检测人脸（忽略）
import os
import dlib
import cv2
if not os.path.exists('test'):
os.makedirs('test')
f = open('images.txt', 'w')
detector = dlib.get_frontal_face_detector()
root = 'images'
for i in os.listdir(root):
img = cv2.imread(os.path.join(root, i))
if img is None:
continue
dets = detector(img, 0)
print(i)
for j in range(len(dets)):
rect = dets[j]
im = img[rect.top():rect.bottom(),rect.left():rect.right()]
if im is None:
continue
cv2.imwrite('test/{}_{}.jpg'.format(i.split('.')[0],j), im)
f.write('test/{}_{}.jpg\n'.format(i.split('.')[0],j))
f.close()
欧拉角计算
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
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
def get_image_points_from_landmark_shape(landmark_shape):
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 image_points
def get_image_points(img):
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),face_rectangle
def get_euler_angle(img):
size=img.shape
image_points,rect =get_image_points(img)
print(image_points)
print(image_points[0][0],image_points[0][1])
cv2.circle(img, (int(image_points[0][0]), int(image_points[0][1])), 3, (0,0,255), -1)
cv2.circle(img, (int(image_points[1][0]), int(image_points[1][1])), 3, (0,0,255), -1)
cv2.circle(img, (int(image_points[2][0]), int(image_points[2][1])), 3, (0,0,255), -1)
cv2.circle(img, (int(image_points[3][0]), int(image_points[3][1])), 3, (0,0,255), -1)
cv2.circle(img, (int(image_points[4][0]), int(image_points[4][1])), 3, (0,0,255), -1)
model_points =np.array([
(0.0, 0.0, 0.0), # Nose tip
#(0.0, -330.0, -65.0), # Chin
(-165.0, 170.0, -135.0), # Left eye left corner
(165.0, 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
center = (size[1]/2, size[0]/2)
focal_length = center[0] / np.tan(60/2 * np.pi / 180)
camera_matrix = np.array(
[[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]], dtype = "double"
)
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)
axis = np.float32([[400,0,0],
[0,400,0],
[0,0,400]])
imgpts, jac = cv2.projectPoints(axis, rotation_vector, translation_vector, camera_matrix, dist_coeffs)
modelpts, jac2 = cv2.projectPoints(model_points, rotation_vector, translation_vector, camera_matrix, dist_coeffs)
rvec_matrix = cv2.Rodrigues(rotation_vector)[0]
proj_matrix = np.hstack((rvec_matrix, translation_vector))
eulerAngles = cv2.decomposeProjectionMatrix(proj_matrix)[6]
pitch, yaw, roll = [math.radians(_) for _ in eulerAngles]
print(pitch, yaw, roll)
pitch = math.degrees(math.asin(math.sin(pitch)))
roll = -math.degrees(math.asin(math.sin(roll)))
yaw = math.degrees(math.asin(math.sin(yaw)))
return imgpts, modelpts, (pitch, roll, yaw),image_points[0],rect
if __name__ == '__main__':
im = cv2.imread('face.png')
imgpts, modelpts,rotation,nose,rect=get_euler_angle(im)
nose=tuple(int(i) for i in nose)
cv2.line(im, nose,tuple(int(i) for i in imgpts[1].ravel()), (0,255,0), 3) #GREEN
cv2.line(im, nose,tuple(int(i) for i in imgpts[0].ravel()), (255,0,), 3) #BLUE
cv2.line(im, nose,tuple(int(i) for i in imgpts[2].ravel()), (0,0,255), 3) #RED
#print(rotation)
b, g, r = cv2.split(im)
im = cv2.merge([r, g, b])
import matplotlib.pyplot as plt
text='pitch:{:.0f}\n roll:{:.0f}\n yaw:{:.0f}'.format(rotation[0],
rotation[1],rotation[2])
print(text)
plt.text(10,50,text,color='r',fontsize=10)
plt.imshow(im)
plt.show()
