自学教程：OpenCV MediaPipe实现颜值打分功能

# 颜值打分--五眼指标import cv2 as cvimport  mediapipe as mpimport numpy as npfrom tqdm import tqdmimport timeimport  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=0.5, #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_stylesdraw_spec=mp_drawing.DrawingSpec(thickness=2,circle_radius=1,color=[66,77,229])img=cv.imread("img.png")# 将图像模型输入，获取预测结果# BGR转RGBimg_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)scaler=1h,w=img.shape[0],img.shape[1]# 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        )        for idx, coord in enumerate(face_landmarks.landmark):            cx = int(coord.x * w)            cy = int(coord.y * h)            img = cv.putText(img, ' FACE DELECTED', (25, 50), cv.FONT_HERSHEY_SIMPLEX, 0.1,                             (218, 112, 214), 1, 1)            img = cv.putText(img, str(idx), (cx, cy), cv.FONT_HERSHEY_SIMPLEX, 0.3,                             (218, 112, 214), 1, 1)else:    img = cv.putText(img, 'NO FACE DELECTED', (25, 50), cv.FONT_HERSHEY_SIMPLEX, 1.25,                     (218, 112, 214), 1, 8)look_img(img)cv.imwrite('face_id.jpg',img)# 连轮廓最左侧点FL=results.multi_face_landmarks[0].landmark[234];FL_X,FL_Y=int(FL.x*w),int(FL.y*h);FL_Color=(234,0,255)img=cv.circle(img,(FL_X,FL_Y),20,FL_Color,-1)look_img(img)# 脸上侧边缘FT=results.multi_face_landmarks[0].landmark[10];#  10 坐标为上图中标注的点的序号FT_X,FT_Y=int(FT.x*w),int(FT.y*h);FT_Color=(231,141,181)img=cv.circle(img,(FT_X,FT_Y),20,FT_Color,-1)look_img(img)# 下侧边缘FB=results.multi_face_landmarks[0].landmark[152];#  152 坐标为上图中标注的点的序号FB_X,FB_Y=int(FB.x*w),int(FB.y*h);FB_Color=(231,141,181)img=cv.circle(img,(FB_X,FB_Y),20,FB_Color,-1)look_img(img)# 右侧FR=results.multi_face_landmarks[0].landmark[454];#  454 坐标为上图中标注的点的序号FR_X,FR_Y=int(FR.x*w),int(FR.y*h);FR_Color=(0,255,0)img=cv.circle(img,(FR_X,FR_Y),20,FR_Color,-1)look_img(img)# 左眼左眼角ELL=results.multi_face_landmarks[0].landmark[33];#  33坐标为上图中标注的点的序号ELL_X,ELL_Y=int(ELL.x*w),int(ELL.y*h);ELL_Color=(0,255,0)img=cv.circle(img,(ELL_X,ELL_Y),20,ELL_Color,-1)look_img(img)#左眼右眼角ELR=results.multi_face_landmarks[0].landmark[133];#  133坐标为上图中标注的点的序号ELR_X,ELR_Y=int(ELR.x*w),int(ELR.y*h);ELR_Color=(0,255,0)img=cv.circle(img,(ELR_X,ELR_Y),20,ELR_Color,-1)look_img(img)# 右眼左眼角362ERL=results.multi_face_landmarks[0].landmark[362];#  133坐标为上图中标注的点的序号ERL_X,ERL_Y=int(ERL.x*w),int(ERL.y*h);ERL_Color=(233,255,128)img=cv.circle(img,(ERL_X,ERL_Y),20,ERL_Color,-1)look_img(img)# 右眼右眼角263ERR=results.multi_face_landmarks[0].landmark[263];#  133坐标为上图中标注的点的序号ERR_X,ERR_Y=int(ERR.x*w),int(ERR.y*h);ERR_Color=(23,255,128)img=cv.circle(img,(ERR_X,ERR_Y),20,ERR_Color,-1)look_img(img)# 从左往右六个点的横坐标Six_X=np.array([FL_X,ELL_X,ELR_X,ERL_X,ERR_X,FR_X])# 从最左到最右的距离Left_Right=FR_X-FL_X# 从左向右六个点的间隔的五个距离一并划归Five_Distance=100*np.diff(Six_X)/Left_Right# 两眼宽度的平均值Eye_Width_Mean=np.mean((Five_Distance[1],Five_Distance[3]))# 五个距离分别与两眼宽度均值的差Five_Eye_Diff=Five_Distance-Eye_Width_Mean# 求L2范数，作为颜值的指标Five_Eye_Metrics=np.linalg.norm(Five_Eye_Diff)cv.line(img,(FL_X,FT_Y),(FL_X,FB_Y),FL_Color,3)cv.line(img,(ELL_X,FT_Y),(ELL_X,FB_Y),ELL_Color,3)cv.line(img,(ELR_X,FT_Y),(ELR_X,FB_Y),ELR_Color,3)cv.line(img,(ERL_X,FT_Y),(ERL_X,FB_Y),ERL_Color,3)cv.line(img,(ERR_X,FT_Y),(ERR_X,FB_Y),ERR_Color,3)cv.line(img,(FR_X,FT_Y),(FR_X,FB_Y),FR_Color,3)cv.line(img,(FL_X,FT_Y),(FR_X,FT_Y),FT_Color,3)cv.line(img,(FL_X,FB_Y),(FR_X,FB_Y),FB_Color,3)scaler=1img = cv.putText(img, 'Five Eye Metrics{:.2f}'.format(Five_Eye_Metrics), (25, 50), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 6, 6)img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[0]), (25, 100), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 5, 5)img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[2]), (25, 150), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 4, 4)img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[4]), (25, 200), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 4)look_img(img)cv.imwrite("yanzhi.jpg",img)

import cv2 as cvimport  mediapipe as mpimport numpy as npfrom tqdm import tqdmimport timeimport  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=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,    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_stylesdraw_spec=mp_drawing.DrawingSpec(thickness=2,circle_radius=1,color=[66,77,229])landmark_drawing_spec=mp_drawing.DrawingSpec(thickness=1,circle_radius=2,color=[66,77,229])# 轮廓可视化connection_drawing_spec=mp_drawing.DrawingSpec(thickness=2,circle_radius=1,color=[233,155,6])# 处理帧函数def process_frame(img):    start_time = time.time()    scaler = 1    h, w = img.shape[0], img.shape[1]    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:            # 连轮廓最左侧点            FL = results.multi_face_landmarks[0].landmark[234];            FL_X, FL_Y = int(FL.x * w), int(FL.y * h);            FL_Color = (234, 0, 255)            img = cv.circle(img, (FL_X, FL_Y), 5, FL_Color, -1)            look_img(img)            # 脸上侧边缘            FT = results.multi_face_landmarks[0].landmark[10];  # 10 坐标为上图中标注的点的序号            FT_X, FT_Y = int(FT.x * w), int(FT.y * h);            FT_Color = (231, 141, 181)            img = cv.circle(img, (FT_X, FT_Y), 5, FT_Color, -1)            look_img(img)            # 下侧边缘            FB = results.multi_face_landmarks[0].landmark[152];  # 152 坐标为上图中标注的点的序号            FB_X, FB_Y = int(FB.x * w), int(FB.y * h);            FB_Color = (231, 141, 181)            img = cv.circle(img, (FB_X, FB_Y), 5, FB_Color, -1)            look_img(img)            # 右侧            FR = results.multi_face_landmarks[0].landmark[454];  # 454 坐标为上图中标注的点的序号            FR_X, FR_Y = int(FR.x * w), int(FR.y * h);            FR_Color = (0, 255, 0)            img = cv.circle(img, (FR_X, FR_Y), 5, FR_Color, -1)            look_img(img)            # 左眼左眼角            ELL = results.multi_face_landmarks[0].landmark[33];  # 33坐标为上图中标注的点的序号            ELL_X, ELL_Y = int(ELL.x * w), int(ELL.y * h);            ELL_Color = (0, 255, 0)            img = cv.circle(img, (ELL_X, ELL_Y), 5, ELL_Color, -1)            look_img(img)            # 左眼右眼角            ELR = results.multi_face_landmarks[0].landmark[133];  # 133坐标为上图中标注的点的序号            ELR_X, ELR_Y = int(ELR.x * w), int(ELR.y * h);            ELR_Color = (0, 255, 0)            img = cv.circle(img, (ELR_X, ELR_Y), 5, ELR_Color, -1)            look_img(img)            # 右眼左眼角362            ERL = results.multi_face_landmarks[0].landmark[362];  # 133坐标为上图中标注的点的序号            ERL_X, ERL_Y = int(ERL.x * w), int(ERL.y * h);            ERL_Color = (233, 255, 128)            img = cv.circle(img, (ERL_X, ERL_Y), 5, ERL_Color, -1)            look_img(img)            # 右眼右眼角263            ERR = results.multi_face_landmarks[0].landmark[263];  # 133坐标为上图中标注的点的序号            ERR_X, ERR_Y = int(ERR.x * w), int(ERR.y * h);            ERR_Color = (23, 255, 128)            img = cv.circle(img, (ERR_X, ERR_Y), 5, ERR_Color, -1)            look_img(img)            # 从左往右六个点的横坐标            Six_X = np.array([FL_X, ELL_X, ELR_X, ERL_X, ERR_X, FR_X])            # 从最左到最右的距离            Left_Right = FR_X - FL_X            # 从左向右六个点的间隔的五个距离一并划归            Five_Distance = 100 * np.diff(Six_X) / Left_Right            # 两眼宽度的平均值            Eye_Width_Mean = np.mean((Five_Distance[1], Five_Distance[3]))            # 五个距离分别与两眼宽度均值的差            Five_Eye_Diff = Five_Distance - Eye_Width_Mean            # 求L2范数，作为颜值的指标            Five_Eye_Metrics = np.linalg.norm(Five_Eye_Diff)            cv.line(img, (FL_X, FT_Y), (FL_X, FB_Y), FL_Color, 3)            cv.line(img, (ELL_X, FT_Y), (ELL_X, FB_Y), ELL_Color, 3)            cv.line(img, (ELR_X, FT_Y), (ELR_X, FB_Y), ELR_Color, 3)            cv.line(img, (ERL_X, FT_Y), (ERL_X, FB_Y), ERL_Color, 3)            cv.line(img, (ERR_X, FT_Y), (ERR_X, FB_Y), ERR_Color, 3)            cv.line(img, (FR_X, FT_Y), (FR_X, FB_Y), FR_Color, 3)            cv.line(img, (FL_X, FT_Y), (FR_X, FT_Y), FT_Color, 3)            cv.line(img, (FL_X, FB_Y), (FR_X, FB_Y), FB_Color, 3)            scaler = 1            img = cv.putText(img, 'Five Eye Metrics{:.2f}'.format(Five_Eye_Metrics), (25, 50), cv.FONT_HERSHEY_SIMPLEX,                             1,                             (218, 112, 214), 2, 6)            img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[0]), (25, 100), cv.FONT_HERSHEY_SIMPLEX, 1,                             (218, 112, 214), 2, 5)            img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[2]), (25, 150), cv.FONT_HERSHEY_SIMPLEX, 1,                             (218, 112, 214), 2, 4)            img = cv.putText(img, 'Distance 1{:.2f}'.format(Five_Eye_Diff[4]), (25, 200), cv.FONT_HERSHEY_SIMPLEX, 1,                             (218, 112, 214), 2, 4)    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, 300 * 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'):        breakcap.release()cv.destroyAllWindows()

import cv2 as cvimport  mediapipe as mpimport numpy as npfrom tqdm import tqdmimport timeimport  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_stylesdraw_spec=mp_drawing.DrawingSpec(thickness=2,circle_radius=1,color=[223,155,6])# 读取图像img=cv.imread("img.png")# 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RGBimg_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)radius=12lw=2scaler=1h,w=img.shape[0],img.shape[1]# 将RGB图像输入模型，获取预测结果# # 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        )        for idx, coord in enumerate(face_landmarks.landmark):            cx = int(coord.x * w)            cy = int(coord.y * h)            img = cv.putText(img, ' FACE DELECTED', (25, 50), cv.FONT_HERSHEY_SIMPLEX, 0.1,                             (218, 112, 214), 1, 1)            img = cv.putText(img, str(idx), (cx, cy), cv.FONT_HERSHEY_SIMPLEX, 0.3,                             (218, 112, 214), 1, 1)else:    img = cv.putText(img, 'NO FACE DELECTED', (25, 50), cv.FONT_HERSHEY_SIMPLEX, 1.25,                     (218, 112, 214), 1, 8)look_img(img)# 连轮廓最左侧点FL=results.multi_face_landmarks[0].landmark[234];FL_X,FL_Y=int(FL.x*w),int(FL.y*h);FL_Color=(234,0,255)img=cv.circle(img,(FL_X,FL_Y),radius,FL_Color,-1)look_img(img)# 脸上侧边缘FT=results.multi_face_landmarks[0].landmark[10];#  10 坐标为上图中标注的点的序号FT_X,FT_Y=int(FT.x*w),int(FT.y*h);FT_Color=(231,141,181)img=cv.circle(img,(FT_X,FT_Y),radius,FT_Color,-1)look_img(img)# 下侧边缘FB=results.multi_face_landmarks[0].landmark[152];#  152 坐标为上图中标注的点的序号FB_X,FB_Y=int(FB.x*w),int(FB.y*h);FB_Color=(231,141,181)img=cv.circle(img,(FB_X,FB_Y),radius,FB_Color,-1)look_img(img)# 右侧FR=results.multi_face_landmarks[0].landmark[454];#  454 坐标为上图中标注的点的序号FR_X,FR_Y=int(FR.x*w),int(FR.y*h);FR_Color=(0,255,0)img=cv.circle(img,(FR_X,FR_Y),radius,FR_Color,-1)look_img(img)# 左眼左眼角ELL=results.multi_face_landmarks[0].landmark[33];#  33坐标为上图中标注的点的序号ELL_X,ELL_Y=int(ELL.x*w),int(ELL.y*h);ELL_Color=(0,255,0)img=cv.circle(img,(ELL_X,ELL_Y),radius,ELL_Color,-1)look_img(img)#左眼右眼角ELR=results.multi_face_landmarks[0].landmark[133];#  133坐标为上图中标注的点的序号ELR_X,ELR_Y=int(ELR.x*w),int(ELR.y*h);ELR_Color=(0,255,0)img=cv.circle(img,(ELR_X,ELR_Y),radius,ELR_Color,-1)look_img(img)# 右眼左眼角362ERL=results.multi_face_landmarks[0].landmark[362];#  133坐标为上图中标注的点的序号ERL_X,ERL_Y=int(ERL.x*w),int(ERL.y*h);ERL_Color=(233,255,128)img=cv.circle(img,(ERL_X,ERL_Y),radius,ERL_Color,-1)look_img(img)# 右眼右眼角263ERR=results.multi_face_landmarks[0].landmark[263];#  133坐标为上图中标注的点的序号ERR_X,ERR_Y=int(ERR.x*w),int(ERR.y*h);ERR_Color=(23,255,128)img=cv.circle(img,(ERR_X,ERR_Y),radius,ERR_Color,-1)look_img(img)# 从左往右六个点的横坐标Six_X=np.array([FL_X,ELL_X,ELR_X,ERL_X,ERR_X,FR_X])# 从最左到最右的距离Left_Right=FR_X-FL_X# 从左向右六个点的间隔的五个距离一并划归Five_Distance=100*np.diff(Six_X)/Left_Right# 两眼宽度的平均值Eye_Width_Mean=np.mean((Five_Distance[1],Five_Distance[3]))# 五个距离分别与两眼宽度均值的差Five_Eye_Diff=Five_Distance-Eye_Width_Mean# 求L2范数，作为颜值的指标Five_Eye_Metrics=np.linalg.norm(Five_Eye_Diff)# 三庭# 眉心MX=results.multi_face_landmarks[0].landmark[9];#  9 坐标为上图中标注的点的序号MX_X,MX_Y=int(MX.x*w),int(MX.y*h);MX_Color=(29,123,234)img=cv.circle(img,(MX_X,MX_Y),radius,MX_Color,-1)look_img(img)# 鼻翼下缘 2NB=results.multi_face_landmarks[0].landmark[2];#  2 坐标为上图中标注的点的序号NB_X,NB_Y=int(NB.x*w),int(NB.y*h);NB_Color=(180,187,28)img=cv.circle(img,(NB_X,NB_Y),radius,NB_Color,-1)look_img(img)# 嘴唇中心  13LC=results.multi_face_landmarks[0].landmark[13];#  17 坐标为上图中标注的点的序号LC_X,LC_Y=int(LC.x*w),int(LC.y*h);LC_Color=(0,0,258)img=cv.circle(img,(LC_X,LC_Y),radius,LC_Color,-1)look_img(img)# 嘴唇下缘  17LB=results.multi_face_landmarks[0].landmark[17];#  17 坐标为上图中标注的点的序号LB_X,LB_Y=int(LB.x*w),int(LB.y*h);LB_Color=(139,0,0)img=cv.circle(img,(LB_X,LB_Y),radius,LB_Color,-1)look_img(img)Six_Y=np.array([FT_Y,MX_Y,NB_Y,LC_Y,LB_Y,FB_Y])Top_Down=FB_Y-FT_YThree_Section_Distance =100*np.diff(Six_Y)/Top_DownThree_Section_Mrtric_A=np.abs(Three_Section_Distance[1]-sum(Three_Section_Distance[2:]))# 鼻下到唇心距离 占第三庭的三分之一Three_Section_Mrtric_B=np.abs(Three_Section_Distance[2]-sum(Three_Section_Distance[2:])/3)#唇心到下巴尖距离 占 第三庭的二分之一Three_Section_Mrtric_C=np.abs(sum(Three_Section_Distance[3:])-sum(Three_Section_Distance[2:])/2)# 达芬奇# 嘴唇左角  61LL=results.multi_face_landmarks[0].landmark[61];#  61 坐标为上图中标注的点的序号LL_X,LL_Y=int(LL.x*w),int(LL.y*h);LL_Color=(255,255,255)img=cv.circle(img,(LL_X,LL_Y),radius,LL_Color,-1)look_img(img)# 嘴唇右角 291LR=results.multi_face_landmarks[0].landmark[291];#  291 坐标为上图中标注的点的序号LR_X,LR_Y=int(LR.x*w),int(LR.y*h);LR_Color=(255,255,255)img=cv.circle(img,(LR_X,LR_Y),radius,LR_Color,-1)look_img(img)# 鼻子左缘 129NL=results.multi_face_landmarks[0].landmark[129];#  291 坐标为上图中标注的点的序号NL_X,NL_Y=int(NL.x*w),int(NL.y*h);NL_Color=(255,255,255)img=cv.circle(img,(NL_X,NL_Y),radius,NL_Color,-1)look_img(img)# 鼻子右缘 358NR=results.multi_face_landmarks[0].landmark[358];#  358 坐标为上图中标注的点的序号NR_X,NR_Y=int(NR.x*w),int(NR.y*h);NR_Color=(255,255,255)img=cv.circle(img,(NR_X,NR_Y),radius,NR_Color,-1)look_img(img)# 嘴宽为鼻宽的1.5/1.6倍Da_Vinci=(LR.x-LL.x)/(NR.x-NL.x)# 眉毛# 左眉毛左眉角 46EBLL=results.multi_face_landmarks[0].landmark[46];#  46 坐标为上图中标注的点的序号EBLL_X,EBLL_Y=int(EBLL.x*w),int(EBLL.y*h);EBLL_Color=(255,355,155)img=cv.circle(img,(EBLL_X,EBLL_Y),radius,EBLL_Color,-1)look_img(img)# 左眉毛眉峰 105EBLT=results.multi_face_landmarks[0].landmark[105];#  105 坐标为上图中标注的点的序号EBLT_X,EBLT_Y=int(EBLT.x*w),int(EBLT.y*h);EBLT_Color=(255,355,155)img=cv.circle(img,(EBLT_X,EBLT_Y),radius,EBLT_Color,-1)look_img(img)#左眉毛右角 107EBLR=results.multi_face_landmarks[0].landmark[107];#  107 坐标为上图中标注的点的序号EBLR_X,EBLR_Y=int(EBLR.x*w),int(EBLR.y*h);EBLR_Color=(255,355,155)img=cv.circle(img,(EBLR_X,EBLR_Y),radius,EBLR_Color,-1)look_img(img)# 右眉毛左角 336EBRL=results.multi_face_landmarks[0].landmark[336];#  336 坐标为上图中标注的点的序号EBRL_X,EBRL_Y=int(EBRL.x*w),int(EBRL.y*h);EBRL_Color=(295,355,105)img=cv.circle(img,(EBRL_X,EBRL_Y),radius,EBRL_Color,-1)look_img(img)# 右眉毛眉峰 334EBRT=results.multi_face_landmarks[0].landmark[334];#  334 坐标为上图中标注的点的序号EBRT_X,EBRT_Y=int(EBRT.x*w),int(EBRT.y*h);EBRT_Color=( 355,155,155)img=cv.circle(img,(EBRT_X,EBRT_Y),radius,EBRT_Color,-1)look_img(img)# 右眉毛右角 276EBRR=results.multi_face_landmarks[0].landmark[276];#  107 坐标为上图中标注的点的序号EBRR_X,EBRR_Y=int(EBRR.x*w),int(EBRR.y*h);EBRR_Color=(155,305,195)img=cv.circle(img,(EBRR_X,EBRR_Y),radius,EBRR_Color,-1)look_img(img)# 眉头是否在眼角的正上方EB_Metric_A=(EBLR_X-ELR_X)/Left_RightEB_Metric_B=(EBRL_X-ERL_X)/Left_RightEB_Metric_C=(EBLT_X-ELL_X)/Left_RightEB_Metric_D=(EBRT_X-ERR_X)/Left_RightEB_Metric_E=0.5*np.linalg.det([[EBLL_X,EBLL_Y,1],[ELL_X,ELL_Y,1],[NL_X,NL_Y,1]])/(Left_Right)**2EB_Metric_F=0.5*np.linalg.det([[EBRR_X,EBRR_Y,1],[ERR_X,ERR_Y,1],[NR_X,NR_Y,1]])/(Left_Right)**2cv.line(img,(EBLL_X,EBLL_Y),(ELL_X,ELL_Y),EBLL_Color,lw)cv.line(img,(ELL_X,ELL_Y),(NL_X,NL_Y),EBLL_Color,lw)cv.line(img,(EBLL_X,EBLL_Y),(NL_X,NL_Y),EBLL_Color,lw)cv.line(img,(EBRR_X,EBRR_Y),(ERR_X,ERR_Y),EBLL_Color,lw)cv.line(img,(EBRR_X,EBRR_Y),(NR_X,NR_Y),EBLL_Color,lw)cv.line(img,(EBRR_X,EBRR_Y),(NR_X,NR_Y),EBLL_Color,lw)look_img(img)#左内眼角上点 157ELRT=results.multi_face_landmarks[0].landmark[157];#  157 坐标为上图中标注的点的序号ELRT_X,ELRT_Y=int(ELRT.x*w),int(ELRT.y*h);ELRT_Color=(155,305,195)img=cv.circle(img,(ELRT_X,ELRT_Y),radius,ELRT_Color,-1)look_img(img)#左内眼角下点   154ELRB=results.multi_face_landmarks[0].landmark[154];#  154 坐标为上图中标注的点的序号ELRB_X,ELRB_Y=int(ELRB.x*w),int(ELRB.y*h);ELRB_Color=(155,305,195)img=cv.circle(img,(ELRB_X,ELRB_Y),radius,ELRB_Color,-1)look_img(img)#右内眼角上点 384ERLT=results.multi_face_landmarks[0].landmark[384];#  384 坐标为上图中标注的点的序号ERLT_X,ERLT_Y=int(ERLT.x*w),int(ERLT.y*h);ERLT_Color=(155,305,195)img=cv.circle(img,(ERLT_X,ERLT_Y),radius,ERLT_Color,-1)look_img(img)# 右内眼角下点  381ERRB=results.multi_face_landmarks[0].landmark[381];#  384 坐标为上图中标注的点的序号ERRB_X,ERRB_Y=int(ERRB.x*w),int(ERRB.y*h);ERRB_Color=(155,305,195)img=cv.circle(img,(ERRB_X,ERRB_Y),radius,ERRB_Color,-1)look_img(img)# 角度vector_a=np.array([ELRT_X-ELR_X,ELRT_Y-ELR_Y])vector_b=np.array([ELRB_X-ELR_X,ELRB_Y-ELR_Y])cos=vector_a.dot(vector_b)/(np.linalg.norm(vector_a)*np.linalg.norm(vector_b))EB_Metric_G=np.degrees(np.arccos(cos))vector_a=np.array([ERLT_X-ERL_X,ERLT_Y-ERL_Y])vector_b=np.array([ERRB_X-ERL_X,ERRB_Y-ERL_Y])cos=vector_a.dot(vector_b)/(np.linalg.norm(vector_a)*np.linalg.norm(vector_b))EB_Metric_H=np.degrees(np.arccos(cos))# 可视化cv.line(img,(FL_X,FT_Y),(FL_X,FB_Y),FL_Color,3)cv.line(img,(ELL_X,FT_Y),(ELL_X,FB_Y),ELL_Color,3)cv.line(img,(ELR_X,FT_Y),(ELR_X,FB_Y),ELR_Color,3)cv.line(img,(ERL_X,FT_Y),(ERL_X,FB_Y),ERL_Color,3)cv.line(img,(ERR_X,FT_Y),(ERR_X,FB_Y),ERR_Color,3)cv.line(img,(FR_X,FT_Y),(FR_X,FB_Y),FR_Color,3)cv.line(img,(FL_X,FT_Y),(FR_X,FT_Y),FT_Color,3)cv.line(img,(FL_X,FB_Y),(FR_X,FB_Y),FB_Color,3)cv.line(img,(FL_X,MX_Y),(FR_X,MX_Y),MX_Color,lw)cv.line(img,(FL_X,NB_Y),(FR_X,NB_Y),NB_Color,lw)cv.line(img,(FL_X,LC_Y),(FR_X,LC_Y),LC_Color,lw)cv.line(img,(FL_X,LB_Y),(FR_X,LB_Y),LB_Color,lw)scaler=1img = cv.putText(img, 'Five Eye Metrics{:.2f}'.format(Five_Eye_Metrics), (25, 50), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 3, 10)img = cv.putText(img, 'A{:.2f}'.format(Five_Eye_Diff[0]), (25, 100), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 3, 10)img = cv.putText(img, 'B{:.2f}'.format(Five_Eye_Diff[2]), (25, 150), cv.FONT_HERSHEY_SIMPLEX, 1,                 (218, 112, 214), 3, 10)img = cv.putText(img, 'C{:.2f}'.format(Five_Eye_Diff[4]), (25, 200), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 10)img = cv.putText(img, 'Three Scetion{:.2f}'.format(Three_Section_Mrtric_A), (25, 300), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 10)img = cv.putText(img, '1/3{:.2f}'.format(Three_Section_Mrtric_B), (25, 400), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 10)img = cv.putText(img, '1/2{:.2f}'.format(Three_Section_Mrtric_C), (25, 500), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 10)img = cv.putText(img, 'Da Vinci{:.2f}'.format(Da_Vinci), (25, 600), cv.FONT_HERSHEY_SIMPLEX,1,                 (218, 112, 214), 3, 10)look_img(img)