Calibrate systematic roof box offsets
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@@ -52,12 +52,19 @@ def metrics(tp: int, fp: int, fn: int) -> dict[str, float | int]:
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}
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def scale_box(box: tuple[float, float, float, float], factor: float) -> tuple[float, float, float, float]:
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def scale_box(
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box: tuple[float, float, float, float], factor: float, offset_x: float = 0.0, offset_y: float = 0.0
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) -> tuple[float, float, float, float]:
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"""Scale a detector box around its center for calibration-only geometry correction."""
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x1, y1, x2, y2 = box
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cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
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half_width, half_height = (x2 - x1) * factor / 2, (y2 - y1) * factor / 2
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return cx - half_width, cy - half_height, cx + half_width, cy + half_height
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return (
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cx - half_width + offset_x,
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cy - half_height + offset_y,
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cx + half_width + offset_x,
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cy + half_height + offset_y,
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)
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def read_references(path: Path, width: int, height: int) -> list[tuple[float, float, float, float]]:
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@@ -97,6 +104,8 @@ def main() -> int:
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help="Maximum detections retained per tile; dense Belgian urban tiles exceed YOLO's default 300.",
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)
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parser.add_argument("--box-scale", type=float, default=1.0)
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parser.add_argument("--box-offset-x", type=float, default=0.0)
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parser.add_argument("--box-offset-y", type=float, default=0.0)
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parser.add_argument("--proposal-classifier", type=Path)
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parser.add_argument("--proposal-classifier-threshold", type=float, default=0.5)
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parser.add_argument("--proposal-crop-scale", type=float, default=1.4)
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@@ -142,7 +151,12 @@ def main() -> int:
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for tile, result in zip(tiles, results, strict=True):
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height, width = result.orig_shape
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predictions = [
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(scale_box(tuple(map(float, box)), args.box_scale), float(score))
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(
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scale_box(
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tuple(map(float, box)), args.box_scale, args.box_offset_x, args.box_offset_y
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),
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float(score),
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)
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for box, score in zip(result.boxes.xyxy.cpu().tolist(), result.boxes.conf.cpu().tolist(), strict=True)
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]
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if proposal_classifier is not None:
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@@ -213,6 +227,8 @@ def main() -> int:
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"inference_imgsz": args.imgsz,
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"max_detections_per_tile": args.max_det,
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"box_scale": args.box_scale,
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"box_offset_x": args.box_offset_x,
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"box_offset_y": args.box_offset_y,
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"proposal_classifier": str(args.proposal_classifier) if args.proposal_classifier else None,
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"proposal_classifier_threshold": args.proposal_classifier_threshold if args.proposal_classifier else None,
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"proposal_crop_scale": args.proposal_crop_scale if args.proposal_classifier else None,
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