[人工智能]【2】MediaPipe面部识别

上次因为课设写了MediaPipe的手势识别后就一直没有更新，今天写一下人脸检测
网页参考：https://google.github.io/mediapipe/solutions/face_detection.html

MediaPipe 人脸检测是一种超快的人脸检测解决方案，具有 6 个特征点和多面支持。它基于BlazeFace，是一种轻量级且性能良好的人脸检测器。

接口：

MODEL_SELECTION
整数索引或 .用于选择最适合距相机 2 米以内的人脸的短距离型号，以及最适合 5 米以内人脸的全范围型号。
MIN_DETECTION_CONFIDENCE
人脸检测模型中的最小置信度值 （），用于将检测视为成功。缺省值为0.5。

输出

检测到的人脸的集合，其中每张人脸都表示为一个检测原型消息，其中包含一个边界框和 6 个关键点（右眼、左眼、鼻尖、嘴巴中心、右耳垂体和左耳垂体）。

运行实例代码

！！！注意：

mediapipe版本：0.8.3.1
python版本：3.7.11

import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

# For webcam input:
cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(
    model_selection=0, min_detection_confidence=0.5) as face_detection:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = face_detection.process(image)

    # Draw the face detection annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.detections:
      for detection in results.detections:
        mp_drawing.draw_detection(image, detection)
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()

当然我们运行时候不出意外会报错

TypeError: __init__() got an unexpected keyword argument 'model_selection'

此时我们将

  with mp_face_detection.FaceDetection(model_selection=1, 
                                       min_detection_confidence=0.5) as face_detection:

中的model_selection删除或者注释掉即可，因为0.8.3的mediapipe不支持。

---------------------手动分隔符------------------

我们随意在网上找一个图片来尝试效果：

import cv2
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

# For static images:
IMAGE_FILES = []
with mp_face_detection.FaceDetection(
    model_selection=1, min_detection_confidence=0.5) as face_detection:
  for idx, file in enumerate(IMAGE_FILES):
    image = cv2.imread(file)
    # Convert the BGR image to RGB and process it with MediaPipe Face Detection.
    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Draw face detections of each face.
    if not results.detections:
      continue
    annotated_image = image.copy()
    for detection in results.detections:
      print('Nose tip:')
      print(mp_face_detection.get_key_point(
          detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
      mp_drawing.draw_detection(annotated_image, detection)
    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)

结果如下：


可以看到虽然右耳朵被遮挡但是依然被很好地推理出来了。