---
license: mit
---
[](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)
## 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
* [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
* 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
* 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
/images/generated_embryos_3.gif)
* 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
/images/resnet_preds_with_input.gif)
* 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/)
## 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)
## 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)!