Python使用dlib实现人脸检测

前期准备

在开始之前，你得先做如下准备：

• opencv
  这个一般没啥问题，通过pip install opencv-python安装即可。
• dlib
  安装dlib之前需要安装好cmake，之后再通过pip install dlib安装，如果报错的话，再自行百度吧，我是折腾了一下午才弄好。
• 下载dlib提供的检测模型文件
  下载地址：http://dlib.net/files/
  文件名shape_predictor_68_face_landmarks.dat

人脸检测

单一图片

代码部分实现起来非常简单，不过十几行的事，不过需要注意的是，通过cv2.imread读取的图片是BRG通道的，需要转成RGB通道，不然通过pyplot显示图片会变色。

• 代码部分

import cv2
import dlib
import matplotlib.pyplot as plt
import numpy as np

predictor_path = 'shape_predictor_68_face_landmarks.dat'
test_img = 'test.png'
img = cv2.imread(test_img)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

faces = detector(img, 0)
if len(faces):
    print '==> Found %d face in this image.' % len(faces)
    for i in range(len(faces)):
        landmarks = np.matrix([[p.x, p.y] for p in predictor(img, faces[i]).parts()])
        for point in landmarks:
            pos = (point[0, 0], point[0, 1])
            cv2.circle(img, pos, 3, color=(0, 255, 0),thickness=3)
else:
    print 'Face not found!'

# opencv读取图片是BRG通道的，需要专成RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10, 8))
plt.subplot(121)
plt.imshow(plt.imread(test_img))
plt.axis('off')
plt.subplot(122)
plt.imshow(img)
plt.axis('off')
plt.show()

• 效果如下

  
  68点人脸检测

摄像头读取

我们可以通过cv2.VideoCapture(0)调起摄像头，camera.read会返回两个参数，第一个代表是否获取到图像帧，第二个代表图像帧内容，剩下的部分就跟上面一样了，传给dlib进行人脸检测就好了。

• 完整代码

# -*- coding: utf-8 -*-

# @author: Awesome_Tang
# @date: 2018-12-31
# @version: python2.7

import cv2
import dlib
import numpy as np
import os


class Config(object):
    predictor_path = 'shape_predictor_68_face_landmarks.dat'
    test_img = 'test.jpg'
    width = 640
    height = 480


class FaceDetective():

    def __init__(self):
        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor(Config.predictor_path)

    def check_file(self,path):
        if os.path.exists(path):
            img = cv2.imread(path)
            return img
        else:
            raise IOError('No such file : "%s", please check!' % path)

    def detective(self, frame):
        faces = self.detector(frame, 0)
        if len(faces):
            print '==> Found %d face in this frame.' % len(faces)
            for i in range(len(faces)):
                landmarks = np.matrix([[p.x, p.y] for p in self.predictor(frame, faces[i]).parts()])
                for point in landmarks:
                    pos = (point[0, 0], point[0, 1])
                    cv2.circle(frame, pos, 3, color=(0, 0, 255),thickness=3)
        else:
            print 'Face not found!'
        return frame

    def run_camera(self):
        camera = cv2.VideoCapture(0)
        camera.set(cv2.CAP_PROP_FRAME_WIDTH, Config.width)
        camera.set(cv2.CAP_PROP_FRAME_HEIGHT, Config.height)
        while True:
            detected, frame = camera.read()

            if detected:
                frame = cv2.flip(frame, 1)
                frame = self.detective(frame)
            cv2.imshow("AwesomeTang", frame)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        camera.release()
        cv2.destroyAllWindows()

    def single_image(self,img_path):
        img = self.check_file(img_path)
        img = self.detective(img)
        cv2.namedWindow("AwesomeTang", 0)
        cv2.resizeWindow("AwesomeTang", Config.width, Config.height)
        cv2.imshow("AwesomeTang",img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()


if __name__ == '__main__':
    p = FaceDetective()
    #p.single_image(Config.test_img)
    p.run_camera()

• 效果如下

  
  

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skr~ skr~~