Merge pull request #170 from kostrykin/dev/simple_filter

Update `ip_filter_standard` tool

Author: kostrykin

tools/2d_simple_filter/.shed.yml

@@ -1,7 +1,7 @@
 categories:
   - Imaging
-description: 2d simple filter
-long_description: Simple filter in 2D
+description: 2-D simple filter
+long_description: Apply 2-D image filter
 name: 2d_simple_filter    
 owner: imgteam
 homepage_url: https://github.com/bmcv

tools/2d_simple_filter/filter.py

@@ -0,0 +1,110 @@
+import argparse
+import json
+from typing import (
+    Any,
+    Callable,
+)
+
+import giatools
+import numpy as np
+import scipy.ndimage as ndi
+from skimage.morphology import disk
+
+
+def image_astype(img: giatools.Image, dtype: np.dtype) -> giatools.Image:
+    return giatools.Image(
+        data=img.data.astype(dtype),
+        axes=img.axes,
+        original_axes=img.original_axes,
+        metadata=img.metadata,
+    )
+
+
+filters = {
+    'gaussian': lambda img, sigma, order=0, axis=None: (
+        apply_2d_filter(
+            ndi.gaussian_filter,
+            img if order == 0 else image_astype(img, float),
+            sigma=sigma,
+            order=order,
+            axes=axis,
+        )
+    ),
+    'uniform': lambda img, size: (
+        apply_2d_filter(ndi.uniform_filter, img, size=size)
+    ),
+    'median': lambda img, radius: (
+        apply_2d_filter(ndi.median_filter, img, footprint=disk(radius))
+    ),
+    'prewitt': lambda img, axis: (
+        apply_2d_filter(ndi.prewitt, img, axis=axis)
+    ),
+    'sobel': lambda img, axis: (
+        apply_2d_filter(ndi.sobel, img, axis=axis)
+    ),
+}
+
+
+def apply_2d_filter(
+    filter_impl: Callable[[np.ndarray, Any, ...], np.ndarray],
+    img: giatools.Image,
+    **kwargs: Any,
+) -> giatools.Image:
+    """
+    Apply the 2-D filter to the 2-D/3-D, potentially multi-frame and multi-channel image.
+    """
+    result_data = None
+    for qtzc in np.ndindex(
+        img.data.shape[ 0],  # Q axis
+        img.data.shape[ 1],  # T axis
+        img.data.shape[ 2],  # Z axis
+        img.data.shape[-1],  # C axis
+    ):
+        sl = np.s_[*qtzc[:3], ..., qtzc[3]]  # noqa: E999
+        arr = img.data[sl]
+        assert arr.ndim == 2  # sanity check, should always be True
+
+        # Perform 2-D filtering
+        res = filter_impl(arr, **kwargs)
+        if result_data is None:
+            result_data = np.empty(img.data.shape, res.dtype)
+        result_data[sl] = res
+
+    # Return results
+    return giatools.Image(result_data, img.axes)
+
+
+def apply_filter(
+    input_filepath: str,
+    output_filepath: str,
+    filter_type: str,
+    **kwargs: Any,
+):
+    # Read the input image
+    img = giatools.Image.read(input_filepath)
+
+    # Perform filtering
+    filter_impl = filters[filter_type]
+    res = filter_impl(img, **kwargs).normalize_axes_like(img.original_axes)
+
+    # Adopt metadata and write the result
+    res.metadata = img.metadata
+    res.write(output_filepath, backend='tifffile')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('input', type=str, help='Input image filepath')
+    parser.add_argument('output', type=str, help='Output image filepath (TIFF)')
+    parser.add_argument('params', type=str)
+    args = parser.parse_args()
+
+    # Read the config file
+    with open(args.params) as cfgf:
+        cfg = json.load(cfgf)
+
+    apply_filter(
+        args.input,
+        args.output,
+        **cfg,
+    )