Revert "V2.1"

.github/workflows/python-app.yml

@@ -5,34 +5,26 @@ name: Python application
 
 on:
   push:
-    branches: [ develop, v2.1 ]
+    branches: [ develop ]
   pull_request:
-    branches: [ develop, v2.1 ]
+    branches: [ develop ]
 
 jobs:
   build:
-
-    strategy:
-      matrix:
-        tfa: [true, false]
-
+
     runs-on: ubuntu-latest
+
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: 3.9
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 pytest numpy
+        pip install flake8 pytest
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-
-    - name: Conditionally install tfa
-      if: ${{ matrix.tfa }}
-      run: |
-        pip install tensorflow-addons
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names

.gitignore

@@ -2,19 +2,11 @@
 **/__pycache__
 deeptrack-app/*
 */datasets/*
-paper-examples/models/*
+*/models/*
 
 build/*
 dist/*
 *.egg-info/
 */datasets/*
 */theory
-_src/build/**/*
-
-ParticleSizing
-3DTracking
-CellData
-ParticleTracking
-data/
-datasets/
-examples/**/*/models/
+_src/build/**/*

README.md

@@ -1,140 +1,149 @@
 <p align="center">
-  <img width="350" src=https://github.com/softmatterlab/DeepTrack-2.0/blob/develop/assets/logo.png?raw=true>
+  <img width="350" src=https://github.com/softmatterlab/DeepTrack-2.0/blob/master/assets/logo.png?raw=true>
 </p>
 
-DeepTrack is a comprehensive deep learning framework for digital microscopy.
-We provide tools to create physical simulations of customizable optical systems, to generate and train neural network models, and to analyze experimental data.
 
-If you use DeepTrack 2.1 in your project, please cite our DeepTrack article:
+DeepTrack is a comprehensive deep learning framework for digital microscopy. 
+We provide tools to create physical simulations of customizable optical systems, to generate and train neural network models, and to analyze experimental data.
 
+If you use DeepTrack 2.0 in your project, please cite our DeepTrack 2.0 article:
 ```
-Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe.
+Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. 
 "Quantitative Digital Microscopy with Deep Learning."
 Applied Physics Reviews 8 (2021), 011310.
 https://doi.org/10.1063/5.0034891
 ```
-
 # Getting started
 
 ## Installation
 
-DeepTrack 2.1 requires at least python 3.6.
+DeepTrack 2.0 requires at least python 3.6
 
-To install DeepTrack 2.1, open a terminal or command prompt and run:
+To install DeepTrack 2.0, open a terminal or command prompt and run
 
     pip install deeptrack
 
-If you have a very recent version of python, you may need to install numpy _before_ DeepTrack. This is a known issue with scikit-image.
-
-## Updating to 2.1 from 2.0
-
-If you are already using DeepTrack 2.0 (pypi version 0.x.x), updating to DeepTrack 2.1 (pypi version 1.x.x) is painless. If you have followed deprecation warnings, no change to your code is needed. There are two breaking changes:
-
-- The deprecated operator `+` to chain features has been removed. It is now only possible using the `>>` operator.
-- The deprecated operator `**` to duplicate a feature has been removed. It is now only possible using the `^` operator.
-
-If you notice any other changes in behavior, please report it to us in the issues tab.
-
-## Learning DeepTrack 2.1
+## Learning DeepTrack 2.0
 
 Everybody learns in different ways! Depending on your preferences, and what you want to do with DeepTrack, you may want to check out one or more of these resources.
 
 ### Fundamentals
 
 First, we have a very general walkthrough of [basic](https://softmatterlab.github.io/DeepTrack-2.0/basics.html) and [advanced](https://softmatterlab.github.io/DeepTrack-2.0/advanced.html) topics. This is a 5-10 minute read, that will get you well on your way to understand the unique interactions available in DeepTrack.
 
-Similarly, you may find the [get-started notebooks](examples/get-started) a rewarding way to start learning DeepTrack
-
-## Documentation
-
-The detailed documentation of DeepTrack 2.1 is available at the following link: https://softmatterlab.github.io/DeepTrack-2.0/deeptrack.html
-
-### DeepTrack 2.1 in action
+### DeepTrack 2.0 in action
 
 To see DeepTrack in action, we provide six well documented tutorial notebooks that create simulation pipelines and train models:
 
-1. [deeptrack_introduction_tutorial](examples/tutorials/deeptrack_introduction_tutorial.ipynb) gives an overview of how to use DeepTrack 2.1.
-2. [tracking_particle_cnn_tutorial](examples/tutorials/tracking_particle_cnn_tutorial.ipynb) demonstrates how to track a point particle with a convolutional neural network (CNN).
-3. [tracking_multiple_particles_unet_tutorial](examples/tutorials/tracking_multiple_particles_unet_tutorial.ipynb) demonstrates how to track multiple particles using a U-net.
-4. [characterizing_aberrations_tutorial](examples/tutorials/characterizing_aberrations_tutorial.ipynb) demonstrates how to add and characterize aberrations of an optical device.
-5. [distinguishing_particles_in_brightfield_tutorial](examples/tutorials/distinguishing_particles_in_brightfield_tutorial.ipynb) demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
-6. [analyzing_video_tutorial](examples/tutorials/analyzing_video_tutorial.ipynb) demonstrates how to create videos and how to train a neural network to analyze them.
+1. [deeptrack_introduction_tutorial](tutorials/deeptrack_introduction_tutorial.ipynb) gives an overview of how to use DeepTrack 2.0.
+2. [tracking_particle_cnn_tutorial](tutorials/tracking_particle_cnn_tutorial.ipynb) demonstrates how to track a point particle with a convolutional neural network (CNN). 
+3. [tracking_multiple_particles_unet_tutorial](tutorials/tracking_multiple_particles_unet_tutorial.ipynb) demonstrates how to track multiple particles using a U-net.
+4. [characterizing_aberrations_tutorial](tutorials/characterizing_aberrations_tutorial.ipynb) demonstrates how to add and characterize aberrations of an optical device.
+5. [distinguishing_particles_in_brightfield_tutorial](tutorials/distinguishing_particles_in_brightfield_tutorial.ipynb) demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
+6. [analyzing_video_tutorial](tutorials/analyzing_video_tutorial.ipynb) demonstrates how to create videos and how to train a neural network to analyze them.
 
 Additionally, we have seven more case studies which are less documented, but gives additional insight in how to use DeepTrack with real datasets
 
-1. [MNIST](examples/paper-examples/1_MNIST.ipynb) classifies handwritted digits.
-2. [single particle tracking](examples/paper-examples/2-single_particle_tracking.ipynb) tracks experimentally captured videos of a single particle. (Requires opencv-python compiled with ffmpeg to open and read a video.)
-3. [single particle sizing](examples/paper-examples/3-particle_sizing.ipynb) extracts the radius and refractive index of particles.
-4. [multi-particle tracking](examples/paper-examples/4-multi-molecule-tracking.ipynb) detects quantum dots in a low SNR image.
-5. [3-dimensional tracking](examples/paper-examples/5-inline_holography_3d_tracking.ipynb) tracks particles in three dimensions.
-6. [cell counting](examples/paper-examples/6-cell_counting.ipynb) counts the number of cells in fluorescence images.
-7. [GAN image generation](examples/paper-examples/7-GAN_image_generation.ipynb) uses a GAN to create cell image from masks.
-
-### Model-specific examples
-
-We also have examples that are specific for certain models. This includes 
-- [*LodeSTAR*](examples/LodeSTAR) for label-free particle tracking.
-- [*MAGIK*](examples/MAGIK) for graph-based particle linking a trace characterization.
+1. [![](https://colab.research.google.com/assets/colab-badge.svg) MNIST](https://colab.research.google.com/drive/1dRehGzf9DNpz7Jo2dw4U6vSyE4STZgpF?usp=sharing) classifies handwritted digits.
+2. [![](https://colab.research.google.com/assets/colab-badge.svg) single particle tracking](https://colab.research.google.com/drive/1rh46w8TuJDF0mnvLpo6dlWkLiLr7MmQ9?usp=sharing) tracks experimentally captured videos of a single particle. (Requires opencv-python compiled with ffmpeg to open and read a video.)
+3. [![](https://colab.research.google.com/assets/colab-badge.svg) single particle sizing](https://colab.research.google.com/drive/1U12f3m3oLKCGp-BAERGrwjMhEZdkWvT5?usp=sharing) extracts the radius and refractive index of particles.
+4. [![](https://colab.research.google.com/assets/colab-badge.svg) multi-particle tracking](https://colab.research.google.com/drive/1TpNZ6ytoDXSZvGDFAFWrSjNs4SXGZmBw?usp=sharing) detects quantum dots in a low SNR image.
+5. [![](https://colab.research.google.com/assets/colab-badge.svg) 3-dimensional tracking](https://colab.research.google.com/drive/1QJXPxsVeDt1ZW1685D5VANsME69s3mqi?usp=sharing) tracks particles in three dimensions.
+6. [![](https://colab.research.google.com/assets/colab-badge.svg) cell counting](https://colab.research.google.com/drive/1C2Gn1Ym8etycOYW9yfDB_WiKlEeyvLtp?usp=sharing) counts the number of cells in fluorescence images.
+7. [![](https://colab.research.google.com/assets/colab-badge.svg) GAN image generation](https://colab.research.google.com/drive/1rfFbeE-qkg3PxHBEa_r7Q9wXq0vdueEC?usp=sharing) uses a GAN to create cell image from masks.
 
 ### Video Tutorials
 
-Videos are currently being updated to match with the current version of DeepTrack.
+DeepTrack 2.0 introduction tutorial video: https://youtu.be/hyfaxF8q6VE  
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=hyfaxF8q6VE
+" target="_blank"><img src="https://img.youtube.com/vi/hyfaxF8q6VE/maxresdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 recognizing handwritten digits tutorial video: https://youtu.be/QD9JUXyLJpc  
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=QD9JUXyLJpc
+" target="_blank"><img src="https://img.youtube.com/vi/QD9JUXyLJpc/maxresdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 single particle tracking tutorial video: https://youtu.be/6Cntik6AfBI   
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=6Cntik6AfBI
+" target="_blank"><img src="https://img.youtube.com/vi/6Cntik6AfBI/maxresdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 single-particle characterization tutorial video: https://youtu.be/ia2H1QO1cHg   
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=ia2H1QO1cHg
+" target="_blank"><img src="https://img.youtube.com/vi/ia2H1QO1cHg/maxresdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 multiple particle tracking tutorial video: https://youtu.be/wFV2VqzpeZs  
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=wFV2VqzpeZs
+" target="_blank"><img src="https://img.youtube.com/vi/wFV2VqzpeZs/maxresdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 multiple particle tracking in 3D tutorial video: https://youtu.be/fzD1QIEIJ04  
+<a href="http://www.youtube.com/watch?feature=player_embedded&v=fzD1QIEIJ04
+" target="_blank"><img src="https://img.youtube.com/vi/fzD1QIEIJ04/mqdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 cell counting tutorial video: https://youtu.be/C6hu_IYoWtI  
+<a href="https://www.youtube.com/watch?feature=player_embedded&v=C6hu_IYoWtI
+" target="_blank"><img src="https://img.youtube.com/vi/C6hu_IYoWtI/mqdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
+
+DeepTrack 2.0 GAN image generation tutorial video: https://youtu.be/8g44Yks7cis  
+<a href="https://www.youtube.com/watch?feature=player_embedded&v=8g44Yks7cis
+" target="_blank"><img src="https://img.youtube.com/vi/8g44Yks7cis/mqdefault.jpg" 
+alt="Tutorial" width="384" height="216" border="10" /></a>
 
 ### In-depth dives
 
 The examples folder contains notebooks which explains the different modules in more detail. These can be read in any order, but we provide a recommended order where more fundamental topics are introduced early.
 This order is as follows:
 
-1. [features_example](examples/module-examples/features_example.ipynb)
-2. [properties_example](examples/module-examples/properties_example.ipynb)
-3. [scatterers_example](examples/module-examples/scatterers_example.ipynb)
-4. [optics_example](examples/module-examples/optics_example.ipynb)
-5. [aberrations_example](examples/module-examples/aberrations_example.ipynb)
-6. [noises_example](examples/module-examples/noises_example.ipynb)
-7. [augmentations_example](examples/module-examples/augmentations_example.ipynb)
-8. [image_example](examples/module-examples/image_example.ipynb)
-9. [generators_example](examples/module-examples/generators_example.ipynb)
-10. [models_example](examples/module-examples/models_example.ipynb)
-11. [losses_example](examples/module-examples/losses_example.ipynb)
-12. [utils_example](examples/module-examples/utils_example.ipynb)
-13. [sequences_example](examples/module-examples/sequences_example.ipynb)
-14. [math_example](examples/module-examples/math_example.ipynb)
+1. [features_example](examples/features_example.ipynb)
+2. [properties_example](examples/properties_example.ipynb)
+3. [scatterers_example](examples/scatterers_example.ipynb)
+4. [optics_example](examples/optics_example.ipynb)
+5. [aberrations_example](examples/aberrations_example.ipynb)
+6. [noises_example](examples/noises_example.ipynb)
+7. [augmentations_example](examples/augmentations_example.ipynb)
+6. [image_example](examples/image_example.ipynb)
+7. [generators_example](examples/generators_example.ipynb)
+8. [models_example](examples/models_example.ipynb)
+10. [losses_example](examples/losses_example.ipynb)
+11. [utils_example](examples/utils_example.ipynb)
+12. [sequences_example](examples/sequences_example.ipynb)
+13. [math_example](examples/math_example.ipynb)
 
+## Graphical user interface
 
+DeepTrack 2.0 provides a completely stand-alone  [graphical  user  interface](https://github.com/softmatterlab/DeepTrack-2.0-app),  which  delivers  all  the power of DeepTrack without requiring programming knowledge.
 
-## Cite us!
+[![InterfaceDemo](https://i.imgur.com/lTy2vhz.gif)](https://i.imgur.com/lTy2vhz.gif)
+
+## Documentation
 
-If you use DeepTrack 2.1 in your project, please cite us here:
+The detailed documentation of DeepTrack 2.0 is available at the following link: https://softmatterlab.github.io/DeepTrack-2.0/deeptrack.html
 
+## Cite us!
+
+If you use DeepTrack 2.0 in your project, please cite us here:
 ```
-Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe.
+Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe. 
 "Quantitative Digital Microscopy with Deep Learning."
 Applied Physics Reviews 8 (2021), 011310.
 https://doi.org/10.1063/5.0034891
 ```
-
 See also:
-
-<https://arxiv.org/abs/2202.06355>:
 ```
-Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel  Midtvedt, Giovanni Volpe,1 and  Carlo  Manzo
-"Geometric deep learning reveals the spatiotemporal fingerprint ofmicroscopic motion."
-arXiv 2202.06355 (2022).
+Saga Helgadottir, Aykut Argun, and Giovanni Volpe. 
+"Digital video microscopy enhanced by deep learning." 
+Optica 6.4 (2019): 506-513. 
+https://doi.org/10.1364/OPTICA.6.000506
 ```
-
-<https://doi.org/10.1364/OPTICA.6.000506>:
-```
-Saga Helgadottir, Aykut Argun, and Giovanni Volpe.
-"Digital video microscopy enhanced by deep learning."
-Optica 6.4 (2019): 506-513.
-```
-
-<https://github.com/softmatterlab/DeepTrack.git>:
 ```
-Saga Helgadottir, Aykut Argun, and Giovanni Volpe.
+Saga Helgadottir, Aykut Argun, and Giovanni Volpe. 
 "DeepTrack." (2019)
+https://github.com/softmatterlab/DeepTrack.git
 ```
-
 ## Funding
-
-This work was supported by the ERC Starting Grant ComplexSwimmers (Grant No. 677511) and the ERC Starting Grant MAPEI (101001267).
+This work was supported by the ERC Starting Grant ComplexSwimmers (Grant No. 677511).

_src/source/augmentations.rst

@@ -76,3 +76,10 @@ PadToMultiplesOf
    :members:
    :exclude-members: get
 
+PreLoad
+^^^^^^^
+
+.. autoclass:: deeptrack.augmentations.PreLoad
+   :members:
+   :exclude-members: get
+