Merge main into feat/hailo-patchcore-e2e - #167

…, localization, Autopilot support, and testing fixes (#166)

## 🔗 Related Issue

Fixes #

## 📝 Description

This PR adds **EfficientAD support to AnomaVision** and integrates it
into the existing anomaly-detection pipeline alongside PaDiM and
PatchCore.

### 🚀 EfficientAD

* Added EfficientAD as a supported anomaly-detection algorithm.
* Integrated EfficientAD with the existing algorithm abstraction used by
PaDiM/PatchCore.
* Reused the existing AnomaVision data loading and preprocessing
pipeline.
* Added batched training/inference to avoid unnecessary repeated
computation.
* Optimized teacher feature extraction and removed unnecessary
inference-time computation.
* Added calibrated anomaly thresholds based on **normal training images
only**.
* Added anomaly-map generation for pixel-level localization.
* Reused the existing post-processing and visualization pipeline for:

  * anomaly heatmaps
  * localization masks
  * contours
  * bounding boxes
  * overlay visualizations

### 🔌 Deployment & Export

* Added EfficientAD support to the existing model/export workflow.
* Maintained compatibility with the existing ONNX-based detection flow.
* Preserved the existing PaDiM/PatchCore inference architecture rather
than introducing a separate EfficientAD pipeline.
* Added EfficientAD support to **Production Autopilot**.
* Autopilot can compare PaDiM, PatchCore, and EfficientAD candidates.
* EfficientAD calibration data is preserved with the model artifact.

### 📊 Production Autopilot

* Restored and preserved the rich `production_autopilot_report.html`
dashboard.
* Added EfficientAD to candidate comparison and model selection.
* Report includes:

  * Image AUROC
  * Pixel AUROC
  * median latency
  * P95 latency
  * anomaly coverage
  * normal false-positive localization rate
  * threshold
  * selected model
  * deployment artifact
  * localization health
  * reproducibility information
* Restored the existing timing/performance summary in the detection
pipeline.

### 🐛 Fixes

* Fixed EfficientAD training failure caused by PyTorch inference-mode
tensors being used in autograd.
* Fixed JSON serialization of NumPy/PyTorch scalar metrics in the
Autopilot deployment manifest.
* Restored Autopilot report/test compatibility.
* Preserved existing helper APIs such as `_format_metric`.
* Fixed report metric naming/capitalization expected by the test suite.

## 🔄 Type of Change

* [x] 🐛 Bug fix (non-breaking change which fixes an issue)
* [x] 🚀 New feature (non-breaking change which adds functionality)
* [ ] 💥 Breaking change (fix or feature that would cause existing
functionality to not work as expected)
* [ ] 📖 Documentation update
* [ ] 🏗️ Infrastructure / CI/CD update

## 🧪 Hardware & Matrix Testing

**I have successfully built and tested this code using `uv` on:**

* [x] `anomavision[cpu]` (Standard/Edge)
* [ ] `anomavision[cu121]` (CUDA 12.1)
* [ ] `anomavision[cu124]` (CUDA 12.4)
* [ ] `anomavision[cu118]` (CUDA 11.8)

**Host OS used for testing:**

* [ ] Linux / Ubuntu
* [x] Windows (Native or WSL2)
* [ ] macOS

## 🧪 Validation

* [x] Existing Autopilot unit tests updated/fixed.
* [x] EfficientAD-specific integration changes covered.
* [x] Autopilot report generation validated.
* [x] Deployment manifest generation validated.
* [ ] Full `uv run pytest` suite verified on all supported environments.
* [ ] Bottle dataset benchmark completed for PaDiM vs PatchCore vs
EfficientAD.
* [ ] Full ONNX/OpenVINO/TensorRT/Hailo hardware validation completed.

## ✅ Developer Checklist

* [x] My code follows the core style guidelines of this project
(Ruff/Black formatting).
* [ ] I have run `uv run pytest` and all unit tests pass locally.
* [ ] **Lockfile Guard:** If I added or modified a dependency in
`pyproject.toml`, I have run `uv lock --python 3.10` and committed the
updated `uv.lock` file.
* [x] I have added tests that prove my fix is effective or that my
feature works.
* [x] I have updated the documentation accordingly (if applicable).

## 📸 Screenshots / Visual Proof

The Production Autopilot report now provides a rich HTML dashboard
comparing PaDiM, PatchCore, and EfficientAD, including localization and
deployment metrics.

Recommended screenshots:

* `production_autopilot_report.html`
* EfficientAD anomaly heatmap
* EfficientAD localization mask/overlay
* PaDiM vs PatchCore vs EfficientAD comparison
* Autopilot model-selection summary