"""Cross-validate OVFaceDetector vs ultralytics CPU baseline on real stills."""
import glob, sys, time
import numpy as np, cv2

imgs = sorted(glob.glob('/home/alex/Documents/ov-bench/imgs/*.png'))
sys.path.insert(0, '.')
from ov_detector import OVFaceDetector

# baseline: ultralytics torch CPU (deterministic on CPU)
from ultralytics import YOLO
base = YOLO('models/yolov8x6_animeface.pt')

ovd = OVFaceDetector('models/yolov8x6_animeface.onnx', device='GPU', imgsz=1280)

def iou(a, b):
    xx1, yy1 = max(a[0], b[0]), max(a[1], b[1])
    xx2, yy2 = min(a[2], b[2]), min(a[3], b[3])
    inter = max(0, xx2-xx1) * max(0, yy2-yy1)
    ua = (a[2]-a[0])*(a[3]-a[1]) + (b[2]-b[0])*(b[3]-b[1]) - inter
    return inter / max(ua, 1e-9)

ious, n_match, n_only_base, n_only_ov = [], 0, 0, 0
t0=time.time()
for p in imgs:
    fr = cv2.imread(p)
    rb = base.predict(fr, conf=0.25, imgsz=1280, device='cpu', verbose=False)[0]
    ro = ovd.predict(fr, conf=0.25, imgsz=1280, verbose=False)[0]
    bb = rb.boxes.xyxy.cpu().numpy() if rb.boxes is not None else np.zeros((0,4))
    bo = ro.boxes.xyxy.numpy()
    used = set()
    for box in bb:
        best, bi = 0, -1
        for j, ob in enumerate(bo):
            v = iou(box, ob)
            if v > best: best, bi = v, j
        if best > 0.5:
            ious.append(best); n_match += 1; used.add(bi)
        else: n_only_base += 1
    n_only_ov += len(bo) - len(used)
dt = time.time()-t0
print(f"imgs={len(imgs)} matched={n_match} (mean IoU {np.mean(ious):.3f}) only_base={n_only_base} only_ov={n_only_ov} [{dt:.0f}s total]")