tensorflow练习8：实现Google的Deep Dream

Google把自家生成图片的技术 Inceptionism 开源化，称之为 Deep Dream ，一个原本用来将图片分类的AI，让我们看到不一样的世界~在把一张图片喂入之后，选择某一层神经网路（Google 的神经网luo有 10-30 层）进行重复处理的次数和变形的程度，就能获得一张非常后现代的「画作」。
1)下载模型，解压。
下载模型，解压
2）加载预训练的模型，训练。

#coding=utf-8
import tensorflow as tf
import numpy as np
import cv2
inception_model = 'tensorflow_inception_graph.pb'
# 加载inception模型
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)

X = tf.placeholder(np.float32, name='input')
with tf.gfile.FastGFile(inception_model, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
imagenet_mean = 117.0
preprocessed = tf.expand_dims(X-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':preprocessed})

layers = [op.name for op in graph.get_operations() if op.type=='Conv2D' and 'import/' in op.name]
feature_nums = [int(graph.get_tensor_by_name(name+':0').get_shape()[-1]) for name in layers]

print('layers:', len(layers))   # 59
print('feature:', sum(feature_nums))  # 7548

# deep dream
def deep_dream(obj, img_noise=np.random.uniform(size=(224,224,3)) + 100.0, iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
    score = tf.reduce_mean(obj)
    gradi = tf.gradients(score, X)[0]

    img = img_noise
    octaves = []

    def tffunc(*argtypes):
        placeholders = list(map(tf.placeholder, argtypes))
        def wrap(f):
            out = f(*placeholders)
            def wrapper(*args, **kw):
                return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))
            return wrapper
        return wrap
    def resize(img, size):
        img = tf.expand_dims(img, 0)
        return tf.image.resize_bilinear(img, size)[0,:,:,:]

    resize = tffunc(np.float32, np.int32)(resize)
    for _ in range(octave_n-1):
        hw = img.shape[:2]
        lo = resize(img, np.int32(np.float32(hw)/octave_scale))
        hi = img-resize(lo, hw)
        img = lo
        octaves.append(hi)

    def calc_grad_tiled(img, t_grad, tile_size=512):
        sz = tile_size
        h, w = img.shape[:2]
        sx, sy = np.random.randint(sz, size=2)
        img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
        grad = np.zeros_like(img)
        for y in range(0, max(h-sz//2, sz),sz):
            for x in range(0, max(w-sz//2, sz),sz):
                sub = img_shift[y:y+sz,x:x+sz]
                g = sess.run(t_grad, {X:sub})
                grad[y:y+sz,x:x+sz] = g
        return np.roll(np.roll(grad, -sx, 1), -sy, 0)   

    for octave in range(octave_n):
        if octave>0:
            hi = octaves[-octave]
            img = resize(img, hi.shape[:2])+hi
        for _ in range(iter_n):
            g = calc_grad_tiled(img, gradi)
            img += g*(step / (np.abs(g).mean()+1e-7))

        # 保存图像
        output_file = 'output' + str(octave+1) + '.jpg'
        cv2.imwrite(output_file, img)
        print(output_file)

# 加载输入图像
input_img = cv2.imread('input.jpg')
input_img = np.float32(input_img)

# 选择层
layer = 'mixed4c'

deep_dream(tf.square(graph.get_tensor_by_name("import/%s:0"%layer)), input_img)