1. Automatic centering with LµCID library
by Etienne Francois

2. Summary
Requisites
How does it work ?
Next step

3. Requisites
PyFAI (Jerome Kiefer, Dimitrios Karkoulis)
OpenCV 2.x

4. How does it work ? Communication with mxCuBE Time performance
Based on the manual procedure with 3 click centering
Step by step with visual information
Time performance
About 200ms to locate the center of the loop in the image
Between 25 and 30s for the entire procedure

5. How does it work ? Algorithm for mxCuBE Find the loop
Minimal zoom (level 1 in mxCuBE)
Usually once (5 try at most)
If not visible, rotate back step by step to estimate the position
Test centering
Middle zoom (level 3 in mxCuBE)
Switch off the back light
Switch on the front light
Usually once (2 try at most)
Inform on fail or success

6. How does it work ? Image processing with LµCID
Pre-treatment (with PyFAI lib)
Treatment (with OpenCV lib)

7. How does it work ? Pre-treatment Light up the corners
Improve the detection and limit false detections
After
Before

8. How does it work ?
Treatment
Adaptive threshold
After
Before

9. How does it work ? Median filter after contours detection and fill.
After
Before

10. How does it work ?
Morphological mathematic
Erosion
Dilatation

11. How does it work ? First erosion then dilatation Erode then dilate
After
Before

12. How does it work ?
Then use the result of erosion/dilatation as a mask to the previous image
Delete the robot and the sample holder to have just the loop

13. How does it work ?
We can now take measures and determine the center of the loop
A third from the right for the moment but it might be improved

14. Next step : edge detection for mesh scan
Create a mesh for the loop
Centered loop in face position
Determine all the consistent points
Mesh the founded area

15. To be continued … The automatic centering is currently in test phase
Some improvement are needed
Some promising results yet
The automatic mesh scan is currently in development