def Execute3D(self, w):
##
## Load image
##
filestack = self.ObtainTarget()
params = self.ObtainParamsBottomTable(self.obj_args, self.args)
output_path = params['Output Folder']
if len(output_path) == 0:
print('Output folder unspecified.')
return False
#
numz = len(filestack)
# size = cv2.imread(filestack[0], cv2.IMREAD_GRAYSCALE).shape
check_attribute = m.imread(filestack[0], flags=cv2.IMREAD_GRAYSCALE)
tsize = check_attribute.shape
tdtype = check_attribute.dtype
input_volume = np.zeros([tsize[0], tsize[1], numz], tdtype)
print('Loading images ...')
for zi, filename in enumerate(filestack):
# input_volume[:, :, zi] = cv2.imread(filename, cv2.IMREAD_GRAYSCALE).astype(tdtype)
input_volume[:, :, zi] = m.imread(filename,
flags=cv2.IMREAD_GRAYSCALE)
##
## 2D/3D filter application
##
for i in range(w.count()):
item = w.item(i)
text = item.text()
instance = item.data(Qt.UserRole)
params = self.ObtainParamsFilter(instance.args)
type = self.fi.get_type(text)
cls = self.fi.get_class(text)
if type == '2d':
for zi in range(numz):
input_image = input_volume[:, :, zi]
output_image = cls.Filter(self, input_image, params)
input_volume[:, :, zi] = output_image
elif type == '3d':
tmp = cls.Filter(self, input_volume, params)
input_volume = tmp.astype(np.uint16)
# Unlock Folder
m.UnlockFolder(self.parent.u_info, output_path)
# Save segmentation
print('Saving images ...')
for zi, filename in enumerate(filestack):
output_name = os.path.basename(filename)
savename = os.path.join(output_path, output_name)
root, ext = os.path.splitext(savename)
if ext == ".tif" or ext == ".tiff" or ext == ".TIF" or ext == ".TIFF":
m.save_tif16(input_volume[:, :, zi], savename)
elif ext == ".png" or ext == ".PNG":
m.save_png16(input_volume[:, :, zi], savename)
print('2D/3D filters were applied!')
# Lock Folder
m.LockFolder(self.parent.u_info, output_path)
def _Run(self, parent, params, comm_title):
##
print('Start postprocesing.')
print(params['Output Filetype'])
data = np.load(params['Target Sementation File (npz)'])
print('File contents :', data.files)
segmentation = data['segmentation']
print('Segmentation image size: ', segmentation.shape)
filetype = params['Output Filetype']
##
##
if (filetype == '8-bit color PNG') or (filetype == '8-bit color TIFF'):
ids = np.max(segmentation)
print('Max segmentation ID: ', ids)
colormap = np.random.randint(255,
size=(ids + 2, 3),
dtype=np.uint64)
colormap[0, :] = 0
##
m.UnlockFolder(parent.u_info, params['Output Segmentation Folder'])
##
for idz in range(segmentation.shape[0]):
image2d = segmentation[idz, :, :]
print('image2d size: ', image2d.shape)
if filetype == '16-bit gray scale TIFF':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.tif'.format(idz))
m.save_tif16(image2d, filename)
elif filetype == '8-bit gray scale TIFF':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.tif'.format(idz))
m.save_tif8(image2d, filename)
elif filetype == '16-bit gray scale PNG':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.png'.format(idz))
m.save_png16(image2d, filename)
elif filetype == '8-bit gray scale PNG':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.png'.format(idz))
m.save_png8(image2d, filename)
elif filetype == '8-bit color PNG':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.png'.format(idz))
m.save_pngc(image2d, filename, colormap)
elif filetype == '8-bit color TIFF':
filename = os.path.join(params['Output Segmentation Folder'],
'z{:0=4}.tif'.format(idz))
m.save_tifc(image2d, filename, colormap)
else:
print('Data was not saved.')
##
print(comm_title, 'was finished.')
flag = m.LockFolder(parent.u_info,
params['Output Segmentation Folder'])
return flag
def Execute2D(self, w):
##
## Input files /Output folder
##
self.filestack = self.ObtainTarget()
params = self.ObtainParamsBottomTable(self.obj_args, self.args)
output_path = params['Output Folder']
if len(output_path) == 0:
print('Output folder unspecified.')
return False
# Unlock Folder
m.UnlockFolder(self.parent.u_info, output_path)
for filename in self.filestack:
print(filename)
output_name = os.path.basename(filename)
# input_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
input_image = m.imread(filename, flags=cv2.IMREAD_GRAYSCALE)
output_image = self.FilterApplication2D(w, input_image)
output_dtype = output_image.dtype
savename = os.path.join(output_path, output_name)
root, ext = os.path.splitext(savename)
if ext == ".tif" or ext == ".tiff" or ext == ".TIF" or ext == ".TIFF":
if output_dtype == 'uint16':
m.save_tif16(output_image, savename)
elif output_dtype == 'uint8':
m.save_tif8(output_image, savename)
else:
print('dtype mismatch: ', output_dtype)
elif ext == ".png" or ext == ".PNG":
if output_dtype == 'uint16':
m.save_png16(output_image, savename)
elif output_dtype == 'uint8':
m.save_png8(output_image, savename)
else:
print('dtype mismatch: ', output_dtype)
print('2D filters were applied!')
# Lock Folder
m.LockFolder(self.parent.u_info, output_path)
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.')