Pytorch安装及学习人脸特征点识别

import torch
torch.__version__

'0.4.1'

torch.cuda.is_available()

True

pip install -U scikit-image

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
# 忽略警告
import warnings
warnings.filterwarnings("ignore")
plt.ion()   # 交互模式

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

landmarks_frame.head(5)

n = 4
img_name = landmarks_frame.iloc[n, 0]
img_name

'1198_0_861.jpg'

# 并不是矩阵化，而是一个数组中！
landmarks = landmarks_frame.iloc[n, 1:].as_matrix()
landmarks

array([138, 392, 141, 427, 145, 464, 152, 501, 166, 536, 186, 567, 214,
       594, 249, 614, 290, 616, 329, 608, 363, 589, 390, 562, 411, 529,
       ...
       294, 516, 279, 518, 265, 516], dtype=object)

# 一行表示点的x与y坐标，形成(n,2)数组！
# -1 表示模糊匹配
landmarks = landmarks.astype('float').reshape(-1, 2)
# 打印输出前5行数据
landmarks[:5,:]

array([[138., 392.],
       [141., 427.],
       [145., 464.],
       [152., 501.],
       [166., 536.]])

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()