| Next changeset 1:859dd1c11ac0 (2022-02-19) |
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Commit message:
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/spot_detection_2d/ commit 481cd51a76341c0ec3759f919454e95139f0cc4e" |
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added:
spot_detection_2d.py spot_detection_2d.xml test-data/spots_detected.tsv test-data/test_img1.tif |
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| diff -r 000000000000 -r d78372040976 spot_detection_2d.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spot_detection_2d.py Wed Jul 21 19:59:00 2021 +0000 |
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| @@ -0,0 +1,86 @@ +""" +Copyright 2021 Biomedical Computer Vision Group, Heidelberg University. +Author: Qi Gao (qi.gao@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 numpy as np +import pandas as pd +from skimage.feature import peak_local_max +from skimage.filters import gaussian + + +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 spot_detection(fn_in, fn_out, frame_1st=1, frame_end=0, typ_br='smoothed', th=10, ssig=1, 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) + + 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) + + txyb_all = np.array([]).reshape(0, 4) + for i in range(frame_1st - 1, frame_end): + tmp = np.copy(ims_smd[i, :, :]) + 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] + + 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) + + 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] + df.to_csv(fn_out, index=False, float_format='%.2f', sep="\t") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Spot detection based on local maxima") + 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("typ_intens", help="smoothed or robust (for measuring the intensities of spots)") + parser.add_argument("thres", type=float, help="Percentage of the global maximal intensity for thresholding candidate spots") + parser.add_argument("ssig", type=float, help="Sigma of the Gaussian filter for noise suppression") + parser.add_argument("bndy", type=int, help="Number of pixels (Spots close to image boundaries 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_br=args.typ_intens, + th=args.thres, + ssig=args.ssig, + bd=args.bndy) |
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| diff -r 000000000000 -r d78372040976 spot_detection_2d.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/spot_detection_2d.xml Wed Jul 21 19:59:00 2021 +0000 |
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| @@ -0,0 +1,54 @@ +<tool id="ip_spot_detection_2d" name="Spot Detection" version="0.0.1" profile="20.05"> + <description>based on local intensity maxima</description> + <requirements> + <requirement type="package" version="2.9.0">imageio</requirement> + <requirement type="package" version="1.20.2">numpy</requirement> + <requirement type="package" version="1.2.4">pandas</requirement> + <requirement type="package" version="0.18.1">scikit-image</requirement> + </requirements> + <command> + <![CDATA[ + python '$__tool_directory__/spot_detection_2d.py' + '$fn_in' + '$fn_out' + '$frame_1st' + '$frame_end' + '$typ_intens' + '$thres' + '$ssig' + '$bndy' + ]]> + </command> + <inputs> + <param name="fn_in" type="data" format="tiff" label="Image sequence (stack)" /> + <param name="frame_1st" type="integer" value="1" label="Starting time point (1 for the first frame of the stack)" /> + <param name="frame_end" type="integer" value="0" label="Ending time point (0 for the last frame of the stack)" /> + <param name="typ_intens" type="select" label="How to measure the intensities"> + <option value="smoothed" selected="True">Smoothed</option> + <option value="robust">Robust</option> + </param> + <param name="thres" type="float" value="10" label="Percentage of the global maximal intensity as the threshold for candidate spots" /> + <param name="ssig" type="float" value="1" label="Sigma of the Gaussian filter for noise suppression" /> + <param name="bndy" type="integer" value="10" label="Number of pixels (Spots within n-pixel image boundaries will be ignored)" /> + </inputs> + <outputs> + <data format="tabular" name="fn_out" /> + </outputs> + <tests> + <test> + <param name="fn_in" value="test_img1.tif"/> + <param name="frame_1st" value="1"/> + <param name="frame_end" value="0"/> + <param name="typ_intens" value="smoothed"/> + <param name="thres" value="10"/> + <param name="ssig" value="1"/> + <param name="bndy" value="10"/> + <output name="fn_out" value="spots_detected.tsv" ftype="tabular" /> + </test> + </tests> + <help> + **What it does** + + This tool detects spots and measures the intensities in a 2D image sequence based on local intensity maxima. + </help> +</tool> |
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| diff -r 000000000000 -r d78372040976 test-data/spots_detected.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/spots_detected.tsv Wed Jul 21 19:59:00 2021 +0000 |
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| diff -r 000000000000 -r d78372040976 test-data/test_img1.tif |
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| Binary file test-data/test_img1.tif has changed |