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To do that, you can use the following command + +.. code-block:: console + + $ spotiflow predict --input PATH + +where ``PATH`` can be either an image or a folder. By default, the command will use the ``general`` pretrained model. You can specify a different model by using the ``--pretrained-model`` flag. Moreover, spots are saved to a subfolder ``spotiflow_results`` created inside the input folder (this can be changed with the ``--out-dir`` flag). For more information, please refer to the help message of the CLI (``spotiflow-predict -h``). diff --git a/_sources/finetune.rst.txt b/_sources/finetune.rst.txt new file mode 100644 index 0000000..e37362d --- /dev/null +++ b/_sources/finetune.rst.txt @@ -0,0 +1,50 @@ +Fine-tuning a Spotiflow model on a custom dataset +------------------------------------------------- + +Data format +^^^^^^^^^^^ + +See :ref:`train:Data format`. + +Fine-tuning (CLI) +^^^^^^^^^^^^^^^^^ + +You can fine-tune from an existing model by simply adding an argument to the CLI call. See :ref:`train:Basic training (CLI)` for more information. + +.. code-block:: console + + spotiflow-train /path/to/spots_data -o /my/trained/model --finetune-from general + +where `/path/to/pretrained/model` is the path to the directory containing the model you want to fine-tune. You can also pass other parameters to the training, such as the number of epochs, the learning rate, etc. For more information on the arguments allowed, see the documentation of the CLI command: + + +Fine-tuning (API) +^^^^^^^^^^^^^^^^^ + +Finetuning a pre-trained model on a custom dataset is very easy. You can load the model very similarly to what you would normally do to predict on new images (you only need to add one extra parameter!): + +.. code-block:: python + + from spotiflow.model import Spotiflow + from spotiflow.utils import get_data + + # Get the data + train_imgs, train_spots, val_imgs, val_spots = get_data("/path/to/spots_data") + + # Initialize the model + model = Spotiflow.from_pretrained( + "general", + inference_mode=False, + ) + + # Train and save the model + model.fit( + train_imgs, + train_spots, + val_imgs, + val_spots, + save_dir="/my/trained/model", + ) + +Of course, you can also fine-tune from a model you have trained before. In that case, use the ``from_folder()`` method instead of ``from_pretrained()`` (see :ref:`index:Predicting spots in an image`). +All the information about training customization from :ref:`train:Customizing the training` applies here as well. However, note that you cannot change the model architecture when fine-tuning! diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..237c8d1 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,102 @@ +:hero: Spotiflow: accurate and robust spot detection for fluorescence microscopy + +========= +Spotiflow +========= + +Spotiflow is a learning-based subpixel-accurate spot detection method for 2D and 3D fluorescence microscopy. It is primarily developed for spatial transcriptomics workflows that require transcript detection in large, multiplexed FISH-images, although it can also be used to detect spot-like structures in general fluorescence microscopy images and volumes. For more information, please refer to our `paper `__. + +Getting Started +--------------- + +Installation +~~~~~~~~~~~~ + + +First, create and activate a fresh ``conda`` environment (we currently support Python 3.9 to 3.12). If you don't have ``conda`` installed, we recommend using `miniforge `__. + +.. code-block:: console + + $ conda create -n spotiflow python=3.12 + $ conda activate spotiflow + +**Note (for MacOS users):** if using MacOS, there is a known bug causing the installation of PyTorch with conda to sometimes break OpenMP. You can avoid installing PyTorch with ``conda`` and let install it automatically via pip instead. +Then, install Pytorch using ``conda``/ ``mamba``. Please follow the `official instructions for your system `__. + +As an example, for a Linux system with CUDA (note that you should change the CUDA version to match the one installed on your system): + +.. code-block:: console + + $ conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia + +**Note (for Windows users):** if using Windows, if using Windows, please install the latest `Build Tools for Visual Studio `__ (make sure to select the C++ build tools during installation) before proceeding to install Spotiflow. + +Finally, install ``spotiflow`` using ``pip``: + +.. code-block:: console + + $ pip install spotiflow + +Predicting spots in an image +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Python API +^^^^^^^^^^ + +The snippet below shows how to retrieve the spots from an image using one of the pretrained models: + +.. code-block:: python + + from skimage.io import imread + from spotiflow.model import Spotiflow + from spotiflow.utils import write_coords_csv + + + # Load the desired image + img = imread("/path/to/your/image") + + # Load a pretrained model + model = Spotiflow.from_pretrained("general") + + # Predict spots + spots, details = model.predict(img) # predict expects a numpy array + + # spots is a numpy array with shape (n_spots, 2) + # details contains additional information about the prediction, like the predicted heatmap, the probability per spot, the flow field, etc. + + # Save the results to a CSV file + write_coords_csv(spots, "/path/to/save/spots.csv") + +If a custom model is used, simply change the model loadings step to: + +.. code-block:: python + + # Load a custom model + model = Spotiflow.from_folder("/path/to/model") + +Command Line Interface (CLI) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command + +.. code-block:: console + + $ spotiflow predict --input PATH + +where ``PATH`` can be either an image or a folder. By default, the command will use the ``general`` pretrained model. You can specify a different model by using the ``--pretrained-model`` flag. Moreover, spots are saved to a subfolder ``spotiflow_results`` created inside the input folder (this can be changed with the ``--out-dir`` flag). For more information, please refer to the help message of the CLI (``spotiflow-predict -h``). + +Napari plugin +^^^^^^^^^^^^^ +Spotiflow also can be run easily in a graphical user interface as a `napari `__ plugin. See :ref:`napari:Predicting spots using the napari plugin` for more information. + +Contents +-------- + +.. toctree:: + :maxdepth: 2 + + napari + cli + pretrained + train + finetune + api diff --git a/_sources/napari.rst.txt b/_sources/napari.rst.txt new file mode 100644 index 0000000..e719dca --- /dev/null +++ b/_sources/napari.rst.txt @@ -0,0 +1,26 @@ +Predicting spots using the napari plugin +---------------------------------------- + +The napari plugin can be used to predict spots in a napari viewer. First, you must install it in the environment containing Spotiflow: + +.. code-block:: console + + (spotiflow) $ pip install napari-spotiflow + +The plugin will then be available in the napari GUI under the name "Spotiflow widget". This is how the GUI looks like: + +.. image:: ./_static/spotiflow_napari_gui.png + :width: 700 + :align: center + +The plugin allows running on two modes: for images (``2D``) and volumes (``3D``), which can be toggled using the corresponding buttons in the GUI. You can also run on movies by setting the appropriate axis order (should be leading with a `T`). + +Upon pressing the button ``Run``, The plugin will create a ``Points`` layer containing the predicted spots: + +.. image:: ./_static/spotiflow_napari_preds.png + :width: 700 + :align: center + +If the option ``Show CNN output`` is checked, the plugin will also create two ``Image`` layers containing the heatmap output of the CNN as well as the stereographic flow. + +Finally, the plugin includes two sample 2D images (HybISS and Terra) as well as a synthetic 3D volume. These samples can be loaded from the ``File`` menu (``File -> Open sample -> napari-spotiflow``). You can try the plugin with these samples to get a better idea of how it works! \ No newline at end of file diff --git a/_sources/pretrained.rst.txt b/_sources/pretrained.rst.txt new file mode 100644 index 0000000..77d37e6 --- /dev/null +++ b/_sources/pretrained.rst.txt @@ -0,0 +1,21 @@ +Available pre-trained models +---------------------------- + +The following pre-trained models are available (for a more detailed description, please refer to the *Methods* section of the paper as well as *Supplementary Table 2*): + +- ``general``: trained on a diverse dataset of spots of different modalities acquired in different microscopes with different settings. This model is the default one used in the CLI (pixel sizes: 0.04µm, 0.1µm, 0.11µm, 0.15µm, 0.32µm, 0.34µm). +- ``hybiss``: trained on HybISS data acquired in 3 different microscopes (pixel sizes: 0.15µm, 0.32µm, 0.34µm). +- ``synth_complex``: trained on synthetic data, which includes simulations of aberrated spots and fluorescence background (pixel size: 0.1µm). +- ``synth_3d``: trained on synthetic 3D data, which includes simulations of aberrated spots and Z-related artifacts (voxel size: 0.2µm). +- ``smfish_3d``: fine-tuned from the ``synth_3d`` model on smFISH 3D data of *Platynereis dumerilii* (voxel size: 0.13µm (YX), 0.48µm (Z)). + +You can use these models to predict spots in images or to fine-tune them on a few annotations of your own data. The models can be loaded via the API as follows: + +.. code-block:: python + + from spotiflow.model import Spotiflow + + pretrained_model_name = "general" + model = Spotiflow.from_pretrained(pretrained_model_name) + +You can also load them from the napari plugin or from the CLI by specifying the name of the model. See :ref:`napari:Predicting spots using the napari plugin` and :ref:`cli:Inference via CLI` for more information respectively. diff --git a/_sources/train.rst.txt b/_sources/train.rst.txt new file mode 100644 index 0000000..e31b388 --- /dev/null +++ b/_sources/train.rst.txt @@ -0,0 +1,153 @@ +Training a Spotiflow model on a custom dataset +---------------------------------------------- + +Data format +^^^^^^^^^^^ + +First of all, make sure that the data is organized in the following format: + +:: + + spots_data + ├── train + │ ├── img_001.csv + │ ├── img_001.tif + | ... + │ ├── img_XYZ.csv + | └── img_XYZ.tif + └── val + ├── val_img_001.csv + ├── val_img_001.tif + ... + ├── val_img_XYZ.csv + └── val_img_XYZ.tif + +The actual naming of the files is not important, but the ``.csv`` and ``.tif`` files corresponding to the same image **must** have the same name! The ``.csv`` files must contain the spot coordinates in the following format: + +.. code-block:: + + y,x + 42.3,24.24 + 252.99, 307.97 + ... + +The column names can also be `axis-0` (instead of `y`) and `axis-1` instead of `x`. For the 3D case, the format is similar but with an additional column corresponding to the `z` coordinate: + +.. code-block:: + + z,y,x + 12.4,42.3,24.24 + 61.2,252.99, 307.97 + ... + +In this case, you can also use `axis-0`, `axis-1`, and `axis-2` instead of `z`, `y`, and `x`, respectively. + + +Basic training (CLI) +^^^^^^^^^^^^^^^^^^^^ + +You can train a model using the CLI as follows: + +.. code-block:: console + + spotiflow-train /path/to/spots_data -o /my/trained/model + +where `/path/to/spots_data` is the path to the directory containing the data in the format described above and `/my/trained/model` is the directory where the trained model will be saved. You can also pass other parameters to the training, such as the number of epochs, the learning rate, etc. For more information on the arguments allowed, see the documentation of the CLI command: + +.. code-block:: console + + spotiflow-train --help + +To illustrate with an example, to train a Spotiflow model on 2-channel 3D data for 100 epochs, you can run: + +.. code-block:: console + + spotiflow-train /path/to/spots_data -o /my/trained/model --is-3d True --num-epochs 100 --in-channels 2 + +Basic training (API) +^^^^^^^^^^^^^^^^^^^^ + +You can easily train a model using the default settings as follows and save it to the directory `/my/trained/model`: + +.. code-block:: python + + from spotiflow.model import Spotiflow + from spotiflow.utils import get_data + + # Get the data + train_imgs, train_spots, val_imgs, val_spots = get_data("/path/to/spots_data") + + # Initialize the model + model = Spotiflow() + + # Train and save the model + model.fit( + train_imgs, + train_spots, + val_imgs, + val_spots, + save_dir="/my/trained/model", + ) + +You can then load it by simply calling: + +.. code-block:: python + + model = Spotiflow.from_folder("/my/trained/model") + + +In the 3D case, you should initialize a :py:mod:`spotiflow.model.config.SpotiflowModelConfig` object and pass it to the `Spotiflow` constructor with the appropriate parameter set (see other options for the configuration at the end of the section): + +.. code-block:: python + + # Same imports as before + from spotiflow.model import SpotiflowModelConfig + + # Create the model config + model_config = SpotiflowModelConfig( + is_3d=True, + grid=2, # subsampling factor for prediction + # you can pass other arguments here + ) + + model = Spotiflow(model_config) + # Train and save the model as before + + +Customizing the training +^^^^^^^^^^^^^^^^^^^^^^^^ + +You can also pass other parameters relevant for training to the `fit` method. For example, you can change the number of epochs, the batch size, the learning rate, etc. You can do that using the `train_config` parameter. For more information on the arguments allowed, see the documentation of :py:func:`spotiflow.model.spotiflow.Spotiflow.fit` method as well as :py:mod:`spotiflow.model.config.SpotiflowTrainingConfig`. As an example, let's change the number of epochs and the learning rate: + +.. code-block:: python + + train_config = { + "num_epochs": 100, + "learning_rate": 0.001, + "smart_crop": True, + # other parameters + } + + model.fit( + train_imgs, + train_spots, + val_imgs, + val_spots, + save_dir="/my/trained/model", + train_config=train_config, + # other parameters + ) + + +In order to change the model architecture (`e.g.` number of input channels, number of layers, variance for the heatmap generation, etc.), you can create a :py:mod:`spotiflow.model.config.SpotiflowModelConfig` object and populate it accordingly. Then you can pass it to the `Spotiflow` constructor (note that this is necessary for 3D). For example, if our image is RGB and we need the network to use 3 input channels, we can do the following: + +.. code-block:: python + + from spotiflow.model import SpotiflowModelConfig + + # Create the model config + model_config = SpotiflowModelConfig( + in_channels=3, + # you can pass other arguments here + ) + model = Spotiflow(model_config) diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..7ebbd6d --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,914 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + 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b/_static/fonts/fb1aaa90783b8cb9375265abeb91b153.woff2 differ diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 0000000..c7fe6c6 --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/sphinx_immaterial_theme.4b4a8c74e2ff2ab5b.min.css b/_static/sphinx_immaterial_theme.4b4a8c74e2ff2ab5b.min.css new file mode 100644 index 0000000..67c585a --- /dev/null +++ b/_static/sphinx_immaterial_theme.4b4a8c74e2ff2ab5b.min.css @@ -0,0 +1,4 @@ +@charset "UTF-8";html{-webkit-text-size-adjust:none;-moz-text-size-adjust:none;text-size-adjust:none;box-sizing:border-box}*,:after,:before{box-sizing:inherit}@media 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h=m.outerHTML;if(!u.has(h)){let b=document.createElement("script");for(let v of m.getAttributeNames())b.setAttribute(v,m.getAttribute(v));b.textContent=m.textContent,document.body.appendChild(b),u.add(h)}}}))).subscribe(),e.pipe(Pe(1),d(()=>_e("container")),_(l=>W("script",l)),mt(l=>{let p=O("script");if(l.src){for(let m of l.getAttributeNames())p.setAttribute(m,l.getAttribute(m));return l.replaceWith(p),new U(m=>{p.onload=()=>m.complete()})}else return p.textContent=l.textContent,l.replaceWith(p),k})).subscribe(),P(i,a).pipe(rr(e)).subscribe(({url:l,offset:p})=>{l.hash&&!p?No(l.hash):window.scrollTo(0,(p==null?void 0:p.y)||0)}),r.pipe(Wr(i),He(250),J("offset")).subscribe(({offset:l})=>{history.replaceState(l,"")}),P(i,a).pipe(Ne(2,1),A(([l,p])=>l.url.pathname===p.url.pathname),d(([,l])=>l)).subscribe(({offset:l})=>{window.scrollTo(0,(l==null?void 0:l.y)||0)})}var $i=Je(rn());function Ii(e,t){let r=new RegExp(e.separator,"img"),n=(o,i,a)=>`${i}${a}`;return o=>{o=o.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator})(${o.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return a=>(t?(0,$i.default)(a):a).replace(i,n).replace(/<\/mark>(\s+)]*>/img,"$1")}}function ji({document$:e}){var a;let t=ce(),r=t.version.staticVersions?C(t.version.staticVersions):Ge(new URL((a=t.version.versionPath)!=null?a:"../versions.json",t.base)),n=new URL("..",t.base),o=c=>new URL(c,n).toString().replace(/\/*$/,""),i=r.pipe(d(c=>{let s=t.base.toString().replace(/\/*$/,"");return c.find(({version:f,aliases:u})=>o(f)===s||u.find(l=>o(l)===s))||c[0]}));r.pipe(d(c=>new Map(c.map(s=>[`${new URL(`../${s.version}/`,t.base)}`,s]))),_(c=>w(document.body,"click").pipe(A(s=>!s.metaKey&&!s.ctrlKey),me(i),_(([s,f])=>{if(s.target instanceof Element){let u=s.target.closest("a");if(u&&!u.target&&c.has(u.href)){let l=u.href;return!s.target.closest(".md-version")&&c.get(l)===f?k:(s.preventDefault(),C(l))}}return k}),_(s=>{let{version:f}=c.get(s);return hr(new URL(s)).pipe(d(u=>{let p=Oe().href.replace(t.base,"");return u.includes(p.split("#")[0])?new URL(`../${f}/${p}`,t.base):new URL(s)}))})))).subscribe(c=>lr(c)),G([r,i]).subscribe(([c,s])=>{B(".md-header__topic").appendChild(oi(c,s))}),e.pipe(_(()=>i)).subscribe(c=>{var f;let s=__md_get("__outdated",sessionStorage);if(s===null){let u=((f=t.version)==null?void 0:f.default)||"latest";s=!c.aliases.includes(u),__md_set("__outdated",s,sessionStorage)}if(s)for(let u of ee("outdated"))u.hidden=!1})}function Fi(e,{location$:t}){let r={lang:[],separator:"\\s+"};return G([t.pipe(q(Oe()),A(n=>!!n.searchParams.get("h")))]).pipe(d(([n])=>Ii(r,!0)(n.searchParams.get("h"))),d(n=>{var a;let o=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let c=i.nextNode();c;c=i.nextNode())if((a=c.parentElement)!=null&&a.offsetHeight){let s=c.textContent,f=n(s);f.length>s.length&&o.set(c,f)}for(let[c,s]of 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r=`https://api.github.com/repos/${e}/${t}`;return Ir(Ge(`${r}/releases/latest`).pipe(le(()=>k),d(n=>({version:n.tag_name})),De({})),Ge(r).pipe(le(()=>k),d(n=>({stars:n.stargazers_count,forks:n.forks_count})),De({}))).pipe(d(([n,o])=>N(N({},n),o)))}else{let r=`https://api.github.com/users/${e}`;return Ge(r).pipe(d(n=>({repositories:n.public_repos})),De({}))}}function Vi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ge(r).pipe(le(()=>k),d(({star_count:n,forks_count:o})=>({stars:n,forks:o})),De({}))}function Ni(e){let[t]=e.match(/(git(?:hub|lab))/i)||[];switch(t.toLowerCase()){case"github":let[,r,n]=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);return Ui(r,n);case"gitlab":let[,o,i]=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i);return Vi(o,i);default:return k}}var Ws;function zs(e){return Ws||(Ws=I(()=>{let t=__md_get("__source",sessionStorage);if(t)return C(t);if(ee("consent").length){let n=__md_get("__consent");if(!(n&&n.github))return k}return Ni(e.href).pipe(M(n=>__md_set("__source",n,sessionStorage)))}).pipe(le(()=>k),A(t=>Object.keys(t).length>0),d(t=>({facts:t})),X(1)))}function Di(e){let t=B(":scope > :last-child",e);return I(()=>{let r=new L;return r.subscribe(({facts:n})=>{t.appendChild(ri(n)),t.classList.add("md-source__repository--active")}),zs(e).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>N({ref:e},n)))})}function qs(e,{viewport$:t,header$:r}){return de(document.body).pipe(_(()=>mr(e,{header$:r,viewport$:t})),d(({offset:{y:n}})=>({hidden:n>=10})),J("hidden"))}function Wi(e,t){return I(()=>{let r=new L;return r.subscribe({next({hidden:n}){e.hidden=n},complete(){e.hidden=!1}}),(Z("navigation.tabs.sticky")?C({hidden:!1}):qs(e,t)).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>N({ref:e},n)))})}function Ks(e,{viewport$:t,header$:r,excludedLinks:n}){let o=new Map,i=W("a[href]",e);for(let s of i){if(n!=null&&n.has(s))continue;let f=s.getAttribute("href"),u;if(f.startsWith("#")){let 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h=u.getBoundingClientRect(),b=l.getBoundingClientRect();l.scrollTo({top:l.scrollTop+(h.y-b.height/2-b.y)})}})}o&&Z("navigation.tracking")&&t.pipe(te(i.pipe(ue(1))),J("offset"),He(250),Pe(1),te(n.pipe(Pe(1))),Ot({delay:250}),me(i)).subscribe(([,{prev:s}])=>{let f=Oe(),u=s[s.length-1];if(u&&u.length){let[l]=u,{hash:p}=new URL(l.href);f.hash!==p&&(f.hash=p,history.replaceState({},"",`${f}`))}else f.hash="",history.replaceState({},"",`${f}`)}),Z("toc.sticky")&&de(document.body).pipe(J("width"),He(0)).subscribe(()=>{let s=new Map,f="--md-nav__header-height";for(let u of W(".md-nav__link",e)){let l=u.nextElementSibling;if(!(l instanceof HTMLElement)||l.tagName!=="NAV")continue;let p="",m=NaN,h=l.parentElement.closest("nav");if(h!==null){let b=s.get(h);b!==void 0&&(p=`${b.height} + `,m=b.zindex-1)}isNaN(m)&&(m=100),p+=`${u.offsetHeight}px + 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i.subscribe({next({hidden:c}){e.hidden=c,c?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(te(a),J("height")).subscribe(({height:c})=>{e.style.top=`${c+16}px`}),Bs(e,{viewport$:t,main$:n,target$:o}).pipe(M(c=>i.next(c)),R(()=>i.complete()),d(c=>N({ref:e},c)))}function qi({document$:e,tablet$:t}){e.pipe(_(()=>W(".md-toggle--indeterminate, [data-md-state=indeterminate]")),M(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>w(r,"change").pipe(zr(()=>r.classList.contains("md-toggle--indeterminate")),d(()=>r))),me(t)).subscribe(([r,n])=>{r.classList.remove("md-toggle--indeterminate"),n&&(r.checked=!1)})}function Ys(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ki({document$:e}){e.pipe(_(()=>W("[data-md-scrollfix]")),M(t=>t.removeAttribute("data-md-scrollfix")),A(Ys),oe(t=>w(t,"touchstart").pipe(d(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Bi({viewport$:e,tablet$:t}){G([ht("search"),t]).pipe(d(([r,n])=>r&&!n),_(r=>C(r).pipe(ke(r?400:100))),me(e)).subscribe(([r,{offset:{y:n}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${n}px`;else{let o=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",o&&window.scrollTo(0,o)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var nt=So(),vr=jo(),rt=Do(),un=Io(),ge=Jo(),gr=Yr("(min-width: 960px)"),Gi=Yr("(min-width: 1220px)"),Ji=Wo(),Gs=ce(),pn=new L;ki({alert$:pn});Z("navigation.instant")&&Pi({document$:nt,location$:vr,viewport$:ge});var Yi;((Yi=Gs.version)==null?void 0:Yi.provider)==="mike"&&ji({document$:nt});P(vr,rt).pipe(ke(125)).subscribe(()=>{Ye("drawer",!1),Ye("search",!1)});un.pipe(A(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ie("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=ie("[href][rel=next]");typeof r!="undefined"&&r.click();break}});qi({document$:nt,tablet$:gr});Ki({document$:nt});Bi({viewport$:ge,tablet$:gr});var We=xi(_e("header"),{viewport$:ge}),br=nt.pipe(d(()=>_e("main")),_(e=>Si(e,{viewport$:ge,header$:We})),X(1)),Js=P(...ee("consent").map(e=>Xo(e,{target$:rt})),...ee("dialog").map(e=>yi(e,{alert$:pn})),...ee("header").map(e=>wi(e,{viewport$:ge,header$:We,main$:br})),...ee("palette").map(e=>Oi(e)),...ee("search").map(e=>Hi(e,{keyboard$:un})),...ee("source").map(e=>Di(e))),Qs=I(()=>P(...ee("announce").map(e=>Qo(e)),...ee("content").map(e=>gi(e,{viewport$:ge,target$:rt,print$:Ji})),...ee("content").map(e=>Z("search.highlight")?Fi(e,{location$:vr}):k),...ee("header-title").map(e=>Ei(e,{viewport$:ge,header$:We})),...ee("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Gr(Gi,()=>fn(e,{viewport$:ge,header$:We,main$:br})):Gr(gr,()=>fn(e,{viewport$:ge,header$:We,main$:br}))),...ee("tabs").map(e=>Wi(e,{viewport$:ge,header$:We})),...ee("toc").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!0})),...ee("sidebar").filter(e=>e.getAttribute("data-md-type")==="navigation").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!1})),...ee("top").map(e=>zi(e,{viewport$:ge,header$:We,main$:br,target$:rt})))),Qi=nt.pipe(_(()=>Qs),tt(Js),X(1));Qi.subscribe();window.document$=nt;window.location$=vr;window.target$=rt;window.keyboard$=un;window.viewport$=ge;window.tablet$=gr;window.screen$=Gi;window.print$=Ji;window.alert$=pn;window.component$=Qi;})(); diff --git a/_static/spotiflow_napari_gui.png b/_static/spotiflow_napari_gui.png new file mode 100644 index 0000000..b742fe5 Binary files /dev/null and b/_static/spotiflow_napari_gui.png differ diff --git a/_static/spotiflow_napari_preds.png b/_static/spotiflow_napari_preds.png new file mode 100644 index 0000000..85c88d3 Binary files /dev/null and b/_static/spotiflow_napari_preds.png differ diff --git a/_static/spotiflow_transp_small.png b/_static/spotiflow_transp_small.png new file mode 100644 index 0000000..ded4b64 Binary files /dev/null and b/_static/spotiflow_transp_small.png differ diff --git a/_static/spotipy_transp_small.png b/_static/spotipy_transp_small.png new file mode 100644 index 0000000..44002b5 Binary files /dev/null and b/_static/spotipy_transp_small.png differ diff --git a/api.html b/api.html new file mode 100644 index 0000000..332f348 --- /dev/null +++ b/api.html @@ -0,0 +1,3384 @@ + + + + + + + + + + + + + + + + API Reference - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + +
+
+ + + +
+
+
+ + + + +
+
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+ + + + + + + + +

API Reference

+
+
+class spotiflow.model.spotiflow.Spotiflow(config: SpotiflowModelConfig | None = None)
+

Supervised spot detector using a multi-stage neural network as a backbone for +feature extraction followed by resolution-dependent post-processing modules +to allow loss computation and optimization at different resolution levels.

+
+
+fit(train_images: Sequence[ndarray], train_spots: Sequence[ndarray], val_images: Sequence[ndarray], val_spots: Sequence[ndarray], augment_train: bool | Pipeline = True, save_dir: str | None = None, train_config: dict | SpotiflowTrainingConfig | None = None, device: 'auto' | 'cpu' | 'cuda' | 'mps' = 'auto', logger: 'none' | 'tensorboard' | 'wandb' = 'tensorboard', number_of_devices: int | None = 1, num_workers: int | None = 0, callbacks: Sequence[Callback] | None = None, deterministic: bool | None = True, benchmark: bool | None = False, **dataset_kwargs)
+

Train a Spotiflow model.

+
+
Parameters:
+
+
train_images : Sequence[np.ndarray]

training images

+
+
train_spots : Sequence[np.ndarray]

training spots

+
+
val_images : Sequence[np.ndarray]

validation images

+
+
val_spots : Sequence[np.ndarray]

validation spots

+
+
augment_train : Union[bool, AugmentationPipeline]

whether to augment the training data. Defaults to True.

+
+
save_dir : Optional[str], optional

directory to save the model to. Must be given if no checkpoint logger is given as a callback. Defaults to None.

+
+
train_config : Optional[SpotiflowTrainingConfig], optional

training config. If not given, will use the default config. Defaults to None.

+
+
device : Literal["cpu", "cuda", "mps"], optional

computing device to use. Can be “cpu”, “cuda”, “mps”. Defaults to “cpu”.

+
+
logger : Optional[pl.loggers.Logger], optional

logger to use. Defaults to “tensorboard”.

+
+
number_of_devices : Optional[int], optional

number of accelerating devices to use. Only applicable to “cuda” acceleration. Defaults to 1.

+
+
num_workers : Optional[int], optional

number of workers to use for data loading. Defaults to 0 (main process only).

+
+
callbacks : Optional[Sequence[pl.callbacks.Callback]], optional

callbacks to use during training. Defaults to no callbacks.

+
+
deterministic : Optional[bool], optional

whether to use deterministic training. Set to True for deterministic behaviour at a cost of performance. Defaults to True.

+
+
benchmark : Optional[bool], optional

whether to use benchmarking. Set to False for deterministic behaviour at a cost of performance. Defaults to False.

+
+
**dataset_kwargs

additional arguments to pass to the SpotsDataset class. Defaults to no additional arguments.

+
+
+
+
+
+ +
+
+classmethod from_folder(pretrained_path: str, inference_mode=True, which: str = 'best', map_location: 'auto' | 'cpu' | 'cuda' | 'mps' = 'auto', verbose: bool = False) → Self
+

Load a pretrained model.

+
+
Parameters:
+
+
pretrained_path : str

path to the model folder

+
+
inference_mode : bool, optional

whether to set the model in eval mode. Defaults to True.

+
+
which : str, optional

which checkpoint to load. Defaults to “best”.

+
+
map_location : str, optional

device string to load the model to. Defaults to ‘auto’ (hardware-based).

+
+
+
+
Returns:
+

loaded model

+
+
Return type:
+

Self

+
+
+
+ +
+
+classmethod from_pretrained(pretrained_name: str, inference_mode: bool = True, which: str = 'best', map_location: 'auto' | 'cpu' | 'cuda' | 'mps' = 'auto', cache_dir: Path | str | None = None, verbose: bool = True, **kwargs) → Self
+

Load a pretrained model with given name

+
+
Parameters:
+
+
pretrained_name : str

name of the pretrained model to be loaded

+
+
inference_mode : bool, optional

whether to set the model in eval mode. Defaults to True.

+
+
which : str, optional

which checkpoint to load. Defaults to “best”.

+
+
map_location : str, optional

device string to load the model to. Defaults to ‘auto’ (hardware-based).

+
+
cache_dir : Optional[Union[Path, str]], optional

directory to cache the model. Defaults to None. If None, will use the default cache directory (given by the env var SPOTIFLOW_CACHE_DIR if set, otherwise ~/.spotiflow).

+
+
+
+
Returns:
+

loaded model

+
+
Return type:
+

Self

+
+
+
+ +
+
+load(path: str, which: 'best' | 'last' = 'best', inference_mode: bool = True, map_location: str = 'cuda') → None
+

Load a model from disk.

+
+
Parameters:
+
+
path : str

folder to load the model from

+
+
which : Literal['best', 'last'], optional

which checkpoint to load. Defaults to “best”.

+
+
inference_mode : bool, optional

whether to set the model in eval mode. Defaults to True.

+
+
map_location : str, optional

device string to load the model to. Defaults to ‘cuda’.

+
+
+
+
+
+ +
+
+optimize_threshold(val_ds: Dataset, cutoff_distance: int = 3, min_distance: int = 1, exclude_border: bool = False, threshold_range: tuple[float, float] = (0.3, 0.7), niter: int = 11, batch_size: int = 1, device: device | 'auto' | 'cpu' | 'cuda' | 'mps' | None = None, subpix: bool | None = None) → None
+
+
Optimize the probability threshold on an annotated dataset.

The metric used to optimize is the F1 score.

+
+
+
+
Parameters:
+
+
val_ds : torch.utils.data.Dataset

dataset to optimize on

+
+
cutoff_distance : int, optional

distance tolerance considered for points matching. Defaults to 3.

+
+
min_distance : int, optional

Minimum distance between spots for NMS. Defaults to 1.. Defaults to 2.

+
+
exclude_border : bool, optional

Whether to exclude spots at the border. Defaults to False.

+
+
threshold_range : Tuple[float, float], optional

Range of thresholds to consider. Defaults to (.3, .7).

+
+
niter : int, optional

number of iterations for both coarse- and fine-grained search. Defaults to 11.

+
+
batch_size : int, optional

batch size to use. Defaults to 2.

+
+
device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional

computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.

+
+
subpix : Optional[bool], optional

whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.

+
+
+
+
+
+ +
+
+predict(img: ndarray | Array, prob_thresh: float | None = None, n_tiles: tuple[int] = None, max_tile_size: int = None, min_distance: int = 1, exclude_border: bool = False, scale: int | None = None, subpix: bool | int | None = None, peak_mode: 'skimage' | 'fast' = 'fast', normalizer: Callable | 'auto' | None = 'auto', verbose: bool = True, progress_bar_wrapper: Callable | None = None, device: device | 'auto' | 'cpu' | 'cuda' | 'mps' | None = None, distributed_params: dict | None = None) → tuple[ndarray, SimpleNamespace]
+

Predict spots in an image.

+
+
Parameters:
+
+
img : Union[np.ndarray, da.Array]

input image

+
+
prob_thresh : Optional[float], optional

Probability threshold for peak detection. If None, will load the optimal one. Defaults to None.

+
+
n_tiles : Tuple[int, int], optional

Number of tiles to split the image into. Defaults to (1,1).

+
+
min_distance : int, optional

Minimum distance between spots for NMS. Defaults to 1.

+
+
exclude_border : bool, optional

Whether to exclude spots at the border. Defaults to False.

+
+
scale : Optional[int], optional

Scale factor to apply to the image. Defaults to None.

+
+
subpix : bool, optional

Whether to use the stereographic flow to compute subpixel localization. If None, will deduce from the model configuration. Defaults to None.

+
+
peak_mode : str, optional

Peak detection mode (can be either “skimage” or “fast”, which is a faster custom C++ implementation). Defaults to “fast”.

+
+
normalizer : Optional[Union[Literal["auto"], callable]], optional

Normalizer to use. If None, will use the default normalizer. Defaults to “auto” (percentile-based normalization with p_min=1, p_max=99.8).

+
+
verbose : bool, optional

Whether to print logs and progress. Defaults to True.

+
+
progress_bar_wrapper : Optional[callable], optional

Progress bar wrapper to use. Defaults to None.

+
+
device : Optional[Union[torch.device, Literal["auto", "cpu", "cuda", "mps"]]], optional

computing device to use. If None, will infer from model location. If “auto”, will infer from available hardware. Defaults to None.

+
+
+
+
Returns:
+

Tuple of (points, details). Points are the coordinates of the spots. Details is a namespace containing the spot-wise probabilities (prob), the heatmap (heatmap), the stereographic flow (flow), the 2D local offset vector field (subpix) and the spot intensities (intens).

+
+
Return type:
+

Tuple[np.ndarray, SimpleNamespace]

+
+
+
+ +
+
+save(path: str, which: 'best' | 'last' = 'best', update_thresholds: bool = False) → None
+

Save the model to disk.

+
+
Parameters:
+
+
path : str

folder to save the model to

+
+
which : Literal["best", "last"]

which checkpoint to save. Should be either “best” or “last”.

+
+
update_thresholds : bool, optional

whether to update the thresholds file. Defaults to False.

+
+
+
+
+
+ +
+ +
+
+class spotiflow.model.config.SpotiflowModelConfig(backbone: 'resnet' | 'unet' | 'unet_res' = 'unet', in_channels: int = 1, out_channels: int = 1, initial_fmaps: int = 32, fmap_inc_factor: Number = 2, n_convs_per_level: int = 3, levels: int = 4, downsample_factor: int = 2, kernel_size: int = 3, padding: int | str = 'same', mode: 'direct' | 'fpn' | 'slim' = 'slim', background_remover: bool = False, compute_flow: bool = True, batch_norm: bool = True, downsample_factors: tuple[tuple[int, int]] | None = None, kernel_sizes: tuple[tuple[int, int]] | None = None, dropout: float = 0.0, sigma: Number = 1.0, is_3d: bool = False, grid: int | tuple[int, int, int] = (1, 1, 1), **kwargs)
+
+ +
+
+class spotiflow.model.config.SpotiflowTrainingConfig(crop_size: int | tuple[int, int] | tuple[int, int, int] = 512, smart_crop: bool = False, heatmap_loss_f: str = 'bce', flow_loss_f: str = 'l1', loss_levels: int | None = None, num_train_samples: int | None = None, pos_weight: Number = 10.0, lr: float = 0.0003, optimizer: str = 'adamw', batch_size: int = 4, lr_reduce_patience: int = 10, num_epochs: int = 200, finetuned_from: str | None = None, early_stopping_patience: int = 0, crop_size_depth: int = 32, **kwargs)
+
+ +
+
+class spotiflow.data.spots.SpotsDataset(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, mode: str = 'max', compute_flow: bool = False, image_files: Sequence[str] | None = None, normalizer: 'auto' | Callable | None = 'auto', add_class_label: bool = True, grid: Sequence[int] | None = None)
+

Base spot dataset class instantiated with loaded images and centers.

+
+
+__init__(images: Sequence[ndarray], centers: Sequence[ndarray], augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, mode: str = 'max', compute_flow: bool = False, image_files: Sequence[str] | None = None, normalizer: 'auto' | Callable | None = 'auto', add_class_label: bool = True, grid: Sequence[int] | None = None)
+

Constructor

+
+
Parameters:
+
+
images : Sequence[np.ndarray]

Sequence of images.

+
+
centers : Sequence[np.ndarray]

Sequence of center coordinates.

+
+
augmenter : Optional[Callable], optional

Augmenter function. If given, function arguments should two (first image, second spots). Defaults to None.

+
+
downsample_factors : Sequence[int], optional

Downsample factors. Defaults to (1,).

+
+
sigma : float, optional

Sigma of Gaussian kernel to generate heatmap. Defaults to 1.

+
+
mode : str, optional

Mode of heatmap generation. Defaults to “max”.

+
+
compute_flow : bool, optional

Whether to compute flow from centers. Defaults to False.

+
+
image_files : Optional[Sequence[str]], optional

Sequence of image filenames. If the dataset was not constructed from a folder, this will be None. Defaults to None.

+
+
normalizer : Union[Literal["auto"], Callable, None], optional

Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).

+
+
+
+
+
+ +
+
+property augmenter : Callable
+

Return augmenter function.

+
+
Returns:
+

Augmenter function. It should take image and centers as input.

+
+
Return type:
+

Callable

+
+
+
+ +
+
+property centers : Sequence[ndarray]
+

Return centers of spots in dataset.

+
+
Returns:
+

Sequence of center coordinates.

+
+
Return type:
+

Sequence[np.ndarray]

+
+
+
+ +
+
+classmethod from_folder(path: Path | str, augmenter: Callable | None = None, downsample_factors: Sequence[int] = (1,), sigma: float = 1.0, image_extensions: Sequence[str] = ('tif', 'tiff', 'png', 'jpg', 'jpeg'), mode: str = 'max', max_files: int | None = None, compute_flow: bool = False, normalizer: Callable | 'auto' | None = 'auto', random_state: int | None = None, add_class_label: bool = True, grid: Sequence[int] | None = None) → Self
+

Build dataset from folder. Images and centers are loaded from disk and normalized.

+
+
Parameters:
+
+
path : Union[Path, str]

Path to folder containing images (with given extensions) and centers.

+
+
augmenter : Callable

Augmenter function.

+
+
downsample_factors : Sequence[int], optional

Downsample factors. Defaults to (1,).

+
+
sigma : float, optional

Sigma of Gaussian kernel to generate heatmap. Defaults to 1.

+
+
image_extensions : Sequence[str], optional

Image extensions to look for in images. Defaults to (“tif”, “tiff”, “png”, “jpg”, “jpeg”).

+
+
mode : str, optional

Mode of heatmap generation. Defaults to “max”.

+
+
max_files : Optional[int], optional

Maximum number of files to load. Defaults to None (all of them).

+
+
compute_flow : bool, optional

Whether to compute flow from centers. Defaults to False.

+
+
normalizer : Optional[Union[Callable, Literal["auto"]]], optional

Normalizer function. Defaults to “auto” (percentile-based normalization with p_min=1 and p_max=99.8).

+
+
random_state : Optional[int], optional

Random state used when shuffling file names when “max_files” is not None. Defaults to None.

+
+
+
+
Returns:
+

Dataset instance.

+
+
Return type:
+

Self

+
+
+
+ +
+
+property image_files : Sequence[str]
+

Return image filenames with the same order as in the dataset.

+
+
Returns:
+

Sequence of image filenames. If the dataset was not constructed from a folder, this will be None.

+
+
Return type:
+

Union[Sequence[str], None]

+
+
+
+ +
+
+property images : Sequence[ndarray]
+

Return images in dataset.

+
+
Returns:
+

Sequence of images.

+
+
Return type:
+

Sequence[np.ndarray]

+
+
+
+ +
+
+property n_classes : int
+

Return number of classes in the dataset.

+
+
Returns:
+

number of spot classes.

+
+
Return type:
+

int

+
+
+
+ +
+ +
+
+spotiflow.utils.get_data(path: Path | str, normalize: bool = True, include_test: bool = False, is_3d: bool = False) → tuple[ndarray, ndarray, ndarray, ndarray]
+

Get data from a given path. The path should contain a ‘train’ and ‘val’ folder.

+
+
Parameters:
+
+
path : Union[Path, str]

Path to the data.

+
+
normalize : bool, optional

Whether to normalize the data. Defaults to True.

+
+
+
+
Returns:
+

A 4-length tuple of arrays corresponding to the training images, training spots, validation images and validation spots.

+
+
Return type:
+

Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]

+
+
+
+ +
+
+spotiflow.utils.normalize(x: ndarray, pmin: float = 1.0, pmax: float = 99.8, subsample: int = 1, clip: bool = False, ignore_val: int | float | None = None) → ndarray
+

Normalizes (percentile-based) a 2d image with the additional option to ignore a value. The normalization is done as follows:

+

x = (x - I_{p_{min}}) / (I_{p_{max}} - I_{p_{min}})

+

where I_{p_{min}} and I_{p_{max}} are the pmin and pmax percentiles of the image intensity, respectively.

+
+
Parameters:
+
+
x : np.ndarray

Image to be normalized

+
+
pmin : float, optional

Minimum percentile. Defaults to 1..

+
+
pmax : float, optional

Maximum percentile. Defaults to 99.8.

+
+
subsample : int, optional

Subsampling factor for percentile calculation. Defaults to 1.

+
+
clip : bool, optional

Whether to clip the normalized image. Defaults to False.

+
+
ignore_val : Optional[Union[int, float]], optional

Value to be ignored. Defaults to None.

+
+
+
+
Returns:
+

Normalized image

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+spotiflow.utils.read_coords_csv(fname: str, add_class_column: bool = False) → ndarray
+

Parses a csv file and returns correctly ordered points array

+
+
Parameters:
+
+
fname : str

Path to the csv file

+
+
add_class_column : bool, optional

Whether to add a class column to the points array. Defaults to False.

+
+
+
+
Returns:
+

A 2D array of spot coordinates. If add_class_column is True, then the array will have shape (N, 3) where N is the number of points. Otherwise, the array will have shape (N, 2)

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+spotiflow.utils.write_coords_csv(pts: ndarray, fname: Path | str) → None
+

Writes points in a NumPy array to a CSV file

+
+
Parameters:
+
+
pts : np.ndarray

2D or 3D array of points

+
+
fname : Union[Path, str]

Path to the CSV file

+
+
+
+
+
+ +
+
+spotiflow.sample_data.test_image_hybiss_2d()
+

Single test HybISS image from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)

+
+ +
+
+spotiflow.sample_data.test_image_synth_3d()
+

Single synthetic volumetric stack from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)

+
+ +
+
+spotiflow.sample_data.test_image_terra_2d()
+

Single test Terra frame from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)

+
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+ + + + + + + + \ No newline at end of file diff --git a/cli.html b/cli.html new file mode 100644 index 0000000..9c7c87e --- /dev/null +++ b/cli.html @@ -0,0 +1,512 @@ + + + + + + + + + + + + + + + + Inference via CLI - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Inference via CLI

+

Command Line Interface (CLI)

+

You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command

+
$ spotiflow predict --input PATH
+
+
+

where PATH can be either an image or a folder. By default, the command will use the general pretrained model. You can specify a different model by using the --pretrained-model flag. Moreover, spots are saved to a subfolder spotiflow_results created inside the input folder (this can be changed with the --out-dir flag). For more information, please refer to the help message of the CLI (spotiflow-predict -h).

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+ + + + + + + + \ No newline at end of file diff --git a/finetune.html b/finetune.html new file mode 100644 index 0000000..b5a8c56 --- /dev/null +++ b/finetune.html @@ -0,0 +1,568 @@ + + + + + + + + + + + + + + + + Fine-tuning a Spotiflow model on a custom dataset - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Fine-tuning a Spotiflow model on a custom dataset

+

Data format

+

See Data format.

+

Fine-tuning (CLI)

+

You can fine-tune from an existing model by simply adding an argument to the CLI call. See Basic training (CLI) for more information.

+
spotiflow-train /path/to/spots_data -o /my/trained/model --finetune-from general
+
+
+

where /path/to/pretrained/model is the path to the directory containing the model you want to fine-tune. You can also pass other parameters to the training, such as the number of epochs, the learning rate, etc. For more information on the arguments allowed, see the documentation of the CLI command:

+

Fine-tuning (API)

+

Finetuning a pre-trained model on a custom dataset is very easy. You can load the model very similarly to what you would normally do to predict on new images (you only need to add one extra parameter!):

+
from spotiflow.model import Spotiflow
+from spotiflow.utils import get_data
+
+# Get the data
+train_imgs, train_spots, val_imgs, val_spots = get_data("/path/to/spots_data")
+
+# Initialize the model
+model = Spotiflow.from_pretrained(
+    "general",
+    inference_mode=False,
+)
+
+# Train and save the model
+model.fit(
+    train_imgs,
+    train_spots,
+    val_imgs,
+    val_spots,
+    save_dir="/my/trained/model",
+)
+
+
+

Of course, you can also fine-tune from a model you have trained before. In that case, use the from_folder() method instead of from_pretrained() (see Predicting spots in an image). +All the information about training customization from Customizing the training applies here as well. However, note that you cannot change the model architecture when fine-tuning!

+ + + + + + + +
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+ + + + + + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..94c084b --- /dev/null +++ b/genindex.html @@ -0,0 +1,407 @@ + + + + + + + + + + + + + + + + None - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ © Copyright 2024, Albert Dominguez Mantes. + +
+ + Created using + Sphinx + 8.1.3. + and + Sphinx-Immaterial + +
+ +
+ +
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+ +
+
+
+
+ + + + + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..e8ec836 --- /dev/null +++ b/index.html @@ -0,0 +1,606 @@ + + + + + + + + + + + + + + + + Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Spotiflow: accurate and robust spot detection for fluorescence microscopy

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Spotiflow

+

Spotiflow is a learning-based subpixel-accurate spot detection method for 2D and 3D fluorescence microscopy. It is primarily developed for spatial transcriptomics workflows that require transcript detection in large, multiplexed FISH-images, although it can also be used to detect spot-like structures in general fluorescence microscopy images and volumes. For more information, please refer to our paper.

+

Getting Started

+

Installation

+

First, create and activate a fresh conda environment (we currently support Python 3.9 to 3.12). If you don’t have conda installed, we recommend using miniforge.

+
$ conda create -n spotiflow python=3.12
+$ conda activate spotiflow
+
+
+

Note (for MacOS users): if using MacOS, there is a known bug causing the installation of PyTorch with conda to sometimes break OpenMP. You can avoid installing PyTorch with conda and let install it automatically via pip instead. +Then, install Pytorch using conda/ mamba. Please follow the official instructions for your system.

+

As an example, for a Linux system with CUDA (note that you should change the CUDA version to match the one installed on your system):

+
$ conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia
+
+
+

Note (for Windows users): if using Windows, if using Windows, please install the latest Build Tools for Visual Studio (make sure to select the C++ build tools during installation) before proceeding to install Spotiflow.

+

Finally, install spotiflow using pip:

+
$ pip install spotiflow
+
+
+

Predicting spots in an image

+

Python API

+

The snippet below shows how to retrieve the spots from an image using one of the pretrained models:

+
from skimage.io import imread
+from spotiflow.model import Spotiflow
+from spotiflow.utils import write_coords_csv
+
+
+# Load the desired image
+img = imread("/path/to/your/image")
+
+# Load a pretrained model
+model = Spotiflow.from_pretrained("general")
+
+# Predict spots
+spots, details = model.predict(img) # predict expects a numpy array
+
+# spots is a numpy array with shape (n_spots, 2)
+# details contains additional information about the prediction, like the predicted heatmap, the probability per spot, the flow field, etc.
+
+# Save the results to a CSV file
+write_coords_csv(spots, "/path/to/save/spots.csv")
+
+
+

If a custom model is used, simply change the model loadings step to:

+
# Load a custom model
+model = Spotiflow.from_folder("/path/to/model")
+
+
+

Command Line Interface (CLI)

+

You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command

+
$ spotiflow predict --input PATH
+
+
+

where PATH can be either an image or a folder. By default, the command will use the general pretrained model. You can specify a different model by using the --pretrained-model flag. Moreover, spots are saved to a subfolder spotiflow_results created inside the input folder (this can be changed with the --out-dir flag). For more information, please refer to the help message of the CLI (spotiflow-predict -h).

+

Napari plugin

+

Spotiflow also can be run easily in a graphical user interface as a napari plugin. See Predicting spots using the napari plugin for more information.

+

Contents

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+ + + + + + + + \ No newline at end of file diff --git a/napari.html b/napari.html new file mode 100644 index 0000000..61019ec --- /dev/null +++ b/napari.html @@ -0,0 +1,465 @@ + + + + + + + + + + + + + + + + Predicting spots using the napari plugin - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Predicting spots using the napari plugin

+

The napari plugin can be used to predict spots in a napari viewer. First, you must install it in the environment containing Spotiflow:

+
(spotiflow) $ pip install napari-spotiflow
+
+
+

The plugin will then be available in the napari GUI under the name “Spotiflow widget”. This is how the GUI looks like:

+_images/spotiflow_napari_gui.png + +

The plugin allows running on two modes: for images (2D) and volumes (3D), which can be toggled using the corresponding buttons in the GUI. You can also run on movies by setting the appropriate axis order (should be leading with a T).

+

Upon pressing the button Run, The plugin will create a Points layer containing the predicted spots:

+_images/spotiflow_napari_preds.png + +

If the option Show CNN output is checked, the plugin will also create two Image layers containing the heatmap output of the CNN as well as the stereographic flow.

+

Finally, the plugin includes two sample 2D images (HybISS and Terra) as well as a synthetic 3D volume. These samples can be loaded from the File menu (File -> Open sample -> napari-spotiflow). You can try the plugin with these samples to get a better idea of how it works!

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+ + + + + + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..c0d2eb7 Binary files /dev/null and b/objects.inv differ diff --git a/pretrained.html b/pretrained.html new file mode 100644 index 0000000..eca0c7d --- /dev/null +++ b/pretrained.html @@ -0,0 +1,468 @@ + + + + + + + + + + + + + + + + Available pre-trained models - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Available pre-trained models

+

The following pre-trained models are available (for a more detailed description, please refer to the Methods section of the paper as well as Supplementary Table 2):

+
    +

  • general: trained on a diverse dataset of spots of different modalities acquired in different microscopes with different settings. This model is the default one used in the CLI (pixel sizes: 0.04µm, 0.1µm, 0.11µm, 0.15µm, 0.32µm, 0.34µm).

    • +

  • hybiss: trained on HybISS data acquired in 3 different microscopes (pixel sizes: 0.15µm, 0.32µm, 0.34µm).

    • +

  • synth_complex: trained on synthetic data, which includes simulations of aberrated spots and fluorescence background (pixel size: 0.1µm).

    • +

  • synth_3d: trained on synthetic 3D data, which includes simulations of aberrated spots and Z-related artifacts (voxel size: 0.2µm).

    • +

  • smfish_3d: fine-tuned from the synth_3d model on smFISH 3D data of Platynereis dumerilii (voxel size: 0.13µm (YX), 0.48µm (Z)).

    • +
+

You can use these models to predict spots in images or to fine-tune them on a few annotations of your own data. The models can be loaded via the API as follows:

+
from spotiflow.model import Spotiflow
+
+pretrained_model_name = "general"
+model = Spotiflow.from_pretrained(pretrained_model_name)
+
+
+

You can also load them from the napari plugin or from the CLI by specifying the name of the model. See Predicting spots using the napari plugin and Inference via CLI for more information respectively.

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+ + + + + + + + \ No newline at end of file diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 0000000..4ddfea0 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,410 @@ + + + + + + + + + + + + + + + + None - Spotiflow 0.5.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ © Copyright 2024, Albert Dominguez Mantes. + +
+ + Created using + Sphinx + 8.1.3. + and + Sphinx-Immaterial + +
+ +
+ +
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+ +
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+
+
+ + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..eab3e7b --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"API Reference": [[0, null]], "Available pre-trained models": [[5, null]], "Basic training (API)": [[6, "basic-training-api"]], "Basic training (CLI)": [[6, "basic-training-cli"]], "Command Line Interface (CLI)": [[1, "command-line-interface-cli"], [3, "command-line-interface-cli"]], "Contents": [[3, "contents"]], "Customizing the training": [[6, "customizing-the-training"]], "Data format": [[2, "data-format"], [6, "data-format"]], "Fine-tuning (API)": [[2, "fine-tuning-api"]], "Fine-tuning (CLI)": [[2, "fine-tuning-cli"]], "Fine-tuning a Spotiflow model on a custom dataset": [[2, null]], "Getting Started": [[3, "getting-started"]], "Inference via CLI": [[1, null]], "Installation": [[3, "installation"]], "Napari plugin": [[3, "napari-plugin"]], "Predicting spots in an image": 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Images and centers are loaded from disk and normalized."], [0, 3, 1, 0, "image_files", "Return image filenames with the same order as in the dataset."], [0, 3, 1, 0, "images", "Return images in dataset."], [0, 3, 1, 0, "n_classes", "Return number of classes in the dataset."]], "spotiflow.data.spots.SpotsDataset.__init__": [[0, 2, 2, "spotiflow.data.spots.SpotsDataset", "add_class_label", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "augmenter", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "centers", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "compute_flow", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "downsample_factors", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "grid", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "image_files", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "images", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "mode", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "normalizer", "Sequence of images."], [0, 2, 2, "spotiflow.data.spots.SpotsDataset", "sigma", "Sequence of images."]], "spotiflow.data.spots.SpotsDataset.from_folder": [[0, 2, 2, "spotiflow.data.spots.SpotsDataset.from_folder", "add_class_label", "Build dataset from folder. 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Images and centers are loaded from disk and normalized."], [0, 2, 2, 0, "image_extensions", "Image extensions to look for in images."], [0, 2, 2, 0, "max_files", "Maximum number of files to load."], [0, 2, 2, 0, "mode", "Mode of heatmap generation."], [0, 2, 2, 0, "normalizer", "Normalizer function."], [0, 2, 2, 0, "path", "Path to folder containing images (with given extensions) and centers."], [0, 2, 2, 0, "random_state", "Random state used when shuffling file names when \"max_files\" is not None."], [0, 2, 2, 0, "sigma", "Sigma of Gaussian kernel to generate heatmap."]], "spotiflow.model.config": [[0, 0, 1, 0, "SpotiflowModelConfig", ""], [0, 0, 1, 0, "SpotiflowTrainingConfig", ""]], "spotiflow.model.config.SpotiflowModelConfig.__init__": [[0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "backbone", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "background_remover", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "batch_norm", ""], [0, 2, 2, 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"spotiflow.model.config.SpotiflowModelConfig", "mode", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "n_convs_per_level", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "out_channels", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "padding", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowModelConfig", "sigma", ""]], "spotiflow.model.config.SpotiflowTrainingConfig.__init__": [[0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "batch_size", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "crop_size", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "crop_size_depth", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "early_stopping_patience", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "finetuned_from", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "flow_loss_f", ""], [0, 2, 2, "spotiflow.model.config.SpotiflowTrainingConfig", "heatmap_loss_f", 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levels."]], "spotiflow.model.spotiflow.Spotiflow": [[0, 1, 1, 0, "fit", "Train a Spotiflow model."], [0, 1, 1, 0, "from_folder", "Load a pretrained model."], [0, 1, 1, 0, "from_pretrained", "Load a pretrained model with given name"], [0, 1, 1, 0, "load", "Load a model from disk."], [0, 1, 1, 0, "optimize_threshold", "The metric used to optimize is the F1 score."], [0, 1, 1, 0, "predict", "Predict spots in an image."], [0, 1, 1, 0, "save", "Save the model to disk."]], "spotiflow.model.spotiflow.Spotiflow.__init__": [[0, 2, 2, "spotiflow.model.spotiflow.Spotiflow", "config", "Supervised spot detector using a multi-stage neural network as a backbone for feature extraction followed by resolution-dependent post-processing modules to allow loss computation and optimization at different resolution levels."]], "spotiflow.model.spotiflow.Spotiflow.fit": [[0, 2, 2, 0, "augment_train", "whether to augment the training data."], [0, 2, 2, 0, "benchmark", "whether to use benchmarking."], [0, 2, 2, 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2, 0, "path", "folder to load the model from"], [0, 2, 2, 0, "which", "which checkpoint to load."]], "spotiflow.model.spotiflow.Spotiflow.optimize_threshold": [[0, 2, 2, 0, "batch_size", "batch size to use."], [0, 2, 2, 0, "cutoff_distance", "distance tolerance considered for points matching."], [0, 2, 2, 0, "device", "computing device to use."], [0, 2, 2, 0, "exclude_border", "Whether to exclude spots at the border."], [0, 2, 2, 0, "min_distance", "Minimum distance between spots for NMS."], [0, 2, 2, 0, "niter", "number of iterations for both coarse- and fine-grained search."], [0, 2, 2, 0, "subpix", "whether to use the stereographic flow to compute subpixel localization."], [0, 2, 2, 0, "threshold_range", "Range of thresholds to consider."], [0, 2, 2, 0, "val_ds", "dataset to optimize on"]], "spotiflow.model.spotiflow.Spotiflow.predict": [[0, 2, 2, 0, "device", "computing device to use."], [0, 2, 2, "spotiflow.model.spotiflow.Spotiflow.predict", "distributed_params", "Predict spots in an image."], [0, 2, 2, 0, "exclude_border", "Whether to exclude spots at the border."], [0, 2, 2, 0, "img", "input image"], [0, 2, 2, "spotiflow.model.spotiflow.Spotiflow.predict", "max_tile_size", "Predict spots in an image."], [0, 2, 2, 0, "min_distance", "Minimum distance between spots for NMS."], [0, 2, 2, 0, "n_tiles", "Number of tiles to split the image into."], [0, 2, 2, 0, "normalizer", "Normalizer to use."], [0, 2, 2, 0, "peak_mode", "Peak detection mode (can be either \"skimage\" or \"fast\", which is a faster custom C++ implementation)."], [0, 2, 2, 0, "prob_thresh", "Probability threshold for peak detection."], [0, 2, 2, 0, "progress_bar_wrapper", "Progress bar wrapper to use."], [0, 2, 2, 0, "scale", "Scale factor to apply to the image."], [0, 2, 2, 0, "subpix", "Whether to use the stereographic flow to compute subpixel localization."], [0, 2, 2, 0, "verbose", "Whether to print logs and progress."]], "spotiflow.model.spotiflow.Spotiflow.save": [[0, 2, 2, 0, "path", "folder to save the model to"], [0, 2, 2, 0, "update_thresholds", "whether to update the thresholds file."], [0, 2, 2, 0, "which", "which checkpoint to save."]], "spotiflow.sample_data": [[0, 5, 1, 0, "test_image_hybiss_2d", "Single test HybISS image from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)"], [0, 5, 1, 0, "test_image_synth_3d", "Single synthetic volumetric stack from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)"], [0, 5, 1, 0, "test_image_terra_2d", "Single test Terra frame from the Spotiflow paper (doi.org/10.1101/2024.02.01.578426)"]], "spotiflow.utils": [[0, 5, 1, 0, "get_data", "Get data from a given path. The path should contain a 'train' and 'val' folder."], [0, 5, 1, 0, "normalize", "Normalizes (percentile-based) a 2d image with the additional option to ignore a value. The normalization is done as follows:"], [0, 5, 1, 0, "read_coords_csv", "Parses a csv file and returns correctly ordered points array"], [0, 5, 1, 0, "write_coords_csv", "Writes points in a NumPy array to a CSV file"]], "spotiflow.utils.get_data": [[0, 2, 2, "spotiflow.utils.get_data", "include_test", "Get data from a given path. The path should contain a 'train' and 'val' folder."], [0, 2, 2, "spotiflow.utils.get_data", "is_3d", "Get data from a given path. The path should contain a 'train' and 'val' folder."], [0, 2, 2, 0, "normalize", "Whether to normalize the data."], [0, 2, 2, 0, "path", "Path to the data."]], "spotiflow.utils.normalize": [[0, 2, 2, 0, "clip", "Whether to clip the normalized image."], [0, 2, 2, 0, "ignore_val", "Value to be ignored."], [0, 2, 2, 0, "pmax", "Maximum percentile."], [0, 2, 2, 0, "pmin", "Minimum percentile."], [0, 2, 2, 0, "subsample", "Subsampling factor for percentile calculation."], [0, 2, 2, 0, "x", "Image to be normalized"]], "spotiflow.utils.read_coords_csv": [[0, 2, 2, 0, "add_class_column", "Whether to add a class column to the points array."], [0, 2, 2, 0, "fname", "Path to the csv file"]], "spotiflow.utils.write_coords_csv": [[0, 2, 2, 0, "fname", "Path to the CSV file"], [0, 2, 2, 0, "pts", "2D or 3D array of points"]]}, "objnames": {"0": ["py", "class", "Python class"], "1": ["py", "method", "Python method"], "2": ["py", "parameter", "Python parameter"], "3": ["py", "property", "Python 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Training a Spotiflow model on a custom dataset

+

Data format

+

First of all, make sure that the data is organized in the following format:

+
spots_data
+├── train
+│   ├── img_001.csv
+│   ├── img_001.tif
+|   ...
+│   ├── img_XYZ.csv
+|   └── img_XYZ.tif
+└── val
+    ├── val_img_001.csv
+    ├── val_img_001.tif
+    ...
+    ├── val_img_XYZ.csv
+    └── val_img_XYZ.tif
+
+
+

The actual naming of the files is not important, but the .csv and .tif files corresponding to the same image must have the same name! The .csv files must contain the spot coordinates in the following format:

+
y,x
+42.3,24.24
+252.99, 307.97
+...
+
+
+

The column names can also be axis-0 (instead of y) and axis-1 instead of x. For the 3D case, the format is similar but with an additional column corresponding to the z coordinate:

+
z,y,x
+12.4,42.3,24.24
+61.2,252.99, 307.97
+...
+
+
+

In this case, you can also use axis-0, axis-1, and axis-2 instead of z, y, and x, respectively.

+

Basic training (CLI)

+

You can train a model using the CLI as follows:

+
spotiflow-train /path/to/spots_data -o /my/trained/model
+
+
+

where /path/to/spots_data is the path to the directory containing the data in the format described above and /my/trained/model is the directory where the trained model will be saved. You can also pass other parameters to the training, such as the number of epochs, the learning rate, etc. For more information on the arguments allowed, see the documentation of the CLI command:

+
spotiflow-train --help
+
+
+

To illustrate with an example, to train a Spotiflow model on 2-channel 3D data for 100 epochs, you can run:

+
spotiflow-train /path/to/spots_data -o /my/trained/model --is-3d True --num-epochs 100 --in-channels 2
+
+
+

Basic training (API)

+

You can easily train a model using the default settings as follows and save it to the directory /my/trained/model:

+
from spotiflow.model import Spotiflow
+from spotiflow.utils import get_data
+
+# Get the data
+train_imgs, train_spots, val_imgs, val_spots = get_data("/path/to/spots_data")
+
+# Initialize the model
+model = Spotiflow()
+
+# Train and save the model
+model.fit(
+    train_imgs,
+    train_spots,
+    val_imgs,
+    val_spots,
+    save_dir="/my/trained/model",
+)
+
+
+

You can then load it by simply calling:

+
model = Spotiflow.from_folder("/my/trained/model")
+
+
+

In the 3D case, you should initialize a spotiflow.model.config.SpotiflowModelConfig object and pass it to the Spotiflow constructor with the appropriate parameter set (see other options for the configuration at the end of the section):

+
# Same imports as before
+from spotiflow.model import SpotiflowModelConfig
+
+# Create the model config
+model_config = SpotiflowModelConfig(
+    is_3d=True,
+    grid=2, # subsampling factor for prediction
+    # you can pass other arguments here
+)
+
+model = Spotiflow(model_config)
+# Train and save the model as before
+
+
+

Customizing the training

+

You can also pass other parameters relevant for training to the fit method. For example, you can change the number of epochs, the batch size, the learning rate, etc. You can do that using the train_config parameter. For more information on the arguments allowed, see the documentation of spotiflow.model.spotiflow.Spotiflow.fit() method as well as spotiflow.model.config.SpotiflowTrainingConfig. As an example, let’s change the number of epochs and the learning rate:

+
train_config = {
+    "num_epochs": 100,
+    "learning_rate": 0.001,
+    "smart_crop": True,
+    # other parameters
+}
+
+model.fit(
+    train_imgs,
+    train_spots,
+    val_imgs,
+    val_spots,
+    save_dir="/my/trained/model",
+    train_config=train_config,
+    # other parameters
+)
+
+
+

In order to change the model architecture (e.g. number of input channels, number of layers, variance for the heatmap generation, etc.), you can create a spotiflow.model.config.SpotiflowModelConfig object and populate it accordingly. Then you can pass it to the Spotiflow constructor (note that this is necessary for 3D). For example, if our image is RGB and we need the network to use 3 input channels, we can do the following:

+
from spotiflow.model import SpotiflowModelConfig
+
+# Create the model config
+model_config = SpotiflowModelConfig(
+    in_channels=3,
+    # you can pass other arguments here
+)
+model = Spotiflow(model_config)
+
+
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