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import os, argparse
import numpy as np
from cellpose import io, transforms
def main():
parser = argparse.ArgumentParser(description='Make slices of XYZ image data for training. Assumes image is ZXYC unless specified otherwise using --channel_axis and --z_axis')
input_img_args = parser.add_argument_group("input image arguments")
input_img_args.add_argument('--dir', default=[], type=str,
help='folder containing data to run or train on.')
input_img_args.add_argument(
'--image_path', default=[], type=str, help=
'if given and --dir not given, run on single image instead of folder (cannot train with this option)'
)
input_img_args.add_argument(
'--look_one_level_down', action='store_true',
help='run processing on all subdirectories of current folder')
input_img_args.add_argument('--img_filter', default=[], type=str,
help='end string for images to run on')
input_img_args.add_argument(
'--channel_axis', default=-1, type=int,
help='axis of image which corresponds to image channels')
input_img_args.add_argument('--z_axis', default=0, type=int,
help='axis of image which corresponds to Z dimension')
input_img_args.add_argument(
'--chan', default=0, type=int, help=
'Deprecated')
input_img_args.add_argument(
'--chan2', default=0, type=int, help=
'Deprecated'
)
input_img_args.add_argument('--invert', action='store_true',
help='invert grayscale channel')
input_img_args.add_argument(
'--all_channels', action='store_true', help=
'deprecated')
input_img_args.add_argument("--anisotropy", required=False, default=1.0, type=float,
help="anisotropy of volume in 3D")
# algorithm settings
algorithm_args = parser.add_argument_group("algorithm arguments")
algorithm_args.add_argument('--sharpen_radius', required=False, default=0.0,
type=float, help='high-pass filtering radius. Default: %(default)s')
algorithm_args.add_argument('--tile_norm', required=False, default=0, type=int,
help='tile normalization block size. Default: %(default)s')
algorithm_args.add_argument('--nimg_per_tif', required=False, default=10, type=int,
help='number of crops in XY to save per tiff. Default: %(default)s')
algorithm_args.add_argument('--crop_size', required=False, default=512, type=int,
help='size of random crop to save. Default: %(default)s')
args = parser.parse_args()
# find images
if len(args.img_filter) > 0:
imf = args.img_filter
else:
imf = None
if len(args.dir) > 0:
image_names = io.get_image_files(args.dir, "_masks", imf=imf,
look_one_level_down=args.look_one_level_down)
dirname = args.dir
else:
if os.path.exists(args.image_path):
image_names = [args.image_path]
dirname = os.path.split(args.image_path)[0]
else:
raise ValueError(f"ERROR: no file found at {args.image_path}")
np.random.seed(0)
nimg_per_tif = args.nimg_per_tif
crop_size = args.crop_size
os.makedirs(os.path.join(dirname, 'train/'), exist_ok=True)
pm = [(0, 1, 2, 3), (2, 0, 1, 3), (1, 0, 2, 3)]
npm = ["YX", "ZY", "ZX"]
for name in image_names:
name0 = os.path.splitext(os.path.split(name)[-1])[0]
img0 = io.imread_3D(name)
try:
img0 = transforms.convert_image(img0, channel_axis=args.channel_axis,
z_axis=args.z_axis, do_3D=True)
except ValueError:
print('Error converting image. Did you provide the correct --channel_axis and --z_axis ?')
for p in range(3):
img = img0.transpose(pm[p]).copy()
print(npm[p], img[0].shape)
Ly, Lx = img.shape[1:3]
imgs = img[np.random.permutation(img.shape[0])[:args.nimg_per_tif]]
if args.anisotropy > 1.0 and p > 0:
imgs = transforms.resize_image(imgs, Ly=int(args.anisotropy * Ly), Lx=Lx)
for k, img in enumerate(imgs):
if args.tile_norm:
img = transforms.normalize99_tile(img, blocksize=args.tile_norm)
if args.sharpen_radius:
img = transforms.smooth_sharpen_img(img,
sharpen_radius=args.sharpen_radius)
ly = 0 if Ly - crop_size <= 0 else np.random.randint(0, Ly - crop_size)
lx = 0 if Lx - crop_size <= 0 else np.random.randint(0, Lx - crop_size)
io.imsave(os.path.join(dirname, f'train/{name0}_{npm[p]}_{k}.tif'),
img[ly:ly + args.crop_size, lx:lx + args.crop_size].squeeze())
if __name__ == '__main__':
main()
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