T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information

This is the official repo of the paper T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information (Russello et al., 2022). If using, please cite our paper:

@article{russello2022t,
title={T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information},
author={Russello, Helena and van der Tol, Rik and Kootstra, Gert},
journal={Computers and Electronics in Agriculture},
volume={192},
pages={106559},
year={2022},
publisher={Elsevier}
}

For questions, contact me at: helena [dot] russello [at] wur [dot] nl.

License

Copyright 2022 Helena Russello

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Requirements

To install the python environment, I recommend to use the environment.yml file with the command:

conda env create -f environment.yml 

Usage

If you already have other pose estimation models working in Pytorch, I'd advise to use the tleap.py file in the models subfolder and run it with your own code.

Configuration

In the sub-folder cfg, you can find an example of a default config file.

• data_folder is the path to your images.
• dataset_csv is the path to the csv file of your training dataset. The expected format of this file is described in the next subsection.
• dataset_test is the path to the csv file of your test dataset.
• device pytorch argument for the device.
• frequent logging frequency.
• save_checkpoint folder where to save the checkpoints.
• load_checkpoint whether to load the model from a checkpoint. (i.e., No, or the path to the checkpoint).
• wandb Whether to log training with Weights and Biases.
• group If you use logging with Weights and Biases, you can give a group name to keep your runs organised.
• file_format file format for the logged images.
• keypoints list of keypoints
• lr learning rate
• epochs number of training epochs.
• batch_size
• lr_decay whether to use a LR scheduler
• seq_length sequence lenght. Use 1 for static LEAP, 2 to 4 for T-LEAP
• optimizer optimizer to use. Choose between sgd, adam or amsgrad
• depth depth of the NN. Use 3 for the initial depth of LEAP, 4 for T-LEAP and deeper LEAP

Data format

Note: the data doesn't follow the most straightforward structure, but (if there is demand for it) I plan to add in the future some boilerplates later on to make this easier.

1. Extract all the frames of your videos as images. You need one folder with the name of the video, in which each frame is named as frame_number.png. For instance, if the name of the video is video1.mp4, there would be a folder named video1 and frames 0.png to say 100,png. Note that the frame number doesn't need to start at 0 (for instance if you want to drop empty frames at the start or end of the video)

2. Have a subfolder named labels_csv that contains the keypoint annotation per video. Each video should have its own csv file and named video_name.csv. Here is how the csv file should look like. Note that the column of the likelihood is not used, but still needed for parsing. Put any value you like.

    video,  frame, Keypoint1_x, Keypoint1_y, Keypoint1_likelihood, Keypoint2_x, Keypoint2_y, Keypoint2_likelihood, ....
    --------------------------------------------------------------------------------------------------------------------
    video1, 0,     92,          136,         1,                    90,          130,         1, ...
   

3. In summary, your data folder should have a structure like in the example bellow.

    ./data
        |__ video1/ 
                 |__ 0.png
                     1.png
                     ...
        |__ video2/ 
                 |__ 35.png
                     36.png
                     ...                 
        |__ labels_csv/ 
                 |__ video1.csv
                     video2.csv
                     ...   
   

4. In addition to that, you should have a separate csv file for training and testing. The location or name of this file doesn't matter, simply write the path to them in the config file. The csv file contains the list of video frames sequences that belong to the train or test set. The format is as follows:

    filename,      start_frame,    end_frame
    ------------------------------------------------
    video1.mp4,    102,            105
    video26.mp4,   10,             13
    video1.mp4,    1,              4   
    ....
   

5. The images can be cropped in advance (faster training), or when they are loaded. Note that the resize operation takes a long time and might drastically increase the training time. For cropping the images in training and testing, add the following transforms:

   transform = [
           SequentialPoseDataset.CropBody(margin=100, random=False, square=True, keep_orig=True),
           SequentialPoseDataset.Rescale(200)
       ]
   

Training and testing the model

Most of the parameters and different paths should be specified in your .yml configuration file (as specified in the configuration section above). Run the script:

train_sep.py --config path/to/your/configfile

Some parameters can be overriden from the .yml config file, and provided as arguments to the python script: frequent, lr, epochs, batch_size, seq_length, seed, optimizer, group, depth.

Once the training is complete, the trained model is evaluated on the test set.

Benchmarks

Because the dataset from the T-LEAP paper is not open-source, you cannot train T-LEAP on that data. Instead, we plan to (expected mid-2022) provide benchmark results on the Horse-10 dataset from Mathis et al. 2020.

Horse-10

Coming soon...