you are expert in camera and lens, im developing m...

I’ll trace the existing zoom/focus state machine first, then use your three measured zoom points to build a predictive OBJ/CAM starting model. After that I’ll replace the slow search with a prediction-first, bracket-and-refine autofocus, verify the four files for consistency, and return all four updated files.

I found an important mismatch in the current flow: the manual Auto Focus button uses your newer two-axis search, but a zoom completion currently launches startObjSearch() instead of that autofocus engine. That means the zoom path is not benefiting from the same “best focus” logic, so I’m unifying those paths and making zoom seed the autofocus near the predicted optimum rather than starting a broad scan.

The research matches the direction I’m implementing: fast microscope autofocus works best as coarse-to-fine hill climbing with robust direction estimation, then a curve fit near the peak instead of exhaustive scanning. Recent microscopy work specifically reports dynamic step reduction and curve fitting near the optimum, while older microscope work combines hill climbing with interval-reduction searches for speed.

Your three measurements are too sparse for a safe single global polynomial—especially because the 2.1× CAM correction is unspecified—so I’m treating them as a bounded calibration prior: piecewise interpolation where data exists, no dangerous long extrapolation for CAM, then local optical verification from the camera score.

The updated design now uses your measurements as zoom-specific focus offsets, moves directly to that predicted OBJ/CAM point during zoom, and then verifies locally with sharpness—not cell count. For 6.6× and 7.5× it will seed from the supplied CAM corrections; around 2.1× it deliberately searches CAM more broadly because that correction was not numerically specified.

The search engine is now two-speed: a zoom-triggered run uses only a small local bracket with fewer samples and shorter settling, while manual/no-model autofocus keeps the wider safety search. I also added a one-shot parabolic peak estimate from stationary focus samples, so the final motor position can land between discrete scan steps instead of being limited to the grid.

One implementation detail surfaced during validation: your existing AFS “position reached” tolerance was larger than the high-zoom fine step, which could make a commanded micro-adjustment count as “already reached” without actually moving. I’m tightening that tolerance only for the new AFS path so the 0.04–0.1 mm refinements and fitted peak move are real motor moves, not skipped checks.

I studied the four files, your measured focus offsets, and microscope autofocus literature, then rebuilt the zoom→focus path around a prediction-first, verify-fast strategy.

Your existing code already has a nominal zoom geometry: OBJ is a cubic in ln(zoom) and CAM is derived from the total OBJ+CAM relation. (mainwindow.cpp) The old zoom path then waited 500 ms and launched startObjSearch(), while the optical autofocus itself used relatively large fixed 1 mm OBJ / 2 mm CAM coarse steps. (mainwindow.cpp) (mainwindow.cpp)

The new relationship is essentially:

OBJ_focus(z) = OBJ_nominal(z) + ΔOBJ(z)

CAM_focus(z) = CAM_nominal(z) + ΔCAM(z)

where ΔOBJ is bounded piecewise interpolation through your (2.1,+1.0), (6.6,+0.9), (7.5,+1.0) measurements. For CAM, the known 6.6–7.5 region gives approximately ΔCAM = 1.0 + 1.3333 × (zoom - 6.6) mm. I intentionally do not extrapolate that CAM slope down to 2.1×, because your 2.1× CAM correction was not specified; at low zoom the algorithm instead makes a wider local CAM search and learns the result for subsequent focuses in that session.

I changed the autofocus to land at this prediction first, measure center/left/right, climb only if necessary, progressively reduce step size with zoom, use robust multi-frame scores, compensate OBJ backlash, and then fit a quadratic through the stationary measurements and physically verify the fitted peak. That coarse-to-fine + curve-fitting strategy is consistent with published rapid microscope autofocus approaches; recent work specifically uses adaptive mountain climbing and curve fitting to reduce stage travel while retaining accuracy. (pmc.ncbi.nlm.nih.gov pubmed.ncbi.nlm.nih.gov)

I also freeze automatic LED/exposure corrections while focus samples are being compared. That matters because changing illumination during the search can change the numerical sharpness score independently of actual optical focus. Your Autofocus class itself is currently the lighting stage; actual sharpness comes from DetectionThread::focusScoreUpdated, so I preserved that architecture. (autofocus.h)

Because your sample is behind glass, keeping the empirical model as a prior rather than an absolute equation is important. Cover-glass thickness, refractive index, mounting medium, and particularly high numerical aperture can shift optimum image quality/focus and introduce spherical aberration. (www.microscopyu.com www.microscopyu.com)

The four updated files are:

• [mainwindow.cpp]
• mainwindow.h
• [autofocus.cpp]
• autofocus.h

I validated symbol consistency, delimiter/bracket balance, the interpolation math, and the quadratic peak calculation. I could not run a full Qt/hardware build because the rest of the project and microscope controller are not included here. One thing you should set before hardware testing is the real OBJ/CAM mechanical maximum travel; your existing autofocus code still has 1000.0 mm placeholder limits, and I did not invent hardware limits without your specifications.

Done — the ZIP contains all 4 full updated files:

• mainwindow.cpp
• mainwindow.h
• autofocus.cpp
• autofocus.h

Download the ZIP