def adapted_rand(self, segmentation):
assert list(segmentation.data.shape) == list(self.groundtruth.data.shape)
assert list(segmentation.resolution) == list(self.groundtruth.resolution)
print "Computing RAND..."
return adapted_rand(np.array(segmentation.data), self.gt)
def run(config):
image_path, labels_path, raw_path = config['SAVE_PRED'], config[
'LABELS_DENSE'], config['DATA']
seg, labels, raw = imread(image_path)[2:12], imread(
labels_path)[-16:-6], imread(raw_path)[-16:-6, :, :, 0]
pixel_error = np.mean(seg != labels)
rand_error = adapted_rand(seg, labels)
print("pixel error: {}, rand error {}".format(pixel_error, rand_error))
def adapted_rand(self, segmentation):
assert list(segmentation.data.shape) == list(
self.groundtruth.data.shape)
assert list(segmentation.resolution) == list(
self.groundtruth.resolution)
print "Computing RAND..."
return adapted_rand(np.array(segmentation.data), self.gt)
for tp in time_points:
for st in seg_types:
seg_path = f"{data_path}{dataset}{tp}{st}{seg_id}"
gt_path = f"{data_path}{dataset}{tp}{st}{gt_id}"
seg_path = glob.glob(seg_path)
gt_path = glob.glob(gt_path)
for _seg_path, _gt_path in zip(seg_path, gt_path):
seg = np.load(_seg_path)
gt = np.load(_gt_path)
seg = np.where(gt == 0, 0, seg)
_rand = adapted_rand(seg, gt)
_voi = voi(seg, gt)
print(" scores: ", _rand, _voi[0], _voi[1])
result = {
"dataset": dataset,
"time_point": tp,
"segmentation_id": seg_id,
"segmentation_path": seg_path,
"groundtruth_path": gt_path,
"adapted_rand": _rand,
"voi": _voi,
"voi_split": _voi[0],
"voi_merge": _voi[1]
}
from rand import adapted_rand
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-seg',
'--segmentation',
type=str,
default='../inference/segmentation.hdf')
parser.add_argument('-gt',
'--groundtruth',
type=str,
default='../data/gt.hdf')
args = parser.parse_args()
test = h5py.File(args.segmentation, 'r')
test = np.asarray(test['labels']).astype(np.int64)
gt = h5py.File(args.groundtruth, 'r')
gt = np.asarray(gt['labels']) # /volumes/labels/neuron_ids
# no_gt = gt>=np.uint64(-10)
# gt[no_gt] = 0
gt = gt.astype(np.int64)
test = test[14:-14, 106:-106, 106:-106]
gt = gt[14:-14, 106:-106, 106:-106]
(voi_split, voi_merge) = voi(test, gt)
rand = adapted_rand(test, gt)
print('voi split: ' + str(voi_split))
print('voi merge: ' + str(voi_merge))
print('rand: ' + str(rand))