| from typing import Union, TextIO
|
|
|
| import numpy as np
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| from numba import jit
|
|
|
| from lib.test.evaluation.data import SequenceList, BaseDataset, Sequence
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|
|
|
|
| class VOTDataset(BaseDataset):
|
| """
|
| VOT2018 dataset
|
|
|
| Publication:
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| The sixth Visual Object Tracking VOT2018 challenge results.
|
| Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pfugfelder, Luka Cehovin Zajc, Tomas Vojir,
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| Goutam Bhat, Alan Lukezic et al.
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| ECCV, 2018
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| https://prints.vicos.si/publications/365
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|
|
| Download the dataset from http://www.votchallenge.net/vot2018/dataset.html
|
| """
|
| def __init__(self, year=18):
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| super().__init__()
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| self.year = year
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| if year == 18:
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| self.base_path = self.env_settings.vot18_path
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| elif year == 20:
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| self.base_path = self.env_settings.vot20_path
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| elif year == 22:
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| self.base_path = self.env_settings.vot22_path
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| self.sequence_list = self._get_sequence_list(year)
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|
|
| def get_sequence_list(self):
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| return SequenceList([self._construct_sequence(s) for s in self.sequence_list])
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|
|
| def _construct_sequence(self, sequence_name):
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| sequence_path = sequence_name
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| nz = 8
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| ext = 'jpg'
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| start_frame = 1
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|
|
| anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name)
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|
|
| if self.year == 18 or self.year == 22:
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| try:
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| ground_truth_rect = np.loadtxt(str(anno_path), dtype=np.float64)
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| except:
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| ground_truth_rect = np.loadtxt(str(anno_path), delimiter=',', dtype=np.float64)
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|
|
| end_frame = ground_truth_rect.shape[0]
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|
|
| frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
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| sequence_path=sequence_path, frame=frame_num, nz=nz, ext=ext)
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| for frame_num in range(start_frame, end_frame+1)]
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|
|
|
|
| if ground_truth_rect.shape[1] > 4:
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| gt_x_all = ground_truth_rect[:, [0, 2, 4, 6]]
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| gt_y_all = ground_truth_rect[:, [1, 3, 5, 7]]
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|
|
| x1 = np.amin(gt_x_all, 1).reshape(-1,1)
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| y1 = np.amin(gt_y_all, 1).reshape(-1,1)
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| x2 = np.amax(gt_x_all, 1).reshape(-1,1)
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| y2 = np.amax(gt_y_all, 1).reshape(-1,1)
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|
|
| ground_truth_rect = np.concatenate((x1, y1, x2-x1, y2-y1), 1)
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|
|
| elif self.year == 20:
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| ground_truth_rect = read_file(str(anno_path))
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| ground_truth_rect = np.array(ground_truth_rect, dtype=np.float64)
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| end_frame = ground_truth_rect.shape[0]
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|
|
| frames = ['{base_path}/{sequence_path}/color/{frame:0{nz}}.{ext}'.format(base_path=self.base_path,
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| sequence_path=sequence_path,
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| frame=frame_num, nz=nz, ext=ext)
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| for frame_num in range(start_frame, end_frame + 1)]
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|
|
| else:
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| raise NotImplementedError
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|
|
| return Sequence(sequence_name, frames, 'vot', ground_truth_rect)
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|
|
| def __len__(self):
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| return len(self.sequence_list)
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|
|
| def _get_sequence_list(self, year):
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| if year == 18:
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| sequence_list= ['ants1',
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| 'ants3',
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| 'bag',
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| 'ball1',
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| 'ball2',
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| 'basketball',
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| 'birds1',
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| 'blanket',
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| 'bmx',
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| 'bolt1',
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| 'bolt2',
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| 'book',
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| 'butterfly',
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| 'car1',
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| 'conduction1',
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| 'crabs1',
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| 'crossing',
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| 'dinosaur',
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| 'drone_across',
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| 'drone_flip',
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| 'drone1',
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| 'fernando',
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| 'fish1',
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| 'fish2',
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| 'fish3',
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| 'flamingo1',
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| 'frisbee',
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| 'girl',
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| 'glove',
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| 'godfather',
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| 'graduate',
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| 'gymnastics1',
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| 'gymnastics2',
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| 'gymnastics3',
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| 'hand',
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| 'handball1',
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| 'handball2',
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| 'helicopter',
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| 'iceskater1',
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| 'iceskater2',
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| 'leaves',
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| 'matrix',
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| 'motocross1',
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| 'motocross2',
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| 'nature',
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| 'pedestrian1',
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| 'rabbit',
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| 'racing',
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| 'road',
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| 'shaking',
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| 'sheep',
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| 'singer2',
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| 'singer3',
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| 'soccer1',
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| 'soccer2',
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| 'soldier',
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| 'tiger',
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| 'traffic',
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| 'wiper',
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| 'zebrafish1']
|
| elif year == 20:
|
|
|
| sequence_list= ['agility',
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| 'ants1',
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| 'ball2',
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| 'ball3',
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| 'basketball',
|
| 'birds1',
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| 'bolt1',
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| 'book',
|
| 'butterfly',
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| 'car1',
|
| 'conduction1',
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| 'crabs1',
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| 'dinosaur',
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| 'dribble',
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| 'drone1',
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| 'drone_across',
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| 'drone_flip',
|
| 'fernando',
|
| 'fish1',
|
| 'fish2',
|
| 'flamingo1',
|
| 'frisbee',
|
| 'girl',
|
| 'glove',
|
| 'godfather',
|
| 'graduate',
|
| 'gymnastics1',
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| 'gymnastics2',
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| 'gymnastics3',
|
| 'hand',
|
| 'hand02',
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| 'hand2',
|
| 'handball1',
|
| 'handball2',
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| 'helicopter',
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| 'iceskater1',
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| 'iceskater2',
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| 'lamb',
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| 'leaves',
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| 'marathon',
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| 'matrix',
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| 'monkey',
|
| 'motocross1',
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| 'nature',
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| 'polo',
|
| 'rabbit',
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| 'rabbit2',
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| 'road',
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| 'rowing',
|
| 'shaking',
|
| 'singer2',
|
| 'singer3',
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| 'soccer1',
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| 'soccer2',
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| 'soldier',
|
| 'surfing',
|
| 'tiger',
|
| 'wheel',
|
| 'wiper',
|
| 'zebrafish1']
|
| elif year == 22:
|
| sequence_list= ['agility',
|
| 'animal',
|
| 'ants1',
|
| 'bag',
|
| 'ball2',
|
| 'ball3',
|
| 'basketball',
|
| 'birds1',
|
| 'birds2',
|
| 'bolt1',
|
| 'book',
|
| 'bubble',
|
| 'butterfly',
|
| 'car1',
|
| 'conduction1',
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| 'crabs1',
|
| 'dinosaur',
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| 'diver',
|
| 'drone1',
|
| 'drone_across',
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| 'fernando',
|
| 'fish1',
|
| 'fish2',
|
| 'flamingo1',
|
| 'frisbee',
|
| 'girl',
|
| 'graduate',
|
| 'gymnastics1',
|
| 'gymnastics2',
|
| 'gymnastics3',
|
| 'hand',
|
| 'hand2',
|
| 'handball1',
|
| 'handball2',
|
| 'helicopter',
|
| 'iceskater1',
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| 'iceskater2',
|
| 'kangaroo',
|
| 'lamb',
|
| 'leaves',
|
| 'marathon',
|
| 'matrix',
|
| 'monkey',
|
| 'motocross1',
|
| 'nature',
|
| 'polo',
|
| 'rabbit',
|
| 'rabbit2',
|
| 'rowing',
|
| 'shaking',
|
| 'singer2',
|
| 'singer3',
|
| 'snake',
|
| 'soccer1',
|
| 'soccer2',
|
| 'soldier',
|
| 'surfing',
|
| 'tennis',
|
| 'tiger',
|
| 'wheel',
|
| 'wiper',
|
| 'zebrafish1']
|
|
|
| else:
|
| raise NotImplementedError
|
|
|
| return sequence_list
|
|
|
|
|
| def parse(string):
|
| """
|
| parse string to the appropriate region format and return region object
|
| """
|
| from vot.region.shapes import Rectangle, Polygon, Mask
|
|
|
|
|
| if string[0] == 'm':
|
|
|
| m_, offset_, region = create_mask_from_string(string[1:].split(','))
|
|
|
| return region
|
| else:
|
|
|
| raise NotImplementedError
|
| print('Unknown region format.')
|
| return None
|
|
|
|
|
| def read_file(fp: Union[str, TextIO]):
|
| if isinstance(fp, str):
|
| with open(fp) as file:
|
| lines = file.readlines()
|
| else:
|
| lines = fp.readlines()
|
|
|
| regions = []
|
|
|
| for i, line in enumerate(lines):
|
| regions.append(parse(line.strip()))
|
| return regions
|
|
|
|
|
| def create_mask_from_string(mask_encoding):
|
| """
|
| mask_encoding: a string in the following format: x0, y0, w, h, RLE
|
| output: mask, offset
|
| mask: 2-D binary mask, size defined in the mask encoding
|
| offset: (x, y) offset of the mask in the image coordinates
|
| """
|
| elements = [int(el) for el in mask_encoding]
|
| tl_x, tl_y, region_w, region_h = elements[:4]
|
| rle = np.array([el for el in elements[4:]], dtype=np.int32)
|
|
|
|
|
| mask = rle_to_mask(rle, region_w, region_h)
|
| region = [tl_x, tl_y, region_w, region_h]
|
|
|
| return mask, (tl_x, tl_y), region
|
|
|
| @jit(nopython=True)
|
| def rle_to_mask(rle, width, height):
|
| """
|
| rle: input rle mask encoding
|
| each evenly-indexed element represents number of consecutive 0s
|
| each oddly indexed element represents number of consecutive 1s
|
| width and height are dimensions of the mask
|
| output: 2-D binary mask
|
| """
|
|
|
| v = [0] * (width * height)
|
|
|
|
|
| idx_ = 0
|
| for i in range(len(rle)):
|
| if i % 2 != 0:
|
|
|
| for j in range(rle[i]):
|
| v[idx_+j] = 1
|
| idx_ += rle[i] |
