Comment exécuter un test privé d'un modèle de reconnaissance InsightFace

import cv2


def crop_face_with_expansion(image, bbox, expansion_factor=0.2, max_side=256):
    """
    Crop a single-person headshot with a small margin around the detected
    face box, and optionally downscale so the longest side is <= max_side.

    image:             input image as a numpy array (BGR or RGB).
    bbox:              face bounding box [x, y, width, height] from your
                       detector of choice (RetinaFace, SCRFD, etc.).
    expansion_factor:  total margin ratio (0.2 means +10% on each side).
    max_side:          if set, the longest side of the result is bounded.
    """
    img_h, img_w = image.shape[:2]
    x, y, w, h = bbox

    # 1. Margin distributed evenly to both sides.
    dw = int(w * expansion_factor / 2)
    dh = int(h * expansion_factor / 2)

    # 2. Expanded box, clamped to image bounds.
    x1 = max(0, x - dw)
    y1 = max(0, y - dh)
    x2 = min(img_w, x + w + dw)
    y2 = min(img_h, y + h + dh)

    face_img = image[y1:y2, x1:x2]

    # 3. Optional bounded downscale with a high-quality filter.
    if max_side is not None:
        curr_h, curr_w = face_img.shape[:2]
        longest = max(curr_h, curr_w)
        if longest > max_side:
            scale = max_side / longest
            new_w = int(curr_w * scale)
            new_h = int(curr_h * scale)
            face_img = cv2.resize(
                face_img, (new_w, new_h), interpolation=cv2.INTER_AREA,
            )

    return face_img


# Save the crop with a lossless format (PNG) before sending it to the API.
# Lossy formats (JPEG, WebP at low quality) introduce compression artifacts
# that can measurably reduce face recognition accuracy.
# cv2.imwrite("probe.png", crop)

"""Example client for the private InsightFace feature extraction service.

Uploads a single-person face image to the test endpoint, reads the returned
embedding, and saves it locally with np.save for offline evaluation.
"""

import argparse
import os.path as osp

import numpy as np
import requests


def extract_feature(image_path, output_path, url, model_name, timeout=120.0):
    filename = osp.basename(image_path)
    data = {"model_name": model_name}

    with open(image_path, "rb") as infile:
        files = {
            "image": (filename, infile, "application/octet-stream"),
        }
        try:
            resp = requests.post(
                url=url, files=files, data=data, timeout=timeout,
            )
            resp.raise_for_status()
        except requests.RequestException as ex:
            raise SystemExit(
                "request failed: %s: %s" % (type(ex).__name__, str(ex))
            )

    payload = resp.json()
    feature = payload.get("feature")
    if not isinstance(feature, list):
        raise SystemExit("server response feature must be a list")
    if len(feature) == 0:
        raise SystemExit(
            "server returned no features; verify the image contains a "
            "detectable face"
        )

    feat = np.asarray(feature, dtype=np.float32)
    np.save(output_path, feat)
    print("saved feature:", output_path, feat.shape, payload.get("model_name"))
    return feat


def main():
    parser = argparse.ArgumentParser(
        description=(
            "Upload a face image to the private InsightFace test server and "
            "save the embedding with np.save."
        ),
    )
    # The --server-url value is delivered to evaluators by email.
    parser.add_argument(
        "--server-url", required=True,
        help="Feature extraction API URL (sent privately by InsightFace)",
    )
    # The set of choices below is illustrative; the models actually unlocked
    # for your account are listed in your MOU and may evolve over time.
    parser.add_argument(
        "--model-name", required=True,
        choices=["buffalo", "dingo", "eagle"],
        help="Recognition model to evaluate (must match an entry in your MOU)",
    )
    parser.add_argument("--image", required=True, help="Input face image path (PNG recommended)")
    parser.add_argument("--output", required=True, help="Output .npy file path")
    parser.add_argument("--timeout", type=float, default=120.0, help="HTTP timeout in seconds")
    args = parser.parse_args()

    extract_feature(
        args.image, args.output, args.server_url, args.model_name,
        timeout=args.timeout,
    )


if __name__ == "__main__":
    main()