tensoflow练习7：生成图片

这一部分主要以生成图片为例。
数据集：
1)https://pan.baidu.com/s/1kVSA8z9 (密码: atqm)
2)https://pan.baidu.com/s/1ctbd9O (密码: kubu)

1）数据的预处理：
将下载的图片放到一个girls文件夹下，进行压缩，去重复操作。
yasuo.py: 图片被缩放成64*64的格式

# -*- coding: utf-8 -*-import os import cv2import numpy as npimage_dir = 'girls'new_girl_dir = 'little_girls'if not os.path.exists(new_girl_dir):    os.makedirs(new_girl_dir)#目录不存在则创建新目录for img_file in os.listdir(image_dir):    img_file_path = os.path.join(image_dir, img_file)    img = cv2.imread(img_file_path)    if img is None:        print("image read fail")        continue    height, weight, channel = img.shape    if height < 200 or weight <200 or channel != 3:        continue    #图片缩放成64*64    img = cv2.resize(img,(64,64))    new_file = os.path.join(new_girl_dir,img_file)    cv2.imwrite(new_file, img)    print(new_file)

remove_chongfu.py(cpu跑的话，非常慢，最好gpu)

# -*- coding: utf-8 -*-import osimport cv2import numpy as np#判断两张图片是否一样def is_same_image(img_file1, img_file2):    img1 = cv2.imread(img_file1)    img2 = cv2.imread(img_file2)    if img1 is None or img2 is None:        return False    if img1.shape == img2.shape and not (np.bitwise_xor(img1,img2).any()):        return True    else:        return False#去重复照片        file_list = os.listdir('little_girls')try:    for img1 in file_list:        print((len(file_list)))        for img2 in file_list:            if img1 != img2:                if is_same_image('little_girls/'+img1, 'little_girls/'+img2) is True:                    print(img1, img2)                    os.remove('little_girls/'+img1)        file_list.remove(img1)except Exception as e:    print(e)

接下来是训练模型和生成过程：
pixelcnn.py:

#coding=utf-8import tensorflow as tfimport numpy as npimport osimport cv2#MiNST=FALSEMINST = Falseif MINST == True:    from tensorflow.examples.tutorials.mnist import input_data    data = input_data.read_data_sets('/tmp/')    image_height = 28    image_width = 28    image_channel = 1    batch_size = 128    n_batches = data.train.num_examples // batch_sizeelse:    picture_dir = 'little_girls'    picture_list = []    # 建议不要将图片一次性加载到内存    for (dirpath,dirnames,filenames) in os.walk(picture_dir):#访问文件目录        for filename in filenames:            if filename.endswith('.jpg'):#是否为jpg图片                picture_list.append(os.sep.join([dirpath,filename]))    print("图像总数：",len(picture_list))    #图像大小和channel    image_height = 64    image_width = 64    image_channel = 3#RGB    # 每次使用多少样本训练    batch_size = 128    n_batches = len(picture_list) // batch_size #300多次    #图片格式对应输入X    img_data = []    for img_file in picture_list:        img_data.append(cv2.imread(img_file))#读入文件    img_data = np.array(img_data)    img_data = img_data / 255.0X= tf.placeholder(tf.float32,shape=[None,image_height,image_width,image_channel])def gated_cnn(W_shape_, fan_in, gated= True, payload = None, mask = None, activation = True):    W_shape = [W_shape_[0], W_shape_[1], fan_in.get_shape()[-1], W_shape_[2]]    b_shape = W_shape_[2]    def get_weights(shape,name,mask=None):        weights_initializer = tf.contrib.layers.xavier_initializer()        W= tf.get_variable(name,shape,tf.float32,weights_initializer)        if mask:            filter_mid_x = shape[0]//2            filter_mid_y = shape[1]//2            mask_filter =np.ones(shape, dtype= np.float32)            mask_filter[filter_mid_x,filter_mid_y+1:, :, :] = 0.            mask_filter[filter_mid_x+1:, :, :, :] = 0.            if mask == 'a':                mask_filter[filter_mid_x, filter_mid_y, :, :] = 0.            W *= mask_filter        return W    if gated:        W_f = get_weights(W_shape, "v_W", mask=mask)        W_g = get_weights(W_shape, "h_W", mask=mask)        b_f = tf.get_variable("v_b" , b_shape, tf.float32, tf.zeros_initializer)        b_g = tf.get_variable("h_b" , b_shape, tf.float32, tf.zeros_initializer)        conv_f = tf.nn.conv2d(fan_in, W_f, strides=[1,1,1,1], padding='SAME')        conv_g = tf.nn.conv2d(fan_in, W_g, strides=[1,1,1,1], padding='SAME')        if payload is not None:            conv_f += payload            conv_g += payload        fan_out = tf.mul(tf.tanh(conv_f + b_f), tf.sigmoid(conv_g+ b_g))    else:        W = get_weights(W_shape, "W", mask=mask)        b = tf.get_variable("b", b_shape, tf.float32, tf.zeros_initializer)        conv = tf.nn.conv2d(fan_in, W, strides=[1,1,1,1],padding='SAME')        if activation:            fan_out = tf.nn.relu(tf.add(conv,b))        else:            fan_out = tf.add(conv, b)    return fan_outdef pixel_cnn(layers=12, f_map=32):    v_stack_in, h_stack_in = X, X    for i in range(layers):        filter_size = 3 if i>0 else 7        mask ='b' if i>0 else 'a'        residual = True if i>0 else False        i = str(i)        with tf.variable_scope("v_stack"+i):            v_stack = gated_cnn([filter_size, filter_size, f_map],v_stack_in, mask =mask)            v_stack_in = v_stack        with tf.variable_scope("vstack_1"+i):            v_stack_1 = gated_cnn([1,1,f_map],v_stack_in,gated=False)        with tf.variable_scope("h_stack"+i):            h_stack = gated_cnn([1, filter_size, f_map], h_stack_in, payload=v_stack_1, mask=mask)        with tf.variable_scope("h_stack_1"+i):            h_stack_1 = gated_cnn([1, 1, f_map], h_stack, gated=False, mask=mask)            if residual:                h_stack_1 += h_stack_in            h_stack_in = h_stack_1    with tf.variable_scope("fc_1"):        fc1 = gated_cnn([1, 1, f_map], h_stack_in, gated=False, mask='b')    color = 256    with tf.variable_scope("fc_2"):        fc2 = gated_cnn([1, 1, image_channel * color], fc1, gated=False, mask='b', activation=False)        fc2 = tf.reshape(fc2, (-1, color))        return fc2def train_pixel_cnn():    output = pixel_cnn()    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(output, tf.cast(tf.reshape(X, [-1]), dtype=tf.int32)))    trainer = tf.train.RMSPropOptimizer(1e-3)    gradients = trainer.compute_gradients(loss)    clipped_gradients = [(tf.clip_by_value(_[0], -1, 1), _[1]) for _ in gradients]    optimizer = trainer.apply_gradients(clipped_gradients)    with tf.Session() as sess:        sess.run(tf.global_variables_initializer())        saver = tf.train.Saver(tf.trainable_variables())        for epoch in range(50):            for batch in range(n_batches):                if MINST == True:                    batch_X, _ = data.train.next_batch(batch_size)                    batch_X = batch_X.reshape([batch_size, image_height, image_width, image_channel])                else:                    batch_X = img_data[batch_size * batch : batch_size * (batch + 1)]                _, cost = sess.run([optimizer, loss], feed_dict={X:batch_X})                print("epoch:", epoch, '  batch:', batch,'  cost:', cost)            if epoch % 7 == 0:                saver.save(sess, "girl.ckpt", global_step=epoch)# 训练train_pixel_cnn()#生成的函数代码def generate_girl():    output = pixel_cnn()    predict = tf.reshape(tf.multinomial(tf.nn.softmax(output), num_samples=1, seed=100), tf.shape(X))    #predict_argmax = tf.reshape(tf.argmax(tf.nn.softmax(output), dimension=tf.rank(output) - 1), tf.shape(X))    with tf.Session() as sess:        sess.run(tf.initialize_all_variables())        saver = tf.train.Saver(tf.trainable_variables())        saver.restore(sess, 'girl.ckpt-49')        pics = np.zeros((1*1, image_height, image_width, image_channel), dtype=np.float32)        for i in range(image_height):            for j in range(image_width):                for k in range(image_channel):                    next_pic = sess.run(predict, feed_dict={X:pics})                    pics[:, i, j, k] = next_pic[:, i, j, k]        cv2.imwrite('girl.jpg', pics[0])        print('生成妹子图: girl.jpg')# 生成图像generate_girl()

50个epoch实在太慢了，一天才跑2个，必须用gpu才可以跑。