Merge pull request #73 from watarungurunnn/wataru/instruction

Add instruction to install from the source

Author: devoworm

README.md

@@ -26,16 +26,19 @@
 ### Installation
 ```python
 pip install devolearn
+
+# or you can build from the source:
+pip install git+https://github.com/DevoLearn/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%">  
+<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 
+### 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>
@@ -54,7 +57,7 @@ plt.imshow(seg_pred)
 plt.show()
 ```
 
-* Running the model on a video and saving the predictions into a folder 
+* 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")
 ```
@@ -73,7 +76,7 @@ df = segmentor.predict_from_video(video_path = "sample_data/videos/seg_sample.mo
 df.to_csv("centroids.csv")
 ```
 
-### Segmenting the Cell Nucleus in C. elegans embryo 
+### 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>
@@ -106,7 +109,7 @@ generator = embryo_generator_model()
 
 * Generating a picture and viewing it with [matplotlib](https://matplotlib.org/)
 ```python
-gen_image = generator.generate()  
+gen_image = generator.generate()
 plt.imshow(gen_image)
 plt.show()
 
@@ -126,7 +129,7 @@ generator.generate_n_images(n = 5, foldername= "generated_images", image_size= (
 <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 
+* Importing the population model for inferences
 ```python
 from devolearn import lineage_population_model
 ```
@@ -156,6 +159,8 @@ plot.show()
 | **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/)
 | 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/)