python人脸姿态_使用opencv和dlib进行人脸姿态估计(python)

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概述

在计算机视觉中，物体的姿态是指相对于相机的相对取向和位置。

本文主要参考了《Head Pose Estimation using OpenCV and Dlib》这篇文章。

进行人脸姿态估计的目的就是获取人脸相对相机的朝向：

人脸姿态估计的思想：旋转三维标准模型一定角度，直到模型上“三维特征点”的“2维投影”，与待测试图像上的特征点(图像上的特征点显然是2维)尽量重合。

代码

从笔记本摄像头取视频，每一帧用dlib检测人脸关键点，用opencv做姿态估计。并实时显示在屏幕上。

姿态估计部分的代码参考了《Head Pose Estimation using OpenCV and Dlib》这篇文章。

opencv输出的是旋转矩阵，我参考《基于Dlib和OpenCV的人脸姿态估计(HeadPoseEstimation)》这篇文章的方法，将旋转矩阵转化为4元数，再转化为欧拉角(弧度)。

这篇文章的坑就是，最终转化出来的欧拉角单位是弧度，需要除以Pi乘以180得到度的单位值。原文的代码是c++，我转化为python3了。

下面的例子是我最终测试完毕没问题的代码。

#!/usr/bin/env python

import cv2

import numpy as np

import dlib

import time

import math

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

POINTS_NUM_LANDMARK = 68

# 获取最大的人脸

def _largest_face(dets):

if len(dets) == 1:

return 0

face_areas = [ (det.right()-det.left())*(det.bottom()-det.top()) for det in dets]

largest_area = face_areas[0]

largest_index = 0

for index in range(1, len(dets)):

if face_areas[index] > largest_area :

largest_index = index

largest_area = face_areas[index]

print("largest_face index is {} in {} faces".format(largest_index, len(dets)))

return largest_index

# 从dlib的检测结果抽取姿态估计需要的点坐标

def get_image_points_from_landmark_shape(landmark_shape):

if landmark_shape.num_parts != POINTS_NUM_LANDMARK:

print("ERROR:landmark_shape.num_parts-{}".format(landmark_shape.num_parts))

return -1, None

#2D image points. If you change the image, you need to change vector

image_points = np.array([

(landmark_shape.part(30).x, landmark_shape.part(30).y), # Nose tip

(landmark_shape.part(8).x, landmark_shape.part(8).y), # Chin

(landmark_shape.part(36).x, landmark_shape.part(36).y), # Left eye left corner

(landmark_shape.part(45).x, landmark_shape.part(45).y), # Right eye right corne

(landmark_shape.part(48).x, landmark_shape.part(48).y), # Left Mouth corner

(landmark_shape.part(54).x, landmark_shape.part(54).y) # Right mouth corner

], dtype="double")

return 0, image_points

# 用dlib检测关键点，返回姿态估计需要的几个点坐标

def get_image_points(img):

#gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY ) # 图片调整为灰色

dets = detector( img, 0 )

if 0 == len( dets ):

print( "ERROR: found no face" )

return -1, None

largest_index = _largest_face(dets)

face_rectangle = dets[largest_index]

landmark_shape = predictor(img, face_rectangle)

return get_image_points_from_landmark_shape(landmark_shape)

# 获取翻转向量和平移向量

def get_pose_estimation(img_size, image_points ):

# 3D model points.

model_points = np.array([

(0.0, 0.0, 0.0), # Nose tip

(0.0, -330.0, -65.0), # Chin

(-225.0, 170.0, -135.0), # Left eye left corner

(225.0, 170.0, -135.0), # Right eye right corne

(-150.0, -150.0, -125.0), # Left Mouth corner

(150.0, -150.0, -125.0) # Right mouth corner

])

# Camera internals

focal_length = img_size[1]

center = (img_size[1]/2, img_size[0]/2)

camera_matrix = np.array(

[[focal_length, 0, center[0]],

[0, focal_length, center[1]],

[0, 0, 1]], dtype = "double"

)

print("Camera Matrix :{}".format(camera_matrix))

dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion

(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE )

print("Rotation Vector:n {}".format(rotation_vector))

print("Translation Vector:n {}".format(translation_vector))

return success, rotation_vector, translation_vector, camera_matrix, dist_coeffs

# 从翻转向量转换为欧拉角

def get_euler_angle(rotation_vector):

# calculate rotation angles

theta = cv2.norm(rotation_vector, cv2.NORM_L2)

# transformed to quaterniond

w = math.cos(theta / 2)

x = math.sin(theta / 2)*rotation_vector[0][0] / theta

y = math.sin(theta / 2)*rotation_vector[1][0] / theta

z = math.sin(theta / 2)*rotation_vector[2][0] / theta

ysqr = y * y

# pitch (x-axis rotation)

t0 = 2.0 * (w * x + y * z)

t1 = 1.0 - 2.0 * (x * x + ysqr)

print('t0:{}, t1:{}'.format(t0, t1))

pitch = math.atan2(t0, t1)

# yaw (y-axis rotation)

t2 = 2.0 * (w * y - z * x)

if t2 > 1.0:

t2 = 1.0

if t2 < -1.0:

t2 = -1.0

yaw = math.asin(t2)

# roll (z-axis rotation)

t3 = 2.0 * (w * z + x * y)

t4 = 1.0 - 2.0 * (ysqr + z * z)

roll = math.atan2(t3, t4)

print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))

# 单位转换：将弧度转换为度

Y = int((pitch/math.pi)*180)

X = int((yaw/math.pi)*180)

Z = int((roll/math.pi)*180)

return 0, Y, X, Z

def get_pose_estimation_in_euler_angle(landmark_shape, im_szie):

try:

ret, image_points = get_image_points_from_landmark_shape(landmark_shape)

if ret != 0:

print('get_image_points failed')

return -1, None, None, None

ret, rotation_vector, translation_vector, camera_matrix, dist_coeffs = get_pose_estimation(im_szie, image_points)

if ret != True:

print('get_pose_estimation failed')

return -1, None, None, None

ret, pitch, yaw, roll = get_euler_angle(rotation_vector)

if ret != 0:

print('get_euler_angle failed')

return -1, None, None, None

euler_angle_str = 'Y:{}, X:{}, Z:{}'.format(pitch, yaw, roll)

print(euler_angle_str)

return 0, pitch, yaw, roll

except Exception as e:

print('get_pose_estimation_in_euler_angle exception:{}'.format(e))

return -1, None, None, None

if __name__ == '__main__':

# rtsp://admin:ts123456@10.20.21.240:554

cap = cv2.VideoCapture(0)

while (cap.isOpened()):

start_time = time.time()

# Read Image

ret, im = cap.read()

if ret != True:

print('read frame failed')

continue

size = im.shape

if size[0] > 700:

h = size[0] / 3

w = size[1] / 3

im = cv2.resize( im, (int( w ), int( h )), interpolation=cv2.INTER_CUBIC )

size = im.shape

ret, image_points = get_image_points(im)

if ret != 0:

print('get_image_points failed')

continue

ret, rotation_vector, translation_vector, camera_matrix, dist_coeffs = get_pose_estimation(size, image_points)

if ret != True:

print('get_pose_estimation failed')

continue

used_time = time.time() - start_time

print("used_time:{} sec".format(round(used_time, 3)))

ret, pitch, yaw, roll = get_euler_angle(rotation_vector)

euler_angle_str = 'Y:{}, X:{}, Z:{}'.format(pitch, yaw, roll)

print(euler_angle_str)

# Project a 3D point (0, 0, 1000.0) onto the image plane.

# We use this to draw a line sticking out of the nose

(nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)

for p in image_points:

cv2.circle(im, (int(p[0]), int(p[1])), 3, (0,0,255), -1)

p1 = ( int(image_points[0][0]), int(image_points[0][1]))

p2 = ( int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))

cv2.line(im, p1, p2, (255,0,0), 2)

# Display image

#cv2.putText( im, str(rotation_vector), (0, 100), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1 )

cv2.putText( im, euler_angle_str, (0, 120), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1 )

cv2.imshow("Output", im)

cv2.waitKey(1)

参考资料

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