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, parent, params, comm_title):
input_files = m.ObtainImageFiles(params['Image Folder'])
if len(input_files) == 0:
print('No images in the Image Folder.')
return False
im = m.imread(input_files[0], cv2.IMREAD_UNCHANGED)
root, ext_image = os.path.splitext(os.path.basename(input_files[0]))
print('')
print('Target file to check color type : ', input_files[0])
print('Image dimensions : ', im.shape)
print('Image filetype : ', im.dtype)
image_size_x = im.shape[1]
image_size_y = im.shape[0]
if (image_size_x <= 256 or image_size_y <= 256):
print('Image size is too small.')
return False
# Generate tmpdir
tmpdir_standardized = os.path.join(
params['Output Segmentation Folder (Empty)'],
"standardized" + str(threading.get_ident()).zfill(6)[-6:])
if os.path.exists(tmpdir_standardized):
shutil.rmtree(tmpdir_standardized)
os.mkdir(tmpdir_standardized)
#
tmpdir_output = os.path.join(
params['Output Segmentation Folder (Empty)'],
"output" + str(threading.get_ident()).zfill(6)[-6:])
if os.path.exists(tmpdir_output):
shutil.rmtree(tmpdir_output)
os.mkdir(tmpdir_output)
## Check image size
max_image_size = params['Maximal unit image size']
if max_image_size == '512':
std_sizes = np.array([512])
elif max_image_size == '1024':
std_sizes = np.array([512, 1024])
elif max_image_size == '2048':
std_sizes = np.array([512, 1024, 2048])
else:
print('Internal error at Maximal unit image size.')
return False
max_std_size = np.max(std_sizes)
if image_size_x > max_std_size:
unit_image_size_x = max_std_size
num_tiles_x = np.int(np.ceil(float(image_size_x) / max_std_size))
else:
unit_image_size_x = np.min(std_sizes[std_sizes >= image_size_x])
num_tiles_x = 1
if image_size_y > max_std_size:
unit_image_size_y = max_std_size
num_tiles_y = np.int(np.ceil(float(image_size_y) / max_std_size))
else:
unit_image_size_y = np.min(std_sizes[std_sizes >= image_size_y])
num_tiles_y = 1
#
converted_size_x = unit_image_size_x * num_tiles_x
converted_size_y = unit_image_size_y * num_tiles_y
fringe_size_x = converted_size_x - image_size_x
fringe_size_y = converted_size_y - image_size_y
#
#
output_files = []
print('Image standardization: ')
for input_file in input_files:
im_col = m.imread(input_file)
# im_col = self._ChangeIntoColor(im_col)
filename = path.basename(input_file)
print(filename + ' ')
for ext in [
'.TIF', '.tif', '.TIFF', '.tiff', '.PNG', '.jpg', '.jpeg',
'.JPG', '.JPEG'
]:
filename = filename.replace(ext, '.png')
output_files.append(filename)
# add fringe X
im_fringe_x = cv2.flip(im_col, 1) # flipcode > 0, left-right
im_fringe_x = im_fringe_x[:, 0:fringe_size_x]
converted_image = cv2.hconcat([im_col, im_fringe_x])
# add fringe Y
im_fringe_y = cv2.flip(converted_image,
0) # flipcode = 0, top-bottom
im_fringe_y = im_fringe_y[0:fringe_size_y, :]
converted_image = cv2.vconcat([converted_image, im_fringe_y])
# Save
if (num_tiles_x == 1) and (num_tiles_y == 1):
converted_filename = os.path.join(tmpdir_standardized,
filename)
m.imwrite(converted_filename, converted_image)
else:
for iy in range(num_tiles_y):
for ix in range(num_tiles_x):
y0 = iy * unit_image_size_y
y1 = y0 + unit_image_size_y
x0 = ix * unit_image_size_x
x1 = x0 + unit_image_size_x
current_tile = converted_image[y0:y1, x0:x1]
converted_filename = str(ix).zfill(3)[-3:] + '_' + str(
iy).zfill(3)[-3:] + '_' + filename
converted_filename = os.path.join(
tmpdir_standardized, converted_filename)
m.imwrite(converted_filename, current_tile)
#Complete
print('')
print('Images were split and changed into RGB 8bit, and stored in ',
tmpdir_standardized)
print('')
tmp = ['--mode' , 'predict' , \
'--save_freq' , '0' , \
'--input_dir' , tmpdir_standardized, \
'--output_dir' , tmpdir_output, \
'--checkpoint' , params['Model Folder'], \
'--image_height', str(unit_image_size_y), \
'--image_width' , str(unit_image_size_x)]
comm = parent.u_info.exec_translate[:]
comm.extend(tmp)
print('')
print(' '.join(comm))
print('')
print('Start inference.')
print('')
m.UnlockFolder(parent.u_info,
params['Output Segmentation Folder (Empty)']
) # Only for shared folder/file
s.run(comm)
print('')
print('Segmentation reconstruction: ')
for output_file in output_files:
##
if (num_tiles_x == 1) and (num_tiles_y == 1):
## Remove fringes
filename = os.path.join(tmpdir_output, output_file)
inferred_segmentation = m.imread(filename,
flags=cv2.IMREAD_GRAYSCALE,
dtype='uint8')
else:
## Merge split images.
inferred_segmentation = np.zeros(
(converted_size_y, converted_size_x), dtype='uint8')
for iy in range(num_tiles_y):
for ix in range(num_tiles_x):
y0 = iy * unit_image_size_y
y1 = y0 + unit_image_size_y
x0 = ix * unit_image_size_x
x1 = x0 + unit_image_size_x
current_tile_filename = str(ix).zfill(
3)[-3:] + '_' + str(iy).zfill(
3)[-3:] + '_' + output_file
current_tile_filename = os.path.join(
tmpdir_output, current_tile_filename)
current_tile = m.imread(current_tile_filename,
flags=cv2.IMREAD_GRAYSCALE,
dtype='uint8')
inferred_segmentation[y0:y1,
x0:x1] = current_tile[:, :]
inferred_segmentation = inferred_segmentation[0:image_size_y,
0:image_size_x]
print('inferred_segmentation: ', inferred_segmentation.shape,
inferred_segmentation.dtype)
## Save
filename_base = os.path.splitext(os.path.basename(output_file))[0]
filename_base = os.path.join(
params['Output Segmentation Folder (Empty)'], filename_base)
filetype = params['Output Filetype']
if filetype == '8-bit gray scale PNG':
filename = filename_base + '.png'
m.save_png8(inferred_segmentation, filename)
elif filetype == '8-bit gray scale TIFF (Uncompressed)':
filename = filename_base + '.tif'
m.save_tif8(inferred_segmentation, filename, compression=1)
elif filetype == '8-bit gray scale TIFF (Compressed)':
filename = filename_base + '.tif'
m.save_tif8(inferred_segmentation, filename)
else:
print('Internel error: bad filetype.')
print(filename)
##
# rm tmpdir
if os.path.exists(tmpdir_standardized):
shutil.rmtree(tmpdir_standardized)
if os.path.exists(tmpdir_output):
shutil.rmtree(tmpdir_output)
parent.parent.ExecuteCloseFileFolder(
params['Output Segmentation Folder (Empty)'])
parent.parent.OpenFolder(params['Output Segmentation Folder (Empty)'])
print('')
print('Finish inference.')
print('')
return True
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.')