最近在做人脸相关课题，找个地方保存一下部分代码。

用的dlib是81个关键点，GitHub网址：https://github.com/codeniko/shape_predictor_81_face_landmarks

#读取视频，检测一帧中的人脸，并在图像中标出关键点，同时打印出关键点的坐标。
import dlib
import numpy as np
import cv2
from matplotlib import pyplot as plt

cap = cv2.VideoCapture('视频路径')

predictor_path = "shape_predictor_81_face_landmarks.dat的路径"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

while(True):
    ret, frame = cap.read()
    # 取灰度
    img_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
    # 人脸数rects
    rects = detector(img_gray, 0)
    for i in range(len(rects)):
        landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, rects[i]).parts()])
        for idx, point in enumerate(landmarks):
            # 81点的坐标
            pos = (point[0, 0], point[0, 1])
            # 利用cv2.circle给每个特征点画一个圈，共81个
            cv2.circle(frame, pos, 2, color=(0, 255, 0))
            # 利用cv2.putText输出1-81
            font = cv2.FONT_HERSHEY_SIMPLEX
            cv2.putText(frame, str(idx + 1), pos, font, 0.3, (0, 0, 255), 1, cv2.LINE_AA)
            print("index=" + str(idx+1) + " x=" + str(pos[0]) + " y=" + str(pos[1]))
    plt.imshow(frame)
    plt.show()
    if cv2.waitKey(1) & 0xFF == ord('q'):
        print("q pressed")
        break
cap.release()
cv2.destroyAllWindows()

接下来根据上面的人脸关键点，选取ROI。

a = np.arange(81)
b = np.arange(81)
# 取灰度
img_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
dets = detector(img_gray, 0)
if (len(dets) != 0):
    # 找到脸颊区域
    for i in range(len(dets)):
        landmarks = np.matrix([[p.x, p.y] for p in predictor(frame, dets[i]).parts()])
        for idx, point in enumerate(landmarks):
            # 68点的坐标
            pos = (point[0, 0], point[0, 1])
             # ROI：脸颊区域
            a = a.tolist()
            b = b.tolist()
            a[idx] = point[0, 0]
            b[idx] = point[0, 1]
            a = np.array(a)
            b = np.array(b)

    # 额头区域
    x1 = int(a[76])
    y1 = int(b[19])
    x2 = int(a[73])
    y2 = int(b[73])
    if (x1 < 0): # 防止人脸转动，x1位置超出图像
        x1 = 0
    forehead_ROI = frame[y2:y1, x1:x2]

    # 脸颊区域
    x1 = int((a[2] + a[4]) / 2)
    y1 = int((b[2] + b[4]) / 2)
    y2 = int((b[2] + b[23]) / 2)
    x2 = int((a[12] + a[14]) / 2)
    if (x1 < 0):
        x1 = 0
    cheek_ROI = frame[y2:y1, x1:x2]

    # 检测整个人脸区域
    for k, d in enumerate(dets):
        [x1, x2, y1, y2] = [d.left(), d.right(), d.top(), d.bottom()]
    face_ROI = frames[y1:y2, x1:x2]