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| license: mit |
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| <p align="center"> |
| <img src = "https://raw.githubusercontent.com/DevoLearn/devolearn/master/images/banner_1.jpg"> |
| </p> |
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| [](https://codecov.io/gh/DevoLearn/devolearn) |
| [](https://github.com/DevoLearn/devolearn/issues) |
| [](https://github.com/DevoLearn/devolearn/graphs/contributors) |
| [](https://github.com/DevoLearn/devolearn/commits/master) |
| [](https://openworm.slack.com/archives/CMVFU7Q4W) |
| [](https://colab.research.google.com/github/DevoLearn/data-science-demos/blob/master/devolearn_docs/devolearn_quickstart.ipynb) |
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| ## Contents |
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| * [Example notebooks](https://github.com/DevoLearn/devolearn#example-notebooks) |
| * [Segmenting the C. elegans embryo](https://github.com/DevoLearn/devolearn#segmenting-the-c-elegans-embryo) |
| * [Generating synthetic images of embryos with a GAN](https://github.com/DevoLearn/devolearn#generating-synthetic-images-of-embryos-with-a-pre-trained-gan) |
| * [Predicting populations of cells within the C. elegans embryo](https://github.com/DevoLearn/devolearn#predicting-populations-of-cells-within-the-c-elegans-embryo) |
| * [Contributing to DevoLearn](https://github.com/DevoLearn/devolearn/blob/master/.github/contributing.md#contributing-to-devolearn) |
| * [Links to datasets](https://github.com/DevoLearn/devolearn#links-to-datasets) |
| * [Contact us](https://github.com/DevoLearn/devolearn#authorsmaintainers) |
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| ### Installation |
| ```python |
| pip install devolearn |
| ``` |
| ### Example notebooks |
| <p align="center"> |
| <img src = "https://raw.githubusercontent.com/DevoLearn/data-science-demos/master/Networks/nodes_matrix_long_smooth.gif" width = "40%"> |
| <img src = "https://raw.githubusercontent.com/DevoLearn/data-science-demos/master/Networks/3d_node_map.gif" width = "40%"> |
| </p> |
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| * [Extracting centroid maps and making 3d centroid models](https://nbviewer.jupyter.org/github/DevoLearn/data-science-demos/blob/master/Networks/experiments_with_devolearn_node_maps.ipynb) |
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| ### Segmenting the Cell Membrane in C. elegans embryo |
| <p align="center"> |
| <img src = "https://raw.githubusercontent.com/DevoLearn/devolearn/master/images/pred_centroids.gif" width = "80%"> |
| </p> |
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| * Importing the model |
| ```python |
| from devolearn import cell_membrane_segmentor |
| segmentor = cell_membrane_segmentor() |
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| ``` |
|
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| * Running the model on an image and viewing the prediction |
| ```python |
| seg_pred = segmentor.predict(image_path = "sample_data/images/seg_sample.jpg") |
| plt.imshow(seg_pred) |
| plt.show() |
| ``` |
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| * Running the model on a video and saving the predictions into a folder |
| ```python |
| filenames = segmentor.predict_from_video(video_path = "sample_data/videos/seg_sample.mov", centroid_mode = False, save_folder = "preds") |
| ``` |
|
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| * Finding the centroids of the segmented features |
| ```python |
| seg_pred, centroids = segmentor.predict(image_path = "sample_data/images/seg_sample.jpg", centroid_mode = True) |
| plt.imshow(seg_pred) |
| plt.show() |
| ``` |
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| * Saving the centroids from each frame into a CSV |
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| ```python |
| df = segmentor.predict_from_video(video_path = "sample_data/videos/seg_sample.mov", centroid_mode = True, save_folder = "preds") |
| df.to_csv("centroids.csv") |
| ``` |
|
|
| ### Segmenting the Cell Nucleus in C. elegans embryo |
| <p align="center"> |
| <img src='https://huggingface.co/devoworm-group/Devolearn/resolve/main/images/nucleus_segmentation.gif' width = "60%"> |
| </p> |
|
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| * Importing the model |
| ```python |
| from devolearn import cell_nucleus_segmentor |
| segmentor = cell_nucleus_segmentor() |
| |
| ``` |
|
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| * Running the model on an image and viewing the prediction |
| ```python |
| seg_pred = segmentor.predict(image_path = "sample_data/images/nucleus_seg_sample.jpg") |
| plt.imshow(seg_pred) |
| plt.show() |
| ``` |
|
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| ### Generating synthetic images of embryos with a Pre-trained GAN |
| <p align="center"> |
| <img src = "https://raw.githubusercontent.com/devoworm/GSoC-2020/master/Pre-trained%20Models%20(DevLearning)/images/generated_embryos_3.gif" width = "30%"> |
| </p> |
|
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| * Importing the model |
| ```python |
| from devolearn import Generator, embryo_generator_model |
| generator = embryo_generator_model() |
| |
| ``` |
|
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| * Generating a picture and viewing it with [matplotlib](https://matplotlib.org/) |
| ```python |
| gen_image = generator.generate() |
| plt.imshow(gen_image) |
| plt.show() |
| |
| ``` |
|
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| * Generating n images and saving them into `foldername` with a custom size |
|
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| ```python |
| generator.generate_n_images(n = 5, foldername= "generated_images", image_size= (700,500)) |
| ``` |
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| --- |
|
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| ### Predicting populations of cells within the C. elegans embryo |
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| <p align="center"> |
| <img src = "https://raw.githubusercontent.com/devoworm/GSoC-2020/master/Pre-trained%20Models%20(DevLearning)/images/resnet_preds_with_input.gif" width = "60%"> |
| </p> |
|
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| * Importing the population model for inferences |
| ```python |
| from devolearn import lineage_population_model |
| ``` |
|
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| * Loading a model instance to be used to estimate lineage populations of embryos from videos/photos. |
| ```python |
| model = lineage_population_model(device = "cpu") |
| ``` |
|
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| * Making a prediction from an image |
| ```python |
| print(model.predict(image_path = "sample_data/images/embryo_sample.png")) |
| ``` |
|
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| * Making predictions from a video and saving the predictions into a CSV file |
| ```python |
| results = model.predict_from_video(video_path = "sample_data/videos/embryo_timelapse.mov", save_csv = True, csv_name = "video_preds.csv", ignore_first_n_frames= 10, ignore_last_n_frames= 10, postprocess = False) |
| ``` |
|
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| * Plotting the model's predictions from a video |
| ```python |
| plot = model.create_population_plot_from_video(video_path = "sample_data/videos/embryo_timelapse.mov", save_plot= True, plot_name= "plot.png", ignore_last_n_frames= 0, postprocess = False) |
| plot.show() |
| ``` |
|
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| ## Links to Datasets |
| | **Model** | **Data source** | |
| |-------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Segmenting the cell membrane in C. elegans embryo | [3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2720-x#Abs1/) | |
| | Segmenting the nucleus in C. elegans embryo | [C. elegans Cell-Tracking-Challenge dataset](http://celltrackingchallenge.net/3d-datasets/) |
| | Cell lineage population prediction + embryo GAN | [EPIC dataset](https://epic.gs.washington.edu/) |
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| ## Links to HuggingFace spaces |
| | **Model** | **Huggingface** | |
| |-------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Segmenting the cell membrane in C. elegans embryo | [Cell Membrane segmentor](https://huggingface.co/spaces/devoworm-group/membrane_segmentation) | |
| | Segmenting the nucleus in C. elegans embryo | [C. elegans Nucleus segmentor](https://huggingface.co/spaces/devoworm-group/nucleus_segmentor) |
| | Cell lineage population prediction | [Lineage population](https://huggingface.co/spaces/devoworm-group/Lineage_Population) |
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| ## Authors/maintainers: |
| * [Mayukh Deb](https://twitter.com/mayukh091) |
| * [Ujjwal Singh](https://twitter.com/ujjjwalll) |
| * [Dr. Bradly Alicea](https://twitter.com/balicea1) |
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| Feel free to join our [Slack workspace](https://openworm.slack.com/archives/CMVFU7Q4W)! |
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