opencv&mediapipe 人脸检测+摄像头实时 |
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opencv&mediapipe 人脸检测+摄像头实时
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文章目录
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 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 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_styles.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)
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])
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( # 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.FONT_HERSHEY_SIMPLEX,1.25*scaler,(0,0,255),1,8) return img # 调用摄像头 cap=cv.VideoCapture(0) cap.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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这是建立的3d的可视化模型，可以通过鼠标拖动将其旋转
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