Awesome
deeptracking
Torch + python implementation of Deep 6-DOF Tracking
The code to parse a ply file is not provided, this framework needs an implementation of plyparser.py
Useful links
Generating data
Generating synthetic data
Run:
python generate_synthetic_data.py config_file.json
dependencies
- cv2
- numpy (tested with 1.13.3)
- tqdm
- pyOpenGL
- glfw
- numpngw
configuration
see this example file
Generating real data
Run:
python generate_real_data.py config_file.json
dependencies
- cv2
- numpy (tested with 1.13.3)
- tqdm
- pyOpenGL
- glfw
- numpngw
configuration
see this example file
Train
Run:
python train.py config_file.json
dependencies
- Hugh Perkins's pytorch
- scipy, skimage, numpy (tested with 1.13.3)
- tqdm
- numpngw
- slackclient (could be removed)
configuration
see this example file
Test
Sensor
Will run the tracker with a sensor (kinect 2)
python test_sensor.py config_file.json
Sequence
Will run the tracker on a sequence (folder) and save the error in a file
python test_sequence.py config_file.json
dependencies
- cv2
- numpy (tested with 1.13.3)
- Hugh Perkins's pytorch
- pyOpenGL
- glfw
- numpngw
- pyfreenect2 (for Kinect2 sensor)
configuration
see this example file