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Running on Zero
Running on Zero
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8bf6610 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import mediapipe as mp
import numpy as np
from typing import Tuple, List
class CPUFaceHandler:
"""Handler for CPU-based face detection using MediaPipe.
(2 ms/frame)
This handler provides a simple interface for face detection using MediaPipe's
face detection model. It's optimized for CPU usage and provides basic face
detection functionality.
"""
def __init__(self, model_selection: int = 1, min_detection_confidence: float = 0.0):
"""Initialize the face detection handler."""
self.detector = mp.solutions.face_detection.FaceDetection(
model_selection=model_selection,
min_detection_confidence=min_detection_confidence,
)
def detect(self, image: np.ndarray) -> Tuple[int, List[int]]:
"""Detect faces in the given image.
Args:
image (np.ndarray): RGB image array.
Returns:
Tuple[int, List[int]]: A tuple containing:
- Number of faces detected (int)
- Bounding box coordinates [x1, y1, x2, y2] if exactly one face is detected,
empty list otherwise
"""
bboxs, scores = [], []
results = self.detector.process(image)
detection_result = results.detections
if detection_result is None:
return bboxs, scores
for detection in detection_result:
bboxC = detection.location_data.relative_bounding_box
x, y, w, h = bboxC.xmin, bboxC.ymin, bboxC.width, bboxC.height
x1, y1, x2, y2 = x, y, x + w, y + h
bboxs.append([x1, y1, x2, y2])
scores.append(detection.score[0])
return bboxs, scores
def __call__(self, image: np.ndarray) -> Tuple[int, List[int]]:
"""Make the handler callable.
Args:
image (np.ndarray): RGB image array.
Returns:
Tuple[int, List[int]]: Same as detect() method.
"""
return self.detect(image)
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