Two self-contained projects in one repo:
- Math Engine — symbolic solver with step-by-step explanations and Monte Carlo verification (SymPy + NumPy + SciPy)
- Edge Vision — multi-algorithm computer vision demo running 4 CV pipelines side-by-side on local video (OpenCV)
Symbolic solver supporting integrals, derivatives, limits, combinatorics, and probability — with LaTeX output and Monte Carlo cross-checks.
cd math-engine
pip install -r requirements.txt
streamlit run streamlit_app.py
| Type | Example |
|---|---|
| Indefinite integral | integral(sin(x)^2, x) |
| Definite integral | integral(x^2, x, 0, 1) |
| Derivative | diff(exp(x)*log(x), x) |
| nth-order derivative | diff(x^5, x, 3) |
| Limit | limit(sin(x)/x, x, 0) |
| Limit at infinity | limit((1 + 1/x)**x, x, oo) |
| Combinations | C(52, 5) |
| Permutations | P(5, 2) |
| Factorial | factorial(10) |
| Dice probability | P(sum_{i=1..3} die() >= 10) |
| Binomial CDF | P(binomial(10, 0.3) >= 4) |
| Normal tail | P(normal(0, 1) > 1.96) |
All probability results include exact + Monte Carlo verification with 95% CI.
Runs 4 CV algorithms simultaneously on a local video, with per-algorithm controls and processing metrics.
- Canny edges — tunable low/high thresholds, edge density metric
- Sobel gradient — magnitude map, mean gradient metric
- Contour detection — Otsu threshold + contour filtering by area
- CLAHE equalisation — adaptive histogram equalisation, contrast before/after
cd edge-vision
pip install -r requirements.txt
streamlit run apps/streamlit_app.py
Add your own video at edge-vision/data/sample.mp4 or use the included sample.
pip install pytest pytest-cov scipy opencv-python-headless sympy numpy
PYTHONPATH=math-engine:edge-vision pytest math-engine/tests/ edge-vision/tests/ -v38 tests — 21 engine (integrals, derivatives, limits, combinatorics, probability) + 17 CV pipeline (shape, dtype, edge cases).