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| import cv2
import numpy as np
import time
from typing import Tuple, List, Optional
class FaceDetector:
"""
人脸检测模块 - 针对边缘硬件优化
方案1: YOLOv8n (精度优先, 需Edge TPU)
方案2: Haar Cascade (速度优先, CPU-only)
方案3: MediaPipe (平衡方案)
论文策略: 根据硬件平台选择不同检测器
"""
def __init__(self, platform: str = 'rpi5'):
self.platform = platform
if platform == 'coral':
from pycoral.adapters import detect
from pycoral.utils.edgetpu import make_interpreter
self.interpreter = make_interpreter('yolov8n_face_edgetpu.tflite')
self.input_size = (320, 320)
elif platform == 'rpi5':
self.detector = cv2.dnn.readNetFromCaffe(
'deploy.prototxt',
'mobilenet_ssd.caffemodel'
)
self.input_size = (300, 300)
else:
import mediapipe as mp
self.mp_detection = mp.solutions.face_detection.FaceDetection(
model_selection=0,
min_detection_confidence=0.5
)
def detect(self, frame: np.ndarray) -> Optional[Tuple[int, int, int, int]]:
"""
检测人脸并返回最大人脸的边界框
Args:
frame: BGR图像 (H, W, 3)
Returns:
bbox: (x1, y1, x2, y2) 或 None
"""
h, w = frame.shape[:2]
if self.platform == 'coral':
return self._detect_coral(frame, w, h)
elif self.platform == 'rpi5':
return self._detect_rpi5(frame, w, h)
else:
return self._detect_mediapipe(frame, w, h)
def _detect_rpi5(self, frame: np.ndarray,
w: int, h: int) -> Optional[Tuple[int, int, int, int]]:
"""树莓派5 CPU检测 - 使用OpenCV DNN"""
blob = cv2.dnn.blobFromImage(
frame, 0.007843, self.input_size,
(127.5, 127.5, 127.5), swapRB=False, crop=False
)
self.detector.setInput(blob)
detections = self.detector.forward()
best_face = None
best_area = 0
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.5:
class_id = int(detections[0, 0, i, 1])
if class_id == 15:
x1 = int(detections[0, 0, i, 3] * w)
y1 = int(detections[0, 0, i, 4] * h)
x2 = int(detections[0, 0, i, 5] * w)
y2 = int(detections[0, 0, i, 6] * h)
area = (x2 - x1) * (y2 - y1)
if area > best_area:
best_area = area
best_face = (x1, y1, x2, y2)
return best_face
def _detect_coral(self, frame: np.ndarray,
w: int, h: int) -> Optional[Tuple[int, int, int, int]]:
"""Google Coral Edge TPU检测"""
input_data = cv2.resize(frame, self.input_size)
input_data = cv2.cvtColor(input_data, cv2.COLOR_BGR2RGB)
input_data = input_data.reshape(1, *self.input_size, 3)
input_data = input_data.astype(np.uint8)
from pycoral.adapters import common
common.set_input(self.interpreter, input_data)
self.interpreter.invoke()
from pycoral.adapters import detect
results = detect.get_objects(
self.interpreter, score_threshold=0.5
)
best_face = None
best_area = 0
for obj in results:
bbox = obj.bbox
x1 = int(bbox.xmin * w / self.input_size[0])
y1 = int(bbox.ymin * h / self.input_size[1])
x2 = int(bbox.xmax * w / self.input_size[0])
y2 = int(bbox.ymax * h / self.input_size[1])
area = (x2 - x1) * (y2 - y1)
if area > best_area:
best_area = area
best_face = (x1, y1, x2, y2)
return best_face
def _detect_mediapipe(self, frame: np.ndarray,
w: int, h: int) -> Optional[Tuple[int, int, int, int]]:
"""MediaPipe检测（跨平台后备）"""
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = self.mp_detection.process(rgb)
if not results.detections:
return None
best_face = None
best_area = 0
for detection in results.detections:
bbox = detection.location_data.relative_bounding_box
x1 = int(bbox.xmin * w)
y1 = int(bbox.ymin * h)
x2 = int((bbox.xmin + bbox.width) * w)
y2 = int((bbox.ymin + bbox.height) * h)
area = (x2 - x1) * (y2 - y1)
if area > best_area:
best_area = area
best_face = (x1, y1, x2, y2)
return best_face
|