CNN識別

'''
Author：Alone
Create Date:2019-1-13 10:23:45
'''
import tensorflow as tf
import numpy as np
import cv2,os
from random import shuffle
abs_path=r'.\Data'#要載入的訓練集的路徑
test_path=r'./face_test'
# 隨機獲取一批打亂順序的樣本，這個需要重寫
def getFaces(batch_size):
    img_list = os.listdir(abs_path)#樣本圖片的文件夾地址
    img_list.pop()
    amount = img_list.__len__()#樣本總數
    batch_x = []  # 本批的圖片
    batch_y = []  # 本批的標籤
    for dir_name in img_list:
        path = abs_path + '\\'+dir_name
        b = os.listdir(path)
        c = b.__len__()
        random_index = np.random.choice(c, size=batch_size, replace=False, p=None)#生成隨機索引
        np.random.shuffle(random_index)#將隨機索引打亂
        for i in random_index:#取數據
            name = b[i]# 文件名格式    圖片序號_圖片類別.jpg   例如100_1.jpg (這裏的1代表某個人的類別編號，100只是爲了區分圖片名)
            img = cv2.imread(path +'\\'+name)
            if img.shape[2]==3:
                img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            # print(img.shape)
            if img.shape[0]!=96:
                img=cv2.resize(img,(96,96))
            img=img.reshape(96, 96, 1)
            label = int(dir_name)
            labels = [0 for n in range(amount)]
            labels[label] = 1
            batch_x.append(img)
            batch_y.append(labels)
    return np.array(batch_x), np.array(batch_y)
# 這個方法沒有使用，它的作用就是獲取測試集，需要重寫
def getTestData(batch_size):
    img_list = os.listdir(test_path)  # 樣本圖片的文件夾地址
    amount = img_list.__len__()  # 樣本總數
    random_index = np.random.choice(amount, size=batch_size, replace=False, p=None)  # 生成隨機索引
    np.random.shuffle(random_index)  # 將隨機索引打亂
    batch_x = []  # 本批的圖片
    batch_y = []  # 本批的標籤
    for i in random_index:  # 取數據
        name = img_list[i]  # 文件名格式    圖片序號_圖片類別.jpg   例如100_1.jpg (這裏的1代表某個人的類別編號，100只是爲了區分圖片名)
        img = cv2.imread(test_path + '\\' + name)
        if channel == 1:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).reshape(96, 96, 1)
        name = name.rstrip('.jpg')
        label = int(name.split('_')[1])
        labels = [0 for n in range(n_class)]
        labels[label] = 1
        batch_x.append(img)
        batch_y.append(labels)
    return np.array(batch_x), np.array(batch_y)
#==================================================
#==================參數設定========================
sn=os.listdir(abs_path)
sn.pop()
n_class=len(sn)#輸出的類別數
print(n_class)
channel=1#圖片的通道數
n_input = 96*96*channel
input_shape=[96,96]
features_shape=[-1,2*2*256]#這裏的-1是因爲批量輸入每一個的特徵是4*4*256
#==================================================
def weight_variable(shape,name=None):
    initial=tf.truncated_normal(shape,stddev=0.1)#這裏的0.1是爲了是產生的正太分佈在[-0.2,0.2]之間，使用random則會超出範圍
    return tf.Variable(initial,name=name)
def bias_variable(shape,name=None):
    initial=tf.constant(0.1,shape=shape)#爲第一次產生一個偏置全爲0.1，如果隨機則可能導致訓練時間增加
    return tf.Variable(initial,name=name)
def convolution2d(tensor,w_kernel,name=None):
    return tf.nn.conv2d(tensor,w_kernel,strides=[1,1,1,1],padding='VALID',name=name)
def matMul(x,w,name=None):
    return tf.matmul(x,w,name=name)
def max_pool(tensor,name=None):
    return tf.nn.max_pool(tensor,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name=name)
def average_pool(tensor,name=None):
    return tf.nn.avg_pool(tensor,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name=name)
def leakyReLu(x, leak=0.2, name="LeakyRelu"):
    with tf.variable_scope(name):
        f1 = 0.5 * (1 + leak)
        f2 = 0.5 * (1 - leak)
        return f1 * x + f2 * tf.abs(x)
def ReLu(f1_,name="ReLu"):
    with tf.variable_scope(name):
        a=(f1_+tf.abs(f1_))/2
        return a
def DIY_Net(x,y,weights,biases,keep_prob):
    cov1=tf.nn.relu(convolution2d(x,weights['w_c1'])+biases['b_c1'])
    pool1=max_pool(cov1)#黑白圖像max_pool_2x2會比average_pool_2x2要好，因爲最大值爲白色與背景的黑色無關
    cov2=tf.nn.relu(convolution2d(pool1,weights['w_c2'])+biases['b_c2'])
    pool2=max_pool(cov2)
    cov3=tf.nn.relu(convolution2d(pool2,weights['w_c3'])+biases['b_c3'])
    pool3=max_pool(cov3)
    cov4=ReLu(convolution2d(pool3,weights['w_c4'])+biases['b_c4'])
    pool4=max_pool(cov4)
    cov5 = ReLu(convolution2d(pool4, weights['w_c5']) + biases['b_c5'])
    pool5 = max_pool(cov5)
    # cov6 = ReLu(convolution2d(pool5, weights['w_c6']) + biases['b_c6'])
    # pool6 = max_pool(cov6)
    features=tf.reshape(pool5,features_shape,name='features')
    f1= ReLu(matMul(features, weights['w_f1']) + biases['b_f1'])
    f2=tf.nn.relu(matMul(f1,weights['w_f2'])+biases['b_f2'],name='f2')
    dropped=tf.nn.dropout(f2,keep_prob,name='dropped')
    out=tf.add(matMul(dropped,weights['w_out']),biases['b_out'],name='out')
    # out1=matMul(dropped,weights['w_out1'])+biases['b_out1']
    softMax_cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=y)
    loss = tf.reduce_mean(softMax_cross_entropy)
    return out,loss
Weights={#96*96
    'w_c1':weight_variable([3,3,1,16],'w_c1'),#94  47*47 定義卷積核
    'w_c2':weight_variable([2,2,16,32],'w_c2'),#46 23*23
    'w_c3':weight_variable([2,2,32,64],'w_c3'),#22 11*11
    'w_c4':weight_variable([2,2,64,128],'w_c4'),#10 5*5
    'w_c5':weight_variable([2,2,128,256],'w_c5'),#4 2*2
    'w_f1':weight_variable([2*2*256,1024],'w_f1'),
    'w_f2':weight_variable([1024,1024],'w_f2'),
    'w_out':weight_variable([1024,n_class],'w_out'),
    # 'w_out1':weight_variable([1024,n_class],'w_out1')
}
Biases={
    'b_c1':bias_variable([16],'b_c1'),
    'b_c2':bias_variable([32],'b_c2'),
    'b_c3':bias_variable([64],'b_c3'),
    'b_c4': bias_variable([128],'b_c4'),
    'b_c5': bias_variable([256],'b_c5'),
    'b_f1':bias_variable([1024],'b_f1'),
    'b_f2':bias_variable([1024],'b_f2'),
    'b_out':bias_variable([n_class],'b_out'),
    # 'b_out1':bias_variable([n_class],'b_out1'),
}
learning_rate=0.001
x = tf.placeholder("float", shape=[None,input_shape[0],input_shape[1],1],name='input_x')
y = tf.placeholder("float", shape=[None, n_class],name='input_y')
keep_prob = tf.placeholder("float")#保留率
if __name__ == '__main__':
    sess=tf.InteractiveSession()
    predict,loss_func=DIY_Net(x,y,Weights,Biases,keep_prob)
    optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_func,name='optimizer')
    correct_pred = tf.equal(tf.argmax(predict, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    batch_size = 50#一批訓練數據的大小
    display_step = 50
    max_iter=1000 #最大迭代次數
    saver = tf.train.Saver()
    try:
        saver.restore(sess, "./model/model.ckpt-1000")
        print('Load successful !')
    except:
        print('Load failed!')
        sess.run(tf.initialize_all_variables())
    current_count = 1
    temp_img=None
    font = cv2.FONT_HERSHEY_SIMPLEX  # 定義字體
    text = 'start training...'

    while current_count <= max_iter:
        seed=np.random.randint(40,90)
        batch_xs, batch_ys = getFaces(batch_size)
        c = list(zip(batch_xs, batch_ys))
        shuffle(c)
        x_batch, y_batch = zip(*c)
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.75})
        if current_count%display_step==0:
            saver.save(sess, './model/model.ckpt', global_step=current_count)
            current_accuracy = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            current_loss = sess.run(loss_func, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            text=str(current_count) + "iterat  ions " + ", current batch loss： " + "{:.6f}".format(
                current_loss) + ", current_accuracy= " + "{:.5f}".format(current_accuracy)
            print(text)
        temp_img=np.zeros((100,1000,3),dtype=np.uint8)
        temp_img =cv2.putText(temp_img, text, (5, 50), font, 0.5, (0, 0, 255), 1)
        cv2.imshow('running',temp_img)
        key_code=cv2.waitKey(1)
        if key_code==(ord('\r') or ord('q')):
            cv2.destroyAllWindows()
            break
        current_count+=1
    print("優化完成!")
    img=cv2.imread('./7.jpg')
    img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY).reshape(96,96,1)
    r=sess.run(predict,{x:[img],keep_prob:1.})
    result=sess.run(tf.math.softmax(r))
    print(result)
    # saver.save(sess, './model/model.ckpt', global_step=current_count-1)
    # testX,testY=getTestData(1)
    # accuracy_rate=sess.run(accuracy,feed_dict={x: testX, y: testY, keep_prob: 1.})
    # print("測試集準確率:",accuracy_rate )
    sess.close()
    cv2.destroyAllWindows()

'''
遷移學習
查找已保存模型中節點名的方法是 graph.get_operations() ，然後從返回的結果中根據運算操作找到該節點
'''
import cv2, numpy as np
import pandas as pd
import random,os
import tensorflow as tf
from random import shuffle
abs_path=r'..\Data'#要載入的訓練集的路徑
def getFaces(batch_size):
    img_list = os.listdir(abs_path)#樣本圖片的文件夾地址
    img_list.pop()
    amount = img_list.__len__()#樣本總數
    batch_x = []  # 本批的圖片
    batch_y = []  # 本批的標籤
    for dir_name in img_list:
        path = abs_path + '\\'+dir_name
        b = os.listdir(path)
        c = b.__len__()
        random_index = np.random.choice(c, size=batch_size, replace=False, p=None)#生成隨機索引
        np.random.shuffle(random_index)#將隨機索引打亂
        for i in random_index:#取數據
            name = b[i]# 文件名格式    圖片序號_圖片類別.jpg   例如100_1.jpg (這裏的1代表某個人的類別編號，100只是爲了區分圖片名)
            img = cv2.imread(path +'\\'+name)
            if img.shape[2]==3:
                img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            # print(img.shape)
            if img.shape[0]!=96:
                img=cv2.resize(img,(96,96))
            img=img.reshape(96, 96, 1)
            label = int(dir_name)
            labels = [0 for n in range(amount)]
            labels[label] = 1
            batch_x.append(img)
            batch_y.append(labels)
    return np.array(batch_x), np.array(batch_y)
saver = tf.train.import_meta_graph('./model02/model.ckpt-1000.meta')
sess=tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
saver.restore(sess, './model02/model.ckpt-1000')
############開始定義網絡###################
sn=os.listdir(abs_path)
sn.pop()
n_class=len(sn)#輸出的類別數
current_count = 1
temp_img=None
font = cv2.FONT_HERSHEY_SIMPLEX  # 定義字體
text = 'start training...'
graph=tf.get_default_graph()
x_old=graph.get_tensor_by_name('Placeholder:0')
keep_prob_old=graph.get_tensor_by_name('Placeholder_2:0')
features=graph.get_tensor_by_name('Reshape:0')
x = graph.get_tensor_by_name('x:0')
y = graph.get_tensor_by_name('y:0')
keep_prob = graph.get_tensor_by_name('keep_prob:0')
predict=graph.get_tensor_by_name('predict:0')
loss_func=graph.get_tensor_by_name('loss:0')
optimizer=graph.get_operations()[880]
correct_pred = tf.equal(tf.argmax(predict, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
batch_size = 50#一批訓練數據的大小
display_step = 50
max_iter=1000 #最大迭代次數
while current_count <= max_iter:
    seed=np.random.randint(40,90)
    batch_xs, batch_ys = getFaces(batch_size)
    c = list(zip(batch_xs, batch_ys))
    shuffle(c)
    batch_xs, batch_ys = zip(*c)
    batch_xs = sess.run(features,{x_old: batch_xs, keep_prob_old: 1.0})
    sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.75})
    if current_count%display_step==0:
        current_loss = sess.run(loss_func, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
        saver.save(sess, './model02/model.ckpt', global_step=current_count)
        batch_xs, batch_ys = getFaces(50)
        batch_xs = sess.run(features, {x_old: batch_xs, keep_prob_old: 1.0})
        current_accuracy = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
        text=str(current_count) + "iterations " + ", current batch loss： " + "{:.6f}".format(
            current_loss) + ", current_accuracy= " + "{:.5f}".format(current_accuracy)
        print(text)
    temp_img=np.zeros((100,1000,3),dtype=np.uint8)
    temp_img =cv2.putText(temp_img, text, (5, 50), font, 0.5, (0, 0, 255), 1)
    cv2.imshow('running',temp_img)
    key_code=cv2.waitKey(1)
    if key_code==(ord('\r') or ord('q')):
        cv2.destroyAllWindows()
        break
    current_count+=1
print("優化完成!")
cv2.destroyAllWindows()

import tensorflow as tf
import  numpy as np
import math

def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)#這裏的0.1是爲了是產生的正太分佈在[-0.2,0.2]之間，使用random則會超出範圍
    return tf.Variable(initial)
def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)#爲第一次產生一個偏置全爲0.1，如果隨機則可能導致訓練時間增加
    return tf.Variable(initial)
def matMul(x,w):
    return tf.matmul(x,w)
def reLu(f1_,name="ReLu"):
    with tf.variable_scope(name):
        a=(f1_+tf.abs(f1_))/2
        return a
def getData():
    x=[i*0.1 for i in range(0,100,1)]
    y=[math.sin(i*0.1) for i in range(0,100,1)]
    return x,y
#定義訓練數據集
x_data,y_data=getData()
x_data=tf.reshape(tf.Variable(x_data),[100,1])

#定義TensorFlow的數學模型
n_class=1
n_input=1
Weights={
    'w_f1':weight_variable([1,1024]),
    'w_f2':weight_variable([1024,1024]),
    'w_out':weight_variable([1024,n_class])
}
Biases={
    'b_f1':bias_variable([1024]),
    'b_f2':bias_variable([1024]),
    'b_out':bias_variable([n_class])
}
f1 = reLu(matMul(x_data, Weights['w_f1']) + Biases['b_f1'])
f2 = tf.nn.relu(matMul(f1, Weights['w_f2']) + Biases['b_f2'])
out = matMul(f2, Weights['w_out']) + Biases['b_out']


# Weights=tf.Variable(tf.random_uniform([1],-1.0,1.0))
# biases=tf.Variable(tf.zeros([1]))
# train_y=Weights*x_data+biases
#定義梯度函數
loss = tf.reduce_mean(tf.square(out-y_data))
#定義反向傳播函數
train=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
for step in range(0,350):
    sess.run(train)
    print(step)
    # curWeight = sess.run(Weights[0])
    # curBias = sess.run(biases[0])
    # print('current_weight:{0}\tcurrent_bias:{1}'.format(curWeight,curBias))
sess.close()