Normalize and score vision regions against OCR layout

This commit is contained in:
Sean McElwain 2026-05-30 19:26:07 -05:00
parent 70e82123a2
commit f3e61e877b
1 changed files with 247 additions and 5 deletions

View File

@ -189,19 +189,261 @@ def analyze_document_image(image_path: str | Path, *, model_name: str = "placeho
}
def _bbox_iou(a: list[float] | tuple[float, ...], b: list[float] | tuple[float, ...]) -> float:
ax1, ay1, ax2, ay2 = [float(v) for v in a[:4]]
bx1, by1, bx2, by2 = [float(v) for v in b[:4]]
ix1 = max(ax1, bx1)
iy1 = max(ay1, by1)
ix2 = min(ax2, bx2)
iy2 = min(ay2, by2)
iw = max(0.0, ix2 - ix1)
ih = max(0.0, iy2 - iy1)
inter = iw * ih
area_a = max(0.0, ax2 - ax1) * max(0.0, ay2 - ay1)
area_b = max(0.0, bx2 - bx1) * max(0.0, by2 - by1)
denom = area_a + area_b - inter
return inter / denom if denom else 0.0
def _region_contains_ratio(region: list[float] | tuple[float, ...], item: list[float] | tuple[float, ...]) -> float:
rx1, ry1, rx2, ry2 = [float(v) for v in region[:4]]
ix1, iy1, ix2, iy2 = [float(v) for v in item[:4]]
x1 = max(rx1, ix1)
y1 = max(ry1, iy1)
x2 = min(rx2, ix2)
y2 = min(ry2, iy2)
inter = max(0.0, x2 - x1) * max(0.0, y2 - y1)
item_area = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
return inter / item_area if item_area else 0.0
def _scale_bbox(
bbox: list[float] | tuple[float, ...],
*,
scale_x: float,
scale_y: float,
) -> list[float]:
x1, y1, x2, y2 = [float(v) for v in bbox[:4]]
return [
x1 * scale_x,
y1 * scale_y,
x2 * scale_x,
y2 * scale_y,
]
def normalize_vision_regions_to_layout(
vision_result: dict[str, Any],
layout_json: dict[str, Any] | None,
) -> dict[str, Any]:
"""
Convert OpenCV rendered-image pixel coordinates into layout_json page coordinates.
"""
pages = (layout_json or {}).get("pages") or []
rendered_pages = vision_result.get("rendered_pages") or []
layers = vision_result.setdefault("layers", {})
regions = layers.get("vision_regions") or []
if not pages or not rendered_pages or not regions:
vision_result["coordinate_space"] = "unknown_or_unscaled"
return vision_result
page = pages[0]
rendered = rendered_pages[0]
layout_w = float(page.get("page_width") or page.get("width") or 0)
layout_h = float(page.get("page_height") or page.get("height") or 0)
rendered_w = float(rendered.get("width") or 0)
rendered_h = float(rendered.get("height") or 0)
if not layout_w or not layout_h or not rendered_w or not rendered_h:
vision_result["coordinate_space"] = "rendered_pixels_unscaled"
return vision_result
scale_x = layout_w / rendered_w
scale_y = layout_h / rendered_h
normalized = []
for region in regions:
bbox = region.get("bbox")
if not bbox:
continue
item = dict(region)
item["rendered_bbox"] = bbox
item["bbox"] = _scale_bbox(bbox, scale_x=scale_x, scale_y=scale_y)
item["coordinate_space"] = "layout_page"
item["scale_x"] = scale_x
item["scale_y"] = scale_y
normalized.append(item)
layers["vision_regions"] = normalized
vision_result["coordinate_space"] = "layout_page"
vision_result["coordinate_normalization"] = {
"source": "rendered_pixels",
"target": "layout_page",
"layout_width": layout_w,
"layout_height": layout_h,
"rendered_width": rendered_w,
"rendered_height": rendered_h,
"scale_x": scale_x,
"scale_y": scale_y,
}
return vision_result
def score_vision_regions_against_layout(
vision_result: dict[str, Any],
layout_json: dict[str, Any] | None,
) -> dict[str, Any]:
"""
Compare OpenCV regions against existing OCR layout lines.
Purpose:
- measure whether CV regions line up with OCR line boxes
- identify OCR lines not covered by CV regions
- identify CV regions with no OCR coverage
"""
pages = (layout_json or {}).get("pages") or []
regions = ((vision_result.get("layers") or {}).get("vision_regions")) or []
if not pages or not regions:
return {
"schema_version": "vision_region_scoring_v1",
"status": "not_enough_data",
"page_scores": [],
"summary": {
"pages": len(pages),
"regions": len(regions),
"lines": 0,
"matched_lines": 0,
"unmatched_lines": 0,
"unmatched_regions": len(regions),
},
}
page_scores: list[dict[str, Any]] = []
total_lines = 0
total_matched_lines = 0
total_unmatched_regions = 0
for page in pages:
page_number = int(page.get("page") or 1)
page_lines = page.get("lines") or []
page_regions = [r for r in regions if int(r.get("page") or 1) == page_number]
matched_region_indexes: set[int] = set()
line_scores: list[dict[str, Any]] = []
for line in page_lines:
bbox = line.get("bbox")
if not bbox:
continue
best = {
"region_index": None,
"iou": 0.0,
"contains_ratio": 0.0,
"region_bbox": None,
}
for idx, region in enumerate(page_regions):
region_bbox = region.get("bbox")
if not region_bbox:
continue
iou = _bbox_iou(region_bbox, bbox)
contains = _region_contains_ratio(region_bbox, bbox)
score = max(iou, contains)
if score > max(best["iou"], best["contains_ratio"]):
best = {
"region_index": idx,
"iou": round(iou, 4),
"contains_ratio": round(contains, 4),
"region_bbox": region_bbox,
}
matched = (best["contains_ratio"] >= 0.55) or (best["iou"] >= 0.10)
if matched and best["region_index"] is not None:
matched_region_indexes.add(int(best["region_index"]))
line_scores.append(
{
"line_text": str(line.get("text") or "")[:120],
"line_bbox": bbox,
"matched": matched,
**best,
}
)
total_lines += len(line_scores)
matched_lines = sum(1 for item in line_scores if item["matched"])
total_matched_lines += matched_lines
unmatched_region_indexes = [
idx for idx in range(len(page_regions)) if idx not in matched_region_indexes
]
total_unmatched_regions += len(unmatched_region_indexes)
page_scores.append(
{
"page": page_number,
"line_count": len(line_scores),
"region_count": len(page_regions),
"matched_line_count": matched_lines,
"unmatched_line_count": len(line_scores) - matched_lines,
"unmatched_region_count": len(unmatched_region_indexes),
"line_scores": line_scores[:200],
"unmatched_regions": [
page_regions[idx] for idx in unmatched_region_indexes[:100]
],
}
)
return {
"schema_version": "vision_region_scoring_v1",
"status": "scored",
"page_scores": page_scores,
"summary": {
"pages": len(pages),
"regions": len(regions),
"lines": total_lines,
"matched_lines": total_matched_lines,
"unmatched_lines": total_lines - total_matched_lines,
"unmatched_regions": total_unmatched_regions,
},
}
def build_vision_assisted_layout(source_layout: dict[str, Any] | None, vision_result: dict[str, Any]) -> dict[str, Any]:
"""
Convert vision analysis into normal layout_json.
Current phase:
- preserves the current source layout
- tags it as vision-assisted
- normalizes CV coordinates into layout page coordinates
- scores CV region coverage against OCR layout lines
- preserves the current source layout for editor stability
- stores diagnostics on the layout candidate
"""
layout = dict(source_layout or {"pages": []})
normalized_vision = normalize_vision_regions_to_layout(vision_result, layout)
region_score = score_vision_regions_against_layout(normalized_vision, layout)
layout["vision_assisted"] = True
layout["vision_assisted_status"] = vision_result.get("status", "unknown")
layout["vision_engine"] = vision_result.get("engine")
layout["vision_model_name"] = vision_result.get("model_name")
layout["vision_assisted_status"] = normalized_vision.get("status", "unknown")
layout["vision_engine"] = normalized_vision.get("engine")
layout["vision_model_name"] = normalized_vision.get("model_name")
layout["vision_coordinate_normalization"] = normalized_vision.get("coordinate_normalization")
layout["vision_region_score"] = region_score
layout["layout_sync_source"] = "vision_assisted"
layout["layout_needs_review"] = True
return layout