From eddbf5ea0e6da57d936553510cda3cbf17a06acf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cl=C3=A9ment=20H=2E=20Benedetti?= Date: Thu, 31 Oct 2024 08:49:57 +0100 Subject: [PATCH] New version of annotator --- docs/Makefile | 20 ++ .../calibration/calibration-1-psize0_65.png | Bin 0 -> 665644 bytes .../calibration/calibration-1-psize0_9.png | Bin 0 -> 650264 bytes docs/_images/calibration/calibration-1.png | Bin 0 -> 524123 bytes docs/_images/helper/annotation-helper.gif | Bin 0 -> 531869 bytes .../_images/patches/activity-map-building.png | Bin 0 -> 344848 bytes docs/_images/patches/physical-size.png | Bin 0 -> 311774 bytes docs/_images/seg-ex/01-in.png | Bin 0 -> 12003 bytes docs/_images/seg-ex/01-out.png | Bin 0 -> 212 bytes docs/_images/seg-ex/02-in.png | Bin 0 -> 12335 bytes docs/_images/seg-ex/02-out.png | Bin 0 -> 247 bytes docs/_images/seg-ex/03-in.png | Bin 0 -> 11888 bytes docs/_images/seg-ex/03-out.png | Bin 0 -> 202 bytes docs/_images/seg-ex/04-in.png | Bin 0 -> 12659 bytes 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| 16 ++ docs/user_guide.rst | 174 ++++++++++++++++++ pyproject.toml | 2 + src/microglia_analyzer/_tests/workflow.py | 4 +- .../_widget_yolo_annotations.py | 81 ++++++++ src/microglia_analyzer/custom-loss.py | 38 ---- src/microglia_analyzer/custom_loss.py | 47 +++++ src/microglia_analyzer/dl/yolov5 | 1 + src/microglia_analyzer/dl/yolov5_training.py | 25 ++- src/microglia_analyzer/microglia_analyzer.py | 21 ++- src/microglia_analyzer/rename-files.py | 46 +++++ 49 files changed, 817 insertions(+), 54 deletions(-) create mode 100644 docs/Makefile create mode 100644 docs/_images/calibration/calibration-1-psize0_65.png create mode 100644 docs/_images/calibration/calibration-1-psize0_9.png create mode 100644 docs/_images/calibration/calibration-1.png create mode 100644 docs/_images/helper/annotation-helper.gif create mode 100644 docs/_images/patches/activity-map-building.png create mode 100644 docs/_images/patches/physical-size.png create mode 100644 docs/_images/seg-ex/01-in.png create mode 100644 docs/_images/seg-ex/01-out.png create mode 100644 docs/_images/seg-ex/02-in.png create mode 100644 docs/_images/seg-ex/02-out.png create mode 100644 docs/_images/seg-ex/03-in.png create mode 100644 docs/_images/seg-ex/03-out.png create mode 100644 docs/_images/seg-ex/04-in.png create mode 100644 docs/_images/seg-ex/04-out.png create mode 100644 docs/_images/seg-ex/05-in.png create mode 100644 docs/_images/seg-ex/05-out.png create mode 100644 docs/_images/seg-ex/06-in.png create mode 100644 docs/_images/seg-ex/06-out.png create mode 100644 docs/_images/yolo/banner.jpg create mode 100644 docs/_static/custom.css create mode 100644 docs/analyze_microglia.rst create mode 100644 docs/annotate_patches.rst create mode 100644 docs/conf.py create mode 100644 docs/icon.png create mode 100644 docs/index.rst create mode 100644 docs/make.bat create mode 100644 docs/make_doc.yml create mode 100644 docs/make_patches.rst create mode 100644 docs/microglia_analyzer.custom-loss.rst create mode 100644 docs/microglia_analyzer.microglia_analyzer.rst create mode 100644 docs/microglia_analyzer.rst create mode 100644 docs/microglia_analyzer.tiles.patch.rst create mode 100644 docs/microglia_analyzer.tiles.plot.rst create mode 100644 docs/microglia_analyzer.tiles.recalibrate.rst create mode 100644 docs/microglia_analyzer.tiles.rst create mode 100644 docs/microglia_analyzer.tiles.tiler.rst create mode 100644 docs/modules.rst create mode 100644 docs/requirements.txt create mode 100644 docs/user_guide.rst delete mode 100644 src/microglia_analyzer/custom-loss.py create mode 100644 src/microglia_analyzer/custom_loss.py create mode 160000 src/microglia_analyzer/dl/yolov5 create mode 100644 src/microglia_analyzer/rename-files.py diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the 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b/docs/analyze_microglia.rst @@ -0,0 +1,3 @@ +================= +Analyze Microglia +================= \ No newline at end of file diff --git a/docs/annotate_patches.rst b/docs/annotate_patches.rst new file mode 100644 index 0000000..b32aa63 --- /dev/null +++ b/docs/annotate_patches.rst @@ -0,0 +1,96 @@ +=========================== +Annotate your data for YOLO +=========================== + +- The goal of this widget is to help you annotate your data for the YOLO model. +- YOLO is a deep learning model that can detect objects in images. +- Detections are composed of bounding-boxes around objects and a class label. +- To train the model, you need to provide a dataset of annotated images. +- The widget "YOLO Annotator" will help you to create this dataset. + +.. image:: _images/yolo/banner.jpg + :align: center + :width: 800px + +1. Prepare your data +==================== + +- First of all, the data that you will annotate must be ready. +- Your images must all be the same size in pixels. +- You can use TIF, PNG, JPG, ... but no proprietary format (CZI, LIF, ...). +- You can use the "Tiles creator" widget to create patches from your images. A common size is 512x512 pixels. +- All the images that you want to annotate must be in the same folder. +- Before you start, you should have a folder structure like this: + +.. code-block:: bash + + 📁 some-folder + ├── 📁 my-experiment + │ ├── 14-25-23.336.tif + │ ├── 14-25-23.379.tif + │ ├── ... + │ └── 14-25-36.949.tif + + +2. Initialize the widget +======================== + +- Open the widget located in: Plugins > Microglia Analyzer > YOLO Annotator. +- Click the "Root folder" button and choose the folder named "some-folder" in the previous example. It is the folder containing the folder in which your images are located. +- In the "Inputs sub-folder" dropdown menu, choose the folder named "my-experiment" in the previous example. It is the folder containing your images. +- The first image of your experiment will be displayed. +- You can navigate through the images using the dropdown menu in the "Annotations management" section. + + +3. Create your classes +====================== + +- The first thing to do is to create new classes. Each one corresponds to a different type of object that you want to detect. +- Simply fill the text field in the "Classes management" section and click the "New class" button. +- You can add as many classes as you want. +- The classes that you add should appear in the layers stack of Napari (on the left side of the window). + + +4. Annotate your data +===================== + +- To annotate an object, you have to draw a bounding-box around it. You are limited to rectangles. Do not use ellipses or polygons or anything else. +- You can choose the rectangle tool in the upper-left section of the Napari's window. +- Your boxes should contain the whole object and a few pixels around it. +- It is not a problem if the boxes overlap. +- If you failed a box, don't edit it: delete it and draw a new one. +- If you don't plan to annotate many images, you should really be careful not to forget any object on your images. +- When you are happy with the annotations you made, click the "Save annotations" button in the "Annotations management" section. +- In the "Classes management" section, a table should have appeared to show you global statistics about your annotations. +- A new folder with the "-labels" suffix should have appeared in the root folder. It contains the annotations you made. +- A new file with the "-classes.txt" suffix should have appeared in the root folder. It contains the classes you created. +- Your files architecture should now look like this: + +.. code-block:: bash + + 📁 some-folder + ├── 📁 my-experiment + │ ├── 14-25-23.336.tif + │ ├── 14-25-23.379.tif + │ ├── ... + │ └── 14-25-36.949.tif + ├── 📁 my-experiment-labels + │ ├── 14-25-23.336.txt + │ ├── 14-25-23.379.txt + │ ├── ... + │ └── 14-25-36.949.txt + ├── 📃 my-experiment-classes.txt + + +Notes +===== + +- If you are working on RGB images, they will be converted to grayscale. +- If you already annotated some images, your annotations will be loaded when you open the widget. +- Whenever it is possible, try to have a balanced dataset. It means that you should have the same number of annotations for each class. +- In the ".txt" files generated for each image by the widget, the content is as follows: + - [integer]: the class index (starting from 0). + - [float]: the x-coordinate of the center of the bounding-box, in percentage of the image width. + - [float]: the y-coordinate of the center of the bounding-box, in percentage of the image height. + - [float]: the width of the bounding-box, in percentage of the image width. + - [float]: the height of the bounding-box, in percentage of the image height. \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..65f50a7 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,72 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# + +import os +import sys + +mod_path = os.path.abspath('../src') +print(mod_path) +sys.path.insert(0, mod_path) + + +# -- Project information ----------------------------------------------------- + +project = 'Protoplasts swelling analyzer' +copyright = '2024, ClĂ©ment H. Benedetti' +author = 'ClĂ©ment H. BENEDETTI' + +# The full version, including alpha/beta/rc tags +release = '1.0.0' + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', + 'sphinx_tabs.tabs', + 'myst_parser', + 'sphinx_rtd_theme', +] + + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. 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Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/make_doc.yml b/docs/make_doc.yml new file mode 100644 index 0000000..e69de29 diff --git a/docs/make_patches.rst b/docs/make_patches.rst new file mode 100644 index 0000000..4f22977 --- /dev/null +++ b/docs/make_patches.rst @@ -0,0 +1,62 @@ +.. _make_patches: + +============================== +Patches from calibrated images +============================== + + +1. Detect the protoplasts +========================= + +A single experiment is 20 GB of size in average. We would like not to waste processing time to launch the segmentation of traps that are actually empty. +To do so, we implemented a way to detect the presence of protoplasts (find their centroĂŻd). + +The first step of the algorithm consists in processing the "activity map" of this experiment. By this, we mean the areas where protoplasts are spinning on themselves. + +This is achieved by summing the absolute difference between each contiguous pairs of frame. + +Then, we apply a sequence of filters (Median blur, Laplacian of Gaussian, threshold et connected components labeling) to find a little spot of pixels per protoplast. We finish this step with a points-set containing one point on each protoplast. + + +.. image:: _images/patches/activity-map-building.png + :align: center + + + + +2. Create the patches +===================== + +For a model to predict correctly objects, the patches you feed it must be similar to the ones it was trained with. + +The model we trained requires patches that are exactly 128×128 pixels of size. +It sounds like a big constraint as it implies that your pixel size should always be the same. +Indeed, if we cut a patch of 128×128 pixels, those 128 pixels are 115.2 ”m if the size of a pixel is 0.9 ”m and they are only 76.8 ”m if a pixel is 0.65 ”m. + +We addressed this problem by doing a two-step patches extraction. + +Instead of cutting patches of 128×128 pixels in the main image, we cut patches of 130×130 ”m, whatever size in pixels it is. +Once we have those patches, we re-sample them by scaling them so they can fit in a 128×128 pixels image. + +.. table:: An object of 20”m of diameter with different pixel sizes + :align: center + + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | .. image:: _images/calibration/calibration-1.png | .. image:: _images/calibration/calibration-1-psize0_9.png | .. image:: _images/calibration/calibration-1-psize0_65.png | + | :height: 250px | :height: 250px | :height: 250px | + | :width: 250px | :width: 250px | :width: 250px | + | :align: center | :align: center | :align: center | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | The object is always 20”m of diameter | Pixel size = 0.9”m | Pixel size = 0.65”m | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + + +.. image:: _images/patches/physical-size.png + :align: center + :width: 400px + + +3. Export the patches +===================== + +The patches are exported in 128×128 pixels, on 32 bits, normalized between 0 and 1. diff --git a/docs/microglia_analyzer.custom-loss.rst b/docs/microglia_analyzer.custom-loss.rst new file mode 100644 index 0000000..9313470 --- /dev/null +++ b/docs/microglia_analyzer.custom-loss.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.custom\-loss module +======================================= + +.. automodule:: microglia_analyzer.custom-loss + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.microglia_analyzer.rst b/docs/microglia_analyzer.microglia_analyzer.rst new file mode 100644 index 0000000..87ae290 --- /dev/null +++ b/docs/microglia_analyzer.microglia_analyzer.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.microglia\_analyzer module +============================================== + +.. automodule:: microglia_analyzer.microglia_analyzer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.rst b/docs/microglia_analyzer.rst new file mode 100644 index 0000000..461db1b --- /dev/null +++ b/docs/microglia_analyzer.rst @@ -0,0 +1,27 @@ +microglia\_analyzer package +=========================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + microglia_analyzer.tiles + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + microglia_analyzer.custom-loss + microglia_analyzer.microglia_analyzer + +Module contents +--------------- + +.. automodule:: microglia_analyzer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.tiles.patch.rst b/docs/microglia_analyzer.tiles.patch.rst new file mode 100644 index 0000000..27a5b6b --- /dev/null +++ b/docs/microglia_analyzer.tiles.patch.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.tiles.patch module +====================================== + +.. automodule:: microglia_analyzer.tiles.patch + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.tiles.plot.rst b/docs/microglia_analyzer.tiles.plot.rst new file mode 100644 index 0000000..65f7e84 --- /dev/null +++ b/docs/microglia_analyzer.tiles.plot.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.tiles.plot module +===================================== + +.. automodule:: microglia_analyzer.tiles.plot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.tiles.recalibrate.rst b/docs/microglia_analyzer.tiles.recalibrate.rst new file mode 100644 index 0000000..80ff5a2 --- /dev/null +++ b/docs/microglia_analyzer.tiles.recalibrate.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.tiles.recalibrate module +============================================ + +.. automodule:: microglia_analyzer.tiles.recalibrate + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.tiles.rst b/docs/microglia_analyzer.tiles.rst new file mode 100644 index 0000000..3821340 --- /dev/null +++ b/docs/microglia_analyzer.tiles.rst @@ -0,0 +1,21 @@ +microglia\_analyzer.tiles package +================================= + +Submodules +---------- + +.. toctree:: + :maxdepth: 4 + + microglia_analyzer.tiles.patch + microglia_analyzer.tiles.plot + microglia_analyzer.tiles.recalibrate + microglia_analyzer.tiles.tiler + +Module contents +--------------- + +.. automodule:: microglia_analyzer.tiles + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/microglia_analyzer.tiles.tiler.rst b/docs/microglia_analyzer.tiles.tiler.rst new file mode 100644 index 0000000..3eb7120 --- /dev/null +++ b/docs/microglia_analyzer.tiles.tiler.rst @@ -0,0 +1,7 @@ +microglia\_analyzer.tiles.tiler module +====================================== + +.. automodule:: microglia_analyzer.tiles.tiler + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/modules.rst b/docs/modules.rst new file mode 100644 index 0000000..48a8863 --- /dev/null +++ b/docs/modules.rst @@ -0,0 +1,7 @@ +Microglia Analyzer (Python API) +========================================== + +.. toctree:: + :maxdepth: 4 + + microglia_analyzer diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..89793d5 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,16 @@ +qtpy +pyqt5 +pytest +opencv-python-headless +napari[all] +cellpose +scipy +termcolor +Sphinx +sphinx-rtd-theme +sphinx-tabs +scikit-image +myst-parser +docstring-parser +stardist +tensorflow[and-cuda] diff --git a/docs/user_guide.rst b/docs/user_guide.rst new file mode 100644 index 0000000..d834d53 --- /dev/null +++ b/docs/user_guide.rst @@ -0,0 +1,174 @@ +========================= +Quick start: A user guide +========================= + + +1. Install the plugin +===================== + ++-----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Method | Instructions | ++=======================+==========================================================================================================================================================================+ +| pip | **Stable version:** Activate your conda environment, and type :code:`pip install microglia-analyzer`. The plugin should then appear in your plugins list in Napari. | ++-----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| NapariHub | **Stable version:** Go in the plugins menu of Napari and search for "Microglia Analyzer" | ++-----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| GitHub | **Dev version:** Activate your conda environment and use the command :code:`pip install git+https://github.com/MontpellierRessourcesImagerie/microglia-analyzer.git`. | ++-----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| From an archive | Uncompress the archive, place your terminal in the folder containing :code:`pyproject.toml` and launch :code:`pip install -e .`. | ++-----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +If the install was successful, you should see the plugin in Napari in the top bar menu: Plugins > Microglia Analyzer > Microglia Analyzer. + +2. Quick demonstration +====================== + +.. raw:: html + + + + +3. Get your data ready +====================== + +This Napari plugin expects your data to respect a precise format: + +* The frames must be dissociated in individual TIFF images (no video). +* The files must have exactly 3 channels (no grayscale, no alpha channel). +* The timestamp until the milliseconds must be encoded in the name of each file, with the form: :code:`hh-mm-ss.SSS.tif`. +* All the frames must be located in the same folder. +* Avoid using special characters in the folder's name. (Tips: `Doranum `_) +* Be aware that the plugin creates data along its execution. This data is located next the folder containing the frames. This data is approximately 60% of the original's size. Be sure to have enough spare space on your disk. + +.. code-block:: bash + + . + ├── 📁 experiment-2024-06-26-v001 + │ ├── 14-25-23.336.tif + │ ├── 14-25-23.379.tif + │ ├── ... + │ └── 14-25-36.949.tif + + +4. Settings +=========== + ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ +| Name | Description | ++===============+====================================================================================================================================+ +| Frame | Index of the frame that you are currently visualizing in the viewport. **Starts at 1**. | ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ +| Calibration | Length, in physical unit, of the line that you drew on the active shape layer. | ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ +| Median blur | For protoplasts detection. Applies a median blur of this radius to the activity map to remove noise. | ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ +| LoG radius | For protoplasts detection. Applies a Laplacian of Gaussian filter to the activity map to find the center of protoplasts. | ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ +| Threshold | For protoplasts detection. Minimum amount of activity to reach to consider that we have a protoplast. | ++---------------+------------------------------------------------------------------------------------------------------------------------------------+ + + +5. Workflow +=========== + +a. Import your experiment (Experiment manager) +---------------------------------------------- + +- In this section, you will import your experiment so you can visualize and analyze it. +- The :code:`Clear state` button is useless unless you just analyzed something else and need to reset the plugin. +- Use the :code:`Experiment directory` to provide the path to the folder containing your frames. If you already performed an analysis or if you stopped in the process, you can select the `.data` folder associated to your experiment. +- The :code:`Backward` and :code:`Forward` buttons allow you to navigate by 15 frames hops. +- The slider allows you to visualize your frames freely. +- If you need to visualize a precise frame, you can provide its index in the :code:`Frame` slot. +- The three last lines in this group are: + 1. The ellapsed time in seconds from the first frame to the one you are looking at. + 2. The name of the experiment. + 3. The total duration of the experiment in seconds. + + +b. Calibration (Calibration) +---------------------------- + +- To create our patches, we need to know the physical size of a pixel. If you know the pixel size, you can simply fill the :code:`Calibration` slot and provide the correct unit. +- If you don't know the pixel size, you can: + - Add a new shape layer in your project. + - Make a line over a structure of which you know the length (ex: the width of the chip). The line is so thin that you won't see it in the viewport. + - Fill the :code:`Calibration` with the length and unit of this structure. + - Make sure to have your shape layer still active (its name must be on a blue background in the layers list), and click :code:`Apply calibration`. + - The last line of text of the box should now indicate the size of a pixel. + +c. Generate the patches (Create patches) +---------------------------------------- + +- For a better understanding of how and why the patches are created, check the ":doc:`make_patches`" chapter. +- Now the goal will be to adjust the three settings :code:`Median blur`, :code:`LoG radius` and :code:`Threshold`. +- To do so, you need a reference. So start by clicking the :code:`Reprocess patches` button to get your starting point. +- If your acquisition are noisy or if you caught small debris, increase the median blur radius. +- Adjust your LoG radius to be approximately half the diameter of a protoplast (in pixels). +- Decrease the threshold value until you removed every slow object and every protoplast that explodes along time. +- For every change you make, don't forget to hit :code:`Reprocess patches` again. + +d. Segment the protoplasts and measure them (Segment & measure) +--------------------------------------------------------------- + +- Start by exporting the patches that you just created with the :code:`Export patches` button. It might take a few seconds to start when you click the button, don't mash it. +- It will write on the disk the patches as they are expected in input of our segmentation algorithm. +- Once its done, you can click the :code:`Segment protoplasts` button. +- This last step takes a bunch of time as it is deep-learning based (with `StarDist `_). +- You can visualize the progress by clicking the "Activity" button in the bottom right corner of Napari's window. +- Once it's over, the segmentation will show up above your image, and a results table will show up. +- In this results table, the measures are areas in ”mÂČ. +- Each line is a time-point. +- The first column is the ellapsed time in seconds. +- The next columns are for the segmentation: Pxxx indicates that this measure comes from the xxx patch, the L is the unique identifier of the protoplast on the chip. + +e. Edit the segmentation (Segment & measure) +-------------------------------------------- + +- The segmentation may produce false positives or false negatives. You can manually edit the labels in the viewer. +- If you want to do so, the easiest way is to keep your left hand on the keyboard, and the right one on the mouse: + - Left ring finger: :code:`E` key: Allows you to get one frame back. + - Left middle finger: :code:`R` key: Allows you to fill and save the current labels. + - Left index finger: :code:`T` key: Allows you to get one frame forward. + - Left thumb: :code:`Space` key: Allows you to switch between the drawing and moving mode. + +6. Examples of segmented patches +================================ + +.. tabs:: + + .. tab:: Input patches + + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | .. image:: _images/seg-ex/01-in.png | .. image:: _images/seg-ex/02-in.png | .. image:: _images/seg-ex/03-in.png | + | :height: 128px | :height: 128px | :height: 128px | + | :width: 128px | :width: 128px | :width: 128px | + | :align: center | :align: center | :align: center | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | .. image:: _images/seg-ex/04-in.png | .. image:: _images/seg-ex/05-in.png | .. image:: _images/seg-ex/06-in.png | + | :height: 128px | :height: 128px | :height: 128px | + | :width: 128px | :width: 128px | :width: 128px | + | :align: center | :align: center | :align: center | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + + .. tab:: Labeled patches + + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | .. image:: _images/seg-ex/01-out.png | .. image:: _images/seg-ex/02-out.png | .. image:: _images/seg-ex/03-out.png | + | :height: 128px | :height: 128px | :height: 128px | + | :width: 128px | :width: 128px | :width: 128px | + | :align: center | :align: center | :align: center | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + | .. image:: _images/seg-ex/04-out.png | .. image:: _images/seg-ex/05-out.png | .. image:: _images/seg-ex/06-out.png | + | :height: 128px | :height: 128px | :height: 128px | + | :width: 128px | :width: 128px | :width: 128px | + | :align: center | :align: center | :align: center | + +----------------------------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+ + + +7. Notes +======== + +- The plugin provides verbose output, so it's recommended to monitor the terminal if you want detailed information about its actions. +- If a crash occurs, please `create an issue `_ and include the relevant image(s) for further investigation. +- Napari currently supports only open file formats, so make sure to convert your images to TIFF format before using them with Napari. diff --git a/pyproject.toml b/pyproject.toml index 661a946..e7155ea 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,8 @@ dependencies = [ "tifffile" ] +# Required for documentation: tabs, sphinx_sphinx, myst_parser, sphinx_rtd_theme + [project.optional-dependencies] testing = [ "tox", diff --git a/src/microglia_analyzer/_tests/workflow.py b/src/microglia_analyzer/_tests/workflow.py index 8303c7c..d1ae527 100644 --- a/src/microglia_analyzer/_tests/workflow.py +++ b/src/microglia_analyzer/_tests/workflow.py @@ -31,10 +31,10 @@ def logging(message): mam.load_image(img_target) mam.set_calibration(0.325, "”m") mam.export_patches() + mam.set_segmentation_model("/home/benedetti/Documents/projects/2060-microglia/”net/”net-V004") + mam.segment_microglia() return - mam.set_segmentation_model("”net-v002") mam.set_classification_model("”yolo-V005") - mam.segment_microglia() mam.classify_microglia() mam.make_skeletons() mam.extract_metrics() diff --git a/src/microglia_analyzer/_widget_yolo_annotations.py b/src/microglia_analyzer/_widget_yolo_annotations.py index 00663ea..ec6de1d 100644 --- a/src/microglia_analyzer/_widget_yolo_annotations.py +++ b/src/microglia_analyzer/_widget_yolo_annotations.py @@ -2,6 +2,7 @@ QHBoxLayout, QPushButton, QLabel, QFileDialog, QComboBox, QGroupBox) +from scipy.ndimage import binary_fill_holes from napari.utils.notifications import show_info import tifffile @@ -14,6 +15,8 @@ _CLASS_PREFIX = "class." # Name of the layer containing the current image. _IMAGE_LAYER = "Image" +# Layer containing the manual annotations (masks) of microglia. +_MASKS_LAYER = "Masks" # Colors assigned to each YOLO class. _COLORS = [ "#FF4D4D", @@ -60,6 +63,8 @@ def __init__(self, napari_viewer): self.sources_directory = None # Name of the folder in which annotations are located self.annotations_directory = None + # Name of the folder in which masks are located + self.masks_directory = None # List of images ('.tif') in the 'images' folder. self.images_list = [] @@ -143,10 +148,33 @@ def add_annotations_management_group_ui(self): self.layout.addWidget(box) + def add_masks_group_ui(self): + box = QGroupBox("Masks") + layout = QVBoxLayout() + box.setLayout(layout) + + # Button to add a new mask layer. + self.add_mask_button = QPushButton("🎭 Add mask") + self.add_mask_button.clicked.connect(self.add_mask_layer) + layout.addWidget(self.add_mask_button) + + # Button to fill the current label. + self.fill_button = QPushButton("🎹 Fill label") + self.fill_button.clicked.connect(self.fill_current_label) + layout.addWidget(self.fill_button) + + # Button to save the masks. + self.save_masks_button = QPushButton("đŸ’Ÿ Save masks") + self.save_masks_button.clicked.connect(self.save_masks) + layout.addWidget(self.save_masks_button) + + self.layout.addWidget(box) + def init_ui(self): self.add_media_management_group_ui() self.add_classes_management_group_ui() self.add_annotations_management_group_ui() + self.add_masks_group_ui() # ----------------- CALLBACKS ------------------------------------------- @@ -167,16 +195,21 @@ def set_sources_directory(self): """ source_folder = self.inputs_name.currentText() annotations_folder = source_folder + "-labels" + masks_folder = source_folder + "-masks" if (source_folder is None) or (source_folder == "---") or (source_folder == ""): return inputs_path = os.path.join(self.root_directory, source_folder) annotations_path = os.path.join(self.root_directory, annotations_folder) + masks_path = os.path.join(self.root_directory, masks_folder) if not os.path.isdir(inputs_path): return False if not os.path.isdir(annotations_path): os.makedirs(annotations_path) + if not os.path.isdir(masks_path): + os.makedirs(masks_path) self.sources_directory = source_folder self.annotations_directory = annotations_folder + self.masks_directory = masks_path self.annotations_name.setText(annotations_folder) self.open_sources_directory() @@ -210,12 +243,15 @@ def open_image(self): Reloads the annotations if some were already made for this image. """ current_image = self.image_selector.currentText() + self.clear_mask() if (self.root_directory is None) or (current_image is None) or (current_image == "---") or (current_image == ""): return image_path = os.path.join(self.root_directory, self.sources_directory, current_image) name, _ = os.path.splitext(current_image) current_as_txt = name + ".txt" + current_as_tif = name + ".tif" labels_path = os.path.join(self.root_directory, self.annotations_directory, current_as_txt) + masks_path = os.path.join(self.root_directory, self.masks_directory, current_as_tif) if not os.path.isfile(image_path): print(f"The image: '{current_image}' doesn't exist.") return @@ -234,6 +270,15 @@ def open_image(self): if os.path.isfile(labels_path): # If some annotations already exist for this image. self.load_annotations(labels_path) self.count_boxes() + if os.path.isfile(masks_path): + self.restore_mask_layer(masks_path) + + def restore_mask_layer(self, masks_path): + data = tifffile.imread(masks_path) + if _MASKS_LAYER in self.viewer.layers: + self.viewer.layers[_MASKS_LAYER].data = data + else: + self.viewer.add_labels(data, name=_MASKS_LAYER) def save_state(self): """ @@ -250,8 +295,44 @@ def save_state(self): self.write_annotations(lines) self.count_boxes() + def add_mask_layer(self): + if not self.is_image_opened(): + return + shape = self.viewer.layers[_IMAGE_LAYER].data.shape[_WIDTH_HEIGHT[0]:_WIDTH_HEIGHT[1]] + self.viewer.add_labels(np.zeros(shape, dtype=np.uint8), name=_MASKS_LAYER) + + def save_masks(self): + name, _ = os.path.splitext(self.image_selector.currentText()) + current_as_tif = name + ".tif" + mask_path = os.path.join(self.masks_directory, current_as_tif) + if _MASKS_LAYER in self.viewer.layers: + tifffile.imwrite(mask_path, self.viewer.layers[_MASKS_LAYER].data) + show_info("Masks saved.") + # ----------------- METHODS ------------------------------------------- + def is_image_opened(self): + """ + Checks if an image is currently opened in the Napari viewer. + """ + return (_IMAGE_LAYER in self.viewer.layers) and (self.viewer.layers[_IMAGE_LAYER].data is not None) and len(self.viewer.layers[_IMAGE_LAYER].data) > 0 + + def clear_mask(self): + """ + Clears the mask layer. + """ + if _MASKS_LAYER in self.viewer.layers: + self.viewer.layers[_MASKS_LAYER].data = np.zeros_like(self.viewer.layers[_IMAGE_LAYER].data) + + def fill_current_label(self): + if not _MASKS_LAYER in self.viewer.layers: + return + current_label = self.viewer.layers[_MASKS_LAYER].selected_label + mask = self.viewer.layers[_MASKS_LAYER].data == current_label + filled = binary_fill_holes(mask) > 0 + filled = filled.astype(np.uint8) * current_label + self.viewer.layers[_MASKS_LAYER].data = np.maximum(self.viewer.layers[_MASKS_LAYER].data, filled) + def upper_corner(self, box): """ Locates the upper-right corner of a bounding-box having the Napari format. diff --git a/src/microglia_analyzer/custom-loss.py b/src/microglia_analyzer/custom-loss.py deleted file mode 100644 index 9e8d325..0000000 --- a/src/microglia_analyzer/custom-loss.py +++ /dev/null @@ -1,38 +0,0 @@ -from skimage.morphology import skeletonize -from skimage.measure import label, regionprops - -def compute_skeleton_penalty(y_pred, min_length=10): - # Binariser la prĂ©diction - y_pred_bin = y_pred > 0.5 - - # Labeliser les composantes connexes - labeled_image = label(y_pred_bin) - - penalty = 0 - for region in regionprops(labeled_image): - # CrĂ©er un masque pour chaque rĂ©gion - region_mask = labeled_image == region.label - - # Calculer le squelette de la rĂ©gion - skeleton = skeletonize(region_mask) - - # Calculer la longueur du squelette - skeleton_length = skeleton.sum() - - # Ajouter une pĂ©nalitĂ© si la longueur du squelette est infĂ©rieure au seuil - if skeleton_length < min_length: - penalty += (min_length - skeleton_length) - - return penalty - -def custom_loss(y_true, y_pred): - # Calcul de ta loss actuelle (par exemple, Dice Loss) - dice_loss = dice_loss_function(y_true, y_pred) - - # Calcul des pĂ©nalitĂ©s basĂ©es sur la longueur des skeletons - skeleton_penalty = compute_skeleton_penalty(y_pred) - - # Combinaison des pĂ©nalitĂ©s - total_loss = dice_loss + gamma * skeleton_penalty - - return total_loss \ No newline at end of file diff --git a/src/microglia_analyzer/custom_loss.py b/src/microglia_analyzer/custom_loss.py new file mode 100644 index 0000000..2a49e78 --- /dev/null +++ b/src/microglia_analyzer/custom_loss.py @@ -0,0 +1,47 @@ +from skimage.morphology import skeletonize +from skimage.measure import label, regionprops +import numpy as np + +def dice_loss_function(y_true, y_pred): + # ImplĂ©mentation classique de la Dice Loss + smooth = 1.0 + intersection = np.sum(y_true * y_pred) + return 1 - (2. * intersection + smooth) / (np.sum(y_true) + np.sum(y_pred) + smooth) + +def compute_connected_components_penalty(y_pred, max_allowed=1): + # Labeliser les composantes connexes + labeled_image = label(y_pred > 0.5) + num_components = np.max(labeled_image) + + # PĂ©naliser si le nombre de composantes dĂ©passe le seuil autorisĂ© + penalty = max(0, num_components - max_allowed) + + return penalty + +def compute_skeleton_reward(y_pred, min_length=10): + # Calculer le squelette des objets + skeleton = skeletonize(y_pred > 0.5) + + # Calculer la longueur du squelette + skeleton_length = np.sum(skeleton) + + # RĂ©compenser si la longueur du squelette est au moins Ă©gale Ă  la longueur minimale + reward = max(0, skeleton_length - min_length) + + return reward + +def custom_loss(y_true, y_pred, alpha=0.5, beta=0.3, gamma=0.2): + # Calcul de la Dice Loss + dice_loss = dice_loss_function(y_true, y_pred) + + # PĂ©nalitĂ© pour les petites composantes connexes + connected_components_penalty = compute_connected_components_penalty(y_pred) + + # RĂ©compense pour la longueur du squelette + skeleton_reward = compute_skeleton_reward(y_pred) + + # Combinaison des pertes et des rĂ©compenses + total_loss = (alpha * dice_loss) + (beta * connected_components_penalty) - (gamma * skeleton_reward) + + return total_loss + diff --git a/src/microglia_analyzer/dl/yolov5 b/src/microglia_analyzer/dl/yolov5 new file mode 160000 index 0000000..2f74455 --- /dev/null +++ b/src/microglia_analyzer/dl/yolov5 @@ -0,0 +1 @@ +Subproject commit 2f74455adc74a587c9e9d5a6e45df880fce8ea3e diff --git a/src/microglia_analyzer/dl/yolov5_training.py b/src/microglia_analyzer/dl/yolov5_training.py index 2ad7296..a5dd6c8 100644 --- a/src/microglia_analyzer/dl/yolov5_training.py +++ b/src/microglia_analyzer/dl/yolov5_training.py @@ -25,7 +25,7 @@ """ -- `data_folder` : Folder in which we can find the images and annotations folders. +- `data_folder` : Folder in which we can find the images and annotations folders, as well as the 'classes.txt' file. - `qc_folder` : Folder in which we can find the quality control images and masks folders. - `inputs_name` : Name of the folder containing the input images (name of the folder in `data_folder` and `qc_folder`.). - `annotations_name` : Name of the folder containing the annotations (name of the folder in `data_folder` and `qc_folder`.). @@ -50,21 +50,21 @@ #@markdown ## 📍 a. Data paths -data_folder = "/home/benedetti/Documents/projects/2060-microglia/training-data/" +data_folder = "/home/benedetti/Documents/projects/2060-microglia/yolo-user-annotations/" qc_folder = None inputs_name = "images" annotations_name = "labels" -models_path = "/home/benedetti/Desktop/eaudissect/yolo_working/models" -working_directory = "/home/benedetti/Desktop/eaudissect/yolo_local" -model_name_prefix = "YOLOv5-mg" +models_path = "/home/benedetti/Documents/projects/2060-microglia/yolo-models/" +working_directory = "/home/benedetti/Documents/projects/2060-microglia/yolo-runs/" +model_name_prefix = "”yolo" reset_local_data = True #@markdown ## 📍 b. Network architecture -validation_percentage = 0.2 -batch_size = 16 +validation_percentage = 0.15 +batch_size = 24 epochs = 1800 -classes_names = ["Debris", "Microglia"] +classes_names = ["garbage", "amoeboid", "rod", "intermediate", "homeostatic"] optimizer = 'Adam' learning_rate = 0.001 @@ -326,6 +326,10 @@ def migrate_data(targets, source, tuples): ) last += n +def get_image_size(): + training_input_path = os.path.join(working_directory, "training", inputs_name) + image = imread(os.path.join(training_input_path, os.listdir(training_input_path)[0])) + return max(image.shape[0], image.shape[1]) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # PREPARE TRAINING # @@ -450,8 +454,11 @@ def main(): epochs=epochs, batch_size=batch_size, project=models_path, - name=version_name + name=version_name, + imgsz=get_image_size(), + optimizer=optimizer ) + # label_smoothing if __name__ == "__main__": diff --git a/src/microglia_analyzer/microglia_analyzer.py b/src/microglia_analyzer/microglia_analyzer.py index 41e1387..d49ac16 100644 --- a/src/microglia_analyzer/microglia_analyzer.py +++ b/src/microglia_analyzer/microglia_analyzer.py @@ -6,6 +6,11 @@ from microglia_analyzer.tiles.recalibrate import recalibrate_image from microglia_analyzer.tiles.tiler import ImageTiler2D +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + +import tensorflow as tf +from tensorflow.keras.models import Model + _PATCH_SIZE = 512 _OVERLAP = 128 @@ -19,8 +24,10 @@ def __init__(self, logging_f=None): # Directory in which we export stuff relative to the `image_path`. self.working_directory = None # Path of the YOLO model that we use to classify microglia. - self.classification_model = None + self.classification_model_path = None # Path of the model that we use to segment microglia on YOLO patches. + self.segmentation_model_path = None + # Segmentation model. self.segmentation_model = None # Pixel size => tuple (pixel size, unit). self.calibration = None @@ -63,11 +70,15 @@ def get_image_shape(self): def set_calibration(self, pixel_size, unit): self.calibration = (pixel_size, unit) - def set_segmentation_model(self, model_path): - best_path = os.path.join(model_path, ) - self.segmentation_model + def set_segmentation_model_path(self, model_path): + best_path = os.path.join(model_path, "best.keras") + if not os.path.isfile(best_path): + raise ValueError(f"Model '{os.path.basename(model_path)}' does not exist.") + self.segmentation_model_path = best_path + self.segmentation_model = tf.keras.models.load_model(self.segmentation_model_path) + self.segmentation_model.summary() - def set_classification_model(self, model_path): + def set_classification_model_path(self, model_path): pass def export_patches(self): diff --git a/src/microglia_analyzer/rename-files.py b/src/microglia_analyzer/rename-files.py new file mode 100644 index 0000000..fe4fb6b --- /dev/null +++ b/src/microglia_analyzer/rename-files.py @@ -0,0 +1,46 @@ +import os +import shutil +import unicodedata +import sys + +def normalize_filename(filename): + # Remplace les accents et autres caractĂšres spĂ©ciaux + normalized = unicodedata.normalize('NFD', filename) + normalized = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn') + + # Remplace les espaces par des tirets + normalized = normalized.replace(" ", "-") + + # Transforme le nom de fichier en minuscules + normalized = normalized.lower() + + return normalized + +def copy_and_rename_files(src_folder, dest_folder): + # VĂ©rifie si les dossiers source et destination existent + if not os.path.isdir(src_folder): + print(f"Le dossier source '{src_folder}' n'existe pas.") + return + if not os.path.isdir(dest_folder): + os.makedirs(dest_folder) # CrĂ©e le dossier de destination s'il n'existe pas encore + + # Parcours des fichiers dans le dossier source + for filename in os.listdir(src_folder): + src_file = os.path.join(src_folder, filename) + + # VĂ©rifie si c'est un fichier + if os.path.isfile(src_file): + # Renomme le fichier selon les rĂšgles spĂ©cifiĂ©es + new_filename = normalize_filename(filename) + dest_file = os.path.join(dest_folder, new_filename) + + # Copie le fichier vers le dossier de destination + shutil.copy2(src_file, dest_file) + print(f"CopiĂ© : '{filename}' -> '{new_filename}'") + +if __name__ == "__main__": + + src_folder = "/home/benedetti/Documents/projects/2060-microglia/data/input_masks/" + dest_folder = "/home/benedetti/Documents/projects/2060-microglia/data/08-patches-no-spe-char-masks/" + + copy_and_rename_files(src_folder, dest_folder)

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