这个有解释：这个代码精简：
https://github.com/JuneoXIE/dlib-opencv_face_pose_estimation/blob/master/pose_estimate_dlib.py
landmarks_3D = np.float32([ [6.825897, 6.760612, 4.402142], # 33 LEFT_EYEBROW_LEFT, EYEBROW:眉毛 [1.330353, 7.122144, 6.903745], # 29 LEFT_EYEBROW_RIGHT, [-1.330353, 7.122144, 6.903745], # 34 RIGHT_EYEBROW_LEFT, [-6.825897, 6.760612, 4.402142], # 38 RIGHT_EYEBROW_RIGHT, [5.311432, 5.485328, 3.987654], # 13 LEFT_EYE_LEFT, [1.789930, 5.393625, 4.413414], # 17 LEFT_EYE_RIGHT, [-1.789930, 5.393625, 4.413414], # 25 RIGHT_EYE_LEFT, [-5.311432, 5.485328, 3.987654], # 21 RIGHT_EYE_RIGHT, [-2.005628, 1.409845, 6.165652], # 55 NOSE_LEFT, [-2.005628, 1.409845, 6.165652], # 49 NOSE_RIGHT, [2.774015, -2.080775, 5.048531], # 43 MOUTH_LEFT, [-2.774015, -2.080775, 5.048531], # 39 MOUTH_RIGHT, [0.000000, -3.116408, 6.097667], # 45 LOWER_LIP, [0.000000, -7.415691, 4.070434], # 6 CHIN ])
#!/usr/bin/env python import os import cv2 import numpy as np import dlib import time import math data_dir = r"...\test_datasets" 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)
这个也稍微有点麻烦：
https://github.com/polarisZhao/PFLD-pytorch/blob/dcf59b4a5aa0c0e750f4d33844ad75c99f7d5436/pfld/utils.py
dlib 68个关键点 人脸姿态,代码麻烦
https://github.com/YujunXie/Tools/blob/4fdf10ea036ea0e0deaf1844aaa2b7637aa597d3/HeadPoseEstimation.py 3
