图片头部姿态检测（dlib+opencv）
–20220430 一些项目笔记
我们实验室最近在做项目，具体的实验题目可能不是很能和大家分享，我就单纯讲一下我负责这一部分的内容，我们具有三个技术组，其中每个人负责的部分是不一样的，我这边是需要，通过摄像头进行头部运动时的Yaw，Pitch，Roll。
但我目前只进行到通过图像检测，就是通过图片的输入，然后就可以对图片进行一个以上三个数据的检测，目前我参考的代码是
dlib_opencv_face_pose_estimation
源码部分如下：
import os
import cv2
import numpy as np
import dlib
import time
import math
data_dir = r"D:Dataset"
//这个是我自己改的代码
//为我的文件地址，可以自己设定
save_dir = r"...results"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(r".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(17).x, landmark_shape.part(17).y), # 17 left brow left corner
(landmark_shape.part(21).x, landmark_shape.part(21).y), # 21 left brow right corner
(landmark_shape.part(22).x, landmark_shape.part(22).y), # 22 right brow left corner
(landmark_shape.part(26).x, landmark_shape.part(26).y), # 26 right brow right corner
(landmark_shape.part(36).x, landmark_shape.part(36).y), # 36 left eye left corner
(landmark_shape.part(39).x, landmark_shape.part(39).y), # 39 left eye right corner
(landmark_shape.part(42).x, landmark_shape.part(42).y), # 42 right eye left corner
(landmark_shape.part(45).x, landmark_shape.part(45).y), # 45 right eye right corner
(landmark_shape.part(31).x, landmark_shape.part(31).y), # 31 nose left corner
(landmark_shape.part(35).x, landmark_shape.part(35).y), # 35 nose right corner
(landmark_shape.part(48).x, landmark_shape.part(48).y), # 48 mouth left corner
(landmark_shape.part(54).x, landmark_shape.part(54).y), # 54 mouth right corner
(landmark_shape.part(57).x, landmark_shape.part(57).y), # 57 mouth central bottom corner
(landmark_shape.part(8).x, landmark_shape.part(8).y), # 8 chin 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)
# 获取旋转向量和平移向量
def get_pose_estimation(img_size, image_points):
# 3D model points.
model_points = np.array([
(6.825897, 6.760612, 4.402142), # 33 left brow left corner
(1.330353, 7.122144, 6.903745), # 29 left brow right corner
(-1.330353, 7.122144, 6.903745), # 34 right brow left corner
(-6.825897, 6.760612, 4.402142), # 38 right brow right corner
(5.311432, 5.485328, 3.987654), # 13 left eye left corner
(1.789930, 5.393625, 4.413414), # 17 left eye right corner
(-1.789930, 5.393625, 4.413414), # 25 right eye left corner
(-5.311432, 5.485328, 3.987654), # 21 right eye right corner
(2.005628, 1.409845, 6.165652), # 55 nose left corner
(-2.005628, 1.409845, 6.165652), # 49 nose right corner
(2.774015, -2.080775, 5.048531), # 43 mouth left corner
(-2.774015, -2.080775, 5.048531), # 39 mouth right corner
(0.000000, -3.116408, 6.097667), # 45 mouth central bottom corner
(0.000000, -7.415691, 4.070434) # 6 chin 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"
)
dist_coeffs = np.array([7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e+000],
dtype="double") # 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
# 从旋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
pitch_degree = int((pitch / math.pi) * 180)
yaw_degree = int((yaw / math.pi) * 180)
roll_degree = int((roll / math.pi) * 180)
return 0, pitch, yaw, roll, pitch_degree, yaw_degree, roll_degree
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 = 'Pitch:{}, Yaw:{}, Roll:{}'.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__':
# Read Image
image_names = os.listdir(data_dir)
for index, image_name in enumerate(image_names):
print("Image:", image_name)
imgpath = data_dir + '' + image_name
im = cv2.imread(imgpath)
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
ret, pitch, yaw, roll, pitch_degree, yaw_degree, roll_degree = get_euler_angle(rotation_vector)
draw = im.copy()
# Yaw:
if yaw_degree < 0:
output_yaw = "face turns left:" + str(abs(yaw_degree)) + " degrees"
# cv2.putText(draw,output_yaw,(20,40),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_yaw)
if yaw_degree == 0:
print("face doesn't turns left or right")
if yaw_degree > 0:
output_yaw = "face turns right:" + str(abs(yaw_degree)) + " degrees"
# cv2.putText(draw,output_yaw,(20,40),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_yaw)
# Pitch:
if pitch_degree > 0:
output_pitch = "face downwards:" + str(abs(pitch_degree)) + " degrees"
# cv2.putText(draw,output_pitch,(20,80),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_pitch)
if pitch_degree == 0:
print("face not downwards or upwards")
if pitch_degree < 0:
output_pitch = "face upwards:" + str(abs(pitch_degree)) + " degrees"
# cv2.putText(draw,output_pitch,(20,80),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_pitch)
# Roll:
if roll_degree < 0:
output_roll = "face bends to the right:" + str(abs(roll_degree)) + " degrees"
# cv2.putText(draw,output_roll,(20,120),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_roll)
if roll_degree == 0:
print("face doesn't bend to the right or the left.")
if roll_degree > 0:
output_roll = "face bends to the left:" + str(abs(roll_degree)) + " degrees"
# cv2.putText(draw,output_roll,(20,120),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print(output_roll)
# Initial status:
if abs(yaw) < 0.00001 and abs(pitch) < 0.00001 and abs(roll) < 0.00001:
# cv2.putText(draw,"Initial ststus",(20,40),cv2.FONT_HERSHEY_SIMPLEX,.5,(0,255,0))
print("Initial ststus")
# cv2.imwrite(save_dir+""+os.path.splitext(imgpath)[0]+'_pose_estimate.jpg',draw)
这份代码是我改了一下的代码，我将文件的输入地址进行了改动，建议找这种比较简单的地址，嘿嘿，好找一点
每张人脸检测出68个关键点（人脸轮廓17个点，左右眉毛各5个点，左右眼睛各6个点，鼻子9个点，嘴巴20个点）
以下是效果图：CSDN上面熟人太多了！所以没用自己的照片，哈哈哈哈哈，用了一张我老公的照片
这边的话，就是我这张照片的三个需要测量的数据
这个的话，还是不太完善，我想实现的效果，是能够通过摄像头来进行一个实时结果的输出，通过我搜的一些资料，这种效果需要我们设置循环，然后类似对于图片来进行一个检测
慢慢改吧，麻烦人工智能大佬教教我！！！！！
本图文内容来源于网友网络收集整理提供，作为学习参考使用，版权属于原作者。
THE END
二维码