def main():
args = process_command_line()
with open(args.bounding_box_file, 'r') as f:
bb_str = f.readline()
bounding_box = bb_str.split(' ')
bounding_box = [int(x) for x in bounding_box]
assert len(bounding_box) == 6, "Bounding box needs 6 coordinates!"
print "Bounding box:", bounding_box
shape = (bounding_box[1] - bounding_box[0],
bounding_box[3] - bounding_box[2],
bounding_box[5] - bounding_box[4] )
print "Resulting shape:", shape
print "Reading in skeletons from", args.skeleton_path
# get the skeleton coordinates first
skeleton_coordinates = coordinates_from_json(args.skeleton_path, bounding_box)
# perform dense reconstruction for all skeleton coordinates we have
print "Projecting skeletons to dense segments"
dense_skeletons = dense_reconstruction(args.prob_folder,
skeleton_coordinates, args.rf_path,
shape)
# for debugging
from volumina_viewer import volumina_n_layer
probs = np.array(np.squeeze(vigra.readVolume(args.prob_folder+"/pmaps_z=000.tif")))
volumina_n_layer([probs, dense_skeletons.astype(np.uint32)])
def vol_to_vol(path):
vol = vigra.readVolume(path + ".tif")
vol = vol.squeeze()
vol = vol.view(np.ndarray)
#print vol.shape
#print type(vol)
return vol
def load_feats_and_gt_pedunculus():
#raw_data = vigra.readHDF5(
# "/home/constantin/Work/data_ssd/data_080515/pedunculus/150401pedunculus_middle_512x512_first30_sliced.h5",
# "data"
# )
gt = vigra.readVolume(
"/home/constantin/Work/data_ssd/data_080515/pedunculus/150401_pedunculus_membrane_labeling.tif")
gt = np.squeeze(gt)
# delete black slice
gt = np.delete(gt, 6, axis = 2)
gt[gt == 0.] = 1
gt[gt == 255.] = 0
gt = gt.astype(np.uint32)
save_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/features"
#compute_ilastik_2dfeatures(raw_data, save_path)
feats_path = os.path.join( save_path, "all_features.h5")
# make sure that features are computed!
#feats = load_precomputed_feats(save_path, raw_data.shape)
#vigra.writeHDF5(feats, feats_path, "data")
feats = vigra.readHDF5(feats_path, "data")
return (feats, gt)
def gt_pedunculus():
labels_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/150401_pedunculus_membrane_labeling.tif"
raw_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/150401pedunculus_middle_512x512_first30_sliced.h5"
labels = vigra.readVolume(labels_path)
labels = np.squeeze(labels)
labels = np.delete(labels, 6, axis = 2)
raw = vigra.readHDF5(raw_path, "data")
labels = preprocess_for_bgsmoothing_pedunculus(labels)
gt = smooth_background(labels).astype(np.uint32)
volumina_n_layer( (raw, labels, gt) )
gt_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/ground_truth_seg.h5"
vigra.writeHDF5(gt, gt_path, "gt")
def project_gt_pedunculus():
labels_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/150401_pedunculus_membrane_labeling.tif"
gt_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/gt_mc.h5"
raw_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/150401pedunculus_middle_512x512_first30_sliced.h5"
labels = vigra.readVolume(labels_path)
labels = np.squeeze(labels)
labels = np.delete(labels, 6, axis = 2)
gt = vigra.readHDF5(gt_path, "gt")
raw = vigra.readHDF5(raw_path, "data")
gt = project_gt(labels, gt)
save_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/gt_mc_bkg.h5"
volumina_n_layer( (raw, gt, labels) )
vigra.writeHDF5(gt, save_path, "gt")
def slices_to_vol(path):
files = os.listdir(path)
vol = vigra.readVolume(os.path.join(path, files[0]))
vol = vol.squeeze()
vol = vol.view(np.ndarray)
return vol
def vol_to_vol(path):
vol = vigra.readVolume( path + ".tif" )
vol = vol.squeeze()
vol = vol.view(np.ndarray)
return vol
# i += 1 # # ##seg = seg.astype( np.float32 ) ##seg /= seg.max() ##seg *= 255.0 # #print np.unique(seg).size #print seg.max() # #save_path = "/home/constantin/Work/data_ssd/data_080515/pedunculus/res_mc/post_processed/final_mitooff.tif" # #vigra.impex.writeVolume( seg, save_path, '' ) path = "/home/constantin/Desktop/results_for_fred/export_Z=00.png" # #for i in range(29): # path_im = path + str(i).zfill(2) + ".png" # im = vigra.impex.readImage(path_im) # vol_save[:,:,:,i] = im.astype(np.uint8) vol_save = vigra.readVolume(path) print vol_save.shape save_path = "/home/constantin/Desktop/results_for_fred/result_mitooff.tif" vigra.impex.writeVolume( vol_save, save_path, '', dtype = 'UINT16', compression = '' )