Jens/geointel
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Calibrate systematic roof box offsets

Browse Source
This commit is contained in:
Jens
2026-07-27 09:03:05 +02:00
parent 3108a907cc
commit f4a139dd76
1 changed files with 19 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/evaluate_belgium_building_candidate.py
+19 -3
View File
@@ -52,12 +52,19 @@ def metrics(tp: int, fp: int, fn: int) -> dict[str, float | int]:
} }
def scale_box(box: tuple[float, float, float, float], factor: float) -> tuple[float, float, float, float]: def scale_box(
box: tuple[float, float, float, float], factor: float, offset_x: float = 0.0, offset_y: float = 0.0
) -> tuple[float, float, float, float]:
"""Scale a detector box around its center for calibration-only geometry correction.""" """Scale a detector box around its center for calibration-only geometry correction."""
x1, y1, x2, y2 = box x1, y1, x2, y2 = box
cx, cy = (x1 + x2) / 2, (y1 + y2) / 2 cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
half_width, half_height = (x2 - x1) * factor / 2, (y2 - y1) * factor / 2 half_width, half_height = (x2 - x1) * factor / 2, (y2 - y1) * factor / 2
return cx - half_width, cy - half_height, cx + half_width, cy + half_height return (
cx - half_width + offset_x,
cy - half_height + offset_y,
cx + half_width + offset_x,
cy + half_height + offset_y,
)
def read_references(path: Path, width: int, height: int) -> list[tuple[float, float, float, float]]: def read_references(path: Path, width: int, height: int) -> list[tuple[float, float, float, float]]:
@@ -97,6 +104,8 @@ def main() -> int:
help="Maximum detections retained per tile; dense Belgian urban tiles exceed YOLO's default 300.", help="Maximum detections retained per tile; dense Belgian urban tiles exceed YOLO's default 300.",
) )
parser.add_argument("--box-scale", type=float, default=1.0) parser.add_argument("--box-scale", type=float, default=1.0)
parser.add_argument("--box-offset-x", type=float, default=0.0)
parser.add_argument("--box-offset-y", type=float, default=0.0)
parser.add_argument("--proposal-classifier", type=Path) parser.add_argument("--proposal-classifier", type=Path)
parser.add_argument("--proposal-classifier-threshold", type=float, default=0.5) parser.add_argument("--proposal-classifier-threshold", type=float, default=0.5)
parser.add_argument("--proposal-crop-scale", type=float, default=1.4) parser.add_argument("--proposal-crop-scale", type=float, default=1.4)
@@ -142,7 +151,12 @@ def main() -> int:
for tile, result in zip(tiles, results, strict=True): for tile, result in zip(tiles, results, strict=True):
height, width = result.orig_shape height, width = result.orig_shape
predictions = [ predictions = [
(scale_box(tuple(map(float, box)), args.box_scale), float(score)) (
scale_box(
tuple(map(float, box)), args.box_scale, args.box_offset_x, args.box_offset_y
),
float(score),
)
for box, score in zip(result.boxes.xyxy.cpu().tolist(), result.boxes.conf.cpu().tolist(), strict=True) for box, score in zip(result.boxes.xyxy.cpu().tolist(), result.boxes.conf.cpu().tolist(), strict=True)
] ]
if proposal_classifier is not None: if proposal_classifier is not None:
@@ -213,6 +227,8 @@ def main() -> int:
"inference_imgsz": args.imgsz, "inference_imgsz": args.imgsz,
"max_detections_per_tile": args.max_det, "max_detections_per_tile": args.max_det,
"box_scale": args.box_scale, "box_scale": args.box_scale,
"box_offset_x": args.box_offset_x,
"box_offset_y": args.box_offset_y,
"proposal_classifier": str(args.proposal_classifier) if args.proposal_classifier else None, "proposal_classifier": str(args.proposal_classifier) if args.proposal_classifier else None,
"proposal_classifier_threshold": args.proposal_classifier_threshold if args.proposal_classifier else None, "proposal_classifier_threshold": args.proposal_classifier_threshold if args.proposal_classifier else None,
"proposal_crop_scale": args.proposal_crop_scale if args.proposal_classifier else None, "proposal_crop_scale": args.proposal_crop_scale if args.proposal_classifier else None,