mediapipe facemesh
时间: 2023-05-02 20:00:32 浏览: 70
MediaPipe FaceMesh是一款功能强大的人脸识别和面部特征提取的开源工具,可用于各种应用,如虚拟现实、面部跟踪、表情识别等。该工具基于机器学习技术,使用卷积神经网络对人脸进行识别和追踪,并且能够准确地提取面部特征点,如眼睛、口唇、鼻子等。
相关问题
mediapipe facemesh实现3维人脸特效实例代码
以下是使用Mediapipe Facemesh实现3D人脸特效的示例代码:
```python
import cv2
import mediapipe as mp
import numpy as np
# 初始化Mediapipe Facemesh
mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh
# 加载3D模型
obj_file = 'path/to/3d/model.obj'
mesh = pymesh.load_mesh(obj_file)
def draw_mesh(image, landmarks):
# 绘制三角形网格
for triangle in mesh.faces:
pt1 = landmarks[triangle[0]]
pt2 = landmarks[triangle[1]]
pt3 = landmarks[triangle[2]]
pt1 = (int(pt1.x), int(pt1.y))
pt2 = (int(pt2.x), int(pt2.y))
pt3 = (int(pt3.x), int(pt3.y))
cv2.line(image, pt1, pt2, (0, 255, 0), 1)
cv2.line(image, pt2, pt3, (0, 255, 0), 1)
cv2.line(image, pt3, pt1, (0, 255, 0), 1)
def main():
# 打开摄像头
cap = cv2.VideoCapture(0)
# 初始化Mediapipe Facemesh
with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
while True:
# 读取视频帧
ret, image = cap.read()
if not ret:
break
# 转换为RGB格式
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# 检测人脸关键点
results = face_mesh.process(image)
# 绘制关键点和三角形网格
if results.multi_face_landmarks:
for landmarks in results.multi_face_landmarks:
mp_drawing.draw_landmarks(image, landmarks)
draw_mesh(image, landmarks.landmark)
# 转换为BGR格式
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# 显示视频帧
cv2.imshow('Facemesh 3D', image)
if cv2.waitKey(1) == ord('q'):
break
# 释放摄像头
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
```
注意:上述代码中的`pymesh`是一个Python库,用于加载和操作3D模型。在运行代码之前,请确保已安装此库。
mediapipe的人脸识别
MediaPipe提供了一个名为"FaceMesh"的模型,用于实时检测人脸的468个特征点。通过将输入数据传递给MediaPipe图的输入节点,并通过图的输出节点获取结果,可以进行人脸特征点检测。这个技术可以用于人脸跟踪,通过检测人脸的关键特征点,可以实现对人脸的跟踪和识别。这在人脸识别、虚拟现实、增强现实等领域有广泛的应用。<span class="em">1</span><span class="em">2</span><span class="em">3</span>