| Previous changeset 4:14f9986800fa (2023-11-13) |
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Commit message:
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/spot_detection_2d/ commit f1b9207ec23c0af1681c929281bcbf1d0638368e |
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modified:
spot_detection_2d.py spot_detection_2d.xml |
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added:
creators.xml test-data/input1.tif test-data/input2.tif test-data/output1.tsv test-data/output2.tsv |
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removed:
test-data/spots_detected.tsv test-data/test_img1.tif |
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| 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 |
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| @@ -0,0 +1,28 @@ +<macros> + + <xml name="creators/bmcv"> + <organization name="Biomedical Computer Vision Group, Heidelberg Universtiy" alternateName="BMCV" url="http://www.bioquant.uni-heidelberg.de/research/groups/biomedical_computer_vision.html" /> + <yield /> + </xml> + + <xml name="creators/rmassei"> + <person givenName="Riccardo" familyName="Massei"/> + <yield/> + </xml> + + <xml name="creators/alliecreason"> + <person givenName="Allison" familyName="Creason"/> + <yield/> + </xml> + + <xml name="creators/bugraoezdemir"> + <person givenName="Bugra" familyName="Oezdemir"/> + <yield/> + </xml> + + <xml name="creators/thawn"> + <person givenName="Till" familyName="Korten"/> + <yield/> + </xml> + +</macros> |
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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 |
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| b'@@ -1,88 +1,128 @@\n """\n Copyright 2021-2022 Biomedical Computer Vision Group, Heidelberg University.\n-Author: Qi Gao (qi.gao@bioquant.uni-heidelberg.de)\n+Authors:\n+- Qi Gao (qi.gao@bioquant.uni-heidelberg.de)\n+- Leonid Kostrykin (leonid.kostrykin@bioquant.uni-heidelberg.de)\n \n Distributed under the MIT license.\n See file LICENSE for detail or copy at https://opensource.org/licenses/MIT\n-\n """\n \n import argparse\n \n-import imageio\n+import giatools.io\n import numpy as np\n import pandas as pd\n-from skimage.feature import peak_local_max\n-from skimage.filters import gaussian, laplace\n+import scipy.ndimage as ndi\n+from numpy.typing import NDArray\n+from skimage.feature import blob_dog, blob_doh, blob_log\n+\n+blob_filters = {\n+ \'dog\': blob_dog,\n+ \'doh\': blob_doh,\n+ \'log\': blob_log,\n+}\n \n \n-def getbr(xy, img, nb, firstn):\n- ndata = xy.shape[0]\n- br = np.empty((ndata, 1))\n- for j in range(ndata):\n- br[j] = np.NaN\n- if not np.isnan(xy[j, 0]):\n- timg = img[xy[j, 1] - nb - 1:xy[j, 1] + nb, xy[j, 0] - nb - 1:xy[j, 0] + nb]\n- br[j] = np.mean(np.sort(timg, axis=None)[-firstn:])\n- return br\n+def mean_intensity(img: NDArray, y: int, x: int, radius: int) -> float:\n+ assert img.ndim == 2\n+ assert radius >= 0\n+ if radius == 0:\n+ return float(img[y, x])\n+ else:\n+ mask = np.ones(img.shape, bool)\n+ mask[y, x] = False\n+ mask = (ndi.distance_transform_edt(mask) <= radius)\n+ return img[mask].mean()\n \n \n-def spot_detection(fn_in, fn_out, frame_1st=1, frame_end=0,\n- typ_filter=\'Gauss\', ssig=1, th=10,\n- typ_br=\'smoothed\', bd=10):\n- ims_ori = imageio.mimread(fn_in, format=\'TIFF\')\n- ims_smd = np.zeros((len(ims_ori), ims_ori[0].shape[0], ims_ori[0].shape[1]), dtype=\'float64\')\n- if frame_end == 0 or frame_end > len(ims_ori):\n- frame_end = len(ims_ori)\n+def spot_detection(\n+ fn_in: str,\n+ fn_out: str,\n+ frame_1st: int,\n+ frame_end: int,\n+ filter_type: str,\n+ min_scale: float,\n+ max_scale: float,\n+ abs_threshold: float,\n+ rel_threshold: float,\n+ boundary: int,\n+) -> None:\n \n- for i in range(frame_1st - 1, frame_end):\n- ims_smd[i, :, :] = gaussian(ims_ori[i].astype(\'float64\'), sigma=ssig)\n- ims_smd_max = np.max(ims_smd)\n+ # Load the single-channel 2-D input image (or stack thereof)\n+ stack = giatools.io.imread(fn_in)\n \n- txyb_all = np.array([]).reshape(0, 4)\n- for i in range(frame_1st - 1, frame_end):\n- tmp = np.copy(ims_smd[i, :, :])\n- if typ_filter == \'LoG\':\n- tmp = laplace(tmp)\n+ # Normalize input image so that it is a stack of images (possibly a stack of a single image)\n+ assert stack.ndim in (2, 3)\n+ if stack.ndim == 2:\n+ stack = stack.reshape(1, *stack.shape)\n+\n+ # Slice the stack\n+ assert frame_1st >= 1\n+ assert frame_end >= 0\n+ stack = stack[frame_1st - 1:]\n+ if frame_end > 0:\n+ stack = stack[:-frame_end]\n \n- tmp[tmp < th * ims_smd_max / 100] = 0\n- coords = peak_local_max(tmp, min_distance=1)\n- idx_to_del = np.where((coords[:, 0] <= bd) | (coords[:, 0] >= tmp.shape[0] - bd) |\n- (coords[:, 1] <= bd) | (coords[:, 1] >= tmp.shape[1] - bd))\n- coords = np.delete(coords, idx_to_del[0], axis=0)\n- xys = coords[:, ::-1]\n+ # Select the blob detection filter\n+ assert filter_type.lower() in blob_filters.keys()\n+ blob_filter = blob_filters[filter_type.lower()]\n+\n+ # Perform blob detection on each image of the stack\n+ detections = list()\n+ for img_idx, img in enumerate(stack):\n+ blobs = blob_filter(img, threshold=abs_threshold, threshold_rel=rel_threshold, min_sigma=min_scale, max_sigma=max_scale)\n+ for blob in blobs:\n+ y, x, scale = blob\n+\n+ # Skip the detection if it is too close to the boundary of the image\n+ if y < boundary or x < boundary or y >= img.shape[0] - boundar'..b'np.sqrt(2) * 2\n+ intensity = mean_intensity(img, round(y), round(x), round(radius))\n+ detections.append(\n+ {\n+ \'frame\': img_idx + 1,\n+ \'pos_x\': round(x),\n+ \'pos_y\': round(y),\n+ \'scale\': scale,\n+ \'radius\': radius,\n+ \'intensity\': intensity,\n+ }\n+ )\n \n- txyb = np.concatenate(((i + 1) * np.ones((xys.shape[0], 1)), xys, intens), axis=1)\n- txyb_all = np.concatenate((txyb_all, txyb), axis=0)\n-\n- df = pd.DataFrame()\n- df[\'FRAME\'] = txyb_all[:, 0].astype(int)\n- df[\'POS_X\'] = txyb_all[:, 1].astype(int)\n- df[\'POS_Y\'] = txyb_all[:, 2].astype(int)\n- df[\'INTENSITY\'] = txyb_all[:, 3]\n+ # Build and save dataframe\n+ df = pd.DataFrame.from_dict(detections)\n df.to_csv(fn_out, index=False, float_format=\'%.2f\', sep="\\t")\n \n \n if __name__ == "__main__":\n+\n parser = argparse.ArgumentParser(description="Spot detection")\n- parser.add_argument("fn_in", help="Name of input image sequence (stack)")\n- parser.add_argument("fn_out", help="Name of output file to save the coordinates and intensities of detected spots")\n- parser.add_argument("frame_1st", type=int, help="Index for the starting frame to detect spots (1 for first frame of the stack)")\n- parser.add_argument("frame_end", type=int, help="Index for the last frame to detect spots (0 for the last frame of the stack)")\n- parser.add_argument("filter", help="Detection filter")\n- parser.add_argument("ssig", type=float, help="Sigma of the Gaussian for noise suppression")\n- parser.add_argument("thres", type=float, help="Percentage of the global maximal for thresholding candidate spots")\n- parser.add_argument("typ_intens", help="smoothed or robust (for measuring the intensities of spots)")\n- parser.add_argument("bndy", type=int, help="Number of pixels (Spots close to image boundaries will be ignored)")\n+\n+ parser.add_argument("fn_in", help="Name of input image or image stack.")\n+ parser.add_argument("fn_out", help="Name of output file to write the detections into.")\n+ parser.add_argument("frame_1st", type=int, help="Index for the starting frame to detect spots (1 for first frame of the stack).")\n+ parser.add_argument("frame_end", type=int, help="Index for the last frame to detect spots (0 for the last frame of the stack).")\n+ parser.add_argument("filter_type", help="Detection filter")\n+ parser.add_argument("min_scale", type=float, help="The minimum scale to consider for multi-scale detection.")\n+ parser.add_argument("max_scale", type=float, help="The maximum scale to consider for multi-scale detection.")\n+ parser.add_argument("abs_threshold", type=float, help=(\n+ "Filter responses below this threshold will be ignored. Only filter responses above this thresholding will be considered as blobs. "\n+ "This threshold is ignored if the relative threshold (below) corresponds to a higher response.")\n+ )\n+ parser.add_argument("rel_threshold", type=float, help=(\n+ "Same as the absolute threshold (above), but as a fraction of the overall maximal filter response of an image. "\n+ "This threshold is ignored if it corresponds to a response below the absolute threshold.")\n+ )\n+ parser.add_argument("boundary", type=int, help="Width of image boundaries (in pixel) where spots will be ignored.")\n+\n args = parser.parse_args()\n spot_detection(args.fn_in, args.fn_out,\n frame_1st=args.frame_1st, frame_end=args.frame_end,\n- typ_filter=args.filter, ssig=args.ssig, th=args.thres,\n- typ_br=args.typ_intens, bd=args.bndy)\n+ filter_type=args.filter_type,\n+ min_scale=args.min_scale, max_scale=args.max_scale,\n+ abs_threshold=args.abs_threshold, rel_threshold=args.rel_threshold,\n+ boundary=args.boundary)\n' |
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| 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 |
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| @@ -1,5 +1,13 @@ -<tool id="ip_spot_detection_2d" name="Perform 2D spot detection" version="0.0.3-2" profile="20.05"> +<tool id="ip_spot_detection_2d" name="Perform 2-D spot detection" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05"> <description></description> + <macros> + <import>creators.xml</import> + <token name="@TOOL_VERSION@">0.1</token> + <token name="@VERSION_SUFFIX@">0</token> + </macros> + <creator> + <expand macro="creators/bmcv" /> + </creator> <edam_operations> <edam_operation>operation_3443</edam_operation> </edam_operations> @@ -7,10 +15,11 @@ <xref type="bio.tools">galaxy_image_analysis</xref> </xrefs> <requirements> - <requirement type="package" version="2.9.0">imageio</requirement> - <requirement type="package" version="1.20.2">numpy</requirement> + <requirement type="package" version="0.2.0">giatools</requirement> + <requirement type="package" version="1.26.4">numpy</requirement> <requirement type="package" version="1.2.4">pandas</requirement> - <requirement type="package" version="0.18.1">scikit-image</requirement> + <requirement type="package" version="0.21">scikit-image</requirement> + <requirement type="package" version="2024.6.18">tifffile</requirement> </requirements> <command detect_errors="aggressive"> <![CDATA[ @@ -19,49 +28,75 @@ '$fn_out' '$frame_1st' '$frame_end' - '$filter' - '$ssig' - '$thres' - '$typ_intens' - '$bndy' + '$filter_type' + '$min_scale' + '$max_scale' + '$abs_threshold' + '$rel_threshold' + '$boundary' ]]> </command> <inputs> - <param name="fn_in" type="data" format="tiff" label="Image sequence (stack)" /> + <param name="fn_in" type="data" format="tiff" label="Intensity image or a stack of images" /> <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="filter" type="select" label="Choose a detection filter"> - <option value="Gauss" selected="True">Gaussian</option> - <option value="LoG">Laplacian-of-Gaussian, LoG (more accurate when spots have similar sizes)</option> + <param name="filter_type" type="select" label="Detection filter"> + <option value="LoG" selected="True">Laplacian of Gaussian</option> + <option value="DoG">Difference of Gaussians</option> + <option value="DoH">Determinant of Hessian</option> </param> - <param name="ssig" type="float" value="1.0" min="0.5" max="10" label="Sigma of the Gaussian (for noise suppression)" /> - <param name="thres" type="float" value="10.0" min="0" max="100" label="Percentage of the global maximal as the threshold for candidate spots" /> - <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="bndy" type="integer" value="10" min="0" max="50" label="Width (in pixel) of image boundaries where spots will be ignored" /> + <param name="min_scale" type="float" value="1.0" min="1.0" label="Minimum scale" /> + <param name="max_scale" type="float" value="2.0" min="1.0" label="Maximum scale" /> + <param name="abs_threshold" type="float" value=".25" min="0" label="Minimum filter response (absolute)" 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." /> + <param name="rel_threshold" type="float" value="0" min="0" max="1" label="Minimum filter response (relative)" help="Same as the absolute threshold (above), but as a fraction of the overall maximum filter response of an image. This threshold is ignored if it corresponds to a response below the absolute threshold." /> + <param name="boundary" type="integer" value="10" min="0" label="Image boundary" help="Width of image boundaries (in pixel) where spots will be ignored." /> </inputs> <outputs> <data format="tabular" name="fn_out" /> </outputs> <tests> + <!-- Multi-frame input --> <test> - <param name="fn_in" value="test_img1.tif"/> + <param name="fn_in" value="input1.tif"/> <param name="frame_1st" value="1"/> <param name="frame_end" value="0"/> - <param name="filter" value="Gauss"/> - <param name="ssig" value="1"/> - <param name="thres" value="10"/> - <param name="typ_intens" value="smoothed"/> - <param name="bndy" value="10"/> - <output name="fn_out" value="spots_detected.tsv" ftype="tabular" /> + <param name="filter_type" value="LoG"/> + <param name="min_scale" value="1"/> + <param name="max_scale" value="2"/> + <param name="abs_threshold" value="0"/> + <param name="rel_threshold" value="0.1"/> + <param name="boundary" value="10"/> + <output name="fn_out" value="output1.tsv" ftype="tabular" /> + </test> + <!-- Single-frame input --> + <test> + <param name="fn_in" value="input2.tif"/> + <param name="frame_1st" value="1"/> + <param name="frame_end" value="0"/> + <param name="filter_type" value="LoG"/> + <param name="min_scale" value="1"/> + <param name="max_scale" value="2"/> + <param name="abs_threshold" value="0.04"/> + <param name="rel_threshold" value="0"/> + <param name="boundary" value="10"/> + <output name="fn_out" value="output2.tsv" ftype="tabular" /> </test> </tests> <help> - **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 + </help> <citations> <citation type="doi">10.1097/j.pain.0000000000002642</citation> |
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| diff -r 14f9986800fa -r e8c9e104e109 test-data/input1.tif |
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| Binary file test-data/input1.tif has changed |
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| diff -r 14f9986800fa -r e8c9e104e109 test-data/input2.tif |
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| Binary file test-data/input2.tif has changed |
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| 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 |
| b |
| b'@@ -0,0 +1,842 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|
| b |
| 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 |
| b |
| @@ -0,0 +1,39 @@ +frame pos_x pos_y scale radius intensity +1 85 32 1.33 3.77 18807.73 +1 190 25 1.78 5.03 24581.44 +1 137 26 1.44 4.09 19037.59 +1 63 42 1.44 4.09 22390.80 +1 107 44 1.33 3.77 23429.96 +1 61 27 1.56 4.40 18052.18 +1 158 39 1.44 4.09 18377.02 +1 190 14 1.33 3.77 18548.86 +1 182 33 1.78 5.03 26467.79 +1 39 39 1.44 4.09 14782.43 +1 169 26 1.33 3.77 14203.41 +1 61 54 1.33 3.77 23248.06 +1 95 52 1.33 3.77 21480.71 +1 23 60 1.89 5.34 25203.43 +1 84 24 1.56 4.40 16630.57 +1 121 47 1.67 4.71 15459.11 +1 66 49 1.11 3.14 23858.07 +1 115 36 2.00 5.66 16389.10 +1 55 51 1.33 3.77 23548.90 +1 130 72 1.67 4.71 15769.02 +1 117 23 1.33 3.77 16763.14 +1 45 52 1.56 4.40 22877.61 +1 36 71 1.56 4.40 20780.96 +1 78 17 1.33 3.77 16844.51 +1 101 38 1.56 4.40 21376.59 +1 147 31 1.78 5.03 16597.14 +1 163 55 2.00 5.66 18301.54 +1 164 23 1.33 3.77 17073.82 +1 150 24 1.56 4.40 15440.02 +1 151 67 1.78 5.03 18419.96 +1 26 53 2.00 5.66 20586.01 +1 79 62 1.33 3.77 15232.88 +1 69 17 1.11 3.14 15601.83 +1 83 52 1.33 3.77 18315.00 +1 16 54 2.00 5.66 22140.66 +1 166 61 1.78 5.03 18488.78 +1 163 43 1.44 4.09 16925.49 +1 130 53 1.78 5.03 15101.96 |
| b |
| diff -r 14f9986800fa -r e8c9e104e109 test-data/spots_detected.tsv --- a/test-data/spots_detected.tsv Mon Nov 13 22:12:30 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,462 +0,0 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| diff -r 14f9986800fa -r e8c9e104e109 test-data/test_img1.tif |
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| Binary file test-data/test_img1.tif has changed |