"""imgutils model wrappers — the figure DETECTOR + the CCIP EMBEDDER.

⚠️ VERIFY ON FIRST RUN: the exact imgutils function names/signatures + the CCIP
model string can drift between dghs-imgutils releases. These are the two seams to
check against your installed version (`pip show dghs-imgutils`):
  - detect_person(image, level=...) -> [((x0,y0,x1,y1), label, score), ...]
  - ccip_extract_feature(image, model=...) -> a vector (768-d for caformer)
imgutils auto-downloads the ONNX models from HuggingFace on first use; GPU is
used when onnxruntime-gpu is installed.
"""
import numpy as np
from PIL import Image


def detect_figures(image: Image.Image, level: str = "m") -> list[tuple[tuple, float | None]]:
    """Person/figure bounding boxes, NORMALIZED (x, y, w, h in [0,1]) + score.
    Returns [] if detection finds nothing (caller falls back to whole-image)."""
    from imgutils.detect import detect_person

    iw, ih = image.size
    out = []
    for (x0, y0, x1, y1), _label, score in detect_person(image, level=level):
        out.append((
            (x0 / iw, y0 / ih, (x1 - x0) / iw, (y1 - y0) / ih),
            float(score),
        ))
    return out


def ccip_vector(image: Image.Image, model: str | None = None) -> list[float]:
    """The CCIP identity embedding of a (cropped) character image, as a plain
    float list ready to POST."""
    from imgutils.metrics import ccip_extract_feature

    feat = (
        ccip_extract_feature(image, model=model)
        if model else ccip_extract_feature(image)
    )
    return np.asarray(feat, dtype=np.float32).reshape(-1).tolist()