使用 OpenCV 和 Dlib 判断用户视线方向

# 导入必要的包
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2

# 构建命令行参数解析器
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
    help="人脸关键点预测模型路径")
ap.add_argument("-i", "--image", required=True,
    help="输入图像路径")
args = vars(ap.parse_args())

# 初始化 dlib 的人脸检测器 (HOG-based) 和人脸关键点预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# 加载输入图像，调整大小，并转换为灰度图
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 在灰度图像中检测人脸
rects = detector(gray, 1)

# 遍历检测到的人脸
for (i, rect) in enumerate(rects):
    # 确定人脸区域的关键点，然后将关键点 (x, y) 坐标转换为 NumPy 数组
    shape = predictor(gray, rect)
    shape = face_utils.shape_to_np(shape)

    # 将 dlib 的矩形转换为 OpenCV 风格的边界框 [即 (x, y, w, h)]，然后绘制人脸边界框
    (x, y, w, h) = face_utils.rect_to_bb(rect)
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # 显示人脸编号
    cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # 遍历人脸关键点的 (x, y) 坐标，并在图像上绘制它们
    for (x, y) in shape:
        cv2.circle(image, (x, y), 1, (0, 0, 255), -1)

# 显示带有面部检测和人脸关键点的输出图像
cv2.imshow("Output", image)
cv2.waitKey(0)

python your_script_name.py --shape-predictor shape_predictor_68_face_landmarks.dat --image input_image.jpg

# 假设 shape 已经包含了人脸关键点
left_eye_pts = shape[36:42]
right_eye_pts = shape[42:48]

# 提取眼部区域
left_eye_region = image[left_eye_pts[:,1].min():left_eye_pts[:,1].max(), left_eye_pts[:,0].min():left_eye_pts[:,0].max()]
right_eye_region = image[right_eye_pts[:,1].min():right_eye_pts[:,1].max(), right_eye_pts[:,0].min():right_eye_pts[:,0].max()]

# 转换为灰度图
left_eye_gray = cv2.cvtColor(left_eye_region, cv2.COLOR_BGR2GRAY)
right_eye_gray = cv2.cvtColor(right_eye_region, cv2.COLOR_BGR2GRAY)

# 计算平均亮度
left_eye_mean = np.mean(left_eye_gray)
right_eye_mean = np.mean(right_eye_gray)

# 判断视线方向 (简化版)
if left_eye_mean > right_eye_mean + 10:  # 阈值可以根据实际情况调整
    print("Looking to the right")
elif right_eye_mean > left_eye_mean + 10:
    print("Looking to the left")
else:
    print("Looking straight")