@@ -30,13 +30,13 @@ Installation time is less than 5 minutes.
1. Clone this repository ("git clone https://github.com/lpbsscientist/YeaZ-GUI").
2. Download the parameters for the neural network:
3. Download the parameters for segmenting phase contrast images from: https://drive.google.com/file/d/1UTivmx_aEMpeGdOkCZO1CS9mcdJ3zmw2/view?usp=sharing. Put the file in the folder `/unet`.
4. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/1VYBzUtgLQcS-w6S9XpjGcSBacKyItYJ_/view?usp=sharing. Put the file in the folder `/unet`.
5. If you don't have conda or miniconda installed, download it from https://docs.conda.io/en/latest/miniconda.html.
6. In the command line, create a virtual environment with python 3.6.8 with the command `conda create -n YeaZ python=3.9`.
7. Activate that environment using `conda activate YeaZ`.
8. Install the necessary packages using `pip install -r requirements.txt`.
9. Run the program from your command line with `python GUI_main.py`
2-1. Download the parameters for segmenting phase contrast images from: https://drive.google.com/file/d/1Wd2QjGlH3tcKFoFvsQ4dzAhmw23XVnYd/view?usp=sharing. Put the file in the folder `/unet`.
2-2. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/1ROQOWdTfxUeKlyrUXyNzA78RENv5HEQZ/view?usp=sharing. Put the file in the folder `/unet`.
3. If you don't have conda or miniconda installed, download it from https://docs.conda.io/en/latest/miniconda.html.
4. In the command line, create a virtual environment with python 3.6.8 with the command `conda create -n YeaZ python=3.9`.
5. Activate that environment using `conda activate YeaZ`.
6. Install the necessary packages using `pip install -r requirements.txt`.
7. Run the program from your command line with `python GUI_main.py`
## Troubleshooting / FAQ
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@@ -50,7 +50,9 @@ If small buds aren't recognized as cells in your image, this is likely linked to
### I just want the CNN, but not the GUI
In case you only want to use the functionalities of the convolutional neural network and the segmentation, but not the full GUI, you only need the files `unet/model.py`, `unet/neural_network.py` (for making predictions), `unet/segment.py` (for doing watershed segmentation) and `unet/hungarian.py` (for tracking), as well as the weights for the neural network which have to be in the same folder. You can create predictions using the `prediction` function in `neural_network.py` (note that before making predictions, you have to use the function `equalize_adapthist` from `skimage.exposure` on the image). The segmentations can be obtained with the `segment` function in `segment.py`, and tracking between two frames is done using the `correspondence` function in `hungarian.py`.
In case you only want to use the functionalities of the convolutional neural network and the segmentation, but not the full GUI, you only need the files `unet/model_pytorch.py`, `unet/neural_network.py` (for making predictions), `unet/segment.py` (for doing watershed segmentation) and `unet/hungarian.py` (for tracking), as well as the weights for the neural network which have to be in the same folder. You can create predictions using the `prediction` function in `neural_network.py` (note that before making predictions, you have to use the function `equalize_adapthist` from `skimage.exposure` on the image). The segmentations can be obtained with the `segment` function in `segment.py`, and tracking between two frames is done using the `correspondence` function in `hungarian.py`.
You can also run `Launch_NN_comand_Line.py` and give input arguments to run the whole pipeline and save final mask. Here is an example for running the CNN on a phase contrast image:
