Tensorflow实现图片StyleTransfer_style transfer数据集

1.效果展示：

原图：

风格图：                                                   

二. 数据集为8000多张图片，训练一个模型，指定一种训练风格的图片

数据集链接：训练数据，8W多 12G蛮大的
http://msvocds.blob.core.windows.net/coco2014/train2014.zip

训练代码：

from __future__ import print_function
import sys, os, pdb
import numpy as np
import scipy.misc
from src.optimize import optimize
from argparse import ArgumentParser
from src.utils import save_img, get_img, exists, list_files
import evaluate  # 迭代优化
 
CONTENT_WEIGHT = 7.5e0
STYLE_WEIGHT = 1e2
TV_WEIGHT = 2e2
 
LEARNING_RATE = 1e-3
NUM_EPOCHS = 2
CHECKPOINT_DIR = 'checkpoints'
CHECKPOINT_ITERATIONS = 2000
VGG_PATH = 'data/imagenet-vgg-verydeep-19.mat'
TRAIN_PATH = 'data/'  # 图片数据路径
BATCH_SIZE = 4
DEVICE = '/gpu:0'   # gpu 计算
FRAC_GPU = 1
 
# 检测模型中的各个 参数是否已设置好
def check_opts(opts):
    exists(opts.checkpoint_dir, "checkpoint dir not found!")
    exists(opts.style, "style path not found!")
    exists(opts.train_path, "train path not found!")
    if opts.test or opts.test_dir:
        exists(opts.test, "test img not found!")
        exists(opts.test_dir, "test directory not found!")
    exists(opts.vgg_path, "vgg network data not found!")
    assert opts.epochs > 0
    assert opts.batch_size > 0
    assert opts.checkpoint_iterations > 0
    assert os.path.exists(opts.vgg_path)
    assert opts.content_weight >= 0
    assert opts.style_weight >= 0
    assert opts.tv_weight >= 0
    assert opts.learning_rate >= 0
 
def _get_files(img_dir):
    files = list_files(img_dir)
    return [os.path.join(img_dir,x) for x in files]
 
    
def main():
    parser = build_parser()
    options = parser.parse_args()
    check_opts(options)
 
    style_target = get_img(options.style)
    if not options.slow:
        content_targets = _get_files(options.train_path)
    elif options.test:
        content_targets = [options.test]
 
    kwargs = {
        "slow":options.slow,
        "epochs":options.epochs,
        "print_iterations":options.checkpoint_iterations,
        "batch_size":options.batch_size,
        "save_path":os.path.join(options.checkpoint_dir,'fns.ckpt'),
        "learning_rate":options.learning_rate
    }
 
    if options.slow:
        if options.epochs < 10:
            kwargs['epochs'] = 1000
        if options.learning_rate < 1:
            kwargs['learning_rate'] = 1e1
 
    args = [
        content_targets,
        style_target,
        options.content_weight,
        options.style_weight,
        options.tv_weight,
        options.vgg_path
    ]
 
    for preds, losses, i, epoch in optimize(*args, **kwargs):
        style_loss, content_loss, tv_loss, loss = losses
 
        print('Epoch %d, Iteration: %d, Loss: %s' % (epoch, i, loss))
        to_print = (style_loss, content_loss, tv_loss)
        print('style: %s, content:%s, tv: %s' % to_print)
        if options.test:
            assert options.test_dir != False
            preds_path = '%s/%s_%s.png' % (options.test_dir,epoch,i)
            if not options.slow:
                ckpt_dir = os.path.dirname(options.checkpoint_dir)
                evaluate.ffwd_to_img(options.test,preds_path,
                                     options.checkpoint_dir)
            else:
                save_img(preds_path, img)
    ckpt_dir = options.checkpoint_dir
    cmd_text = 'python evaluate.py --checkpoint %s ...' % ckpt_dir
    print("Training complete. For evaluation:\n    `%s`" % cmd_text)
 
if __name__ == '__main__':
    main()

  VGG训练好的模型：
http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

三. 测试代码，指定一种风格的model，测试便可生成混合图片

from __future__ import print_function
import sys
sys.path.insert(0, 'src')
import numpy as np, src.vgg, pdb, os
from src import transform
import scipy.misc
import tensorflow as tf
from src.utils import save_img, get_img, exists, list_files
from argparse import ArgumentParser
from collections import defaultdict
import time
import json
import subprocess
import numpy
 
BATCH_SIZE = 4
DEVICE = '/gpu:0'
 
def from_pipe(opts):
    command = ["ffprobe",
               '-v', "quiet",
               '-print_format', 'json',
               '-show_streams', opts.in_path]
 
    info = json.loads(str(subprocess.check_output(command), encoding="utf8"))
    width = int(info["streams"][0]["width"])
    height = int(info["streams"][0]["height"])
    fps = round(eval(info["streams"][0]["r_frame_rate"]))
 
    command = ["ffmpeg",
               '-loglevel', "quiet",
               '-i', opts.in_path,
               '-f', 'image2pipe',
               '-pix_fmt', 'rgb24',
               '-vcodec', 'rawvideo', '-']
 
    pipe_in = subprocess.Popen(command, stdout=subprocess.PIPE, bufsize=10 ** 9, stdin=None, stderr=None)
 
    command = ["ffmpeg",
               '-loglevel', "info",
               '-y',  # (optional) overwrite output file if it exists
               '-f', 'rawvideo',
               '-vcodec', 'rawvideo',
               '-s', str(width) + 'x' + str(height),  # size of one frame
               '-pix_fmt', 'rgb24',
               '-r', str(fps),  # frames per second
               '-i', '-',  # The imput comes from a pipe
               '-an',  # Tells FFMPEG not to expect any audio
               '-c:v', 'libx264',
               '-preset', 'slow',
               '-crf', '18',
               opts.out]
 
    pipe_out = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=None, stderr=None)
    g = tf.Graph()
    soft_config = tf.ConfigProto(allow_soft_placement=True)
    soft_config.gpu_options.allow_growth = True
 
    with g.as_default(), g.device(opts.device), \
         tf.Session(config=soft_config) as sess:
        batch_shape = (opts.batch_size, height, width, 3)
        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
                                         name='img_placeholder')
        preds = transform.net(img_placeholder)
        saver = tf.train.Saver()
        if os.path.isdir(opts.checkpoint):
            ckpt = tf.train.get_checkpoint_state(opts.checkpoint)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
            else:
                raise Exception("No checkpoint found...")
        else:
            saver.restore(sess, opts.checkpoint)
 
        X = np.zeros(batch_shape, dtype=np.float32)
        nbytes = 3 * width * height
        read_input = True
        last = False
 
        while read_input:
            count = 0
            while count < opts.batch_size:
                raw_image = pipe_in.stdout.read(width * height * 3)
 
                if len(raw_image) != nbytes:
                    if count == 0:
                        read_input = False
                    else:
                        last = True
                        X = X[:count]
                        batch_shape = (count, height, width, 3)
                        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
                                                     name='img_placeholder')
                        preds = transform.net(img_placeholder)
                    break
 
                image = numpy.fromstring(raw_image, dtype='uint8')
                image = image.reshape((height, width, 3))
                X[count] = image
                count += 1
 
            if read_input:
                if last:
                    read_input = False
                _preds = sess.run(preds, feed_dict={img_placeholder: X})
 
                for i in range(0, batch_shape[0]):
                    img = np.clip(_preds[i], 0, 255).astype(np.uint8)
                    try:
                        pipe_out.stdin.write(img)
                    except IOError as err:
                        ffmpeg_error = pipe_out.stderr.read()
                        error = (str(err) + ("\n\nFFMPEG encountered"
                                             "the following error while writing file:"
                                             "\n\n %s" % ffmpeg_error))
                        read_input = False
                        print(error)
        pipe_out.terminate()
        pipe_in.terminate()
        pipe_out.stdin.close()
        pipe_in.stdout.close()
        del pipe_in
        del pipe_out
 
# get img_shape
def ffwd(data_in, paths_out, checkpoint_dir, device_t='/gpu:0', batch_size=4):
    assert len(paths_out) > 0
    is_paths = type(data_in[0]) == str
    if is_paths:
        assert len(data_in) == len(paths_out)
        img_shape = get_img(data_in[0]).shape
    else:
        assert data_in.size[0] == len(paths_out)
        # img_shape = X[0].shape
 
    g = tf.Graph()
    batch_size = min(len(paths_out), batch_size)
    curr_num = 0
    soft_config = tf.ConfigProto(allow_soft_placement=True)
    soft_config.gpu_options.allow_growth = True
    with g.as_default(), g.device(device_t), tf.Session(config=soft_config) as sess:
        batch_shape = (batch_size,) + img_shape
        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape,
                                         name='img_placeholder')
 
        preds = transform.net(img_placeholder)
        saver = tf.train.Saver()
        if os.path.isdir(checkpoint_dir):
            ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
            else:
                raise Exception("No checkpoint found...")
        else:
            saver.restore(sess, checkpoint_dir)
 
        num_iters = int(len(paths_out)/batch_size)
        for i in range(num_iters):
            pos = i * batch_size
            curr_batch_out = paths_out[pos:pos+batch_size]
            if is_paths:
                curr_batch_in = data_in[pos:pos+batch_size]
                X = np.zeros(batch_shape, dtype=np.float32)
                for j, path_in in enumerate(curr_batch_in):
                    img = get_img(path_in)
                    assert img.shape == img_shape, \
                        'Images have different dimensions. ' +  \
                        'Resize images or use --allow-different-dimensions.'
                    X[j] = img
            else:
                X = data_in[pos:pos+batch_size]
 
            _preds = sess.run(preds, feed_dict={img_placeholder:X})
            for j, path_out in enumerate(curr_batch_out):
                save_img(path_out, _preds[j])
                
        remaining_in = data_in[num_iters*batch_size:]
        remaining_out = paths_out[num_iters*batch_size:]
    if len(remaining_in) > 0:
        ffwd(remaining_in, remaining_out, checkpoint_dir, 
            device_t=device_t, batch_size=1)
 
def ffwd_to_img(in_path, out_path, checkpoint_dir, device='/cpu:0'):
    paths_in, paths_out = [in_path], [out_path]
    ffwd(paths_in, paths_out, checkpoint_dir, batch_size=1, device_t=device)
 
def ffwd_different_dimensions(in_path, out_path, checkpoint_dir, 
            device_t=DEVICE, batch_size=4):
    in_path_of_shape = defaultdict(list)
    out_path_of_shape = defaultdict(list)
    for i in range(len(in_path)):
        in_image = in_path[i]
        out_image = out_path[i]
        shape = "%dx%dx%d" % get_img(in_image).shape
        in_path_of_shape[shape].append(in_image)
        out_path_of_shape[shape].append(out_image)
    for shape in in_path_of_shape:
        print('Processing images of shape %s' % shape)
        ffwd(in_path_of_shape[shape], out_path_of_shape[shape], 
            checkpoint_dir, device_t, batch_size)
 
def check_opts(opts):
    exists(opts.checkpoint_dir, 'Checkpoint not found!')
    exists(opts.in_path, 'In path not found!')
    if os.path.isdir(opts.out_path):
        exists(opts.out_path, 'out dir not found!')
        assert opts.batch_size > 0
 
def build_parser():
    parser = ArgumentParser()
    parser.add_argument('--checkpoint', type=str,
                        dest='checkpoint_dir',
                        help='dir or .ckpt file to load checkpoint from',
                        metavar='CHECKPOINT', required=True,default='./model/la_muse.ckpt')
 
    parser.add_argument('--in-path', type=str,
                        dest='in_path',help='dir or file to transform',
                        metavar='IN_PATH', required=True,default='./examples/content/stata.jpg')
 
    help_out = 'destination (dir or file) of transformed file or files'
    parser.add_argument('--out-path', type=str,
                        dest='out_path', help=help_out, metavar='OUT_PATH',
                        required=True,default='./')
 
    parser.add_argument('--device', type=str,
                        dest='device',help='device to perform compute on',
                        metavar='DEVICE', default=DEVICE)
 
    parser.add_argument('--batch-size', type=int,
                        dest='batch_size',help='batch size for feedforwarding',
                        metavar='BATCH_SIZE', default=BATCH_SIZE)
 
    parser.add_argument('--allow-different-dimensions', action='store_true',
                        dest='allow_different_dimensions', 
                        help='allow different image dimensions')
    return parser
 
def main():
    parser = build_parser()
    opts = parser.parse_args()
    # 确认输入参数是否已存在，若不存在，重新创建
    check_opts(opts)
    if not os.path.isdir(opts.in_path):
        if os.path.exists(opts.out_path) and os.path.isdir(opts.out_path):
            # 获取图片的名称，作为输出图片名
            out_path = os.path.join(opts.out_path,os.path.basename(opts.in_path))
        else:
            out_path = opts.out_path
        ffwd_to_img(opts.in_path, out_path, opts.checkpoint_dir,
                    device=opts.device)
    else:
        files = list_files(opts.in_path)
        full_in = [os.path.join(opts.in_path,x) for x in files]
        full_out = [os.path.join(opts.out_path,x) for x in files]
        if opts.allow_different_dimensions:
            ffwd_different_dimensions(full_in, full_out, opts.checkpoint_dir, 
                    device_t=opts.device, batch_size=opts.batch_size)
        else :
            ffwd(full_in, full_out, opts.checkpoint_dir, device_t=opts.device,
                    batch_size=opts.batch_size)
 
 
if __name__ == '__main__':
    main()

四.应用的神经网络模型

import tensorflow as tf, pdb
 
WEIGHTS_INIT_STDEV = .1
# 网络结构
def net(image):
    conv1 = _conv_layer(image, 32, 9, 1)
    conv2 = _conv_layer(conv1, 64, 3, 2)
    conv3 = _conv_layer(conv2, 128, 3, 2)
    # 残差网络结构
    resid1 = _residual_block(conv3, 3)
    resid2 = _residual_block(resid1, 3)
    resid3 = _residual_block(resid2, 3)
    resid4 = _residual_block(resid3, 3)
    resid5 = _residual_block(resid4, 3)
 
    conv_t1 = _conv_tranpose_layer(resid5, 64, 3, 2)
    conv_t2 = _conv_tranpose_layer(conv_t1, 32, 3, 2)
    conv_t3 = _conv_layer(conv_t2, 3, 9, 1, relu=False)
    preds = tf.nn.tanh(conv_t3) * 150 + 255./2
    return preds
 
def _conv_layer(net, num_filters, filter_size, strides, relu=True):
    weights_init = _conv_init_vars(net, num_filters, filter_size)
    strides_shape = [1, strides, strides, 1]
    net = tf.nn.conv2d(net, weights_init, strides_shape, padding='SAME')
    net = _instance_norm(net)
    if relu:
        net = tf.nn.relu(net)
    return net
 
# 反卷积操作
def _conv_tranpose_layer(net, num_filters, filter_size, strides):
    weights_init = _conv_init_vars(net, num_filters, filter_size, transpose=True) #True 反卷积
 
    batch_size, rows, cols, in_channels = [i.value for i in net.get_shape()]
    new_rows, new_cols = int(rows * strides), int(cols * strides)  # 反卷积变换
    # new_shape = #tf.pack([tf.shape(net)[0], new_rows, new_cols, num_filters])
 
    new_shape = [batch_size, new_rows, new_cols, num_filters] # 新的shape
    tf_shape = tf.stack(new_shape)
    strides_shape = [1,strides,strides,1]
 
    net = tf.nn.conv2d_transpose(net, weights_init, tf_shape, strides_shape, padding='SAME')
    net = _instance_norm(net)
    return tf.nn.relu(net)
 
# 残差网络的 模块
def _residual_block(net, filter_size=3):
    tmp = _conv_layer(net, 128, filter_size, 1)
    return net + _conv_layer(tmp, 128, filter_size, 1, relu=False)
 
# batch_normalization 模块
def _instance_norm(net, train=True):
    batch, rows, cols, channels = [i.value for i in net.get_shape()] # 特征图
    var_shape = [channels]
    # 当前特征图中的均值，方差
    mu, sigma_sq = tf.nn.moments(net, [1,2], keep_dims=True)
    shift = tf.Variable(tf.zeros(var_shape))
    scale = tf.Variable(tf.ones(var_shape))
    epsilon = 1e-3
    normalized = (net-mu)/(sigma_sq + epsilon)**(.5)
    return scale * normalized + shift
 
def _conv_init_vars(net, out_channels, filter_size, transpose=False):
    _, rows, cols, in_channels = [i.value for i in net.get_shape()]
    if not transpose:
        weights_shape = [filter_size, filter_size, in_channels, out_channels]
    else:
        weights_shape = [filter_size, filter_size, out_channels, in_channels]   # 反卷积
 
    weights_init = tf.Variable(tf.truncated_normal(weights_shape, stddev=WEIGHTS_INIT_STDEV, seed=1), dtype=tf.float32)
    return weights_init

由于代码过多，不易全部展示，完整Demo参加GitHub链接：

https://github.com/Whq123/Style-transfer-of-picture