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| import cv2
import numpy as np
from typing import Dict, Tuple, Optional
class FaceDetector:
"""
面部检测器
使用OpenCV进行实时面部检测
Euro NCAP要求：
- 实时检测（≥15fps）
- 遮挡容忍（眼镜/口罩）
- 光照鲁棒性
"""
def __init__(self,
cascade_path: str = None,
detection_confidence: float = 0.5):
"""
初始化
Args:
cascade_path: Haar级联文件路径
detection_confidence: 检测置信度阈值
"""
self.face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)
self.eye_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_eye.xml'
)
self.detection_confidence = detection_confidence
self.face_roi = None
self.left_eye_roi = None
self.right_eye_roi = None
def detect(self, frame: np.ndarray) -> Dict:
"""
检测面部和眼部
Args:
frame: 输入帧 (H, W, C)
Returns:
result: 检测结果
"""
result = {
'face_detected': False,
'face_bbox': None,
'eyes_detected': False,
'left_eye': None,
'right_eye': None,
'eye_openness': []
}
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = self.face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(60, 60)
)
if len(faces) == 0:
return result
face = max(faces, key=lambda x: x[2] * x[3])
x, y, w, h = face
result['face_detected'] = True
result['face_bbox'] = (x, y, w, h)
self.face_roi = gray[y:y+h, x:x+w]
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
eyes = self.eye_cascade.detectMultiScale(roi_gray)
if len(eyes) >= 2:
result['eyes_detected'] = True
eyes = sorted(eyes, key=lambda e: e[0])
ex, ey, ew, eh = eyes[0]
result['left_eye'] = (x + ex, y + ey, ew, eh)
self.left_eye_roi = roi_gray[ey:ey+eh, ex:ex+ew]
openness = self._calculate_eye_openness(self.left_eye_roi)
result['eye_openness'].append(('left', openness))
ex, ey, ew, eh = eyes[-1]
result['right_eye'] = (x + ex, y + ey, ew, eh)
self.right_eye_roi = roi_gray[ey:ey+eh, ex:ex+ew]
openness = self._calculate_eye_openness(self.right_eye_roi)
result['eye_openness'].append(('right', openness))
return result
def _calculate_eye_openness(self, eye_roi: np.ndarray) -> float:
"""
计算眼睑开度
Args:
eye_roi: 眼部区域灰度图
Returns:
openness: 开度值 (0-1)
"""
if eye_roi is None or eye_roi.size == 0:
return 0.0
_, binary = cv2.threshold(eye_roi, 50, 255, cv2.THRESH_BINARY)
white_pixels = np.sum(binary == 255)
total_pixels = binary.size
openness = white_pixels / total_pixels if total_pixels > 0 else 0.0
return openness
if __name__ == "__main__":
detector = FaceDetector()
frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
result = detector.detect(frame)
print(f"面部检测: {result['face_detected']}")
print(f"眼部检测: {result['eyes_detected']}")
if result['eye_openness']:
for eye, openness in result['eye_openness']:
print(f" {eye}眼开度: {openness:.2f}")
|