mediapipe之人脸特征点468检测

官网地址：

https://github.com/google/mediapipehttps://github.com/google/mediapipe

  

Mediapipe是google的一个开源项目，支持跨平台的常用ML方案。项目主页戳这里，可以看到很多常用的AI功能它都支持，举几个常用的例子:

1. 人脸检测
2. FaceMesh: 从图像/视频中重建出人脸的3D Mesh，可以用于AR渲染
3. 人像分割: 从图像/视频中把人分割出来，可用于视频会议，像Zoom/钉钉都有这样的功能
4. 手势跟踪：可以标出21个关键点的3D坐标
5. 人体姿态估计: 可以给出33个关键点的3D坐标
6. 头发上色：可以把头发检测出来，并图上颜色

直接上demo：

import cv2
import mediapipe as mp
import time

class FaceMeshDetector():
    def __init__(self,staticMode=False,maxFaces=2,minDetectionCon=0.5,minTrackCon=0.5):
        self.staticMode = staticMode
        self.maxFaces = maxFaces
        self.minDetectionCon = minDetectionCon
        self.minTrackCon = minTrackCon

        self.mpDraw = mp.solutions.drawing_utils
        self.mpFaceMesh = mp.solutions.face_mesh
        self.faceMesh = self.mpFaceMesh.FaceMesh(self.staticMode,self.maxFaces,False,self.minDetectionCon,self.minTrackCon)  # 一张脸显示
        # 設置点的大小和半径
        self.drawSpec = self.mpDraw.DrawingSpec(thickness=1, circle_radius=2)

    def findFaceMesh(self, img, draw=True):
        self.imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.faceMesh.process(self.imgRGB)
        faces = []
        if self.results.multi_face_landmarks:
            for faceLms in self.results.multi_face_landmarks:
                if draw:
                   self.mpDraw.draw_landmarks(img, faceLms, self.mpFaceMesh.FACEMESH_LEFT_EYEBROW, self.drawSpec,
                                           self.drawSpec)  # 设置点的大小
                face =[]
                for id,lm in enumerate(faceLms.landmark):
                    # print(lm)
                    ih, iw, ic = img.shape
                    x, y = int(lm.x * iw), int(lm.y * ih)
                    #绘制出坐标点的数字
                    cv2.putText(img, str(id), (x, y), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
                    face.append([x,y])
                faces.append(face)
        return img,faces


def main():
    cap = cv2.VideoCapture("../videos/heng.mp4")
    pTime = 0
    detector = FaceMeshDetector(maxFaces=2)
    while True:
        success, img = cap.read()
        img,faces = detector.findFaceMesh(img,False) #True表示绘制人脸，False表示不绘制人脸
        if len(faces)!=0:
            print(faces[0])
        cTime = time.time()
        fps = 1 / (cTime - pTime)
        pTime = cTime
        cv2.putText(img, f'FPS{int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 3)
        cv2.imshow("Image", img)
        cv2.waitKey(1)


if __name__ == "__main__":
    main()

效果：