Complete Projects
Count candidate seeds in a tray
Let bright objects touch
Botie's tray image contains bright seed candidates and dust. Counting connected regions becomes a seed count only while the image assumptions hold.
BOTIE
The inputs we are comparing
Grayscale tray image
Seeds and dust
| 0 | 9 | 9 | 0 | 0 |
| 0 | 9 | 9 | 0 | 8 |
| 0 | 0 | 0 | 0 | 0 |
| 7 | 7 | 0 | 8 | 8 |
Touching foreground
| 9 | 9 | 0 |
| 0 | 9 | 9 |
Strict intensity threshold
Same in both cases
5
Minimum component area
Same in both cases
2
Result for seeds and dust
3
Two seeds touch only at their corners
This check uses the pieces from “Follow pixels through foreground, regions, and area filtering”.
BOTIE
- Grayscale tray image
1 1 0 0 0 0 1 1 0 0 0 0 - Strict intensity threshold
0.5
- Minimum component area
2
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
Which saved intermediate would distinguish a dim seed disappearing from two touching seeds merging?
Save original images, masks, labels, areas, and human counts. Evaluate dust, brightness, size, and contact failures before changing the method.
Counts assume each accepted connected region corresponds to one seed. Touching objects and lighting changes can violate that assumption.
Work through the Python challenges →