dlib 计算面部朝向 示例

好的，我能回答您的问题。以下是一个使用dlib计算面部朝向的示例代码：

import dlib
import cv2
import numpy as np

# 加载模型
predictor_path = "shape_predictor_68_face_landmarks.dat"
predictor = dlib.shape_predictor(predictor_path)

# 初始化人脸检测器
detector = dlib.get_frontal_face_detector()

# 读取图像
img = cv2.imread("test.jpg")

# 灰度转换
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 人脸检测
faces = detector(gray, 1)

for face in faces:
    # 特征点检测
    landmarks = predictor(gray, face)
    landmarks = np.array([(landmarks.part(i).x, landmarks.part(i).y) for i in range(68)])
    
    # 计算面部朝向
    image_points = np.array([
                                landmarks[30],     # 鼻尖
                                landmarks[8],      # 下巴
                                landmarks[36],     # 左眼角
                                landmarks[45],     # 右眼角
                                landmarks[48],     # 嘴左角
                                landmarks[54]      # 嘴右角
                            ], dtype="double")
    model_points = np.array([
                                (0.0, 0.0, 0.0),
                                (0.0, -330.0, -65.0),
                                (-225.0, 170.0, -135.0),
                                (225.0, 170.0, -135.0),
                                (-150.0, -150.0, -125.0),
                                (150.0, -150.0, -125.0)
                            ])
    focal_length = img.shape[1]
    center = (img.shape[1]/2, img.shape[0]/2)
    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))
    success, rotation_vector, translation_vector = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
    nose_end_point2D, jacobian = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
    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(img, p1, p2, (0, 255, 0), 2)
    
# 显示结果图像
cv2.imshow("Output", img)
cv2.waitKey(0)

此代码使用dlib库来检测面部朝向，首先需要加载预训练好的特征点检测器。然后使用人脸检测器检测图像中的人脸，然后计算每个人脸的面部朝向。最后，将结果绘制在原始图像上并显示。