cuda-chen / fish-yolo-grabcut

YOLOv3 object detection then use GrabCut to do semantic segmentation to fish market images.

Fish YOLO GrabCut

YOLOv3 object detection then use GrabCut to do semantic segmentation to fish market images.

Demo on Heroku

I have deployed this project on Heroku. You can try here: https://fish-yolo-grabcut.herokuapp.com/

Set up and Run Demo Locally

First, create a virtual environment and install the dependencies (assume you are using conda):

$ conda create -n fish-yolo-grabcut python=3.6 pip 
$ conda activate fish-yolo-grabcut
$ pip install -r requirements.txt

Then, use gdown to download the pretrained weights from here and put it to yolo-fish directory:

$ gdown --output ./yolo-fish/fish.weights --id 1L6JgzbFhC7Bb_5w_V-stAkPSgMplvsmq

Next, choose one of the following approaches you like.

1. Command Line Approach

$ python main.py --image ./images/DSC_0061.JPG --yolo yolo-fish

When finishing, you should find 8 jpg images 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).

3. Docker Approach

$ docker image build -t fish-yolo-grabcut:app .
$ docker container run -p 8501:8501 --rm -d grabcut:app

Then upload fish market image to run the program.

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

To shutdown the docker type this:

$ docker kill <weird id of fish-yolo-grabcut.app>

fish-yolo-grabcut's Issues

GrabCut.py

Try to :
python yoloUtils/GrabCut.py --image ./images/person.jpg --yolo yolo-fish

File "yoloUtils/GrabCut.py", line 44, in
img, boxes, idxs = yolo.runYOLODetection(args)
ValueError: too many values to unpack (expected 3)

Thaks