python校正人脸_人脸矫正代码

def face_alignment(faces,show=False):

'''

faces: num * width * height * channels ,value = 0~255, dtype = np.uint8,

note: width must equal to height

'''

print(faces.shape)

if len(faces.shape)==4 and faces.shape[3]==1:

faces = faces.reshape(faces.shape[:-1]) # if gray, turns to num * width * height, no channel axis 如果是灰度图，去掉最后一维，否则predictor会报错

num = faces.shape[0]

import numpy as np

faces_aligned = np.zeros(shape=faces.shape,dtype=np.uint8)

import dlib

predictor_path = "./shape_predictor_68_face_landmarks.dat" # dlib提供的训练好的68个人脸关键点的模型，网上可以下

predictor = dlib.shape_predictor(predictor_path) # 用来预测关键点

for i in range(num):

img = faces[i]

rec = dlib.rectangle(0,0,img.shape[0],img.shape[1])

shape = predictor(np.uint8(img),rec) # 注意输入的必须是uint8类型

order=[36,45,30,48,54] # left eye, right eye, nose, left mouth, right mouth 注意关键点的顺序，这个在网上可以找

if show:

plt.pyplot.figure()

plt.pyplot.imshow(img,cmap='gray')

for j in order:

x = shape.part(j).x

y = shape.part(j).y

plt.pyplot.scatter(x,y) # 可以plot出来看看效果，这里我只plot5个点

eye_center =( (shape.part(36).x + shape.part(45).x) * 1./2, # 计算两眼的中心坐标

(shape.part(36).y + shape.part(45).y) * 1./2)

dx = (shape.part(45).x - shape.part(36).x) # note: right - right

dy = (shape.part(45).y - shape.part(36).y)

import math

angle = math.atan2(dy,dx) * 180. / math.pi # 计算角度

# print angle

import cv2

RotateMatrix = cv2.getRotationMatrix2D(eye_center, angle, scale=1) # 计算仿射矩阵

RotImg = cv2.warpAffine(img, RotateMatrix, (img.shape[0], img.shape[1])) # 进行放射变换，即旋转

faces_aligned[i] = RotImg

return faces_aligned # uint8

import numpy as np

import os

import cv2

import math

import face_recognition

class face_detector():

def __init__(self):

self.cv2_base_dir = os.path.dirname(os.path.abspath(cv2.__file__))

self.haar_model1 = os.path.join(self.cv2_base_dir, 'data/haarcascade_frontalface_default.xml')

self.haar_model2 = os.path.join(self.cv2_base_dir, 'data/haarcascade_frontalface_alt.xml')

self.haar_model3 = os.path.join(self.cv2_base_dir, 'data/haarcascade_frontalface_alt2.xml')

self.haar_model4 = os.path.join(self.cv2_base_dir, 'data/haarcascade_frontalface_alt_tree.xml')

self.face_cascade = cv2.CascadeClassifier(self.haar_model2)

self.cap = cv2.VideoCapture(0)

self.total = 0

self.detected_num = 0

self.angle = 0

def capture(self):

while(1):

# get a frame

ret, frame = self.cap.read()

# show a frame

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

faces = self.face_cascade.detectMultiScale(gray, 1.3,5)

if(len(faces)>0):

self.detected_num += 1

self.total += 1

percent = self.detected_num/self.total

percent = str(percent)

#print("Detected " + percent + "\n" + "total number is " + str(self.total) + "\n" +

#"detected nunber is " + str(self.detected_num))

#from frame to detect angle

is_angle,angle,direction = self.Horizontal_angle(frame)

if(is_angle == 1):

#print("angle is " + str(angle) + "direction is " + str(direction))

if angle > self.angle:

self.angle = angle

print("max angle = {}".format(angle))

#print()

for(x,y,w,h) in faces:

self.x = x

self.y = y

cv2.putText(frame,str(angle),(400,50),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),2)

#cv2.addText(frame,str(angle),(10,10),cv2.FONT_HERSHEY_SIMPLEX )

cv2.rectangle(frame, (x,y), (x+w, y+h), (255,0,0),2)

else:

self.total += 1

#print("No face detected")

#print()

cv2.imshow("Detecting", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

self.cap.release()

cv2.destroyAllWindows()

def Horizontal_angle(self,input_frame):

face_landmark = face_recognition.face_landmarks(input_frame,model="large")

if len(face_landmark) == 0:

#no face landmark has been detected

return 0,-1,"no"

face_landmark_dict = face_landmark[0]

self.landmark = face_landmark_dict

right_eye_list = face_landmark_dict['right_eye']

left_eye_list = face_landmark_dict['left_eye']

right_eye_centre_x = 0

right_eye_centre_y = 0

left_eye_centre_x = 0

left_eye_centre_y = 0

for i in range(len(right_eye_list)):

right_eye_centre_x += right_eye_list[i][0]

right_eye_centre_y += right_eye_list[i][1]

for j in range(len(left_eye_list)):

left_eye_centre_x += left_eye_list[i][0]

left_eye_centre_y += left_eye_list[i][1]

right_eye_centre_x,right_eye_centre_y = right_eye_centre_x/float(len(right_eye_list)),right_eye_centre_y/float(len(right_eye_list))

left_eye_centre_x, left_eye_centre_y = left_eye_centre_x/float(len(left_eye_list)), left_eye_centre_y/float(len(left_eye_list))

#print("right is {}".format((right_eye_centre_x,right_eye_centre_y)))

#print("left is {}".format((left_eye_centre_x,left_eye_centre_y)))

direction = 'clockwise'

if right_eye_centre_y>left_eye_centre_y:

#C_x,C_y = 0,right_eye_centre_y

direction = 'counterclockwise'

AB_length = math.sqrt(math.pow((right_eye_centre_x-left_eye_centre_x),2)+math.pow((right_eye_centre_y-left_eye_centre_y),2))

AC_length = right_eye_centre_x

BC_length = math.sqrt(math.pow(left_eye_centre_x,2)+math.pow((left_eye_centre_y-right_eye_centre_y),2))

cosA = float((math.pow(AB_length,2)+math.pow(AC_length,2)-math.pow(BC_length,2)))/float(2*AB_length*AC_length)

#print(cosA)

#human head angle

if cosA > 1.0 or cosA < -1.0:

return 0

angle = (math.acos(cosA))*(180/float(math.pi))

#print(angle,direction)

#self.angle = angle

#self.direction = direction

return 1,angle,direction

if __name__ == '__main__':

work = face_detector()

work.capture()