diff save_tifffile_pyramid.py @ 2:96d0d969ebc9 draft default tip

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/s3segmenter commit 0f4f17235c5961c2fd3d4c30180507f66214c11d

--- a/save_tifffile_pyramid.py	Fri Mar 11 23:37:49 2022 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,114 +0,0 @@
-import numpy as np
-import tifffile
-import skimage.transform
-
-PHYSICAL_SIZE_UNIT = ['Ym', 'Zm', 'Em', 'Pm', 'Tm', 'Gm', 'Mm', 'km', 'hm', 'dam', 'm', 'dm', 'cm', 'mm', 'µm', 'nm', 'pm', 'fm', 'am', 'zm', 'ym', 'Å', 'thou', 'li', 'in', 'ft', 'yd', 'mi', 'ua', 'ly', 'pc', 'pt', 'pixel', 'reference frame']
-
-def normalize_image_shape(img):
-    assert img.ndim in (2, 3), (
-        'image must be 2D (Y, X) or 3D (C, Y, X)'
-    )
-    if img.ndim == 2:
-        img = img.reshape(1, *img.shape)
-    assert np.argmin(img.shape) == 0, (
-        '3D image must be in (C, Y, X) order'
-    )
-    return img
-
-def save_pyramid(
-    out_img, output_path,
-    pixel_sizes=(1, 1),
-    pixel_size_units=('µm', 'µm'),
-    channel_names=None,
-    software=None,
-    is_mask=False
-):
-    assert '.ome.tif' in str(output_path)
-    assert len(pixel_sizes) == len(pixel_size_units) == 2
-    assert out_img.ndim in (2, 3), (
-        'image must be either 2D (Y, X) or 3D (C, Y, X)'
-    )
-    
-    img_shape_ori = out_img.shape
-    out_img = normalize_image_shape(out_img)
-    img_shape = out_img.shape
-
-    size_x, size_y = np.array(pixel_sizes, dtype=float)
-    unit_x, unit_y = pixel_size_units
-
-    assert (unit_x in PHYSICAL_SIZE_UNIT) & (unit_y in PHYSICAL_SIZE_UNIT), (
-        f'pixel_size_units must be a tuple of the followings: '
-        f'{", ".join(PHYSICAL_SIZE_UNIT)}'
-    )
-
-    n_channels = img_shape[0]
-    if channel_names == None:
-        channel_names = [f'Channel {i}' for i in range(n_channels)]
-    else:
-        if type(channel_names) == str:
-            channel_names = [channel_names]
-        n_channel_names = len(channel_names)
-        assert n_channel_names == n_channels, (
-            f'number of channel_names ({n_channel_names}) must match '
-            f'number of channels ({n_channels})'
-        )
-    
-    if software == None:
-        software = ''
-        
-    metadata = {
-        'Creator': software,
-        'Pixels': {
-            'PhysicalSizeX': size_x,
-            'PhysicalSizeXUnit': unit_x,
-            'PhysicalSizeY': size_y,
-            'PhysicalSizeYUnit': unit_y,
-        },
-        'Channel': {'Name': channel_names},
-        
-    }
-
-    max_size = np.max(img_shape)
-    subifds = np.ceil(np.log2(max_size / 1024)).astype(int)
-    
-    # use optimal tile size for disk space
-    tile_size = 16*np.ceil(
-        np.array(img_shape[1:]) / (2**subifds) / 16
-    ).astype(int)
-    options = {
-        'tile': tuple(tile_size)
-    }
-
-    with tifffile.TiffWriter(output_path, bigtiff=True) as tiff_out:
-        tiff_out.write(
-            data=out_img,
-            metadata=metadata,
-            software=software,
-            subifds=subifds,
-            **options
-        )
-        for i in range(subifds):
-            if i == 0:
-                down_2x_img = downsize_img_channels(out_img, is_mask=is_mask)
-            else:
-                down_2x_img = downsize_img_channels(down_2x_img, is_mask=is_mask)
-            tiff_out.write(
-                data=down_2x_img,
-                subfiletype=1,
-                **options
-            )
-
-    out_img = out_img.reshape(img_shape_ori)
-    return
-
-def downsize_channel(img, is_mask):
-    if is_mask:
-        return img[::2, ::2]
-    else:
-        return skimage.transform.downscale_local_mean(img, (2, 2)).astype(img.dtype)
-
-def downsize_img_channels(img, is_mask):
-    return np.array([
-        downsize_channel(c, is_mask=is_mask)
-        for c in img
-    ])