标签：程序 转换 href 文件夹 image tps spl ike array
本节的代码参考了TensorFlow 源码中的示例程序https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/deepdream，并做了适当修改。
4.2.1 导入Inception 模型
在chapter_4_data/中或者网址https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip 下载解压得到模型文件tensorflow_inception_graph.pb，将该文件拷贝到当前文件夹中（即chapter_4/中）。
使用下面的命令加载模型并打印一些基础信息：
python load_inception.py# coding:utf-8
# 导入要用到的基本模块。
from __future__ import print_function
import numpy as np
import tensorflow as tf
# 创建图和Session
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
# tensorflow_inception_graph.pb文件中，既存储了inception的网络结构也存储了对应的数据
# 使用下面的语句将之导入
model_fn = 'tensorflow_inception_graph.pb'
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# 定义t_input为我们输入的图像
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
# 输入图像需要经过处理才能送入网络中
# expand_dims是加一维，从[height, width, channel]变成[1, height, width, channel]
# t_input - imagenet_mean是减去一个均值
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
# 找到所有卷积层
layers = [op.name for op in graph.get_operations() if op.type == 'Conv2D' and 'import/' in op.name]
# 输出卷积层层数
print('Number of layers', len(layers))
# 特别地，输出mixed4d_3x3_bottleneck_pre_relu的形状
name = 'mixed4d_3x3_bottleneck_pre_relu'
print('shape of %s: %s' % (name, str(graph.get_tensor_by_name('import/' + name + ':0').get_shape())))
4.2.2 生成原始的Deep Dream 图像
python gen_naive.py# coding: utf-8
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
import scipy.misc
import tensorflow as tf
graph = tf.Graph()
model_fn = 'tensorflow_inception_graph.pb'
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
def savearray(img_array, img_name):
scipy.misc.toimage(img_array).save(img_name)
print('img saved: %s' % img_name)
def render_naive(t_obj, img0, iter_n=20, step=1.0):
# t_score是优化目标。它是t_obj的平均值
# 结合调用处看，实际上就是layer_output[:, :, :, channel]的平均值
t_score = tf.reduce_mean(t_obj)
# 计算t_score对t_input的梯度
t_grad = tf.gradients(t_score, t_input)[0]
# 创建新图
img = img0.copy()
for i in range(iter_n):
# 在sess中计算梯度，以及当前的score
g, score = sess.run([t_grad, t_score], {t_input: img})
# 对img应用梯度。step可以看做“学习率”
g /= g.std() + 1e-8
img += g * step
print('score(mean)=%f' % (score))
# 保存图片
savearray(img, 'naive.jpg')
# 定义卷积层、通道数，并取出对应的tensor
name = 'mixed4d_3x3_bottleneck_pre_relu'
channel = 139
layer_output = graph.get_tensor_by_name("import/%s:0" % name)
# 定义原始的图像噪声
img_noise = np.random.uniform(size=(224, 224, 3)) + 100.0
# 调用render_naive函数渲染
render_naive(layer_output[:, :, :, channel], img_noise, iter_n=20)
4.2.3 生成更大尺寸的Deep Dream 图像
python gen_multiscale.py# coding:utf-8
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
import scipy.misc
import tensorflow as tf
graph = tf.Graph()
model_fn = 'tensorflow_inception_graph.pb'
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
def savearray(img_array, img_name):
scipy.misc.toimage(img_array).save(img_name)
print('img saved: %s' % img_name)
def resize_ratio(img, ratio):
min = img.min()
max = img.max()
img = (img - min) / (max - min) * 255
img = np.float32(scipy.misc.imresize(img, ratio))
img = img / 255 * (max - min) + min
return img
def calc_grad_tiled(img, t_grad, tile_size=512):
# 每次只对tile_size×tile_size大小的图像计算梯度，避免内存问题
sz = tile_size
h, w = img.shape[:2]
# img_shift：先在行上做整体移动，再在列上做整体移动
# 防止在tile的边缘产生边缘效应
sx, sy = np.random.randint(sz, size=2)
img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
grad = np.zeros_like(img)
# y, x是开始位置的像素
for y in range(0, max(h - sz // 2, sz), sz):
for x in range(0, max(w - sz // 2, sz), sz):
# 每次对sub计算梯度。sub的大小是tile_size×tile_size
sub = img_shift[y:y + sz, x:x + sz]
g = sess.run(t_grad, {t_input: sub})
grad[y:y + sz, x:x + sz] = g
# 使用np.roll移动回去
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
def render_multiscale(t_obj, img0, iter_n=10, step=1.0, octave_n=3, octave_scale=1.4):
# 同样定义目标和梯度
t_score = tf.reduce_mean(t_obj)
t_grad = tf.gradients(t_score, t_input)[0]
img = img0.copy()
for octave in range(octave_n):
if octave > 0:
# 每次将将图片放大octave_scale倍
# 共放大octave_n - 1 次
img = resize_ratio(img, octave_scale)
for i in range(iter_n):
# 调用calc_grad_tiled计算任意大小图像的梯度
g = calc_grad_tiled(img, t_grad)
g /= g.std() + 1e-8
img += g * step
print('.', end=' ')
savearray(img, 'multiscale.jpg')
if __name__ == '__main__':
name = 'mixed4d_3x3_bottleneck_pre_relu'
channel = 139
img_noise = np.random.uniform(size=(224, 224, 3)) + 100.0
layer_output = graph.get_tensor_by_name("import/%s:0" % name)
render_multiscale(layer_output[:, :, :, channel], img_noise, iter_n=20)
4.2.4 生成更高质量的Deep Dream 图像
python gen_lapnorm.py# coding:utf-8
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
import scipy.misc
import tensorflow as tf
graph = tf.Graph()
model_fn = 'tensorflow_inception_graph.pb'
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input')
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
def savearray(img_array, img_name):
scipy.misc.toimage(img_array).save(img_name)
print('img saved: %s' % img_name)
def resize_ratio(img, ratio):
min = img.min()
max = img.max()
img = (img - min) / (max - min) * 255
img = np.float32(scipy.misc.imresize(img, ratio))
img = img / 255 * (max - min) + min
return img
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) # 先在行上做整体移动，再在列上做整体移动
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, {t_input: sub})
grad[y:y + sz, x:x + sz] = g
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
k = np.float32([1, 4, 6, 4, 1])
k = np.outer(k, k)
k5x5 = k[:, :, None, None] / k.sum() * np.eye(3, dtype=np.float32)
# 这个函数将图像分为低频和高频成分
def lap_split(img):
with tf.name_scope('split'):
# 做过一次卷积相当于一次“平滑”，因此lo为低频成分
lo = tf.nn.conv2d(img, k5x5, [1, 2, 2, 1], 'SAME')
# 低频成分放缩到原始图像一样大小得到lo2，再用原始图像img减去lo2，就得到高频成分hi
lo2 = tf.nn.conv2d_transpose(lo, k5x5 * 4, tf.shape(img), [1, 2, 2, 1])
hi = img - lo2
return lo, hi
# 这个函数将图像img分成n层拉普拉斯金字塔
def lap_split_n(img, n):
levels = []
for i in range(n):
# 调用lap_split将图像分为低频和高频部分
# 高频部分保存到levels中
# 低频部分再继续分解
img, hi = lap_split(img)
levels.append(hi)
levels.append(img)
return levels[::-1]
# 将拉普拉斯金字塔还原到原始图像
def lap_merge(levels):
img = levels[0]
for hi in levels[1:]:
with tf.name_scope('merge'):
img = tf.nn.conv2d_transpose(img, k5x5 * 4, tf.shape(hi), [1, 2, 2, 1]) + hi
return img
# 对img做标准化。
def normalize_std(img, eps=1e-10):
with tf.name_scope('normalize'):
std = tf.sqrt(tf.reduce_mean(tf.square(img)))
return img / tf.maximum(std, eps)
# 拉普拉斯金字塔标准化
def lap_normalize(img, scale_n=4):
img = tf.expand_dims(img, 0)
tlevels = lap_split_n(img, scale_n)
# 每一层都做一次normalize_std
tlevels = list(map(normalize_std, tlevels))
out = lap_merge(tlevels)
return out[0, :, :, :]
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 render_lapnorm(t_obj, img0,
iter_n=10, step=1.0, octave_n=3, octave_scale=1.4, lap_n=4):
# 同样定义目标和梯度
t_score = tf.reduce_mean(t_obj)
t_grad = tf.gradients(t_score, t_input)[0]
# 将lap_normalize转换为正常函数
lap_norm_func = tffunc(np.float32)(partial(lap_normalize, scale_n=lap_n))
img = img0.copy()
for octave in range(octave_n):
if octave > 0:
img = resize_ratio(img, octave_scale)
for i in range(iter_n):
g = calc_grad_tiled(img, t_grad)
# 唯一的区别在于我们使用lap_norm_func来标准化g！
g = lap_norm_func(g)
img += g * step
print('.', end=' ')
savearray(img, 'lapnorm.jpg')
if __name__ == '__main__':
name = 'mixed4d_3x3_bottleneck_pre_relu'
channel = 139
img_noise = np.random.uniform(size=(224, 224, 3)) + 100.0
layer_output = graph.get_tensor_by_name("import/%s:0" % name)
render_lapnorm(layer_output[:, :, :, channel], img_noise, iter_n=20)
4.2.5 最终的Deep Dream 模型
python gen_deepdream.py# coding:utf-8
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
import scipy.misc
import tensorflow as tf
graph = tf.Graph()
model_fn = 'tensorflow_inception_graph.pb'
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input - imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input': t_preprocessed})
def savearray(img_array, img_name):
scipy.misc.toimage(img_array).save(img_name)
print('img saved: %s' % img_name)
def visstd(a, s=0.1):
return (a - a.mean()) / max(a.std(), 1e-4) * s + 0.5
def resize_ratio(img, ratio):
min = img.min()
max = img.max()
img = (img - min) / (max - min) * 255
img = np.float32(scipy.misc.imresize(img, ratio))
img = img / 255 * (max - min) + min
return img
def resize(img, hw):
min = img.min()
max = img.max()
img = (img - min) / (max - min) * 255
img = np.float32(scipy.misc.imresize(img, hw))
img = img / 255 * (max - min) + min
return img
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) # 先在行上做整体移动，再在列上做整体移动
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, {t_input: sub})
grad[y:y + sz, x:x + sz] = g
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
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 render_deepdream(t_obj, img0,
iter_n=10, step=1.5, octave_n=4, octave_scale=1.4):
t_score = tf.reduce_mean(t_obj)
t_grad = tf.gradients(t_score, t_input)[0]
img = img0
# 同样将图像进行金字塔分解
# 此时提取高频、低频的方法比较简单。直接缩放就可以
octaves = []
for i 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)
# 先生成低频的图像，再依次放大并加上高频
for octave in range(octave_n):
if octave > 0:
hi = octaves[-octave]
img = resize(img, hi.shape[:2]) + hi
for i in range(iter_n):
g = calc_grad_tiled(img, t_grad)
img += g * (step / (np.abs(g).mean() + 1e-7))
print('.', end=' ')
img = img.clip(0, 255)
savearray(img, 'deepdream.jpg')
if __name__ == '__main__':
img0 = PIL.Image.open('test.jpg')
img0 = np.float32(img0)
name = 'mixed4d_3x3_bottleneck_pre_relu'
channel = 139
layer_output = graph.get_tensor_by_name("import/%s:0" % name)
render_deepdream(layer_output[:, :, :, channel], img0)
# name = 'mixed4c'
# layer_output = graph.get_tensor_by_name("import/%s:0" % name)
# render_deepdream(tf.square(layer_output), img0)
标签：程序 转换 href 文件夹 image tps spl ike array
原文地址：https://www.cnblogs.com/chenxiangzhen/p/10532911.html
