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Artificial Intelligence · Mini Project
Object Counting in Images
Counts coins, cells or seeds in a photo with contours and watershed
1-2 weeks build
Overview
A classical computer vision kit with no training required. Images are thresholded and cleaned with morphology, then contours give a first count. Touching objects are separated with a distance transform and watershed, and a tuning panel lets the user adjust thresholds and minimum size until the overlay matches what they see.
What makes it stand up
- 01Adaptive thresholding and morphological clean-up
- 02Contour counting with size and circularity filters
- 03Watershed segmentation to split touching objects
- 04Live tuning sliders with numbered overlays
- 05Manual-count comparison table in the report
Module breakdown
- 01
Preprocessing
Greyscale, blur and adaptive threshold.
- 02
Morphology
Opening and closing to remove noise and fill gaps.
- 03
Contour counting
Area and shape filters on detected contours.
- 04
Watershed
Distance-transform markers to separate touching objects.
- 05
App
Streamlit sliders with an annotated output image.
After this, you will be able to
- Explain how watershed separates objects that touch
- Discuss when classical methods beat deep learning
- Demo counting coins scattered on a desk
Technology stack
- Python
- OpenCV
- NumPy
- scikit-image
- Streamlit
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support