/oocystMeter

Perform oocyst segmentation in mercurochrome stained mosquito midgut

Primary LanguagePythonMIT LicenseMIT

Segment oocyst from images of mosquito images stained with mercurochrome

image

  • Generate oocyst count data each image (in Excel format)
  • Generate area data for each oocyst (in Excel format)
  • Fast runtime (even on CPU)

Installation using docker (recommended):

(1) Clone the repository

git clone https://github.com/duopeng/midgut_oocyst_segmentation

(2) Build docker image (or alternatively, pull a pre-built image)

for Intel/AMD x86_64 CPUs:

build new image

cd midgut_oocyst_segmentation/docker/intel_amd_x86_64

docker build --build-arg USER_ID=1000 -t pengxunduo/oocyst:d2_v0.6_py38_x86_64 .

[alternative] pull pre-built image (built on and tested with windows 64bit)

docker pull pengxunduo/oocyst:d2_v0.6_py38_x86_64

for Apple silicon (e.g. M1/M2/M3 processors):

build new image

cd midgut_oocyst_segmentation/docker/apple_silicon

docker build --build-arg USER_ID=1000 -t pengxunduo/oocyst:d2_v0.6_py38_apple_silicon .

[alternative] pull pre-built image

docker pull pengxunduo/oocyst:d2_v0.6_py38_ARM

(3) Run docker image

for Intel/AMD x86_64 CPUs:

docker run -it --shm-size=8gb --name=oocyst_container pengxunduo/oocyst:d2_v0.6_py38_x86_64

for Apple silicon (e.g. M1/M2/M3 processors):

docker run -it --shm-size=8gb --name=oocyst_container pengxunduo/oocyst:d2_v0.6_py38_apple_silicon

(4) Run example to verify installation

from inside a container started by (3), execute the following commands:

cd midgut_oocyst_segmentation

python oocyst_segmentation.py --dir test_images

Notes:

  • Warnings can be ignored, e.g.: "...image_list.py:88: UserWarning: floordiv is deprecated...".
  • The docker image is based on Ubuntu 18.04, with Python 3.8.10, PyTorch 1.9.1, and Detectron2 v0.6.1.

Manual installation (not recommended):

(1) Clone the repository

git clone https://github.com/duopeng/midgut_oocyst_segmentation

(2) Create conda environment and install python dependencies

cd midgut_oocyst_segmentation
conda create -y -n oocyst python=3.9 && conda activate oocyst
pip install -r requirements.txt

(3) Install detectron2 v0.5

MacOS and Linux:
python -m pip install 'git+https://github.com/facebookresearch/detectron2/tree/v0.5.git' --user
Windows:
python -m pip install git+https://github.com/facebookresearch/detectron2/tree/v0.5.git --user

(4) Model file preparation

  • unzip the two model weight files and keep it in the "model" directory:
       <1> model_0002399.MG.pth (unzip from model_0002399.MG.zip.001 and model_0002399.MG.zip.002)
       <2> model_0006199.pth (unzip from model_0006199.zip.001 and model_0006199.zip.002)

Usage:

  • prepare your own jpeg images and place them in a folder, for example "test_images" folder

  • Run oocyst segementation with the following command:
    python oocyst_segmentation.py --dir [path to your folder]

  • Results
          count_N_size.xlsx               oocyst count, area and coordiate of each oocyst, average area
          [prefix].oocyst.jpg                oocyst annotated on the original image
          [prefix].midgut.jpg                midgut annotated on the original image
          [prefix].midgut.MASK.jpg       A full-resolution black-whight MASK of the midgut identified



Installation issues

  • Make sure the verion of your Python >=3.7. Versions 3.8.10 and 3.9.7 are tested.
  • Try manully install the following packages with pip:
pip install torch==1.9.1  
pip install torchvision==0.10.0  
pip install torchaudio==0.9.1  
pip install pandas==1.3.3  
pip install pycocotools==2.0.2   
pip install dataclasses==0.6  
pip install typing==3.7.4.3  
pip install opencv-python==4.5.3.56  
pip install xlsxwriter==3.0.1  
pip install scipy==1.7.1  
pip install detectron2==0.5
  • For the detectron2 package, you need build tools to compile it from source code.