def save_color_data(self, u_info, dir):
colordata = m.load_hdf5(u_info.color_map_file, u_info.hdf_color_name)
filename = dir + os.sep + u_info.export_col_name + '.csv'
print(filename)
with open(filename, 'w') as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(colordata)
def run(self, u_info):
## Load DB
db = DB(u_info)
self.u_info = u_info
##
## Update split and adjust
##
for iz in range(db.num_tiles_z):
print('Saving: ', iz, '/', db.num_tiles_z)
# Check teemporary data
data_path = u_info.tmp_tile_ids_path + u_info.tile_path_wz.format(0, iz)
if not os.path.isdir(data_path):
continue
# print('Copy from ', data_path)
for iw in range(db.num_tiles_w):
source_dir = u_info.tmp_tile_ids_path \
+ u_info.tile_path_wz.format(iw, iz)
destination_dir = u_info.tile_ids_path \
+ u_info.tile_path_wz.format(iw, iz)
shutil.rmtree(destination_dir)
shutil.move(source_dir, destination_dir)
## Remove temp file
# shutil.rmtree(source_dir)
##
## Update merges
##
# print(u_info.merge_table)
for iw in range(db.num_tiles_w):
for iz, iy, ix in itertools.product(range(db.num_tiles_z), range(db.num_tiles_y_at_w[iw]), range(db.num_tiles_x_at_w[iw])):
### Load tile file
tile_ids_filename = u_info.tile_ids_path + u_info.tile_ids_filename_wzyx.format( iw, iz, iy, ix )
tile_ids = m.load_hdf5( tile_ids_filename, u_info.tile_var_name )
## Color exchange [for merge? check __merge_table.keys() ]
for mm in u_info.merge_table.keys():
mm_id = self.lookup_label(mm, u_info.merge_table)
tile_ids[ tile_ids == int(mm) ] = mm_id
### Save tile file
m.save_hdf5(tile_ids_filename, u_info.tile_var_name, tile_ids)
u_info.merge_table = {}
u_info.flag_undo = 0
u_info.flag_redo = 0
## Update
print('Updating database.')
db.Update()
print('Successfully saved.')
def ObtainUpdateIdsInfo(self):
###
id_tile_list = []
id_max = 0
id_counts = np.zeros(0, dtype=np.int64)
for iw in range(self.num_tiles_w):
for iz, iy, ix in itertools.product(
range(self.num_tiles_z), range(self.num_tiles_y_at_w[iw]),
range(self.num_tiles_x_at_w[iw])):
### Load tile file
### tile_ids( ( tile_num_pixels_y, tile_num_pixels_x ), np.uint32 )
tile_ids_filename = self.u_info.tile_ids_path + self.u_info.tile_ids_filename_wzyx.format(
iw, iz, iy, ix)
tile_ids = m.load_hdf5(tile_ids_filename,
self.u_info.tile_var_name)
unique_tile_ids = np.unique(tile_ids)
## Update database
# Max id
current_max = np.max(unique_tile_ids)
if id_max < current_max:
id_max = current_max
id_counts.resize(id_max + 1)
# print id_max
# id list
for unique_tile_id in unique_tile_ids:
id_tile_list.append((unique_tile_id, iw, iz, iy, ix))
# Pixel number of each id
if iw == 0:
current_ids_counts = np.bincount(tile_ids.ravel())
current_ids_counts_ids = np.nonzero(current_ids_counts)[0]
id_counts[current_ids_counts_ids] = \
id_counts[current_ids_counts_ids] + np.int64(current_ids_counts[current_ids_counts_ids])
## Sort the tile list so that the same id appears together
id_tile_list = np.array(sorted(id_tile_list), np.uint32)
## Max color number check
if (id_max >= self.u_info.ncolors):
print('Number of panels exceeds max_number')
return id_tile_list, id_max, id_counts
def __init__(self, u_info):
## User info
self.u_info = u_info
## Makedir
if os.path.isdir(self.u_info.data_annotator_path) == False:
os.makedirs(self.u_info.data_annotator_path)
## Load color file
colordata = m.load_hdf5(self.u_info.color_map_file,
self.u_info.hdf_color_name)
colnum = colordata.shape[0]
## Load database file
query = "select * from segmentInfo;"
con = sqlite3.connect(self.u_info.segment_info_db_file)
cur = con.cursor()
cur.execute(query) # Obtain max id
#data = cur.fetchone()
data = cur.fetchall()
con.close()
keys = ['id', 'name', 'size', 'confidence']
data_dict = [dict(zip(keys, valuerecord)) for valuerecord in data]
for i, datum_dict in enumerate(data_dict):
id = datum_dict['id']
if id >= colnum:
col = {'r': 128, 'g': 128, 'b': 128, 'act': 0}
else:
col = {
'r': int(colordata[id][0]),
'g': int(colordata[id][1]),
'b': int(colordata[id][2]),
'act': 0
}
data_dict[i].update(col)
##
## Save
##
with open(
os.path.join(self.u_info.data_annotator_path,
"segmentInfo.json"), 'w') as f:
json.dump(data_dict, f, indent=2, ensure_ascii=False)
def run(self, u_info, dir, fname, ftype, startid, numdigit, flag):
## Load DB
db = DB(u_info)
print("Tile Num: z {0}, y {1}, x {2}".format(db.num_tiles_z, db.num_tiles_y, db.num_tiles_x))
## Makedir
#self.mkdir_safe(dir)
## Save ColorInfo & SegmentationInfo
if flag == 'ids':
self.save_color_data(u_info, dir)
self.save_segmentInfo(u_info, dir)
## Volume storage
VOLUME_FORMAT = ["MTIF16G", "MTIF8G", "MTIF8C","NUMPY32", "NUMPY32C","HDF64"]
if ftype in VOLUME_FORMAT:
volume_images_ids = np.zeros((db.num_voxels_z, db.num_voxels_y, db.num_voxels_x), np.uint32)
##
## Export image/segmentation files
##
print("Tile Num: z {0}, y {1}, x {2}".format(db.num_tiles_z, db.num_tiles_y, db.num_tiles_x))
iw = 0
for iz in range(db.num_tiles_z): # 100): #
print("iz ", iz)
merged_images_ids = np.zeros( ( db.canvas_size_y, db.canvas_size_x ), np.uint32 )
tile_image = []
for iy, ix in itertools.product( range(db.num_tiles_y), range(db.num_tiles_x)):
if flag == 'images':
filename = u_info.tile_images_path + u_info.tile_images_filename_wzyx.format(iw, iz, iy, ix)
print(filename)
tile_image = PIL.Image.open(filename)
elif flag == 'ids' :
filename = u_info.tile_ids_path + u_info.tile_ids_filename_wzyx.format(iw, iz, iy, ix)
print(filename)
tile_image = m.load_hdf5(filename, u_info.tile_var_name)
else:
return False
y = iy * db.num_voxels_per_tile_y
x = ix * db.num_voxels_per_tile_x
merged_images_ids[ y : y + db.num_voxels_per_tile_y, x : x + db.num_voxels_per_tile_x ] = tile_image
# Crop by original image size
merged_images_ids = merged_images_ids[ 0:db.num_voxels_y, 0:db.num_voxels_x ]
# Filename setting
# u_info, dir, fname, ftype, startid, numdigit
current_frefix = dir + os.sep + fname + str(int(iz+startid)).zfill(numdigit)
print(current_frefix)
#
if ftype in VOLUME_FORMAT:
volume_images_ids[iz,:,:] = merged_images_ids
elif ftype == "PNG16G":
m.save_png16(merged_images_ids, current_frefix+".png")
elif ftype == "PNG8G":
m.save_png8(merged_images_ids, current_frefix+".png")
elif ftype == "PNG8C":
colordata = m.load_hdf5(u_info.color_map_file, u_info.hdf_color_name)
m.save_pngc(merged_images_ids, current_frefix+".png", colordata)
elif ftype == "TIF16G":
m.save_tif16(merged_images_ids, current_frefix+".tif")
elif ftype == "TIF8G":
m.save_tif8(merged_images_ids, current_frefix+".tif")
elif ftype == "TIF8C":
colordata = m.load_hdf5(u_info.color_map_file, u_info.hdf_color_name)
m.save_tifc(merged_images_ids, current_frefix+".tif", colordata)
else:
print("Export filetype error (Internal Error).")
###
###
current_frefix = dir + os.sep + fname
print('Save file to ', current_frefix)
if ftype == "MTIF16G":
volume_images_ids = volume_images_ids.astype(np.uint16)
tifffile.imsave(current_frefix + ".tif", volume_images_ids)
elif ftype == "MTIF8G":
volume_images_ids = volume_images_ids.astype(np.uint8)
tifffile.imsave(current_frefix + ".tif", volume_images_ids)
elif ftype == "MTIF8C":
print('Multi-tiff 8 color, save.')
colordata = m.load_hdf5(u_info.color_map_file, u_info.hdf_color_name)
volume_images_ids = self.gen_col_multi(volume_images_ids, colordata)
tifffile.imsave(current_frefix + ".tif", volume_images_ids)
elif ftype == "NUMPY32":
volume_images_ids = volume_images_ids.astype(np.uint32)
np.save(current_frefix + ".npy", volume_images_ids)
elif ftype == "NUMPY32C":
volume_images_ids = volume_images_ids.astype(np.uint32)
np.savez(current_frefix + ".npz", stack=volume_images_ids)
elif ftype == "HDF64":
volume_images_ids = volume_images_ids.astype(np.int64)
m.save_hdf5(current_frefix + ".h5", "stack", volume_images_ids)
print('Images/segmentations were Exported.')
