【代码学习】Mediapipe人脸检测使用记录_mediapipe 五官

Mediapipe，每秒200-300帧的实时人脸检测，提取画面中的人脸框，实现后续各种应用：人脸属性识别、表情识别、关键点检测、三维重建、增强现实、AI换妆等

code：google/mediapipe: Cross-platform, customizable ML solutions for live and streaming media. (github.com)

理论

  检测到的人脸集合，其中每个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、右耳和左耳）。边界框由xmin和width（均由[0.0, 1.0]图像宽度归一化）和ymin和（均由图像高度height归一化）组成。[0.0, 1.0]每个关键点由x和组成，分别由图像宽度和高度y归一化。[0.0, 1.0]

 
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()
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468个人脸关键点：

Diffusion Video Editing

结合论文：Speech Driven Video Editing via an Audio-Conditioned Diffusion Model

video preprocess

每个视频帧需要有一个矩形区域的脸掩盖。使用mediapipe，提取面部landmark来确定颌骨的位置。使用这些信息，mask掉覆盖鼻子下方区域的脸部矩形部分，如图1所示。这个掩码是在数据加载器运行时计算并应用到帧上的。

参考：一起来学MediaPipe（一）人脸及五官定位检测-阿里云开发者社区 (aliyun.com)