Image Processing Map (OpenCV → Production)

Image Processing Map (OpenCV → Production)

KW: OpenCV beginner / production  |  Lead: Books × USB camera × Lighting

Goal (shortest path to “usable”): build a loop of stable input × reproducible processing × quantitative evaluation with three pillars: (1) imaging (lens & lighting included), (2) an OpenCV pipeline, (3) evaluation & operations.

1) Learning Path (Core → Applied)

  • Basics: image I/O, BGR/RGB & HSV, histogram equalization, gamma, thresholding (Otsu), morphology, Canny.
  • Shapes / Features: contours & moments, Hough (lines/circles), template matching, ORB/SIFT (keypoints).
  • Geometry / Calibration: checkerboard for intrinsics/extrinsics, distortion correction, homography / perspective (bird’s-eye).
  • Markers: ArUco/ChArUco for coordinate alignment & solvePnP (pose).
  • Motion / Tracking: background subtraction (MOG2), Lucas–Kanade / Farnebäck optical flow, Kalman filter.
  • DNN: OpenCV DNN with ONNX / YOLO family inference (CPU → CUDA/NNAPI and other accelerations).

2) Pipeline Design for Production

  • Flow: requirements → sample capture → fix lighting & FOV first → calibration → preprocessing → detection/measurement → thresholding → decision.
  • Non-functionals: latency, throughput, logging/observability, re-training playbook (data versioning).
  • Evaluation: manage PSNR/SSIM (preprocessing quality) separately from mAP/F1 (detection accuracy).
  • Data ops: annotate with CVAT/labelImg; harden models with augmentations (e.g., albumentations).

3) Hardware (Input) Essentials

  • Camera: sensor (sensitivity/dynamic range), global vs rolling shutter, lens (focal length/distortion).
  • Lens: working distance, field of view, depth of field (aperture), distortion control.
  • Lighting: diffuse / ring / coaxial / low-angle; use cross-polarization (polarizer on light + analyzer on lens) to suppress specular glare.

4) Common Task Patterns

  • Dimensional measurement: calibrate → edge extraction → sub-pixel localization → mm conversion → control charting.
  • Appearance inspection: stable lighting → background subtraction or learning-based → defect region → features → pass/fail.
  • Positioning / robot pick: ring/coaxial lighting → ArUco for coordinates → PnP (pose) → robot command.

5) Deployment Template

  1. PoC (bench): USB camera + variable lighting to secure a reproducible image.
  2. Pilot (near line): rigid mounts, power/heat/dust handling, logging pipeline.
  3. Production: versioning (model/threshold/calibration), monitoring, drift detection & refresh rules.

Lead: Books × USB Camera × Lighting (Start Here)

Books (foundation to implementation)

  • Learning OpenCV (Gary Bradski, Adrian Kaehler; O’Reilly): the classic end-to-end introduction from the project’s founders.

USB Cameras (UVC for speed; industrial-leaning for headroom)

  • Arducam IMX477 12MP USB3.0: high-resolution, high-speed board camera; interchangeable lenses make it versatile.
  • e-con Systems See3CAM series: UVC-compliant lineup with options like global shutter, low-light, and 4K for industrial use.

Lighting (start with ring + polarization to “make the image”)

  • CCS ring lights: widely used in inspection; variants for longer working distance and larger fields.
  • Cross-polarization kit (e.g., Edmund Optics): polarizer on the light + analyzer on the lens to cut glare and boost contrast.

Appendix: On-Site Checklist

  • Lighting fixed (color temperature / intensity / diffusion / polarization)
  • Field of view & resolution (derive mm/px from requirements)
  • Calibration procedure (with temperature / daily re-do plan)
  • Image logging and exact reproduction steps (version control)
  • Document the reasoning for decisions (rules vs learning)
  • SLOs for accuracy & throughput (and approval flow for threshold changes)

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