opencv+mediapipe实现人脸检测及摄像头实时示例
目录
- 单张人脸关键点检测
- 单张图像人脸检测
- 摄像头实时关键点检测
单张人脸关键点检测
定义可视化图像函数
导入三维人脸关键点检测模型 导入可视化函数和可视化样式 读取图像 将图像模型输入，获取预测结果 BGR转RGB 将RGB图像输入模型，获取预测结果 预测人人脸个数 可视化人脸关键点检测效果 绘制人来脸和重点区域轮廓线，返回annotated_image 绘制人脸轮廓、眼睫毛、眼眶、嘴唇 在三维坐标中分别可视化人脸网格、轮廓、瞳孔
import cv2 as cv
import mediapipe as mp
from tqdm import tqdm
import time
import matAbBCJplotlib.pyplot as plt
# 定义可视化图像函数
def look_img(img):
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
plt.imshow(img_RGB)
plt.show()
# 导入三维人脸关键点检测模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)
model=mp_face_mesh.FaceMesh(
static_image_mode=True,#TRUE:静态图片/False:摄像头实时读取
refine_landmarks=True,#使用Attention Mesh模型
min_detection_confidence=0.5, #置信度阈值，越接近1越准
min_tracking_confidence=0.5,#追踪阈值
)
# 导入可视化函数和可视化样式
mp_drawing=mp.solutions.drawing_utils
mp_drawing_styles=mp.solutions.drawing_styles
# 读取图像
img=cv.imread('img.png')
# look_img(img)
# 将图像模型输入，获取预测结果
# BGR转RGB
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
# 将RGB图像输入模型，获取预测结果
results=model.process(img_RGB)
# 预测人人脸个数
len(results.multi_face_landmarks)
print(len(results.multi_face_landmarks))
# 结果：1
# 可视化人脸关键点检测效果
# 绘制人来脸和重点区域轮廓线，返回annotated_image
annotated_image=img.copy()
if results.multi_face_landmarks: #如果检测出人脸
for face_landmarks in results.multi_face_landmarks:#遍历每一张脸
#绘制人脸网格
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_TESSELATION,
#landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
# landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1,circle_radius=2,color=[66,77,229]),
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
)
#绘制人脸轮廓、眼睫毛、眼眶、嘴唇
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_CONTOURS,
# landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
# landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1,circle_radius=2,color=[66,77,229]),
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
)
#绘制瞳孔区域
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_IRISES,
# landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1,circle_radius=2,color=[128,256,229]),
# landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_sAbBCJtyles.get_default_face_mesh_tesselation_style()
)
cv.imwrite('test.jpg',annotated_image)
look_img(annotated_image)
# 在三维坐标中分别可视化人脸网格、轮廓、瞳孔
mp_drawing.plot_landmarks(results.multi_face_landmarks[0],mp_face_mesh.FACEMESH_TESSELATION)
mp_drawing.plot_landmarks(results.multi_face_landmarks[0],mp_face_mesh.FACEMESH_CONTOURS)
mp_drawing.plot_landmarks(results.multi_face_landmarks[0],mp_face_mesh.FACEMESH_IRISES)
单张图像人脸检测
可以通过调用open3d实现3d模型建立，部分代码与上面类似
import cv2 as cv
import mediapipe as mp
import numpy as np
from tqdm import tqdm
import time
import matplotlib.pyplot as plt
# 定义可视化图像函数
def look_img(img):
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
plt.imshow(img_RGB)
plt.show()
# 导入三维人脸关键点检测模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)
model=mp_face_mesh.FaceMesh(
static_image_mode=True,#TRUE:静态图片/False:摄像头实时读取
refine_landmarks=True,#使用Attention Mesh模型
max_num_faces=40,
min_detection_confidence=0.2, #置信度阈值，越接近1越准
min_tracking_confidence=0.5,#追踪阈值
)
# 导入可视化函数和可视化样式
mp_drawing=mp.solutions.drawing_utils
# mp_drawing_styles=mp.solutions.drawing_styles
draw_spec=mp_drawing.DrawingSpec(thickness=2,circle_radius=1,color=[223,155,6])
# 读取图像
img=cv.imread('../人脸三维关键点检测/dkx.jpg')
# width=img1.shape[1]
# height=img1.shape[0]
# img=cv.resize(img1,(width*10,height*10))
# look_img(img)
# 将图像模型输入，获取预测结果
# BGR转RGB
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
# 将RGB图像输入模型，获取预测结果
results=model.process(img_RGB)
# # 预测人人脸个数
# len(results.multi_face_landmarks)
#
# print(len(results.multi_face_landmarks))
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
mp_drawing.draw_landmarks(
image=img,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_CONTOURS,
landmark_drawing_spec=draw_spec,
connection_drawing_spec=draw_spec
)
else:
print('未检测出人脸')
look_img(img)
mp_drawing.plot_landmarks(results.multi_face_landmarks[0],mp_face_mesh.FACEMESH_TESSELATION)
mp_drawing.plot_landmarks(results.multi_face_landmarks[1],mp_face_mesh.FACEMESH_CONTOURS)
mp_drawing.plot_landmarks(results.multi_face_landmarks[1],mp_face_mesh.FACEMESH_IRISES)
# 交互式三维可视化
coords=np.array(results.multi_face_landmarks[0].landmark)
# print(len(coords))
# print(coords)
def get_x(each):
return each.x
def get_y(each):
return each.y
def get_z(each):
return each.z
# 分别获取所有关键点的XYZ坐标
points_x=np.array(list(map(get_x,coords)))
points_y=np.array(list(map(get_y,coords)))
points_z=np.array(list(map(get_z,coords)))
# 将三个方向的坐标合并
points=np.vstack((points_x,points_y,points_z)).T
print(points.shape)
import open3d
point_cloud=open3d.geometry.PointCloud()
point_cloud.points=open3d.utility.Vector3dVector(points)
open3d.visualization.draw_geometries([point_cloud])
这是建立的3d的可视化模型，可以通过鼠标拖动将其旋转
摄像头实时关键点检测
定义可视化图像函数
导入三维人脸关键点检测模型 导入可视化函数和可视化样式 读取单帧函数 主要代码和上面的图像类似
import cv2 as cv
import mediapipe as mp
from tqdm import tqdm
import time
import matplotlib.pyplot as plt
# 导入三维人脸关键点检测模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)
model=mp_face_mesh.FaceMesh(
static_image_mode=False,#TRUE:静态图片/False:摄像头实时读取
refine_landmarks=True,#使用Attention Mesh模型
max_num_faces=5,#最多检测几张人脸
min_detection_confidence=0.5, #置信度阈值，越接近1越准
min_tracking_confidence=0.5,#追踪阈值
)
# 导入可视化函数和可视化样式
mp_drawing=mp.solutions.drawing_utils
mp_drawing_styles=mp.solutions.drawing_styles
# 处理单帧的函数
def process_frame(img):
#记录该帧处理的开始时间
start_time=time.time()
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
results=model.process(img_RGB)
if results.multi_face_landmarks:
for face_landmarks in results.multi_face_landmarks:
# mp_drawing.draw_detection(
编程客栈 # image=img,
# landmarks_list=face_landmarks,
# connections=mp_face_mesh.FACEMESH_TESSELATION,
# landmarks_drawing_spec=None,
# landmarks_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
# )
# 绘制人脸网格
mp_drawing.draw_landmarks(
image=img,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_TESSELATION,
# landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
# landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1,circle_radius=2,color=[66,77,229]),
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
)
# 绘制人脸轮廓、眼睫毛、眼眶、嘴唇
mp_drawing.draw_landmarks(
image=img,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_CONTOURS,
# landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
# landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1,circle_radius=2,color=[66,77,229]),
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style()
)
# 绘制瞳孔区域
mp_drawing.draw_landmarks(
image=img,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_IRISES,
# landmark_drawing_spec为关键点可视化样式，None为默认样式（不显示关键点）
# landmark_drawing_spec=mp_drawing_styles.DrawingSpec(thickness=1, circle_radius=2, color=[0, 1, 128]),
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_tesselation_style())
else:
img = cv.putText(img, 'NO FACE DELECTED', (25 , 50 ), cv.FONT_HERSHEY_SIMPLEX, 1.25,
(218, 112, 214), 1, 8)
#记录该帧处理完毕的时间
end_time=time.time()
#计算每秒处理图像的帧数FPS
FPS=1/(end_time-start_time)
scaler=1
img=cv.putText(img,'FPS'+str(int(FPS)),(25*scaler,100*scaler),cv.FOAbBCJNT_HERSHEY_SIMPLEX,1.25*scaler,(0,0,255),1,8)
return img
# 调用摄像头
cap=cv.VideoCapture(0)
c编程客栈ap.open(0)
# 无限循环，直到break被触发
while cap.isOpened():
success,frame=cap.read()
# if not success:
# print('ERROR')
# break
frame=process_frame(frame)
#展示处理后的三通道图像
cv.imshow('my_window',frame)
if cv.waitKey(1) &0xff==ord('q'):
break
cap.release()
cv.destroyAllWindows()
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