_file, 'r') as f: |
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traj = [list(map(float, x.strip().split(','))) |
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for x in f.readlines()] |
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pred_traj.append(traj) |
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if store: |
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self.pred_trajs[name] = pred_traj |
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else: |
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return pred_traj |
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class VOTDataset(Dataset): |
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""" |
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Args: |
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name: dataset name, should be 'VOT2018', 'VOT2016' |
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dataset_root: dataset root |
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load_img: wether to load all imgs |
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""" |
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def __init__(self, name, dataset_root): |
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super(VOTDataset, self).__init__(name, dataset_root) |
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try: |
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with open(os.path.join(dataset_root, name+'.json'), 'r') as f: |
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meta_data = json.load(f) |
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except: |
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download_str = '# download json file for eval toolkit\n'+\ |
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'cd $SiamMask/data\n'+\ |
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'wget http://www.robots.ox.ac.uk/~qwang/VOT2016.json\n'+\ |
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'wget http://www.robots.ox.ac.uk/~qwang/VOT2018.json' |
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print(download_str) |
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exit() |
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pbar = tqdm(meta_data.keys(), desc='loading '+name, ncols=100) |
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self.videos = {} |
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for video in pbar: |
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pbar.set_postfix_str(video) |
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self.videos[video] = VOTVideo(video, |
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dataset_root, |
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meta_data[video]['video_dir'], |
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meta_data[video]['init_rect'], |
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meta_data[video]['img_names'], |
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meta_data[video]['gt_rect'], |
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meta_data[video]['camera_motion'], |
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meta_data[video]['illum_change'], |
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meta_data[video]['motion_change'], |
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meta_data[video]['size_change'], |
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meta_data[video]['occlusion'], |
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meta_data[video]['width'], |
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meta_data[video]['height']) |
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self.tags = ['all', 'camera_motion', 'illum_change', 'motion_change', |
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'size_change', 'occlusion', 'empty'] |
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