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Fixes #98 - Enable the use of dataset_volume.py for unsupervised cases (label/mask = None) #101

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merged 1 commit into from
Jan 30, 2022
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Fixes #98 - Enable the use of dataset_volume.py for unsupervised cases (label/mask = None) #101

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41 changes: 23 additions & 18 deletions connectomics/data/dataset/dataset_volume.py
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Original file line number Diff line number Diff line change
Expand Up @@ -179,11 +179,14 @@ def _process_targets(self, sample):
out_volume = normalize_image(out_volume, self.data_mean, self.data_std)

# output list
out_target = seg_to_targets(
out_label, self.target_opt, self.erosion_rates, self.dilation_rates)
out_weight = seg_to_weights(
out_target, self.weight_opt, out_valid, out_label)
return pos, out_volume, out_target, out_weight
if out_label is None:
return pos, out_volume, None, None
else:
out_target = self._seg_to_targets(
out_label, self.target_opt, self.erosion_rates, self.dilation_rates)
out_weight = self._seg_to_weights(
out_target, self.weight_opt, out_valid, out_label)
return pos, out_volume, out_target, out_weight

#######################################################
# Position Calculator
Expand Down Expand Up @@ -271,27 +274,29 @@ def _crop_with_pos(self, pos, vol_size):
out_volume = (crop_volume(
self.volume[pos[0]], vol_size, pos[1:])/255.0).astype(np.float32)
# position in the label and valid mask
pos_l = np.round(pos[1:]*self.label_vol_ratio)
out_label = crop_volume(
self.label[pos[0]], self.sample_label_size, pos_l)
# For warping: cv2.remap requires input to be float32.
# Make labels index smaller. Otherwise uint32 and float32 are not
# the same for some values.
out_label = relabel(out_label.copy()).astype(np.float32)

out_label = None
out_valid = None
if self.valid_mask is not None:
out_valid = crop_volume(self.label[pos[0]],
self.sample_label_size, pos_l)
out_valid = (out_valid != 0).astype(np.float32)
if self.label is not None:
pos_l = np.round(pos[1:]*self.label_vol_ratio)
out_label = self._crop_volume(
self.label[pos[0]], self.sample_label_size, pos_l)
# For warping: cv2.remap requires input to be float32.
# Make labels index smaller. Otherwise uint32 and float32 are not
# the same for some values.
out_label = self._relabel(out_label.copy()).astype(np.float32)

if self.valid_mask is not None:
out_valid = self._crop_volume(self.label[pos[0]],
self.sample_label_size, pos_l)
out_valid = (out_valid != 0).astype(np.float32)

return pos, out_volume, out_label, out_valid

def _is_valid(self, out_valid: np.ndarray) -> bool:
"""Decide whether the sampled region is valid or not using
the corresponding valid mask.
"""
if self.valid_mask is None:
if self.valid_mask is None or out_valid is None:
return True
ratio = float(out_valid.sum()) / np.prod(np.array(out_valid.shape))
return ratio > self.valid_ratio
Expand Down