livecell-tracker 0.0.3
live-cell tracking package including instance segmentation and tracker
Requires Python: >=3.7


# livecell-tracker
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Livecell-tracker is a pure python framework for extracting sinlge cell trajectories from raw long live-cell imaging data, computing and analyzing single cell features in latent space.

**This is a placeholder for livecell-tracker future releases. Currently this repo showcases a basic use case to segment images, track cells with opencv/SORT and generate cell features in our CX-A label-free dataset.   
The majority of our analysis methods/notebooks are in `https://github.com/xing-lab-pitt/xing-vimentin-dic-pipeline` maintained by Xing lab, and being added to this repo. Please check later in Nov. 2022 for a complete version with our manuscript.**
## Installation

**Pytorch**  
`conda install pytorch torchvision -c pytorch`

**General package requirements**  
`pip install -r requirements.txt`

**Detectron2**  
Please refer to latest detectron2 documentation to install detectron2 for segmentation if you cannot build from source with the following commands.
https://detectron2.readthedocs.io/en/latest/tutorials/install.html#build-detectron2-from-source

```
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
```

**{avi, mp4} movie generation**
`conda install -c conda-forge ffmpeg`


## Expected input/output for each submodule

**Note**  
If you already have satisfying segmentation models or segmentation results, you may skip **Annotation** and **Segmentation** part below.
### Annotation
input: raw image files
After annotating imaging datasets, you should have json files in COCO format ready for segmentation training. 

#### Labelme
Apply labelme to your datasets following our annotation protocol. 
#### Convert labelme json to COCO format. 
A fixed version of labelme2coco implementation is included in our package. Please refer to our tutorial on how to convert your labelme json to COCO format.  
For CVAT, please export the annotation results as COCO, as shown in our annotation protocol.

### Segmentation
Segmentation has two phase. If you already have pytorch or tensorflow models trained on your dataset, you may skip training phase.

### training phase
input: COCO json files

output: pytorch model (.pth file)

### prediction phase
input: raw images, a trained machine-learning based model  
outputs: SingleCellStatic json outputs

### Track
input: SingleCellStatic
- contour
- bounding box

output: SingleCellTrajectoryColletion
- holding a collection of singleCellTrajectory each containing single cell time-lapse data
- trajectory-wise feature can be calculated after track stage or at trajectory stage.

### Trajectory
input: SingleCellTrajectoryColletion

output: 


### Visualizer
track.movie: generate_single_trajectory_movie()

visualizer: viz_traj, viz_traj_collection

{Documentation placeholder} [Move to docs/ and auto generate by readthedocs]

### Analyze trajectories based on specific research topics


## SingleCellStatic  
class designed to hold all information about a single cell at some timepoint  
**attributes**
- time point
- id (optional)
- contour coordinates
- cell bounding box
- img crop (lazy)
- feature map 
- original img (reference/pointer)

## SingleCellTrajectory
- timeframe_set

## SingleCellTrajectoryCollection