# HG changeset patch
# User imgteam
# Date 1727252370 0
# Node ID e8c9e104e10924dcb6414f6ff5c18f58ad2dfe60
# Parent 14f9986800faaaab02c1b3c39064dda369ddb214
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/spot_detection_2d/ commit f1b9207ec23c0af1681c929281bcbf1d0638368e
diff -r 14f9986800fa -r e8c9e104e109 creators.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/creators.xml Wed Sep 25 08:19:30 2024 +0000
@@ -0,0 +1,28 @@
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diff -r 14f9986800fa -r e8c9e104e109 spot_detection_2d.py
--- a/spot_detection_2d.py Mon Nov 13 22:12:30 2023 +0000
+++ b/spot_detection_2d.py Wed Sep 25 08:19:30 2024 +0000
@@ -1,88 +1,128 @@
"""
Copyright 2021-2022 Biomedical Computer Vision Group, Heidelberg University.
-Author: Qi Gao (qi.gao@bioquant.uni-heidelberg.de)
+Authors:
+- Qi Gao (qi.gao@bioquant.uni-heidelberg.de)
+- Leonid Kostrykin (leonid.kostrykin@bioquant.uni-heidelberg.de)
Distributed under the MIT license.
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
-
"""
import argparse
-import imageio
+import giatools.io
import numpy as np
import pandas as pd
-from skimage.feature import peak_local_max
-from skimage.filters import gaussian, laplace
+import scipy.ndimage as ndi
+from numpy.typing import NDArray
+from skimage.feature import blob_dog, blob_doh, blob_log
+
+blob_filters = {
+ 'dog': blob_dog,
+ 'doh': blob_doh,
+ 'log': blob_log,
+}
-def getbr(xy, img, nb, firstn):
- ndata = xy.shape[0]
- br = np.empty((ndata, 1))
- for j in range(ndata):
- br[j] = np.NaN
- if not np.isnan(xy[j, 0]):
- timg = img[xy[j, 1] - nb - 1:xy[j, 1] + nb, xy[j, 0] - nb - 1:xy[j, 0] + nb]
- br[j] = np.mean(np.sort(timg, axis=None)[-firstn:])
- return br
+def mean_intensity(img: NDArray, y: int, x: int, radius: int) -> float:
+ assert img.ndim == 2
+ assert radius >= 0
+ if radius == 0:
+ return float(img[y, x])
+ else:
+ mask = np.ones(img.shape, bool)
+ mask[y, x] = False
+ mask = (ndi.distance_transform_edt(mask) <= radius)
+ return img[mask].mean()
-def spot_detection(fn_in, fn_out, frame_1st=1, frame_end=0,
- typ_filter='Gauss', ssig=1, th=10,
- typ_br='smoothed', bd=10):
- ims_ori = imageio.mimread(fn_in, format='TIFF')
- ims_smd = np.zeros((len(ims_ori), ims_ori[0].shape[0], ims_ori[0].shape[1]), dtype='float64')
- if frame_end == 0 or frame_end > len(ims_ori):
- frame_end = len(ims_ori)
+def spot_detection(
+ fn_in: str,
+ fn_out: str,
+ frame_1st: int,
+ frame_end: int,
+ filter_type: str,
+ min_scale: float,
+ max_scale: float,
+ abs_threshold: float,
+ rel_threshold: float,
+ boundary: int,
+) -> None:
- for i in range(frame_1st - 1, frame_end):
- ims_smd[i, :, :] = gaussian(ims_ori[i].astype('float64'), sigma=ssig)
- ims_smd_max = np.max(ims_smd)
+ # Load the single-channel 2-D input image (or stack thereof)
+ stack = giatools.io.imread(fn_in)
- txyb_all = np.array([]).reshape(0, 4)
- for i in range(frame_1st - 1, frame_end):
- tmp = np.copy(ims_smd[i, :, :])
- if typ_filter == 'LoG':
- tmp = laplace(tmp)
+ # Normalize input image so that it is a stack of images (possibly a stack of a single image)
+ assert stack.ndim in (2, 3)
+ if stack.ndim == 2:
+ stack = stack.reshape(1, *stack.shape)
+
+ # Slice the stack
+ assert frame_1st >= 1
+ assert frame_end >= 0
+ stack = stack[frame_1st - 1:]
+ if frame_end > 0:
+ stack = stack[:-frame_end]
- tmp[tmp < th * ims_smd_max / 100] = 0
- coords = peak_local_max(tmp, min_distance=1)
- idx_to_del = np.where((coords[:, 0] <= bd) | (coords[:, 0] >= tmp.shape[0] - bd) |
- (coords[:, 1] <= bd) | (coords[:, 1] >= tmp.shape[1] - bd))
- coords = np.delete(coords, idx_to_del[0], axis=0)
- xys = coords[:, ::-1]
+ # Select the blob detection filter
+ assert filter_type.lower() in blob_filters.keys()
+ blob_filter = blob_filters[filter_type.lower()]
+
+ # Perform blob detection on each image of the stack
+ detections = list()
+ for img_idx, img in enumerate(stack):
+ blobs = blob_filter(img, threshold=abs_threshold, threshold_rel=rel_threshold, min_sigma=min_scale, max_sigma=max_scale)
+ for blob in blobs:
+ y, x, scale = blob
+
+ # Skip the detection if it is too close to the boundary of the image
+ if y < boundary or x < boundary or y >= img.shape[0] - boundary or x >= img.shape[1] - boundary:
+ continue
- if typ_br == 'smoothed':
- intens = getbr(xys, ims_smd[i, :, :], 0, 1)
- elif typ_br == 'robust':
- intens = getbr(xys, ims_ori[i], 1, 4)
- else:
- intens = getbr(xys, ims_ori[i], 0, 1)
+ # Add the detection to the list of detections
+ radius = scale * np.sqrt(2) * 2
+ intensity = mean_intensity(img, round(y), round(x), round(radius))
+ detections.append(
+ {
+ 'frame': img_idx + 1,
+ 'pos_x': round(x),
+ 'pos_y': round(y),
+ 'scale': scale,
+ 'radius': radius,
+ 'intensity': intensity,
+ }
+ )
- txyb = np.concatenate(((i + 1) * np.ones((xys.shape[0], 1)), xys, intens), axis=1)
- txyb_all = np.concatenate((txyb_all, txyb), axis=0)
-
- df = pd.DataFrame()
- df['FRAME'] = txyb_all[:, 0].astype(int)
- df['POS_X'] = txyb_all[:, 1].astype(int)
- df['POS_Y'] = txyb_all[:, 2].astype(int)
- df['INTENSITY'] = txyb_all[:, 3]
+ # Build and save dataframe
+ df = pd.DataFrame.from_dict(detections)
df.to_csv(fn_out, index=False, float_format='%.2f', sep="\t")
if __name__ == "__main__":
+
parser = argparse.ArgumentParser(description="Spot detection")
- parser.add_argument("fn_in", help="Name of input image sequence (stack)")
- parser.add_argument("fn_out", help="Name of output file to save the coordinates and intensities of detected spots")
- parser.add_argument("frame_1st", type=int, help="Index for the starting frame to detect spots (1 for first frame of the stack)")
- parser.add_argument("frame_end", type=int, help="Index for the last frame to detect spots (0 for the last frame of the stack)")
- parser.add_argument("filter", help="Detection filter")
- parser.add_argument("ssig", type=float, help="Sigma of the Gaussian for noise suppression")
- parser.add_argument("thres", type=float, help="Percentage of the global maximal for thresholding candidate spots")
- parser.add_argument("typ_intens", help="smoothed or robust (for measuring the intensities of spots)")
- parser.add_argument("bndy", type=int, help="Number of pixels (Spots close to image boundaries will be ignored)")
+
+ parser.add_argument("fn_in", help="Name of input image or image stack.")
+ parser.add_argument("fn_out", help="Name of output file to write the detections into.")
+ parser.add_argument("frame_1st", type=int, help="Index for the starting frame to detect spots (1 for first frame of the stack).")
+ parser.add_argument("frame_end", type=int, help="Index for the last frame to detect spots (0 for the last frame of the stack).")
+ parser.add_argument("filter_type", help="Detection filter")
+ parser.add_argument("min_scale", type=float, help="The minimum scale to consider for multi-scale detection.")
+ parser.add_argument("max_scale", type=float, help="The maximum scale to consider for multi-scale detection.")
+ parser.add_argument("abs_threshold", type=float, help=(
+ "Filter responses below this threshold will be ignored. Only filter responses above this thresholding will be considered as blobs. "
+ "This threshold is ignored if the relative threshold (below) corresponds to a higher response.")
+ )
+ parser.add_argument("rel_threshold", type=float, help=(
+ "Same as the absolute threshold (above), but as a fraction of the overall maximal filter response of an image. "
+ "This threshold is ignored if it corresponds to a response below the absolute threshold.")
+ )
+ parser.add_argument("boundary", type=int, help="Width of image boundaries (in pixel) where spots will be ignored.")
+
args = parser.parse_args()
spot_detection(args.fn_in, args.fn_out,
frame_1st=args.frame_1st, frame_end=args.frame_end,
- typ_filter=args.filter, ssig=args.ssig, th=args.thres,
- typ_br=args.typ_intens, bd=args.bndy)
+ filter_type=args.filter_type,
+ min_scale=args.min_scale, max_scale=args.max_scale,
+ abs_threshold=args.abs_threshold, rel_threshold=args.rel_threshold,
+ boundary=args.boundary)
diff -r 14f9986800fa -r e8c9e104e109 spot_detection_2d.xml
--- a/spot_detection_2d.xml Mon Nov 13 22:12:30 2023 +0000
+++ b/spot_detection_2d.xml Wed Sep 25 08:19:30 2024 +0000
@@ -1,5 +1,13 @@
-
+
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+ creators.xml
+ 0.1
+ 0
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+
operation_3443
@@ -7,10 +15,11 @@
galaxy_image_analysis
- imageio
- numpy
+ giatools
+ numpy
pandas
- scikit-image
+ scikit-image
+ tifffile
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- **What it does**
+
+ **Perform spot detection and measure the image intensities.**
+
+ This tool detects spots (blobs) and measures the image intensities in a single-channel 2-D image (or a stack of such images).
+
+ The tool produces a TSV file containing all detections, with the following columns:
- This tool detects spots and measures the intensities in a 2D image (sequence).
+ - ``frame``: The frame of the image stack
+ - ``pos_x``: The horizontal coordinate of the detection
+ - ``pos_y``: The vertical coordinate of the detection
+ - ``scale``: The scale at which the detection was found
+ - ``radius``: The radius of the detected spot
+ - ``intensity``: The mean intensity of the spot
+
10.1097/j.pain.0000000000002642
diff -r 14f9986800fa -r e8c9e104e109 test-data/input1.tif
Binary file test-data/input1.tif has changed
diff -r 14f9986800fa -r e8c9e104e109 test-data/input2.tif
Binary file test-data/input2.tif has changed
diff -r 14f9986800fa -r e8c9e104e109 test-data/output1.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output1.tsv Wed Sep 25 08:19:30 2024 +0000
@@ -0,0 +1,842 @@
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diff -r 14f9986800fa -r e8c9e104e109 test-data/output2.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output2.tsv Wed Sep 25 08:19:30 2024 +0000
@@ -0,0 +1,39 @@
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diff -r 14f9986800fa -r e8c9e104e109 test-data/spots_detected.tsv
--- a/test-data/spots_detected.tsv Mon Nov 13 22:12:30 2023 +0000
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diff -r 14f9986800fa -r e8c9e104e109 test-data/test_img1.tif
Binary file test-data/test_img1.tif has changed