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Explain the method from first principles (docs/theory.md) - #75
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Add a tutorial that builds the method up from what magnifying motion means: local phase and why scaling it moves an edge, the DTCWT (two trees, level-1 and Q-shift filters, six orientations, levels and redundancy), following phase over time, the two temporal filtering modes, reconstruction and the lowpass residual, colour, parameter choice, a comparison with Eulerian and steerable/Riesz methods, a glossary and further reading. Its figures come from scripts/make_theory_figures.py, which runs the pipeline code. It also measures the per-level phase slope, which shows where the ~3 px displacement limit comes from: the two finest levels reach a half turn at 2-3 px. README: a "How to learn this" path, a pipeline diagram that matches the code (conjugate multiply, band mode, luma mode), and the unsourced "10-100x", "~21x overcomplete" and "~5x faster" claims replaced with sourced or measured statements. Closes #74
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Closes #74
New:
docs/theory.md(≈3,500 words)A tutorial from first principles, linked from a new README section, "How to learn this":
(1+α)δ < λ/8bound.--biort/--qshiftoption with its tap counts), six orientations, levels/sizes/4:1 redundancy, what--nlevelsmeans spatially.G(f) = H2(f)·[1 + (k−1)(1 − Hlow(f))], zero-phase windows, the 0.2327 factor, band mode, frequency resolution.yiq).New:
scripts/make_theory_figures.pyBuilds the five figures in
docs/images/theory/(536 KB total) from the actual pipeline functions. Needs matplotlib, which the script's docstring says; it isn't a project dependency.README corrections
--color-space yiq.I left out statements I couldn't verify, e.g. whether Wadhwa et al. magnify luma only. The example coefficient's oscillation is described as measured: about 0.3 Hz, slow head motion rather than the pulse.