def create_results_dict():
results = {}
for test_set in ALL_VOLUME_IDS:
if test_set not in results: results[test_set] = {}
gt_path = get_paths("gala-evaluate", "train", SIZE,
test_set, "XX", "XX", "XX", "XX")["groundtruth"]
gt = io.read_image_stack(gt_path)
for train_set in ALL_VOLUME_IDS:
if train_set not in results[test_set]: results[test_set][train_set] = {}
for feature in ALL_FEATURES:
if feature not in results[test_set][train_set]:
results[test_set][train_set][feature] = {}
for cue in ALL_CUES:
seg_path = get_paths("gala-evaluate", "train", SIZE,
test_set, cue, feature, "", train_set)["segmentation"]
try:
seg = io.read_image_stack(seg_path)
err = rand_error(seg,gt)
print "test %s, train %s, %s, %s: %f" % (test_set,
train_set, feature, cue, err)
results[test_set][train_set][feature][cue] = err
except:
print " -- missing: test %s, train %s, %s, %s: %s" % (
test_set, train_set, feature, cue, seg_path)
return results
def add_gradient_channel(traintest, size, volume_id, cues_id, datatype, group):
last_component = cues_id.split(ID_DELIMITER)[-1]
new_cues_id = cues_id+ID_DELIMITER+DERIVATIVE_ID+last_component
src_paths = get_paths("XX", traintest, size, volume_id, cues_id, "XX", "XX", "XX")
dest_paths = get_paths("XX", traintest, size, volume_id, new_cues_id, "XX", "XX", "XX")
src_arr = imio.read_h5_stack(src_paths[datatype], group=group)
dest_arr = add_2d_derivative_channel(src_arr, concat=True)
write_h5(dest_arr, dest_paths[datatype], group)
def concatenate(traintest, size, volume_id, cues_id_1, cues_id_2, datatype, group):
cues_id_new = cues_id_1+ID_DELIMITER+cues_id_2
src_paths_1 = get_paths("XX", traintest, size, volume_id, cues_id_1, "XX", "XX", "XX")
src_paths_2 = get_paths("XX", traintest, size, volume_id, cues_id_2, "XX", "XX", "XX")
dest_paths = get_paths("XX", traintest, size, volume_id, cues_id_new, "XX", "XX", "XX")
src_arr_1 = imio.read_h5_stack(src_paths_1[datatype], group=group)
src_arr_2 = imio.read_h5_stack(src_paths_2[datatype], group=group)
if src_arr_1.ndim < 4: src_arr_1 = src_arr_1[...,np.newaxis]
if src_arr_2.ndim < 4: src_arr_2 = src_arr_2[...,np.newaxis]
dest_arr = np.concatenate((src_arr_1, src_arr_2), axis=3)
write_h5(dest_arr, dest_paths[datatype], group)
def main(args):
ws_id = generate_ws_id(args.min_seed_size, args.connectivity,
args.smooth_thresh, args.keep_speckles, args.override_skimage)
paths = get_paths("XX", args.traintest, args.size, args.volume_id,
"idsia", "XX", "XX", "XX", ws_id)
stack = read_h5(paths["hypercubes"], args.h5_key)
if args.dry_run: stack = stack[:2,:,:]
if not args.dont_invert: stack[...] = invert_gray(stack)
ws = np.zeros(stack.shape)
cur_max = 0
for ii in range(stack.shape[0]):
print ii
ws[ii,:,:] = morpho.watershed(stack[ii,:,:], minimum_seed_size=args.min_seed_size,
connectivity=args.connectivity, smooth_thresh=args.smooth_thresh,
override_skimage=args.override_skimage) + cur_max
cur_max = ws[ii,:,:].max()
ws = ws.astype('int64')
print "unique labels in ws:",np.unique(ws).size
if not args.keep_speckles:
ws = agglo.despeckle_watershed(ws)
print "unique labels after despeckling:",np.unique(ws).size
ws, _, _ = evaluate.relabel_from_one(ws)
if ws.min() < 1: ws += (1-ws.min())
write_h5(ws, paths["watersheds"], dry=args.dry_run)
if args.no_quarters or (args.size != "whole"): return
for row_start, row_end, col_start, col_end, frame_start, frame_end, label, size in fractions:
path = generate_path(args.traintest, size, label, ws_id)
write_h5(ws[frame_start:frame_end, row_start:row_end, col_start:col_end], path)
def format(cues_id, features_id, exec_id, outfile_name):
paths = get_paths("gala-segment", "test", "whole", "only", cues_id, features_id, exec_id, "only")
seg_path = os.path.join(paths["output_dir"], paths["experiment_name"]+H5_SUFFIX)
if len(outfile_name) < 1:
out_path = os.path.join(paths["output_dir"], paths["experiment_name"]+TIF_SUFFIX)
else:
out_path = os.path.join(paths["output_dir"], outfile_name)
seg_h5 = h5py.File(seg_path, "r")
seg_np = np.array(seg_h5[H5_KEY]).astype(np.int16)
print "saving to",out_path
tifffile.imsave(out_path, seg_np)