longmalongma
commented
3 years ago Have you considered also supporting DAVIS2016 exclusively? Mostly because of the single object segmentation. I managed to adjust the code, appending the diff in case anyone will need it. I can also do a pull request. Props on the code man :)
diff --git a/davis2017/davis.py b/davis2017/davis.py index d831be6..8891b88 100644 --- a/davis2017/davis.py +++ b/davis2017/davis.py @@ -8,10 +8,11 @@ from PIL import Image class DAVIS(object): SUBSET_OPTIONS = ['train', 'val', 'test-dev', 'test-challenge'] TASKS = ['semi-supervised', 'unsupervised'] + YEARS = ['2016', '2017', '2019'] DATASET_WEB = 'https://davischallenge.org/davis2017/code.html' VOID_LABEL = 255 - def __init__(self, root, task='unsupervised', subset='val', sequences='all', resolution='480p', codalab=False): + def __init__(self, root, task='unsupervised', subset='val', sequences='all', resolution='480p', codalab=False, year='2017'): """ Class to read the DAVIS dataset :param root: Path to the DAVIS folder that contains JPEGImages, Annotations, etc. folders. @@ -24,6 +25,8 @@ class DAVIS(object): raise ValueError(f'Subset should be in {self.SUBSET_OPTIONS}') if task not in self.TASKS: raise ValueError(f'The only tasks that are supported are {self.TASKS}') + if year not in self.YEARS: + raise ValueError(f'Year should be one of the following {self.YEARS}') self.task = task self.subset = subset @@ -31,8 +34,12 @@ class DAVIS(object): self.img_path = os.path.join(self.root, 'JPEGImages', resolution) annotations_folder = 'Annotations' if task == 'semi-supervised' else 'Annotations_unsupervised' self.mask_path = os.path.join(self.root, annotations_folder, resolution) - year = '2019' if task == 'unsupervised' and (subset == 'test-dev' or subset == 'test-challenge') else '2017' - self.imagesets_path = os.path.join(self.root, 'ImageSets', year) + + self.year = year + if self.year == '2019' and not (task == 'unsupervised' and (subset == 'test-dev' or subset == 'test-challenge')): + raise ValueError("Set 'task' to 'unsupervised' and subset to 'test-dev' or 'test-challenge'") + + self.imagesets_path = os.path.join(self.root, 'ImageSets', self.year) self._check_directories() @@ -95,6 +102,10 @@ class DAVIS(object): tmp = tmp * np.arange(1, num_objects + 1)[:, None, None, None] masks = (tmp == masks[None, ...]) masks = masks > 0 + else: + # for single object evaluation (e.g. DAVIS2016) + masks = np.expand_dims(masks, axis=0) + masks = masks > 0 return masks, masks_void, masks_id def get_sequences(self): diff --git a/davis2017/evaluation.py b/davis2017/evaluation.py index 7bfb80f..eae777c 100644 --- a/davis2017/evaluation.py +++ b/davis2017/evaluation.py @@ -12,7 +12,7 @@ from scipy.optimize import linear_sum_assignment class DAVISEvaluation(object): - def __init__(self, davis_root, task, gt_set, sequences='all', codalab=False): + def __init__(self, davis_root, task, gt_set, sequences='all', codalab=False, year='2017'): """ Class to evaluate DAVIS sequences from a certain set and for a certain task :param davis_root: Path to the DAVIS folder that contains JPEGImages, Annotations, etc. folders. @@ -22,7 +22,8 @@ class DAVISEvaluation(object): """ self.davis_root = davis_root self.task = task - self.dataset = DAVIS(root=davis_root, task=task, subset=gt_set, sequences=sequences, codalab=codalab) + self.year = year + self.dataset = DAVIS(root=davis_root, task=task, subset=gt_set, sequences=sequences, codalab=codalab, year=self.year) @staticmethod def _evaluate_semisupervised(all_gt_masks, all_res_masks, all_void_masks, metric): @@ -77,10 +78,12 @@ class DAVISEvaluation(object): if 'F' in metric: metrics_res['F'] = {"M": [], "R": [], "D": [], "M_per_object": {}} + separate_objects_masks = self.year != '2016' + # Sweep all sequences results = Results(root_dir=res_path) for seq in tqdm(list(self.dataset.get_sequences())): - all_gt_masks, all_void_masks, all_masks_id = self.dataset.get_all_masks(seq, True) + all_gt_masks, all_void_masks, all_masks_id = self.dataset.get_all_masks(seq, separate_objects_masks) if self.task == 'semi-supervised': all_gt_masks, all_masks_id = all_gt_masks[:, 1:-1, :, :], all_masks_id[1:-1] all_res_masks = results.read_masks(seq, all_masks_id) diff --git a/evaluation_method.py b/evaluation_method.py index 04f67d1..d364f81 100644 --- a/evaluation_method.py +++ b/evaluation_method.py @@ -20,6 +20,8 @@ parser.add_argument('--task', type=str, help='Task to evaluate the results', def choices=['semi-supervised', 'unsupervised']) parser.add_argument('--results_path', type=str, help='Path to the folder containing the sequences folders', required=True) +parser.add_argument("--year", type=str, help="Davis dataset year (default: 2017)", default='2017', + choices=['2016', '2017', '2019']) args, _ = parser.parse_known_args() csv_name_global = f'global_results-{args.set}.csv' csv_name_per_sequence = f'per-sequence_results-{args.set}.csv' @@ -34,7 +36,7 @@ if os.path.exists(csv_name_global_path) and os.path.exists(csv_name_per_sequence else: print(f'Evaluating sequences for the {args.task} task...') # Create dataset and evaluate - dataset_eval = DAVISEvaluation(davis_root=args.davis_path, task=args.task, gt_set=args.set) + dataset_eval = DAVISEvaluation(davis_root=args.davis_path, task=args.task, gt_set=args.set, year=args.year) metrics_res = dataset_eval.evaluate(args.results_path) J, F = metrics_res['J'], metrics_res['F']@stashvala For convenience, can you provide a complete code for evaluating davis2016?
zhouweii234
bereket-meley
Have you considered also supporting DAVIS2016 exclusively? Mostly because of the single object segmentation. I managed to adjust the code, appending the diff in case anyone will need it. I can also do a pull request. Props on the code man :)