Marker Tracking Algorithm

Requirement

• opencv
• pybind11
• numpy

pip3 install pybind11 numpy opencv-python

Example

make
python3 src/tracking.py

Shear force test

• Pink arrow: the marker is out of boundary/not detected, but can be inferred from surrounding flow (assume same first derivative)

Twist force test

Configuration

Configuration based on different marker settings (marker number/color/size/interval)

Step 1: Marker detection

The marker detection is in src/marker_detection.py

Modify the code based on the marker color & size.

To verify, run

python src/tracking.py calibrate

And the mask should looks like:

Set Parameters:

• src/marker_detection/find_marker: The kernel size in GaussianBlur, it depends on marker size. should could cover the whole marker.
• src/marker_detection/find_marker: change yellowMin, yellowMax based on markers' color in HSV space.
• src/marker_detection/marker_center: change the areaThresh1, areaThresh2 for the minimal and maximal size of markers

Step 2: Marker matching

Set Parameters

src/setting.py

• RESCALE: scale down
• N, M: the row and column of the marker array
• x0, y0: the coordinate of upper-left marker (in original size)
• dx, dy: the horizontal and vertical interval between adjacent markers (in original size)
• fps_: the desired frame per second, the algorithm will find the optimal solution in 1/fps seconds

Output

The Matching Class has a function get_flow. It return the flow information:

flow = m.get_flow()

output: (Ox, Oy, Cx, Cy, Occupied) = flow
    Ox, Oy: N*M matrix, the x and y coordinate of each marker at frame 0
    Cx, Cy: N*M matrix, the x and y coordinate of each marker at current frame
    Occupied: N*M matrix, the index of the marker at each position, -1 means inferred. 
        e.g. Occupied[i][j] = k, meaning the marker mc[k] lies in row i, column j.