leo9344/Implementation-of-GelSight

An implementation of Gelsight Wedge.

Implementation-of-GelSight

An implementation of GelSight Wedge.

Test passed on Ubuntu 18.04 and Windows 10. 🚩

Requirements

python>=3.7

opencv-python

opencv4.x (C++ Version)

pybind11

pyyaml

argparse

glob

numpy

matplotlib

scipy

skimage

open3d

Visual Studio 2022

Hardware

🔨 🔨

Camera: RaspberryPi Zero Camera Module (22Pin)

We connect the camera module to a raspberrypi zero for image capture.

Illumination: Three colors of LED arrays (RGB)

We used surface mounted LEDs with 120 degree angle (in RGB colors) as the light source. Three LED arrays (each contains 2 LEDs) are circly distributted under the silicone.

Silicone: Composed of transparent base, black markers and the reflective membrane

Transparent base:

1. We 3D printed the mould (with resin) and laser-cut the acrylic sheet (placed at the bottom of the mould) for transparent silicone base manufacturing.
2. we use Solaris (part A and part B) with Shore A 15 and Slacker (used to increase softness) from vendor Smooth-on® to produce the transparent elastomeric base. A ratio of 1:1:3 for each component has proven to be ideal for making an elastomeric base with the appropriate hardness. The mixture is then degassed and cured for 12 hours.
   

Black markers:

We painted markers on top surface of the transparent base with Silc-Pig (black colorant). The distance between each marker is around 1 mm.

Reflective membrane:

1. We dip a small amount of aluminum powder and spread it evenly upon the black markers.
2. we use aluminum powder, Psycho Paint (part A and part B) and Novocs Matte (silicone diluter) to produce the reflective membrane. A ratio of 1:5:5:30 for each component has proven to be ideal for making a moderate membrane. The mixture is degassed and then sprayed on top of the transparent base surface. The membrane is cured for 4 hours.
   

Software

Step -1: set config.yaml

camid for cv2.VideoCapture(camid).

sample: from for {data_path}/sample_{sample_from}.jpg.

sample: to for {data_path}/sample_{sample_to}.jpg.

data_path for {data_path}

Step 0: run pref_ref_and_sample.py

python pref_ref_and_sample.py -r -s

-r or --ref for capturing {data_path}/ref.jpg.

Click left button to take ref.jpg, you can click more than once until you are satiesfied. Then press q to exit or continue.

-s or --ref for capturing {data_path}/sample_xx.jpg.

Click left button to take sample_xx.jpg. If sample_to - sample_from pictures are captured, it will terminate automatically. Or you can manually press q to exit in advance or continue.

Step 1: run camera_config.py

Step 2: Get prepared for running calibration.py

2.1 Measure self.BallRad eg: 3

2.2 Measure self.Pixmm = $\frac{Length\ (mm)}{Pixel}$

2.2.1 Capture & save Pixmm.jpg

Eg:

2.2.2 Get measurement of mm

Eg: mm = 3.40(mm)

2.2.3 Use mesaure_Pixmm.py to select 2 keypoints and calculate their distance (in pixel).

Click once on the first keypoint, then click once on the other keypoint, you will see an arrow linking 2 keypoints with their distance. Eg: distance = 103.07764

2.2.4 Calculate the Pixmm and fill it into calibration.py

In line 13 of calibration.py:

self.Pixmm = $\frac{Length\ (mm)}{Pixel} = \frac{3.40}{103.0776}=0.03298$

2.3 Capture & save 30 x sample_xx.jpg, from sample_1.jpg to sample_30,jpg. eg:

2.4 run calibration.py

Step 3: run Test0Cable.py

Get reconstruction result.

Step 4: run tracking.py

Get tracking result.

Improvements

🔨Coming soon!🔨 Thin plate spline for inpaint of markers?

Use model to map color to gradients.

References

We used https://github.com/siyuandong16/gelsight_heightmap_reconstruction for calibration and heightmap reconstruction and https://github.com/GelSight/tracking for tracking.

Acknowledgements

https://arxiv.org/abs/2106.08851

https://github.com/siyuandong16/gelsight_heightmap_reconstruction

https://github.com/GelSight/tracking

https://tutorial.cytron.io/2020/12/29/raspberry-pi-zero-usb-webcam/