@@ -20,13 +20,13 @@ 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:
2.1. Download the parameters for segmenting phase contrast images from: https://drive.google.com/file/d/1UTivmx_aEMpeGdOkCZO1CS9mcdJ3zmw2. Put the file in the folder `/unet`.
2.2. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/16SRF-bDkxXO4nYzEPk9bxXaDjZsNcP5n. 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=3.6.8`.
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`
3. Download the parameters for segmenting phase contrast images from: https://drive.google.com/file/d/1UTivmx_aEMpeGdOkCZO1CS9mcdJ3zmw2. Put the file in the folder `/unet`.
4. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/1oxZpbJOwXxH6g453lCrBkdPB_2KD6rNo. 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=3.6.8`.
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`
