用OpenCV实现对颜值打分的功能的方法是什么
Admin 2022-05-25 群英技术资讯 438 次浏览
在这篇文章中，我们来学习一下“用OpenCV实现对颜值打分的功能的方法是什么”的相关知识，下文有详细的讲解，易于大家学习和理解，有需要的朋友可以借鉴参考，下面就请大家跟着小编的思路一起来学习一下吧。颜值打分
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
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
获取脸上关键点轮廓的坐标，并且将相应的坐标标注出来，在标注点之间绘制连线（例如：左眼左眼角的识别点标号为33号）
# 颜值打分--五眼指标
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=0.5, #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
draw_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RGB
img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)
scaler=1
h,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),20,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),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=1
img = 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)
摄像头实时检测颜值打分
最后一部分代码是调用摄像头的模板，可以直接使用
关键步骤在代码注释中有体现
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=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_styles
draw_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'):
break
cap.release()
cv.destroyAllWindows()
好像也可以识别出哈士奇
达芬奇指标
这里更加深化了上面的代码，增加了更多的指标
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("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)
radius=12
lw=2
scaler=1
h,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),radius,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),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 2
NB=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 13
LC=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 17
LB=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_Y
Three_Section_Distance =100*np.diff(Six_Y)/Top_Down
Three_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 61
LL=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 291
LR=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 129
NL=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 358
NR=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 46
EBLL=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 105
EBLT=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 107
EBLR=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 336
EBRL=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 334
EBRT=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 276
EBRR=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_Right
EB_Metric_B=(EBRL_X-ERL_X)/Left_Right
EB_Metric_C=(EBLT_X-ELL_X)/Left_Right
EB_Metric_D=(EBRT_X-ERR_X)/Left_Right
EB_Metric_E=0.5*np.linalg.det([[EBLL_X,EBLL_Y,1],[ELL_X,ELL_Y,1],[NL_X,NL_Y,1]])/(Left_Right)**2
EB_Metric_F=0.5*np.linalg.det([[EBRR_X,EBRR_Y,1],[ERR_X,ERR_Y,1],[NR_X,NR_Y,1]])/(Left_Right)**2
cv.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 157
ELRT=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 154
ELRB=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 384
ERLT=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 381
ERRB=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=1
img = 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)
在这张图上体现了更加细致的指标
摄像头实时达芬奇颜值指标
与上面操作流程类似，可参考上面的流程
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=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_styles
draw_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
scaler = 1
radius = 12
lw = 2
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)
# 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 2
NB = 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 13
LC = 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 17
LB = 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_Y
Three_Section_Distance = 100 * np.diff(Six_Y) / Top_Down
Three_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 61
LL = 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 291
LR = 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 129
NL = 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 358
NR = 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 46
EBLL = 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 105
EBLT = 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 107
EBLR = 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 336
EBRL = 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 334
EBRT = 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 276
EBRR = 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_Right
EB_Metric_B = (EBRL_X - ERL_X) / Left_Right
EB_Metric_C = (EBLT_X - ELL_X) / Left_Right
EB_Metric_D = (EBRT_X - ERR_X) / Left_Right
EB_Metric_E = 0.5 * np.linalg.det([[EBLL_X, EBLL_Y, 1], [ELL_X, ELL_Y, 1], [NL_X, NL_Y, 1]]) / (Left_Right) ** 2
EB_Metric_F = 0.5 * np.linalg.det([[EBRR_X, EBRR_Y, 1], [ERR_X, ERR_Y, 1], [NR_X, NR_Y, 1]]) / (Left_Right) ** 2
cv.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 157
ELRT = 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 154
ELRB = 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 384
ERLT = 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 381
ERRB = 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)
img = 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)
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, 700 * 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()以上就是关于“用OpenCV实现对颜值打分的功能的方法是什么”的相关知识，感谢各位的阅读，想要掌握这篇文章的知识点还需要大家自己动手实践使用过才能领会，如果想了解更多相关内容的文章，欢迎关注群英网络，小编每天都会为大家更新不同的知识。
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OpenCV 颜值打分
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