Astigmatic Particle Tracking Velocimetry (APTV) using a Deep Neural Network (DNN)

Source code of the DNN used in: "On the use of a cascaded convolutional neural network for three-dimensional flow measurements using astigmatic PTV"

DOI: https://doi.org/10.1088/1361-6501/ab7bfd

Please cite as:

@article{10.1088/1361-6501/ab7bfd,
	author={Jörg König and Minqian Chen and Wiebke Rösing and David Boho and Patrick Mäder and Christian Cierpka},
	title={On the use of a cascaded convolutional neural network for three-dimensional flow measurements using astigmatic PTV},
	journal={Measurement Science and Technology},
	url={http://iopscience.iop.org/10.1088/1361-6501/ab7bfd},
	year={2020}
}

Dependencies

Particle regression depends on the following libraries:

• Keras==2.2.4
• scikit-image==0.15.0
• scikit-learn==0.20.2
• opencv-contrib-python==3.4.0.12
• pandas==0.23.4
• tqdm==4.31.1

This implementation is based on Tensorflow object Detection Installation instructions. Therefore the installation is the same as original object detection API.

Particle detection

• In order to generate TFRecord file format, you need to convert your dataset to this file format.

    python3 generate_tfrecord.py --csv_input=data/train_labels.csv  --output_path=train.record

• The dataset (TFRecord files) and its corresponding label map. Example of how to create label maps can be found in the folder data.

  item {
  id: 1
    name: 'particle'
  }
  

• Configuring the Particle Detection Training Pipeline in particle_detection/configs

  • Users should substitute the input_path and label_map_path arguments and insert the input configuration into the train_input_reader and eval_input_reader fields in the skeleton configuration.

  • The train_config defines parts of the training process:

    1. Model parameter initialization.
    2. Input preprocessing.
    3. SGD parameters.

  • In order to speed up the training process, it is recommended to reuse the pre-existing object detection checkpoint. The pre-trained checkpoints can be found here, please download the pre-trained model faster_rcnn_resnet101_coco. fine_tune_checkpoint should provide a path to the pre-existing checkpoint (ie:"/usr/home/username/checkpoint/model.ckpt-#####").

• After you created the required input file, in research/object_detection you can train your model.

  python3 train.py  --logtostderr --pipeline_config_path=/faster_rcnn_resnet101_kali.config  --train_dir=

• After training the model, you can get bounding box coordinates by running particle_detection_bb.ipynb.

Particle regression

• Before you train the particle regression model you should crop the particle images to 180 x 180 fixed size images. In particle_regression:

    python3 particle_crop.py

• You can train particle regression by running the following command in particle_regression.

    python3 train_regression_pos.py