DZPeru / fishv3

Fish segmentation based on YOLOv3 + fish.weights + OpenCv DNN + Docker + Heroku

Fishv3 - Fish segmentation

Fish detection and segmentation based on YOLOv3 that use GrabCut to do semantic segmentation to fish market images. Trained by FISH9003

How to run

Web version

You can try clicking here

Docker version

To downloand the image and run the contaider in detach mode, run the code below.

docker container run -p 8501:8501 --rm -d pablogod/fishv3

To shutdown the docker type this:

docker kill <weird id of fishv3> # Type the id

On your computer

Locally:

git clone https://github.com/DZPeru/fishv3
cd fishv3
pip3 install -r requirements.txt

Conda version

Conda:

conda create -n fishv3 python=3.6 pip 
conda activate fishv3
pip install -r requirements.txt

Download the weights of the neural network to your local repository. Or do it manually, downloading from Google Drive.

gdown --output ./fishv3/fish.weights --id 1M8dKL0mjh5QkdH2UeFQN9RF3pXCV6hao

1. Command Line Approach

python main.py --image ./path_to/my_image.jpg --yolo  fishv3

When finishing, you should find images (.jpg) in the project root directory.

2. Streamlit Approach

streamlit run app.py

You can upload fish market image to run the program. The results are shown in the browser (make sure to scroll down).

The results are shown in the browser (make sure to scroll down).

Docker version

To downloand the image and run the contaider in detach mode, run the code below.

$ docker container run -p 8501:8501 --rm -d pablogod/fishv3

To shutdown the docker type this:

$ docker kill <weird id of fishv3.app>