olivettifaces数据集实现人脸识别代码

数据集：

# -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 18:21:21 2019

@author: 92958
"""

import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
import numpy
from PIL import Image

dataset_path='./olivettifaces.gifa'
#获取dataset
def load_data(dataset_path):
    img = Image.open(dataset_path)
    # 定义一个20 × 20的训练样本，一共有40个人，每个人都10张样本照片
    img_ndarray = np.asarray(img, dtype='float64') / 256
    #img_ndarray = np.asarray(img, dtype='float32') / 32

    # 记录脸数据矩阵，57 * 47为每张脸的像素矩阵
    faces = np.empty((400, 57 * 47))

    for row in range(20):
        for column in range(20):
            faces[20 * row + column] = np.ndarray.flatten(
                img_ndarray[row * 57: (row + 1) * 57, column * 47 : (column + 1) * 47]
            )

    label = np.zeros((400, 40))
    for i in range(40):
        label[i * 10: (i + 1) * 10, i] = 1

    # 将数据分成训练集，验证集，测试集
    train_data = np.empty((320, 57 * 47))
    train_label = np.zeros((320, 40))
    vaild_data = np.empty((40, 57 * 47))
    vaild_label = np.zeros((40, 40))
    test_data = np.empty((40, 57 * 47))
    test_label = np.zeros((40, 40))

    for i in range(40):
        train_data[i * 8: i * 8 + 8] = faces[i * 10: i * 10 + 8]
        train_label[i * 8: i * 8 + 8] = label[i * 10: i * 10 + 8]

        vaild_data[i] = faces[i * 10 + 8]
        vaild_label[i] = label[i * 10 + 8]

        test_data[i] = faces[i * 10 + 9]
        test_label[i] = label[i * 10 + 9]

    train_data = train_data.astype('float32')
    vaild_data = vaild_data.astype('float32')
    test_data = test_data.astype('float32')

    return [
        (train_data, train_label),
        (vaild_data, vaild_label),
        (test_data, test_label)
    ]

def convolutional_layer(data, kernel_size, bias_size, pooling_size):
    kernel = tf.get_variable("conv", kernel_size, initializer=tf.random_normal_initializer())
    bias = tf.get_variable('bias', bias_size, initializer=tf.random_normal_initializer())

    conv = tf.nn.conv2d(data, kernel, strides=[1, 1, 1, 1], padding='SAME')
    linear_output = tf.nn.relu(tf.add(conv, bias))
    pooling = tf.nn.max_pool(linear_output, ksize=pooling_size, strides=pooling_size, padding="SAME")
    return pooling

def linear_layer(data, weights_size, biases_size):
    weights = tf.get_variable("weigths", weights_size, initializer=tf.random_normal_initializer())
    biases = tf.get_variable("biases", biases_size, initializer=tf.random_normal_initializer())
    return tf.add(tf.matmul(data, weights), biases)

def convolutional_neural_network(data):
    # 根据类别个数定义最后输出层的神经元
    n_ouput_layer = 40

    kernel_shape1=[5, 5, 1, 32]
    kernel_shape2=[5, 5, 32, 64]
    full_conn_w_shape = [15 * 12 * 64, 1024]
    out_w_shape = [1024, n_ouput_layer]

    bias_shape1=[32]
    bias_shape2=[64]
    full_conn_b_shape = [1024]
    out_b_shape = [n_ouput_layer]

    data = tf.reshape(data, [-1, 57, 47, 1])

    # 经过第一层卷积神经网络后，得到的张量shape为：[batch, 29, 24, 32]
    with tf.variable_scope("conv_layer1") as layer1:
        layer1_output = convolutional_layer(
            data=data,
            kernel_size=kernel_shape1,
            bias_size=bias_shape1,
            pooling_size=[1, 2, 2, 1]
        )
    # 经过第二层卷积神经网络后，得到的张量shape为：[batch, 15, 12, 64]
    with tf.variable_scope("conv_layer2") as layer2:
        layer2_output = convolutional_layer(
            data=layer1_output,
            kernel_size=kernel_shape2,
            bias_size=bias_shape2,
            pooling_size=[1, 2, 2, 1]
        )
    with tf.variable_scope("full_connection") as full_layer3:
        # 讲卷积层张量数据拉成2-D张量只有有一列的列向量
        layer2_output_flatten = tf.contrib.layers.flatten(layer2_output)
        layer3_output = tf.nn.relu(
            linear_layer(
                data=layer2_output_flatten,
                weights_size=full_conn_w_shape,
                biases_size=full_conn_b_shape
            )
        )
        # layer3_output = tf.nn.dropout(layer3_output, 0.8)
    with tf.variable_scope("output") as output_layer4:
        output = linear_layer(
            data=layer3_output,
            weights_size=out_w_shape,
            biases_size=out_b_shape
        )

    return output;

def train_facedata(dataset, model_dir,model_path):
    # train_set_x = data[0][0]
    # train_set_y = data[0][1]
    # valid_set_x = data[1][0]
    # valid_set_y = data[1][1]
    # test_set_x = data[2][0]
    # test_set_y = data[2][1]
    # X = tf.placeholder(tf.float32, shape=(None, None), name="x-input")  # 输入数据
    # Y = tf.placeholder(tf.float32, shape=(None, None), name='y-input')  # 输入标签

    batch_size = 40

    # train_set_x, train_set_y = dataset[0]
    # valid_set_x, valid_set_y = dataset[1]
    # test_set_x, test_set_y = dataset[2]
    train_set_x = dataset[0][0]
    train_set_y = dataset[0][1]
    valid_set_x = dataset[1][0]
    valid_set_y = dataset[1][1]
    test_set_x = dataset[2][0]
    test_set_y = dataset[2][1]

    X = tf.placeholder(tf.float32, [batch_size, 57 * 47])
    Y = tf.placeholder(tf.float32, [batch_size, 40])

    predict = convolutional_neural_network(X)
    cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=Y))
    optimizer = tf.train.AdamOptimizer(1e-2).minimize(cost_func)

    # 用于保存训练的最佳模型
    saver = tf.train.Saver()
    #model_dir = './model'
    #model_path = model_dir + '/best.ckpt'
    with tf.Session() as session:
        # 若不存在模型数据，需要训练模型参数
        if not os.path.exists(model_path + ".index"):
            session.run(tf.global_variables_initializer())
            best_loss = float('Inf')
            for epoch in range(20):
                epoch_loss = 0
                for i in range((int)(np.shape(train_set_x)[0] / batch_size)):
                    x = train_set_x[i * batch_size: (i + 1) * batch_size]
                    y = train_set_y[i * batch_size: (i + 1) * batch_size]
                    _, cost = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})
                    epoch_loss += cost

                print(epoch, ' : ', epoch_loss)
                if best_loss > epoch_loss:
                    best_loss = epoch_loss
                    if not os.path.exists(model_dir):
                        os.mkdir(model_dir)
                        print("create the directory: %s" % model_dir)
                    save_path = saver.save(session, model_path)
                    print("Model saved in file: %s" % save_path)

        # 恢复数据并校验和测试
        saver.restore(session, model_path)
        correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))
        valid_accuracy = tf.reduce_mean(tf.cast(correct,'float'))
        print('valid set accuracy: ', valid_accuracy.eval({X: valid_set_x, Y: valid_set_y}))

        test_pred = tf.argmax(predict, 1).eval({X: test_set_x})
        test_true = np.argmax(test_set_y, 1)
        test_correct = correct.eval({X: test_set_x, Y: test_set_y})
        incorrect_index = [i for i in range(np.shape(test_correct)[0]) if not test_correct[i]]
        for i in incorrect_index:
            print('picture person is %i, but mis-predicted as person %i'
                %(test_true[i], test_pred[i]))
        plot_errordata(incorrect_index, "olivettifaces.gif")

#画出在测试集中错误的数据
def plot_errordata(error_index, dataset_path):
    img = mpimg.imread(dataset_path)
    plt.imshow(img)
    currentAxis = plt.gca()
    for index in error_index:
        row = index // 2
        column = index % 2
        currentAxis.add_patch(
            patches.Rectangle(
                xy=(
                     47 * 9 if column == 0 else 47 * 19,
                     row * 57
                    ),
                width=47,
                height=57,
                linewidth=1,
                edgecolor='r',
                facecolor='none'
            )
    )
    plt.savefig("result.png")
    plt.show()

def main():
    dataset_path = "olivettifaces.gif"
    data = load_data(dataset_path)
    model_dir = './model'
    model_path = model_dir + '/best.ckpt'
    train_facedata(data, model_dir, model_path)

if __name__ == "__main__" :
    main()

控制台信息：

runfile('F:/python/TensorFlow/人脸识别/olive1.py', wdir='F:/python/TensorFlow/人脸识别')

WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.

Instructions for updating:

Colocations handled automatically by placer.

WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.

For more information, please see:

• https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
• https://github.com/tensorflow/addons
  
  If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.

Instructions for updating:

Use keras.layers.flatten instead.

WARNING:tensorflow:From F:/python/TensorFlow/人脸识别/olive1.py:158: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.

Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow

into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

0 : 2671140.984375

create the directory: ./model

Model saved in file: ./model/best.ckpt

1 : 610905.9375

Model saved in file: ./model/best.ckpt

2 : 181258.35693359375

Model saved in file: ./model/best.ckpt

3 : 54391.228271484375

Model saved in file: ./model/best.ckpt

4 : 24234.38525390625

Model saved in file: ./model/best.ckpt

5 : 9868.018524169922

Model saved in file: ./model/best.ckpt

6 : 3433.5851974487305

Model saved in file: ./model/best.ckpt

7 : 826.4495697021484

Model saved in file: ./model/best.ckpt

8 : 200.12329292297363

Model saved in file: ./model/best.ckpt

9 : 194.84842109680176

Model saved in file: ./model/best.ckpt

10 : 63.74338483810425

Model saved in file: ./model/best.ckpt

11 : 10.006996154785156

Model saved in file: ./model/best.ckpt

12 : 7.118054211139679

Model saved in file: ./model/best.ckpt

13 : 0.0

Model saved in file: ./model/best.ckpt

14 : 0.0

15 : 0.0

16 : 0.0

17 : 0.0

18 : 0.0

19 : 0.0

WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.

Instructions for updating:

Use standard file APIs to check for files with this prefix.

INFO:tensorflow:Restoring parameters from ./model/best.ckpt

valid set accuracy: 0.8

picture person is 4, but mis-predicted as person 8

picture person is 18, but mis-predicted as person 14

picture person is 21, but mis-predicted as person 27

picture person is 35, but mis-predicted as person 17

原文：https://blog.csdn.net/hanghangaidoudou/article/details/79347080