文章目錄

• • 單張人臉關(guān)鍵點檢測
  • 單張圖像人臉檢測
  • 攝像頭實時關(guān)鍵點檢測

單張人臉關(guān)鍵點檢測

定義可視化圖像函數(shù)
導(dǎo)入三維人臉關(guān)鍵點檢測模型
導(dǎo)入可視化函數(shù)和可視化樣式
讀取圖像
將圖像模型輸入，獲取預(yù)測結(jié)果
BGR轉(zhuǎn)RGB
將RGB圖像輸入模型，獲取預(yù)測結(jié)果
預(yù)測人人臉個數(shù)
可視化人臉關(guān)鍵點檢測效果
繪制人來臉和重點區(qū)域輪廓線，返回annotated_image
繪制人臉輪廓、眼睫毛、眼眶、嘴唇
在三維坐標(biāo)中分別可視化人臉網(wǎng)格、輪廓、瞳孔

import cv2 as cv
import  mediapipe as mp
from tqdm import tqdm
import time
import  matplotlib.pyplot as plt# 定義可視化圖像函數(shù)
def look_img(img):img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)plt.imshow(img_RGB)plt.show()# 導(dǎo)入三維人臉關(guān)鍵點檢測模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)model=mp_face_mesh.FaceMesh(static_image_mode=True,#TRUE:靜態(tài)圖片/False:攝像頭實時讀取refine_landmarks=True,#使用Attention Mesh模型min_detection_confidence=0.5, #置信度閾值，越接近1越準(zhǔn)min_tracking_confidence=0.5,#追蹤閾值
)# 導(dǎo)入可視化函數(shù)和可視化樣式
mp_drawing=mp.solutions.drawing_utils
mp_drawing_styles=mp.solutions.drawing_styles# 讀取圖像img=cv.imread('img.png')# look_img(img)# 將圖像模型輸入，獲取預(yù)測結(jié)果# BGR轉(zhuǎn)RGB
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)# 將RGB圖像輸入模型，獲取預(yù)測結(jié)果results=model.process(img_RGB)
# 預(yù)測人人臉個數(shù)
len(results.multi_face_landmarks)print(len(results.multi_face_landmarks))# 結(jié)果：1# 可視化人臉關(guān)鍵點檢測效果# 繪制人來臉和重點區(qū)域輪廓線，返回annotated_image
annotated_image=img.copy()
if results.multi_face_landmarks: #如果檢測出人臉for face_landmarks in results.multi_face_landmarks:#遍歷每一張臉#繪制人臉網(wǎng)格mp_drawing.draw_landmarks(image=annotated_image,landmark_list=face_landmarks,connections=mp_face_mesh.FACEMESH_TESSELATION,#landmark_drawing_spec為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）# 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())#繪制人臉輪廓、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為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）# 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())#繪制瞳孔區(qū)域mp_drawing.draw_landmarks(image=annotated_image,landmark_list=face_landmarks,connections=mp_face_mesh.FACEMESH_IRISES,# landmark_drawing_spec為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）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)
# 在三維坐標(biāo)中分別可視化人臉網(wǎng)格、輪廓、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)

單張圖像人臉檢測

可以通過調(diào)用open3d實現(xiàn)3d模型建立，部分代碼與上面類似

import cv2 as cv
import  mediapipe as mp
import numpy as np
from tqdm import tqdm
import time
import  matplotlib.pyplot as plt# 定義可視化圖像函數(shù)
def look_img(img):img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)plt.imshow(img_RGB)plt.show()# 導(dǎo)入三維人臉關(guān)鍵點檢測模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)model=mp_face_mesh.FaceMesh(static_image_mode=True,#TRUE:靜態(tài)圖片/False:攝像頭實時讀取refine_landmarks=True,#使用Attention Mesh模型max_num_faces=40,min_detection_confidence=0.2, #置信度閾值，越接近1越準(zhǔn)min_tracking_confidence=0.5,#追蹤閾值
)# 導(dǎo)入可視化函數(shù)和可視化樣式
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])
# 讀取圖像img=cv.imread('../人臉三維關(guān)鍵點檢測/dkx.jpg')
# width=img1.shape[1]
# height=img1.shape[0]
# img=cv.resize(img1,(width*10,height*10))
# look_img(img)# 將圖像模型輸入，獲取預(yù)測結(jié)果# BGR轉(zhuǎn)RGB
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)# 將RGB圖像輸入模型，獲取預(yù)測結(jié)果results=model.process(img_RGB)
# # 預(yù)測人人臉個數(shù)
# 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('未檢測出人臉')
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)# 交互式三維可視化
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# 分別獲取所有關(guān)鍵點的XYZ坐標(biāo)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)))# 將三個方向的坐標(biāo)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])

這是建立的3d的可視化模型，可以通過鼠標(biāo)拖動將其旋轉(zhuǎn)

攝像頭實時關(guān)鍵點檢測

定義可視化圖像函數(shù)
導(dǎo)入三維人臉關(guān)鍵點檢測模型
導(dǎo)入可視化函數(shù)和可視化樣式
讀取單幀函數(shù)
主要代碼和上面的圖像類似

import cv2 as cv
import  mediapipe as mp
from tqdm import tqdm
import time
import  matplotlib.pyplot as plt# 導(dǎo)入三維人臉關(guān)鍵點檢測模型
mp_face_mesh=mp.solutions.face_mesh
# help(mp_face_mesh.FaceMesh)model=mp_face_mesh.FaceMesh(static_image_mode=False,#TRUE:靜態(tài)圖片/False:攝像頭實時讀取refine_landmarks=True,#使用Attention Mesh模型max_num_faces=5,#最多檢測幾張人臉min_detection_confidence=0.5, #置信度閾值，越接近1越準(zhǔn)min_tracking_confidence=0.5,#追蹤閾值
)# 導(dǎo)入可視化函數(shù)和可視化樣式
mp_drawing=mp.solutions.drawing_utils
mp_drawing_styles=mp.solutions.drawing_styles# 處理單幀的函數(shù)def process_frame(img):#記錄該幀處理的開始時間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()# )# 繪制人臉網(wǎng)格mp_drawing.draw_landmarks(image=img,landmark_list=face_landmarks,connections=mp_face_mesh.FACEMESH_TESSELATION,# landmark_drawing_spec為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）# 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())# 繪制人臉輪廓、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為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）# 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())# 繪制瞳孔區(qū)域mp_drawing.draw_landmarks(image=img,landmark_list=face_landmarks,connections=mp_face_mesh.FACEMESH_IRISES,# landmark_drawing_spec為關(guān)鍵點可視化樣式，None為默認(rèn)樣式（不顯示關(guān)鍵點）# 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)#記錄該幀處理完畢的時間end_time=time.time()#計算每秒處理圖像的幀數(shù)FPSFPS=1/(end_time-start_time)scaler=1img=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# 調(diào)用攝像頭
cap=cv.VideoCapture(0)cap.open(0)
# 無限循環(huán)，直到break被觸發(fā)
while cap.isOpened():success,frame=cap.read()# if not success:#     print('ERROR')#     breakframe=process_frame(frame)#展示處理后的三通道圖像cv.imshow('my_window',frame)if cv.waitKey(1) &0xff==ord('q'):breakcap.release()
cv.destroyAllWindows()

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