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README.md

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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>
+
+![Build Status](https://github.com/DevoLearn/devolearn/actions/workflows/main.yml/badge.svg)
+[![codecov](https://codecov.io/gh/DevoLearn/devolearn/branch/master/graph/badge.svg?token=F8AJZSGWXJ)](https://codecov.io/gh/DevoLearn/devolearn)
+[![](https://img.shields.io/github/issues/DevoLearn/devolearn)](https://github.com/DevoLearn/devolearn/issues)
+[![](https://img.shields.io/github/contributors/DevoLearn/devolearn)](https://github.com/DevoLearn/devolearn/graphs/contributors)
+[![](https://img.shields.io/github/last-commit/DevoLearn/devolearn)](https://github.com/DevoLearn/devolearn/commits/master)
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+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DevoLearn/data-science-demos/blob/master/devolearn_docs/devolearn_quickstart.ipynb)
+
+
+## Contents
+
+* [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)
+
+
+### 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>
+
+* [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)
+
+### 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>
+
+* Importing the model
+```python
+from devolearn import cell_membrane_segmentor
+segmentor = cell_membrane_segmentor()
+
+```
+
+* 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()
+```
+
+* 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")
+```
+
+* 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()
+```
+
+* Saving the centroids from each frame into a CSV
+
+```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://github.com/Mainakdeb/devolearn/blob/master/images/nucleus_segmentation.gif" width = "60%">
+</p>
+
+* Importing the model
+```python
+from devolearn import cell_nucleus_segmentor
+segmentor = cell_nucleus_segmentor()
+
+```
+
+* 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()
+```
+
+### 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>
+
+* Importing the model
+```python
+from devolearn import Generator, embryo_generator_model
+generator = embryo_generator_model()
+
+```
+
+* Generating a picture and viewing it with [matplotlib](https://matplotlib.org/)
+```python
+gen_image = generator.generate()  
+plt.imshow(gen_image)
+plt.show()
+
+```
+
+* Generating n images and saving them into `foldername` with a custom size
+
+```python
+generator.generate_n_images(n = 5, foldername= "generated_images", image_size= (700,500))
+```
+
+---
+
+### Predicting populations of cells within the C. elegans embryo
+
+<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>
+
+* Importing the population model for inferences 
+```python
+from devolearn import lineage_population_model
+```
+
+* Loading a model instance to be used to estimate lineage populations of embryos from videos/photos.
+```python
+model = lineage_population_model(device = "cpu")
+```
+
+* Making a prediction from an image
+```python
+print(model.predict(image_path = "sample_data/images/embryo_sample.png"))
+```
+
+* 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)
+```
+
+* 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()
+```
+
+## 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/)                                                                                    
+
+## Authors/maintainers:
+* [Mayukh Deb](https://twitter.com/mayukh091)
+* [Ujjwal Singh](https://twitter.com/ujjjwalll)
+* [Dr. Bradly Alicea](https://twitter.com/balicea1)
+
+Feel free to join our [Slack workspace](https://openworm.slack.com/archives/CMVFU7Q4W)!