implementation without docs and tutorials

.gitignore

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+.*
+*.pyc
+__pycache__/
+data
+data/
+cache/
+results/
+reports/
+venv/
+!.gitignore

LICENSE

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+MIT License
+
+Copyright (c) 2018 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

got10k/datasets/__init__.py

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+from __future__ import absolute_import
+
+from .got10k import GOT10k
+from .otb import OTB
+from .vot import VOT
+from .vid import ImageNetVID

got10k/datasets/got10k.py

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+from __future__ import absolute_import, print_function
+
+import os
+import glob
+import numpy as np
+import six
+
+
+class GOT10k(object):
+    r"""`GOT-10K <https://got-10k.github.io/>`_ Dataset.
+
+    Publication:
+        ``GOT-10k: A Large High-Diversity Benchmark for Generic Object
+        Tracking in the Wild``, L. Huang, X. Zhao and K. Huang, ArXiv 2018.
+    
+    Args:
+        root_dir (string): Root directory of dataset where ``train``,
+            ``val`` and ``test`` folders exist.
+        subset (string, optional): Specify ``train``, ``val`` or ``test``
+            subset of GOT-10k.
+        return_meta (string, optional): If True, returns ``meta``
+            of each sequence in ``__getitem__`` function, otherwise
+            only returns ``img_files`` and ``anno``.
+    """
+
+    def __init__(self, root_dir, subset='test', return_meta=False):
+        super(GOT10k, self).__init__()
+        assert subset in ['train', 'val', 'test'], 'Unknown subset.'
+
+        self.root_dir = root_dir
+        self.subset = subset
+        self.return_meta = False if subset == 'test' else return_meta
+        self._check_integrity(root_dir, subset)
+
+        list_file = os.path.join(root_dir, subset, 'list.txt')
+        with open(list_file, 'r') as f:
+            self.seq_names = f.read().strip().split('\n')
+        self.seq_dirs = [os.path.join(root_dir, subset, s)
+                         for s in self.seq_names]
+        self.anno_files = [os.path.join(d, 'groundtruth.txt')
+                           for d in self.seq_dirs]
+
+    def __getitem__(self, index):
+        r"""        
+        Args:
+            index (integer or string): Index or name of a sequence.
+        
+        Returns:
+            tuple: (img_files, anno) if ``return_meta`` is False, otherwise
+                (img_files, anno, meta), where ``img_files`` is a list of
+                file names, ``anno`` is a N x 4 (rectangles) numpy array, while
+                ``meta`` is a dict contains meta information about the sequence.
+        """
+        if isinstance(index, six.string_types):
+            if not index in self.seq_names:
+                raise Exception('Sequence {} not found.'.format(index))
+            index = self.seq_names.index(index)
+
+        img_files = sorted(glob.glob(os.path.join(
+            self.seq_dirs[index], '*.jpg')))
+        anno = np.loadtxt(self.anno_files[index], delimiter=',')
+
+        if self.subset == 'test':
+            assert anno.ndim == 1
+            anno = anno[np.newaxis, :]
+        else:
+            assert len(img_files) == len(anno)
+
+        if self.return_meta:
+            meta = self._fetch_meta(self.seq_dirs[index])
+            return img_files, anno, meta
+        else:
+            return img_files, anno
+
+    def __len__(self):
+        return len(self.seq_names)
+
+    def _check_integrity(self, root_dir, subset):
+        assert subset in ['train', 'val', 'test']
+        list_file = os.path.join(root_dir, subset, 'list.txt')
+
+        if os.path.isfile(list_file):
+            with open(list_file, 'r') as f:
+                seq_names = f.read().strip().split('\n')
+
+            # check each sequence folder
+            for seq_name in seq_names:
+                seq_dir = os.path.join(root_dir, subset, seq_name)
+                if not os.path.isdir(seq_dir):
+                    print('Warning: sequence %s not exist.' % seq_name)
+        else:
+            # dataset not exist
+            raise Exception('Dataset not found or corrupted.')
+
+    def _fetch_meta(self, seq_dir):
+        # meta information
+        meta_file = os.path.join(seq_dir, 'meta_info.ini')
+        with open(meta_file) as f:
+            meta = f.read().strip().split('\n')[1:]
+        meta = [line.split(': ') for line in meta]
+        meta = {line[0]: line[1] for line in meta}
+
+        # attributes
+        attributes = ['cover', 'absence', 'cut_by_image']
+        for att in attributes:
+            meta[att] = np.loadtxt(os.path.join(seq_dir, att + '.label'))
+
+        return meta

got10k/datasets/otb.py

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+from __future__ import absolute_import, print_function
+
+import os
+import glob
+import numpy as np
+import io
+import six
+from itertools import chain
+
+from ..utils.ioutils import download, extract
+
+
+class OTB(object):
+    r"""`OTB <http://cvlab.hanyang.ac.kr/tracker_benchmark/>`_ Datasets.
+
+    Publication:
+        ``Object Tracking Benchmark``, Y. Wu, J. Lim and M.-H. Yang, IEEE TPAMI 2015.
+    
+    Args:
+        root_dir (string): Root directory of dataset where sequence
+            folders exist.
+        version (integer or string): Specify the benchmark version, specify as one of
+            ``2013``, ``2015``, ``tb50`` and ``tb100``.
+        download (boolean, optional): If True, downloads the dataset from the internet
+            and puts it in root directory. If dataset is downloaded, it is not
+            downloaded again.
+    """
+
+    __otb13_seqs = ['Basketball', 'Bolt', 'Boy', 'Car4', 'CarDark',
+                    'CarScale', 'Coke', 'Couple', 'Crossing', 'David',
+                    'David2', 'David3', 'Deer', 'Dog1', 'Doll', 'Dudek',
+                    'FaceOcc1', 'FaceOcc2', 'Fish', 'FleetFace',
+                    'Football', 'Football1', 'Freeman1', 'Freeman3',
+                    'Freeman4', 'Girl', 'Ironman', 'Jogging', 'Jumping',
+                    'Lemming', 'Liquor', 'Matrix', 'Mhyang', 'MotorRolling',
+                    'MountainBike', 'Shaking', 'Singer1', 'Singer2',
+                    'Skating1', 'Skiing', 'Soccer', 'Subway', 'Suv',
+                    'Sylvester', 'Tiger1', 'Tiger2', 'Trellis', 'Walking',
+                    'Walking2', 'Woman']
+
+    __tb50_seqs = ['Basketball', 'Biker', 'Bird1', 'BlurBody', 'BlurCar2',
+                   'BlurFace', 'BlurOwl', 'Bolt', 'Box', 'Car1', 'Car4',
+                   'CarDark', 'CarScale', 'ClifBar', 'Couple', 'Crowds',
+                   'David', 'Deer', 'Diving', 'DragonBaby', 'Dudek',
+                   'Football', 'Freeman4', 'Girl', 'Human3', 'Human4',
+                   'Human6', 'Human9', 'Ironman', 'Jump', 'Jumping',
+                   'Liquor', 'Matrix', 'MotorRolling', 'Panda', 'RedTeam',
+                   'Shaking', 'Singer2', 'Skating1', 'Skating2', 'Skiing',
+                   'Soccer', 'Surfer', 'Sylvester', 'Tiger2', 'Trellis',
+                   'Walking', 'Walking2', 'Woman']
+
+    __tb100_seqs = ['Bird2', 'BlurCar1', 'BlurCar3', 'BlurCar4', 'Board',
+                    'Bolt2', 'Boy', 'Car2', 'Car24', 'Coke', 'Coupon',
+                    'Crossing', 'Dancer', 'Dancer2', 'David2', 'David3',
+                    'Dog', 'Dog1', 'Doll', 'FaceOcc1', 'FaceOcc2', 'Fish',
+                    'FleetFace', 'Football1', 'Freeman1', 'Freeman3',
+                    'Girl2', 'Gym', 'Human2', 'Human5', 'Human7', 'Human8',
+                    'Jogging', 'KiteSurf', 'Lemming', 'Man', 'Mhyang',
+                    'MountainBike', 'Rubik', 'Singer1', 'Skater',
+                    'Skater2', 'Subway', 'Suv', 'Tiger1', 'Toy', 'Trans',
+                    'Twinnings', 'Vase'] + __tb50_seqs
+
+    __otb15_seqs = __tb100_seqs
+
+    __version_dict = {
+        2013: __otb13_seqs,
+        2015: __otb15_seqs,
+        'otb2013': __otb13_seqs,
+        'otb2015': __otb15_seqs,
+        'tb50': __tb50_seqs,
+        'tb100': __tb100_seqs}
+
+    def __init__(self, root_dir, version=2015, download=True):
+        super(OTB, self).__init__()
+        assert version in self.__version_dict
+
+        self.root_dir = root_dir
+        self.version = version
+        if download:
+            self._download(root_dir, version)
+        self._check_integrity(root_dir, version)
+
+        valid_seqs = self.__version_dict[version]
+        self.anno_files = sorted(list(chain.from_iterable(glob.glob(
+            os.path.join(root_dir, s, 'groundtruth*.txt')) for s in valid_seqs)))
+        # remove empty annotation files
+        # (e.g., groundtruth_rect.1.txt of Human4)
+        self.anno_files = self._filter_files(self.anno_files)
+        self.seq_dirs = [os.path.dirname(f) for f in self.anno_files]
+        self.seq_names = [os.path.basename(d) for d in self.seq_dirs]
+        # rename repeated sequence names
+        # (e.g., Jogging and Skating2)
+        self.seq_names = self._rename_seqs(self.seq_names)
+
+    def __getitem__(self, index):
+        r"""        
+        Args:
+            index (integer or string): Index or name of a sequence.
+        
+        Returns:
+            tuple: (img_files, anno), where ``img_files`` is a list of
+                file names and ``anno`` is a N x 4 (rectangles) numpy array.
+        """
+        if isinstance(index, six.string_types):
+            if not index in self.seq_names:
+                raise Exception('Sequence {} not found.'.format(index))
+            index = self.seq_names.index(index)
+
+        img_files = sorted(glob.glob(
+            os.path.join(self.seq_dirs[index], 'img/*.jpg')))
+
+        # special sequences
+        # (visit http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html for detail)
+        seq_name = self.seq_names[index]
+        if seq_name.lower() == 'david':
+            img_files = img_files[300-1:770]
+        elif seq_name.lower() == 'football1':
+            img_files = img_files[:74]
+        elif seq_name.lower() == 'freeman3':
+            img_files = img_files[:460]
+        elif seq_name.lower() == 'freeman4':
+            img_files = img_files[:283]
+        elif seq_name.lower() == 'diving':
+            img_files = img_files[:215]
+
+        # to deal with different delimeters
+        with open(self.anno_files[index], 'r') as f:
+            anno = np.loadtxt(io.StringIO(f.read().replace(',', ' ')))
+        assert len(img_files) == len(anno)
+        assert anno.shape[1] == 4
+
+        return img_files, anno
+
+    def __len__(self):
+        return len(self.seq_names)
+
+    def _filter_files(self, filenames):
+        filtered_files = []
+        for filename in filenames:
+            with open(filename, 'r') as f:
+                if f.read().strip() == '':
+                    print('Warning: %s is empty.' % filename)
+                else:
+                    filtered_files.append(filename)
+
+        return filtered_files
+
+    def _rename_seqs(self, seq_names):
+        # in case some sequences may have multiple targets
+        renamed_seqs = []
+        for i, seq_name in enumerate(seq_names):
+            if seq_names.count(seq_name) == 1:
+                renamed_seqs.append(seq_name)
+            else:
+                ind = seq_names[:i + 1].count(seq_name)
+                renamed_seqs.append('%s.%d' % (seq_name, ind))
+
+        return renamed_seqs
+
+    def _download(self, root_dir, version):
+        assert version in self.__version_dict
+        seq_names = self.__version_dict[version]
+
+        if not os.path.isdir(root_dir):
+            os.makedirs(root_dir)
+        elif all([os.path.isdir(os.path.join(root_dir, s)) for s in seq_names]):
+            print('Files already downloaded.')
+            return
+
+        url_fmt = 'http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/%s.zip'
+        for seq_name in seq_names:
+            seq_dir = os.path.join(root_dir, seq_name)
+            if os.path.isdir(seq_dir):
+                continue
+            url = url_fmt % seq_name
+            zip_file = os.path.join(root_dir, seq_name + '.zip')
+            print('Downloading to %s...' % zip_file)
+            download(url, zip_file)
+            print('\nExtracting to %s...' % root_dir)
+            extract(zip_file, root_dir)
+
+        return root_dir
+
+    def _check_integrity(self, root_dir, version):
+        assert version in self.__version_dict
+        seq_names = self.__version_dict[version]
+
+        if os.path.isdir(root_dir) and len(os.listdir(root_dir)) > 0:
+            # check each sequence folder
+            for seq_name in seq_names:
+                seq_dir = os.path.join(root_dir, seq_name)
+                if not os.path.isdir(seq_dir):
+                    print('Warning: sequence %s not exist.' % seq_name)
+        else:
+            # dataset not exist
+            raise Exception('Dataset not found or corrupted. ' +
+                            'You can use download=True to download it.')