完整示例

"""Detect faces in one image and save an annotated copy."""
import argparse
from pathlib import Path

import cv2
import inspireface as isf


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("image", type=Path)
    parser.add_argument("--model", type=Path, help="Path to an unpacked resource pack")
    parser.add_argument("--output", type=Path, default=Path("detected.jpg"))
    args = parser.parse_args()

    image = cv2.imread(str(args.image))
    if image is None:
        parser.error(f"Cannot read image: {args.image}")
    if args.model is None:
        isf.launch("Pikachu")  # Downloads the model on first use.
    else:
        isf.launch(resource_path=str(args.model))

    session = None
    try:
        session = isf.InspireFaceSession(
            isf.HF_ENABLE_NONE, isf.HF_DETECT_MODE_ALWAYS_DETECT,
            max_detect_num=10, detect_pixel_level=320,
        )
        faces = session.face_detection(image)
        print(f"Detected {len(faces)} faces")
        output = image.copy()
        for face in faces:
            x1, y1, x2, y2 = face.location
            cv2.rectangle(output, (x1, y1), (x2, y2), (60, 170, 80), 2)
            print(face.location, face.detection_confidence)
        if not cv2.imwrite(str(args.output), output):
            raise RuntimeError(f"Cannot write image: {args.output}")
        print(f"Saved {args.output}")
    finally:
        if session is not None:
            session.release()
        isf.terminate()


if __name__ == "__main__":
    main()

"""Compare two images that each contain exactly one face."""
import argparse
import cv2
import inspireface as isf


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("first")
    parser.add_argument("second")
    parser.add_argument("--model", required=True)
    args = parser.parse_args()
    images = [cv2.imread(path) for path in (args.first, args.second)]
    if any(image is None for image in images):
        parser.error("Both image paths must be readable")

    isf.launch(resource_path=args.model)
    session = None
    try:
        session = isf.InspireFaceSession(
            isf.HF_ENABLE_FACE_RECOGNITION, isf.HF_DETECT_MODE_ALWAYS_DETECT,
            max_detect_num=10, detect_pixel_level=320,
        )
        features = []
        for image in images:
            faces = session.face_detection(image)
            if len(faces) != 1:
                raise ValueError(f"Expected exactly one face; found {len(faces)}")
            features.append(session.face_feature_extract(image, faces[0]))
        similarity = isf.feature_comparison(features[0], features[1])
        threshold = isf.get_recommended_cosine_threshold()
        print(f"Cosine similarity: {similarity:.4f}")
        print(f"Model threshold: {threshold:.4f}")
        print(f"Above threshold: {similarity >= threshold}")
    finally:
        if session is not None:
            session.release()
        isf.terminate()


if __name__ == "__main__":
    main()

"""Select a stable face frame from a video using InspireFace 1.2.4."""
import argparse
import math
from pathlib import Path

import cv2
import inspireface as isf


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("video")
    parser.add_argument("--model", required=True)
    parser.add_argument("--output", default="captured.jpg")
    args = parser.parse_args()
    video = cv2.VideoCapture(args.video)
    if not video.isOpened():
        parser.error("Cannot open the input video")
    fps = video.get(cv2.CAP_PROP_FPS)
    if not math.isfinite(fps) or not 0 < fps <= 1000:
        video.release()
        parser.error("This example needs a video with a valid frame rate")

    launched = False
    try:
        isf.launch(resource_path=args.model)
        launched = True
        with isf.InspireFaceSession(
            isf.HF_ENABLE_NONE, isf.HF_DETECT_MODE_LIGHT_TRACK,
            max_detect_num=5, detect_pixel_level=320, auto_launch=False,
        ) as session:
            config = isf.FaceCaptureConfig.defaults()
            with session.create_face_capture(config) as capture:
                candidates = {}
                frame_id = 0
                while True:
                    ok, frame = video.read()
                    if not ok:
                        break
                    # Use video time, not the speed of this offline processing loop.
                    timestamp_ms = round(frame_id * 1000 / fps)
                    progress = capture.update(frame, frame_id, timestamp_ms)
                    results = capture.results()
                    kept_ids = {result.frame_id for result in results}
                    if frame_id in kept_ids:
                        candidates[frame_id] = frame.copy()
                    candidates = {key: value for key, value in candidates.items()
                                  if key in kept_ids}
                    print(frame_id, progress.state.name, int(progress.reject_reasons))
                    frame_id += 1
                    if progress.state == isf.FaceCaptureState.READY:
                        capture.finish()
                        selected = capture.results()[0]
                        output = Path(args.output)
                        output.parent.mkdir(parents=True, exist_ok=True)
                        if not cv2.imwrite(str(output), candidates[selected.frame_id]):
                            raise RuntimeError(f"Cannot write {output}")
                        print(f"Saved frame {selected.frame_id} to {output}")
                        return
                    if progress.state == isf.FaceCaptureState.FINISHED:
                        raise RuntimeError("Capture finished before becoming ready; start a new round")
                raise RuntimeError("Video ended before capture was ready")
    finally:
        video.release()
        if launched:
            isf.terminate()


if __name__ == "__main__":
    main()

"""Measure synchronous still-image detection latency (not camera FPS)."""
import argparse
import math
import statistics
import time

import cv2
import inspireface as isf


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("image")
    parser.add_argument("--model", required=True)
    parser.add_argument("--runs", type=int, default=100)
    parser.add_argument("--warmup", type=int, default=20)
    args = parser.parse_args()
    if args.runs < 1 or args.warmup < 0:
        parser.error("runs must be positive and warmup must be non-negative")
    image = cv2.imread(args.image)
    if image is None:
        parser.error("Cannot read the input image")
    isf.launch(resource_path=args.model)
    try:
        with isf.InspireFaceSession(
            isf.HF_ENABLE_NONE, isf.HF_DETECT_MODE_ALWAYS_DETECT,
            max_detect_num=10, detect_pixel_level=320, auto_launch=False,
        ) as session:
            with isf.ImageStream.load_from_cv_image(image) as stream:
                for _ in range(args.warmup):
                    session.face_detection(stream)
                elapsed = []
                counts = set()
                for _ in range(args.runs):
                    start = time.perf_counter_ns()
                    faces = session.face_detection(stream)
                    elapsed.append((time.perf_counter_ns() - start) / 1_000_000)
                    counts.add(len(faces))
        ordered = sorted(elapsed)
        p95 = ordered[math.ceil(0.95 * len(ordered)) - 1]
        print(f"native={isf.version()}, image={image.shape}, face_counts={sorted(counts)}")
        print(f"ALWAYS_DETECT, level=320, max_faces=10, runs={args.runs}")
        print(f"median={statistics.median(elapsed):.3f} ms, p95={p95:.3f} ms")
    finally:
        isf.terminate()


if __name__ == "__main__":
    main()