From ee45ab5aae8c37ed998da509903bca5d4683b62d Mon Sep 17 00:00:00 2001 From: Leonid Kostrykin Date: Tue, 24 Sep 2024 16:24:29 +0200 Subject: [PATCH 1/4] Overhaul the tool and add support for single-image processing --- tools/spot_detection_2d/creators.xml | 1 + tools/spot_detection_2d/spot_detection_2d.py | 172 ++-- tools/spot_detection_2d/spot_detection_2d.xml | 92 +- tools/spot_detection_2d/test-data/input1.tif | Bin 0 -> 714096 bytes tools/spot_detection_2d/test-data/input2.tif | Bin 0 -> 35545 bytes tools/spot_detection_2d/test-data/output1.tsv | 844 ++++++++++++++++++ tools/spot_detection_2d/test-data/output2.tsv | 48 + .../test-data/spots_detected.tsv | 462 ---------- .../spot_detection_2d/test-data/test_img1.tif | Bin 1252163 -> 0 bytes 9 files changed, 1059 insertions(+), 560 deletions(-) create mode 120000 tools/spot_detection_2d/creators.xml create mode 100644 tools/spot_detection_2d/test-data/input1.tif create mode 100644 tools/spot_detection_2d/test-data/input2.tif create mode 100644 tools/spot_detection_2d/test-data/output1.tsv create mode 100644 tools/spot_detection_2d/test-data/output2.tsv delete mode 100644 tools/spot_detection_2d/test-data/spots_detected.tsv delete mode 100644 tools/spot_detection_2d/test-data/test_img1.tif diff --git a/tools/spot_detection_2d/creators.xml b/tools/spot_detection_2d/creators.xml new file mode 120000 index 00000000..5d2b71e0 --- /dev/null +++ b/tools/spot_detection_2d/creators.xml @@ -0,0 +1 @@ +../../macros/creators.xml \ No newline at end of file diff --git a/tools/spot_detection_2d/spot_detection_2d.py b/tools/spot_detection_2d/spot_detection_2d.py index 66c7a5b0..8ddd8b8a 100644 --- a/tools/spot_detection_2d/spot_detection_2d.py +++ b/tools/spot_detection_2d/spot_detection_2d.py @@ -1,88 +1,122 @@ """ 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 - - -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_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) - - 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, :, :]) - if typ_filter == 'LoG': - tmp = laplace(tmp) - - 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] +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 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: 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: + + # Load the single-channel 2-D input image (or stack thereof) + stack = giatools.io.imread(fn_in) + + # 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] + + # 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 + 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, + } + ) + + # 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 sequence (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 --git a/tools/spot_detection_2d/spot_detection_2d.xml b/tools/spot_detection_2d/spot_detection_2d.xml index e098c529..6661185a 100644 --- a/tools/spot_detection_2d/spot_detection_2d.xml +++ b/tools/spot_detection_2d/spot_detection_2d.xml @@ -1,5 +1,13 @@ - + + + creators.xml + 0.1 + 0 + + + + operation_3443 @@ -7,10 +15,10 @@ galaxy_image_analysis - imageio - numpy + giatools + numpy pandas - scikit-image + scikit-image - + - - - + + + + - - - - - - - + + + + + + + + + + + + + + + + + + + - + - - - - - - + + + + + + + - **What it does** - This tool detects spots and measures the intensities in a 2D image (sequence). + **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 sequence thereof). + + The tool produces a TSV file containing all detections, with the following columns: + + - ``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 --git a/tools/spot_detection_2d/test-data/input1.tif b/tools/spot_detection_2d/test-data/input1.tif new file mode 100644 index 0000000000000000000000000000000000000000..c2e1424cb88ed08758c78cb4a1c672f4c2ac16a4 GIT binary patch literal 714096 zcmeFahkw`Q)&GC4>;2B&3E7M6y%%JVKsF>HkT3#agb-$!VNY3#3k6CEU|NSh_7%PtFc_?q3|Ng9{y#L4NV6l{WklzuGFnX`ftI zTv%RKSmr;lX=tQwW`1A(EdSx5m5bLc8L2C)sLU@JJ0`!ve`IiAnAatR#rgmJ>%V^G z(sd(stCwyX8qWV8|My?O%V04>Y=qG>jnmw*R5N-e#n2ts)a*!gR54oUNy3G-H@lVpZfBLd!O?n{>Mau z`b9$1MZ!N7iF`mLW}8TSlSopa$mmx^(oTwGE)>bh7s>llr0|?b$zGB24w0%+B4gha zsXZf7KP)n#T%_qck=Ca~CLR*$>=o%w5SjX($h12}X08*NT`Mxz6`6ltWbl~CqJEL3 z=_1QN6zYJ128wKcRb<;qk(~=gcIS)i{Zizjb0P=!iX83`IWkJ*vNuJJ zpAorgSmc^=k!!ybx&A4U(}zTE?iIN$LFA72MDD&*tJ{9@(10v6D6M3OYC?4vGBOE5;^>ao-c;bEg>pbz*{Q#e}+I!q1C|JSHZl zUrcATPUVHUrgnfV#b~mQ@dA8eTSF{qr^15 zDW>&|n2Ez;I?KgO{!Yx)r^HMnAr(p=Da6n{+(h5*NItFD`u%HX8CzBE02j; z(=TRSx|j{0irM^tm~Gp{>}(RVJ5bEtSH)a(Qp|ycVh-nvx%5jhmz@)He6N_RI>cNv zO3bxyin;!bnA5{zZY~#d>vv-AcuLIOhs505E9QX&F+YD#%rEW~b8elOM{C7A;fi_c zyqI4f6Z2fZm>1H;y!5GT1$vH8f z?G^LS4l!Sj67%(&V!k~i=DT4rKbDI%--&gf66`%q!Js@I#^S{6UD}(>V!2b^eS6y*|i1U&B_urp}J&{21 zmVBvWM30wbNsw9mJtEU&#JnJtQYx9oM@C7ijJAuUM_TQr5-asGPikefwA*2c<6Eb6 zORI>N1c)5NcXvyNETn!R^%HFbl>1nJ8D}psf0jAom}2Tn-nD=y41`U6M!LF_cS@naCm&`RAIS({3Z*^2LWYU>RvuNx0bq4g2YF z2%df^Z_BgLwI7Kck~^tA9U1$X1Sm~0l~C$WU57qX)J@ddmc;uY{Ty%16LW2u~-NTuC*J@O_x~V|YIYtryVC270cQ zSJCdUTp+WFFMp6f(*9l~Vc?<^U1o`&&6a2>u$8nG1Z58+$#s18k?k_s9Du@HS?zTl za!-|uWs;jCwJ7TG1zV+|BVK$8zBiADZJ=ti~gLiGZ4sOZ>*WfT*? 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zki}L@WQsa2rh_?d6u6C&XDY!Mlg#cJj%dd75pC7Mu09tYtRx3FiWSa6viI_7xi9{YLKmvaY&H6;)_%pz z^bg~6dHPpyDtHj6qw#7_wgpI39cw-8tqnPd6iwwR#fUZVt}+k@1OGInWfMH3(=VDF z=&AB$pJZ%R45ND|bX{a4(f0@N(QU=9eSw%+pEt$^$4CLo3A{^D$q7-V14Md+2DT^-J%K749Fr(^Cirw-|8d~9UI9NGJe}qe?g0nWGjEO2ZP#ro` zm?aci)F6K^0COv6D@8-h2k6VXrBW9B=mU+ zxR{9MY~Xy^8$0AD!PWKw`u#u|#%*c{vbP^gY6#EAvcA49)BFkvqEiX7dpj7I4<&Aa zzGbF2YaNT`Dl`vs)1P6MAj^w+Px%zo2JlntETl$#SyeNa=t;MY>hL4MTZf4QhM90c zBmTrBL(~qw;Tia+YIyEJR^8X`vg!z=yO8U!QmgTgYy)q~V@koxO5bL)gH=BVeP{Db zDw=#7o~U{v3{=HR@pjp8Y!&fyzeYM!-Lt?&%$2 Date: Tue, 24 Sep 2024 17:16:00 +0200 Subject: [PATCH 2/4] Add workaround for TIFF dimension issue --- tools/spot_detection_2d/spot_detection_2d.py | 17 +++++++++++++++-- tools/spot_detection_2d/spot_detection_2d.xml | 7 ++++--- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/tools/spot_detection_2d/spot_detection_2d.py b/tools/spot_detection_2d/spot_detection_2d.py index 8ddd8b8a..80f5a0f0 100644 --- a/tools/spot_detection_2d/spot_detection_2d.py +++ b/tools/spot_detection_2d/spot_detection_2d.py @@ -14,6 +14,7 @@ import numpy as np import pandas as pd import scipy.ndimage as ndi +import tifffile from numpy.typing import NDArray from skimage.feature import blob_dog, blob_doh, blob_log @@ -36,6 +37,18 @@ def mean_intensity(img: NDArray, y: int, x: int, radius: int) -> float: return img[mask].mean() +def load_image(fn_in: str) -> NDArray: + """ + Load the input image using ``tifffile`` if possible. + + This is necessary to properly load multi-page TIFF files. + """ + try: + return giatools.io.imread(fn_in, impl=tifffile.imread) + except tifffile.TiffFileError: + return giatools.io.imread(fn_in) # Not a TIFF file + + def spot_detection( fn_in: str, fn_out: str, @@ -50,7 +63,7 @@ def spot_detection( ) -> None: # Load the single-channel 2-D input image (or stack thereof) - stack = giatools.io.imread(fn_in) + stack = load_image(fn_in) # Normalize input image so that it is a stack of images (possibly a stack of a single image) assert stack.ndim in (2, 3) @@ -96,7 +109,7 @@ def spot_detection( parser = argparse.ArgumentParser(description="Spot detection") - parser.add_argument("fn_in", help="Name of input image or image sequence (stack).") + 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).") diff --git a/tools/spot_detection_2d/spot_detection_2d.xml b/tools/spot_detection_2d/spot_detection_2d.xml index 6661185a..613b1fb7 100644 --- a/tools/spot_detection_2d/spot_detection_2d.xml +++ b/tools/spot_detection_2d/spot_detection_2d.xml @@ -1,4 +1,4 @@ - + creators.xml @@ -19,6 +19,7 @@ numpy pandas scikit-image + tifffile - + @@ -85,7 +86,7 @@ **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 sequence thereof). + 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: From 55e484086414eb90ba195ca82669b93eb29756df Mon Sep 17 00:00:00 2001 From: Leonid Kostrykin Date: Tue, 24 Sep 2024 17:19:32 +0200 Subject: [PATCH 3/4] Add implementation of `boundary` parameter --- tools/spot_detection_2d/spot_detection_2d.py | 6 ++++++ tools/spot_detection_2d/test-data/output1.tsv | 2 -- tools/spot_detection_2d/test-data/output2.tsv | 9 --------- 3 files changed, 6 insertions(+), 11 deletions(-) diff --git a/tools/spot_detection_2d/spot_detection_2d.py b/tools/spot_detection_2d/spot_detection_2d.py index 80f5a0f0..473fba3a 100644 --- a/tools/spot_detection_2d/spot_detection_2d.py +++ b/tools/spot_detection_2d/spot_detection_2d.py @@ -87,6 +87,12 @@ def spot_detection( 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 + + # 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( diff --git a/tools/spot_detection_2d/test-data/output1.tsv b/tools/spot_detection_2d/test-data/output1.tsv index a2eb0825..15d59c59 100644 --- a/tools/spot_detection_2d/test-data/output1.tsv +++ b/tools/spot_detection_2d/test-data/output1.tsv @@ -406,7 +406,6 @@ frame pos_x pos_y scale radius intensity 3 214 178 1.00 2.83 9065.83 3 42 86 1.00 2.83 11837.97 3 265 177 1.56 4.40 20759.84 -3 256 188 1.00 2.83 19457.31 3 67 154 1.00 2.83 39978.07 3 318 154 1.33 3.77 9323.84 3 170 176 1.11 3.14 13875.28 @@ -740,7 +739,6 @@ frame pos_x pos_y scale radius intensity 5 214 178 1.00 2.83 9065.83 5 42 86 1.00 2.83 11837.97 5 265 177 1.56 4.40 20759.84 -5 256 188 1.00 2.83 19457.31 5 67 154 1.00 2.83 39978.07 5 318 154 1.33 3.77 9323.84 5 170 176 1.11 3.14 13875.28 diff --git a/tools/spot_detection_2d/test-data/output2.tsv b/tools/spot_detection_2d/test-data/output2.tsv index ba0c7e74..47efffa6 100644 --- a/tools/spot_detection_2d/test-data/output2.tsv +++ b/tools/spot_detection_2d/test-data/output2.tsv @@ -7,11 +7,9 @@ frame pos_x pos_y scale radius intensity 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 24 75 1.56 4.40 23031.16 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 10 83 2.00 5.66 21493.92 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 @@ -20,16 +18,12 @@ frame pos_x pos_y scale radius intensity 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 -1 201 21 2.00 5.66 25498.97 1 130 72 1.67 4.71 15769.02 -1 151 78 1.78 5.03 18372.90 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 0 50 1.56 4.40 19538.34 1 78 17 1.33 3.77 16844.51 1 101 38 1.56 4.40 21376.59 -1 183 6 1.78 5.03 16774.07 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 @@ -40,9 +34,6 @@ frame pos_x pos_y scale radius intensity 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 12 0 2.00 5.66 17703.35 -1 175 2 1.11 3.14 14856.11 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 -1 204 32 2.00 5.66 25289.27 From 1e11896fc317c9a213ff02be8a36203c2aab2849 Mon Sep 17 00:00:00 2001 From: Leonid Kostrykin Date: Tue, 24 Sep 2024 23:15:22 +0200 Subject: [PATCH 4/4] Bump giatools to 0.2.0, remove image loading workaround --- tools/spot_detection_2d/spot_detection_2d.py | 15 +-------------- tools/spot_detection_2d/spot_detection_2d.xml | 2 +- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/tools/spot_detection_2d/spot_detection_2d.py b/tools/spot_detection_2d/spot_detection_2d.py index 473fba3a..ab8e00ce 100644 --- a/tools/spot_detection_2d/spot_detection_2d.py +++ b/tools/spot_detection_2d/spot_detection_2d.py @@ -14,7 +14,6 @@ import numpy as np import pandas as pd import scipy.ndimage as ndi -import tifffile from numpy.typing import NDArray from skimage.feature import blob_dog, blob_doh, blob_log @@ -37,18 +36,6 @@ def mean_intensity(img: NDArray, y: int, x: int, radius: int) -> float: return img[mask].mean() -def load_image(fn_in: str) -> NDArray: - """ - Load the input image using ``tifffile`` if possible. - - This is necessary to properly load multi-page TIFF files. - """ - try: - return giatools.io.imread(fn_in, impl=tifffile.imread) - except tifffile.TiffFileError: - return giatools.io.imread(fn_in) # Not a TIFF file - - def spot_detection( fn_in: str, fn_out: str, @@ -63,7 +50,7 @@ def spot_detection( ) -> None: # Load the single-channel 2-D input image (or stack thereof) - stack = load_image(fn_in) + stack = giatools.io.imread(fn_in) # Normalize input image so that it is a stack of images (possibly a stack of a single image) assert stack.ndim in (2, 3) diff --git a/tools/spot_detection_2d/spot_detection_2d.xml b/tools/spot_detection_2d/spot_detection_2d.xml index 613b1fb7..b2fe46d4 100644 --- a/tools/spot_detection_2d/spot_detection_2d.xml +++ b/tools/spot_detection_2d/spot_detection_2d.xml @@ -15,7 +15,7 @@ galaxy_image_analysis - giatools + giatools numpy pandas scikit-image