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