def generate_obj_proposals(davis_root, subset, num_proposals, save_path):
dataset = DAVIS(davis_root, subset=subset, codalab=True)
for seq in dataset.get_sequences():
save_dir = os.path.join(save_path, seq)
if os.path.exists(save_dir):
continue
all_gt_masks, all_masks_id = dataset.get_all_masks(seq, True)
img_size = all_gt_masks.shape[2:]
num_rows = int(np.ceil(np.sqrt(num_proposals)))
proposals = np.zeros((num_proposals, len(all_masks_id), *img_size))
height_slices = np.floor(
np.arange(0, img_size[0] + 1,
img_size[0] / num_rows)).astype(np.uint).tolist()
width_slices = np.floor(
np.arange(0, img_size[1] + 1,
img_size[1] / num_rows)).astype(np.uint).tolist()
ii = 0
prev_h, prev_w = 0, 0
for h in height_slices[1:]:
for w in width_slices[1:]:
proposals[ii, :, prev_h:h, prev_w:w] = 1
prev_w = w
ii += 1
if ii == num_proposals:
break
prev_h, prev_w = h, 0
if ii == num_proposals:
break
os.makedirs(save_dir, exist_ok=True)
for i, mask_id in enumerate(all_masks_id):
mask = np.sum(proposals[:, i, ...] *
np.arange(1, proposals.shape[0] + 1)[:, None, None],
axis=0)
save_mask(mask, os.path.join(save_dir, f'{mask_id}.png'))
def __init__(self, dataset_root, task, gt_set, sequences='all', codalab=False):
"""
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.
:param task: Task to compute the evaluation, chose between semi-supervised or unsupervised.
:param gt_set: Set to compute the evaluation
:param sequences: Sequences to consider for the evaluation, 'all' to use all the sequences in a set.
"""
self.davis_root = dataset_root
self.task = task
self.dataset = DAVIS(root=davis_root, task=task, subset=gt_set, sequences=sequences, codalab=codalab)
def generate_random_permutation_gt_obj_proposals(davis_root, subset,
save_path):
dataset = DAVIS(davis_root, subset=subset, codalab=True)
for seq in dataset.get_sequences():
gt_masks, all_masks_id = dataset.get_all_masks(seq, True)
obj_swap = np.random.permutation(np.arange(gt_masks.shape[0]))
gt_masks = gt_masks[obj_swap, ...]
save_dir = os.path.join(save_path, seq)
os.makedirs(save_dir, exist_ok=True)
for i, mask_id in enumerate(all_masks_id):
mask = np.sum(gt_masks[:, i, ...] *
np.arange(1, gt_masks.shape[0] + 1)[:, None, None],
axis=0)
save_mask(mask, os.path.join(save_dir, f'{mask_id}.png'))
bmap = np.zeros((height, width))
for x in range(w):
for y in range(h):
if b[y, x]:
j = 1 + math.floor((y - 1) + height / h)
i = 1 + math.floor((x - 1) + width / h)
bmap[j, i] = 1
return bmap
if __name__ == '__main__':
from davis2017.davis import DAVIS
from davis2017.results import Results
dataset = DAVIS(root='input_dir/ref', subset='val', sequences='aerobatics')
results = Results(root_dir='examples/osvos')
# Test timing F measure
for seq in dataset.get_sequences():
all_gt_masks, _, all_masks_id = dataset.get_all_masks(seq, True)
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)
f_metrics_res = np.zeros(all_gt_masks.shape[:2])
for ii in range(all_gt_masks.shape[0]):
f_metrics_res[ii, :] = db_eval_boundary(all_gt_masks[ii, ...],
all_res_masks[ii, ...])
# Run using to profile code: python -m cProfile -o f_measure.prof metrics.py
# snakeviz f_measure.prof
class DAVISEvaluation(object):
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.
:param task: Task to compute the evaluation, chose between semi-supervised or unsupervised.
:param gt_set: Set to compute the evaluation
:param sequences: Sequences to consider for the evaluation, 'all' to use all the sequences in a set.
"""
self.davis_root = davis_root
self.task = task
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):
if all_res_masks.shape[0] > all_gt_masks.shape[0]:
sys.stdout.write(
"\nIn your PNG files there is an index higher than the number of objects in the sequence!"
)
sys.exit()
elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
zero_padding = np.zeros(
(all_gt_masks.shape[0] - all_res_masks.shape[0],
*all_res_masks.shape[1:]))
all_res_masks = np.concatenate([all_res_masks, zero_padding],
axis=0)
j_metrics_res, f_metrics_res = np.zeros(
all_gt_masks.shape[:2]), np.zeros(all_gt_masks.shape[:2])
for ii in range(all_gt_masks.shape[0]):
if 'J' in metric:
j_metrics_res[ii, :] = db_eval_iou(all_gt_masks[ii, ...],
all_res_masks[ii, ...],
all_void_masks)
if 'F' in metric:
f_metrics_res[ii, :] = db_eval_boundary(
all_gt_masks[ii, ...], all_res_masks[ii, ...],
all_void_masks)
return j_metrics_res, f_metrics_res
@staticmethod
def _evaluate_unsupervised(all_gt_masks,
all_res_masks,
all_void_masks,
metric,
max_n_proposals=20):
if all_res_masks.shape[0] > max_n_proposals:
sys.stdout.write(
f"\nIn your PNG files there is an index higher than the maximum number ({max_n_proposals}) of proposals allowed!"
)
sys.exit()
elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
zero_padding = np.zeros(
(all_gt_masks.shape[0] - all_res_masks.shape[0],
*all_res_masks.shape[1:]))
all_res_masks = np.concatenate([all_res_masks, zero_padding],
axis=0)
j_metrics_res = np.zeros(
(all_res_masks.shape[0], all_gt_masks.shape[0],
all_gt_masks.shape[1]))
f_metrics_res = np.zeros(
(all_res_masks.shape[0], all_gt_masks.shape[0],
all_gt_masks.shape[1]))
for ii in range(all_gt_masks.shape[0]):
for jj in range(all_res_masks.shape[0]):
if 'J' in metric:
j_metrics_res[jj,
ii, :] = db_eval_iou(all_gt_masks[ii, ...],
all_res_masks[jj, ...],
all_void_masks)
if 'F' in metric:
f_metrics_res[jj, ii, :] = db_eval_boundary(
all_gt_masks[ii, ...], all_res_masks[jj, ...],
all_void_masks)
if 'J' in metric and 'F' in metric:
all_metrics = (np.mean(j_metrics_res, axis=2) +
np.mean(f_metrics_res, axis=2)) / 2
else:
all_metrics = np.mean(j_metrics_res,
axis=2) if 'J' in metric else np.mean(
f_metrics_res, axis=2)
row_ind, col_ind = linear_sum_assignment(-all_metrics)
return j_metrics_res[row_ind, col_ind, :], f_metrics_res[row_ind,
col_ind, :]
def evaluate(self, res_path, metric=('J', 'F'), debug=False):
metric = metric if isinstance(metric, tuple) or isinstance(
metric, list) else [metric]
if 'T' in metric:
raise ValueError('Temporal metric not supported!')
if 'J' not in metric and 'F' not in metric:
raise ValueError(
'Metric possible values are J for IoU or F for Boundary')
# Containers
metrics_res = {}
if 'J' in metric:
metrics_res['J'] = {"M": [], "R": [], "D": [], "M_per_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, 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)
if self.task == 'unsupervised':
j_metrics_res, f_metrics_res = self._evaluate_unsupervised(
all_gt_masks, all_res_masks, all_void_masks, metric)
elif self.task == 'semi-supervised':
j_metrics_res, f_metrics_res = self._evaluate_semisupervised(
all_gt_masks, all_res_masks, None, metric)
for ii in range(all_gt_masks.shape[0]):
seq_name = f'{seq}_{ii+1}'
if 'J' in metric:
[JM, JR, JD] = utils.db_statistics(j_metrics_res[ii])
metrics_res['J']["M"].append(JM)
metrics_res['J']["R"].append(JR)
metrics_res['J']["D"].append(JD)
metrics_res['J']["M_per_object"][seq_name] = JM
if 'F' in metric:
[FM, FR, FD] = utils.db_statistics(f_metrics_res[ii])
metrics_res['F']["M"].append(FM)
metrics_res['F']["R"].append(FR)
metrics_res['F']["D"].append(FD)
metrics_res['F']["M_per_object"][seq_name] = FM
# Show progress
if debug:
sys.stdout.write(seq + '\n')
sys.stdout.flush()
return metrics_res
