Mercurial > repos > imgteam > 2d_auto_threshold
diff auto_threshold.py @ 9:50fa6150e340 draft default tip
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/2d_auto_threshold/ commit 01343602708de3cc7fa4986af9000adc36dd0651
author | imgteam |
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date | Sat, 07 Jun 2025 18:38:31 +0000 |
parents | 699a5e9146b3 |
children |
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--- a/auto_threshold.py Wed Apr 24 08:11:33 2024 +0000 +++ b/auto_threshold.py Sat Jun 07 18:38:31 2025 +0000 @@ -7,49 +7,111 @@ import argparse -import giatools.io import numpy as np import skimage.filters import skimage.util -import tifffile +from giatools.image import Image + + +class DefaultThresholdingMethod: + + def __init__(self, thres, accept: list[str] | None = None, **kwargs): + self.thres = thres + self.accept = accept if accept else [] + self.kwargs = kwargs + + def __call__(self, image, *args, offset=0, **kwargs): + accepted_kwargs = self.kwargs.copy() + for key, val in kwargs.items(): + if key in self.accept: + accepted_kwargs[key] = val + thres = self.thres(image, *args, **accepted_kwargs) + return image > thres + offset + + +class ManualThresholding: + + def __call__(self, image, thres1: float, thres2: float | None, **kwargs): + if thres2 is None: + return image > thres1 + else: + thres1, thres2 = sorted((thres1, thres2)) + return skimage.filters.apply_hysteresis_threshold(image, thres1, thres2) th_methods = { - 'manual': lambda thres, **kwargs: thres, + 'manual': ManualThresholding(), - 'otsu': lambda img_raw, **kwargs: skimage.filters.threshold_otsu(img_raw), - 'li': lambda img_raw, **kwargs: skimage.filters.threshold_li(img_raw), - 'yen': lambda img_raw, **kwargs: skimage.filters.threshold_yen(img_raw), - 'isodata': lambda img_raw, **kwargs: skimage.filters.threshold_isodata(img_raw), + 'otsu': DefaultThresholdingMethod(skimage.filters.threshold_otsu), + 'li': DefaultThresholdingMethod(skimage.filters.threshold_li), + 'yen': DefaultThresholdingMethod(skimage.filters.threshold_yen), + 'isodata': DefaultThresholdingMethod(skimage.filters.threshold_isodata), - 'loc_gaussian': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='gaussian'), - 'loc_median': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='median'), - 'loc_mean': lambda img_raw, bz, **kwargs: skimage.filters.threshold_local(img_raw, bz, method='mean') + 'loc_gaussian': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='gaussian'), + 'loc_median': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='median'), + 'loc_mean': DefaultThresholdingMethod(skimage.filters.threshold_local, accept=['block_size'], method='mean'), } -def do_thresholding(in_fn, out_fn, th_method, block_size, offset, threshold, invert_output=False): - img = giatools.io.imread(in_fn) - img = np.squeeze(img) - assert img.ndim == 2 +def do_thresholding( + input_filepath: str, + output_filepath: str, + th_method: str, + block_size: int, + offset: float, + threshold1: float, + threshold2: float | None, + invert_output: bool, +): + assert th_method in th_methods, f'Unknown method "{th_method}"' + + # Load image + img_in = Image.read(input_filepath) - th = offset + th_methods[th_method](img_raw=img, bz=block_size, thres=threshold) - res = img > th + # Perform thresholding + result = th_methods[th_method]( + image=img_in.data, + block_size=block_size, + offset=offset, + thres1=threshold1, + thres2=threshold2, + ) if invert_output: - res = np.logical_not(res) + result = np.logical_not(result) + + # Convert to canonical representation for binary images + result = (result * 255).astype(np.uint8) - tifffile.imwrite(out_fn, skimage.util.img_as_ubyte(res)) + # Write result + Image( + data=skimage.util.img_as_ubyte(result), + axes=img_in.axes, + ).normalize_axes_like( + img_in.original_axes, + ).write( + output_filepath, + ) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Automatic image thresholding') - parser.add_argument('im_in', help='Path to the input image') - parser.add_argument('im_out', help='Path to the output image (uint8)') + parser.add_argument('input', type=str, help='Path to the input image') + parser.add_argument('output', type=str, help='Path to the output image (uint8)') parser.add_argument('th_method', choices=th_methods.keys(), help='Thresholding method') - parser.add_argument('block_size', type=int, default=5, help='Odd size of pixel neighborhood for calculating the threshold') - parser.add_argument('offset', type=float, default=0, help='Offset of automatically determined threshold value') - parser.add_argument('threshold', type=float, default=0, help='Manual threshold value') + parser.add_argument('block_size', type=int, help='Odd size of pixel neighborhood for calculating the threshold') + parser.add_argument('offset', type=float, help='Offset of automatically determined threshold value') + parser.add_argument('threshold1', type=float, help='Manual threshold value') + parser.add_argument('--threshold2', type=float, help='Second manual threshold value (for hysteresis thresholding)') parser.add_argument('--invert_output', default=False, action='store_true', help='Values below/above the threshold are labeled with 0/255 by default, and with 255/0 if this argument is used') args = parser.parse_args() - do_thresholding(args.im_in, args.im_out, args.th_method, args.block_size, args.offset, args.threshold, args.invert_output) + do_thresholding( + args.input, + args.output, + args.th_method, + args.block_size, + args.offset, + args.threshold1, + args.threshold2, + args.invert_output, + )