Add crop refinement, finger removal, and local LLM naming

- vision/refine.py: tighten crops to the paper band (removes mat
  margins and hands beside receipts) and inpaint border-connected
  skin regions so fingers disappear from output
- llm/vision.py: identify documents with a local Ollama vision model
  (qwen2.5vl); extracts vendor/date/total/form code and flags quality
  issues (fingers, blur, glare); falls back to Tesseract when down
- pipeline: drop blank pages, dedupe consecutive captures of the same
  document, record refine/LLM fields in report and export summary

tests/test_capture_events.py

@@ -0,0 +1,68 @@
+from paperpod.capture.video import Frame
+from paperpod.vision.capture_events import find_document_events
+from paperpod.vision.document import DocumentDetection
+import numpy as np
+
+
+def _frame(t: float, idx: int = 0) -> Frame:
+    return Frame(index=idx, t=t, image=np.zeros((100, 100, 3), dtype=np.uint8))
+
+
+def _det() -> DocumentDetection:
+    quad = np.array([[0, 0], [1, 0], [1, 1], [0, 1]], dtype=np.float32)
+    return DocumentDetection(quad=quad, area_ratio=0.3, method="quad", source="edges", confidence=0.8)
+
+
+def test_single_event_from_consecutive_detections():
+    samples = [(_frame(1.0, 0), _det()), (_frame(1.2, 1), _det()), (_frame(1.4, 2), _det())]
+    events = find_document_events(samples, gap_s=2.0, min_samples=2)
+    assert len(events) == 1
+    assert events[0].t_start == 1.0
+    assert events[0].sample_count == 3
+
+
+def test_gap_splits_separate_documents():
+    samples = [
+        (_frame(1.0, 0), _det()),
+        (_frame(1.2, 1), _det()),
+        (_frame(5.0, 2), _det()),
+        (_frame(5.2, 3), _det()),
+    ]
+    events = find_document_events(samples, gap_s=2.0, min_samples=2)
+    assert len(events) == 2
+    assert events[0].t_end == 1.2
+    assert events[1].t_start == 5.0
+
+
+def test_min_samples_filters_single_frame_noise():
+    samples = [(_frame(1.0, 0), _det()), (_frame(5.0, 1), _det()), (_frame(5.2, 2), _det())]
+    events = find_document_events(samples, gap_s=2.0, min_samples=2)
+    assert len(events) == 1
+    assert events[0].t_start == 5.0
+
+
+def test_area_change_splits_documents():
+    small = DocumentDetection(
+        quad=np.array([[0, 0], [1, 0], [1, 1], [0, 1]], dtype=np.float32),
+        area_ratio=0.25,
+        method="quad",
+        source="threshold",
+        confidence=0.8,
+    )
+    large = DocumentDetection(
+        quad=np.array([[0, 0], [1, 0], [1, 1], [0, 1]], dtype=np.float32),
+        area_ratio=0.65,
+        method="quad",
+        source="threshold",
+        confidence=0.8,
+    )
+    samples = [
+        (_frame(1.0, 0), small),
+        (_frame(1.2, 1), small),
+        (_frame(1.4, 2), large),
+        (_frame(1.6, 3), large),
+    ]
+    events = find_document_events(samples, gap_s=2.0, min_samples=2)
+    assert len(events) == 2
+    assert events[0].detection.area_ratio == 0.25
+    assert events[1].detection.area_ratio == 0.65