Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

This repo contains all the code used in Bi et al.(2023).

This repository includes:

1. Quick sample code.
2. Thresholding methods
   • 2.1 Single threshold method.
   • 2.2 Adaptive threshold method.
3. Mask RCNN method
   • 3.1 LabelMe.
   • 3.2 Mask-RCNN method
     • 3.2.1 Training Mask-RCNN model
     • 3.2.2 Predict depth images by trained Mask-RCNN model.
4. Build Body Weight Regression Model
   • 4.1 Data Preprocessing
   • 4.2 Goodness of fit
   • 4.3 Cross-Validation 1
   • 4.4 Cross-Validation 2

1. Quick sample code.

Attention: This sample code works with example images. For your customized images, some hyperparameters need to be adjusted.

1.1 Establish your environment.

• Download python folder from this github into your local postion.
• Install all the packages you will use in requirement.txt. Recreate one conda environment follow the lines.

# $ conda create --name <env> --file <requirement.txt>

1.2 Run image analysis.

Step1. Create outputs directory.

• Build one empty folder named outputs within your local folder python which you downloaded from this github repository.
• If you want to run for your own figures, please make sure your files following our Sample_files folder structure.

• Please make sure the outputs folder is empty before running each image analysis method.

Step2. Choose one image analysis method.

Select one image analysis method below and run the related code block

• Single threshold method:

python single_thr/ImageAnalysis_single_thr.py D1

• Adaptive threshold method:

python adaptive_thr/ImageAnalysis_adaptive_thr.py D1

• Mask R-CNN method:

python maskrcnn/ImageAnalysis_mrcnn.py D1

Step3. Check your outputs.

After running, each approach will generate one csv file in outputs/D1folder similar to the following.

2. Thresholding methods

2.1 Single threshold method.

• .py file

2.2 Adaptive threshold method.

• .py file

Main steps

3. Mask RCNN method

3.1 LabelMe.

• The code was forked from https://github.com/uf-aiaos/ShinyAnimalCV
• How to install LabelMe
• How to use LabelMe to label image

## How to used our customized LabelMe.
## Code used to start label png into json files.
labelme ./depth/cow.png -O ./outputs/cow.json

# Code used to transfer json files into folders.
labelme_json_to_dataset ./outputs/cow.json -o ./outputs/cow_json

3.2 Mask-RCNN method

3.2.1 Training Mask-RCNN model

• The code was forked from https://github.com/uf-aiaos/ShinyAnimalCV
• .ipynb file
• .h5 file

3.2.2 Predict depth images by trained Mask-RCNN model.

• .py file

4. Build Body Weight Regression Model

4.1 Data Preprocessing

• .Rmd file

4.2 Goodness of fit

• .Rmd file

4.3 Cross-Validation 1

• .Rmd file

4.4 Cross-Validation 2

• .Rmd file