深度学习（PYTORCH）-2.python调用dlib提取人脸68个特征点

在看官方教程时，无意中发现别人写的一个脚本，非常简洁。

官方教程地址：http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#sphx-glr-beginner-data-loading-tutorial-py

使用的是dlib自带的特征点检测库，初期用来测试还是不错的

 """Create a sample face landmarks dataset.

 Adapted from dlib/python_examples/face_landmark_detection.py
 See this file for more explanation.

 Download a trained facial shape predictor from:
     http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
 """
 import dlib
 import glob
 import csv
 from skimage import io

 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
 num_landmarks = 68

 with open('face_landmarks.csv', 'w', newline='') as csvfile:
     csv_writer = csv.writer(csvfile)

     header = ['image_name']
     for i in range(num_landmarks):
         header += ['part_{}_x'.format(i), 'part_{}_y'.format(i)]

     csv_writer.writerow(header)

     for f in glob.glob('*.jpg'):
         img = io.imread(f)
         dets = detector(img, 1)  # face detection

         # ignore all the files with no or more than one faces detected.
         if len(dets) == 1:
             row = [f]

             d = dets[0]
             # Get the landmarks/parts for the face in box d.
             shape = predictor(img, d)
             for i in range(num_landmarks):
                 part_i_x = shape.part(i).x
                 part_i_y = shape.part(i).y
                 row += [part_i_x, part_i_y]

             csv_writer.writerow(row)

附上使用matplotlib显示特征点的脚本：

 from __future__ import print_function, division
 import os
 import torch
 import pandas as pd
 from skimage import io, transform
 import numpy as np
 import matplotlib.pyplot as plt
 from torch.utils.data import Dataset, DataLoader
 from torchvision import transforms, utils

 # Ignore warnings
 import warnings
 warnings.filterwarnings("ignore")

 plt.ion()   # interactive mode

 landmarks_frame = pd.read_csv('faces/face_landmarks.csv')

 n = 5
 img_name = landmarks_frame.iloc[n, 0]
 landmarks = landmarks_frame.iloc[n, 1:].as_matrix()
 landmarks = landmarks.astype('float').reshape(-1, 2)

 print('Image name: {}'.format(img_name))
 print('Landmarks shape: {}'.format(landmarks.shape))
 print('First 4 Landmarks: {}'.format(landmarks[:4]))

 def show_landmarks(image, landmarks):
     """Show image with landmarks"""
     plt.imshow(image)
     plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')
     plt.pause(0.001)  # pause a bit so that plots are updated

 plt.figure()
 show_landmarks(io.imread(os.path.join('faces/', img_name)),
                landmarks)
 plt.show()

效果图：