%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import random
import math
# Image manipulation.import PIL.Image
from scipy.ndimage.filters import gaussian_filter複製代碼
defsave_image(image, filename):# Ensure the pixel-values are between 0 and 255.
image = np.clip(image, 0.0, 255.0)
# Convert to bytes.
image = image.astype(np.uint8)
# Write the image-file in jpeg-format.with open(filename, 'wb') as file:
PIL.Image.fromarray(image).save(file, 'jpeg')複製代碼
這是繪製圖像的函數。使用matplotlib將獲得低分辨率的圖像。使用PIL效果比較好。
defplot_image(image):# Assume the pixel-values are scaled between 0 and 255.ifFalse:
# Convert the pixel-values to the range between 0.0 and 1.0
image = np.clip(image/255.0, 0.0, 1.0)
# Plot using matplotlib.
plt.imshow(image, interpolation='lanczos')
plt.show()
else:
# Ensure the pixel-values are between 0 and 255.
image = np.clip(image, 0.0, 255.0)
# Convert pixels to bytes.
image = image.astype(np.uint8)
# Convert to a PIL-image and display it.
display(PIL.Image.fromarray(image))複製代碼
歸一化圖像，則像素值在0.0到1.0之間。這個在繪製梯度時頗有用。
defnormalize_image(x):# Get the min and max values for all pixels in the input.
x_min = x.min()
x_max = x.max()
# Normalize so all values are between 0.0 and 1.0
x_norm = (x - x_min) / (x_max - x_min)
return x_norm複製代碼
對梯度作歸一化以後，用這個函數繪製。
defplot_gradient(gradient):# Normalize the gradient so it is between 0.0 and 1.0
gradient_normalized = normalize_image(gradient)
# Plot the normalized gradient.
plt.imshow(gradient_normalized, interpolation='bilinear')
plt.show()複製代碼
defresize_image(image, size=None, factor=None):# If a rescaling-factor is provided then use it.if factor isnotNone:
# Scale the numpy array's shape for height and width.
size = np.array(image.shape[0:2]) * factor
# The size is floating-point because it was scaled.# PIL requires the size to be integers.
size = size.astype(int)
else:
# Ensure the size has length 2.
size = size[0:2]
# The height and width is reversed in numpy vs. PIL.
size = tuple(reversed(size))
# Ensure the pixel-values are between 0 and 255.
img = np.clip(image, 0.0, 255.0)
# Convert the pixels to 8-bit bytes.
img = img.astype(np.uint8)
# Create PIL-object from numpy array.
img = PIL.Image.fromarray(img)
# Resize the image.
img_resized = img.resize(size, PIL.Image.LANCZOS)
# Convert 8-bit pixel values back to floating-point.
img_resized = np.float32(img_resized)
return img_resized複製代碼
defget_tile_size(num_pixels, tile_size=400):""" num_pixels is the number of pixels in a dimension of the image. tile_size is the desired tile-size. """# How many times can we repeat a tile of the desired size.
num_tiles = int(round(num_pixels / tile_size))
# Ensure that there is at least 1 tile.
num_tiles = max(1, num_tiles)
# The actual tile-size.
actual_tile_size = math.ceil(num_pixels / num_tiles)
return actual_tile_size複製代碼
deftiled_gradient(gradient, image, tile_size=400):# Allocate an array for the gradient of the entire image.
grad = np.zeros_like(image)
# Number of pixels for the x- and y-axes.
x_max, y_max, _ = image.shape
# Tile-size for the x-axis.
x_tile_size = get_tile_size(num_pixels=x_max, tile_size=tile_size)
# 1/4 of the tile-size.
x_tile_size4 = x_tile_size // 4# Tile-size for the y-axis.
y_tile_size = get_tile_size(num_pixels=y_max, tile_size=tile_size)
# 1/4 of the tile-size
y_tile_size4 = y_tile_size // 4# Random start-position for the tiles on the x-axis.# The random value is between -3/4 and -1/4 of the tile-size.# This is so the border-tiles are at least 1/4 of the tile-size,# otherwise the tiles may be too small which creates noisy gradients.
x_start = random.randint(-3*x_tile_size4, -x_tile_size4)
while x_start < x_max:
# End-position for the current tile.
x_end = x_start + x_tile_size
# Ensure the tile's start- and end-positions are valid.
x_start_lim = max(x_start, 0)
x_end_lim = min(x_end, x_max)
# Random start-position for the tiles on the y-axis.# The random value is between -3/4 and -1/4 of the tile-size.
y_start = random.randint(-3*y_tile_size4, -y_tile_size4)
while y_start < y_max:
# End-position for the current tile.
y_end = y_start + y_tile_size
# Ensure the tile's start- and end-positions are valid.
y_start_lim = max(y_start, 0)
y_end_lim = min(y_end, y_max)
# Get the image-tile.
img_tile = image[x_start_lim:x_end_lim,
y_start_lim:y_end_lim, :]
# Create a feed-dict with the image-tile.
feed_dict = model.create_feed_dict(image=img_tile)
# Use TensorFlow to calculate the gradient-value.
g = session.run(gradient, feed_dict=feed_dict)
# Normalize the gradient for the tile. This is# necessary because the tiles may have very different# values. Normalizing gives a more coherent gradient.
g /= (np.std(g) + 1e-8)
# Store the tile's gradient at the appropriate location.
grad[x_start_lim:x_end_lim,
y_start_lim:y_end_lim, :] = g
# Advance the start-position for the y-axis.
y_start = y_end
# Advance the start-position for the x-axis.
x_start = x_end
return grad複製代碼
defoptimize_image(layer_tensor, image, num_iterations=10, step_size=3.0, tile_size=400, show_gradient=False):""" Use gradient ascent to optimize an image so it maximizes the mean value of the given layer_tensor. Parameters: layer_tensor: Reference to a tensor that will be maximized. image: Input image used as the starting point. num_iterations: Number of optimization iterations to perform. step_size: Scale for each step of the gradient ascent. tile_size: Size of the tiles when calculating the gradient. show_gradient: Plot the gradient in each iteration. """# Copy the image so we don't overwrite the original image.
img = image.copy()
print("Image before:")
plot_image(img)
print("Processing image: ", end="")
# Use TensorFlow to get the mathematical function for the# gradient of the given layer-tensor with regard to the# input image. This may cause TensorFlow to add the same# math-expressions to the graph each time this function is called.# It may use a lot of RAM and could be moved outside the function.
gradient = model.get_gradient(layer_tensor)
for i in range(num_iterations):
# Calculate the value of the gradient.# This tells us how to change the image so as to# maximize the mean of the given layer-tensor.
grad = tiled_gradient(gradient=gradient, image=img)
# Blur the gradient with different amounts and add# them together. The blur amount is also increased# during the optimization. This was found to give# nice, smooth images. You can try and change the formulas.# The blur-amount is called sigma (0=no blur, 1=low blur, etc.)# We could call gaussian_filter(grad, sigma=(sigma, sigma, 0.0))# which would not blur the colour-channel. This tends to# give psychadelic / pastel colours in the resulting images.# When the colour-channel is also blurred the colours of the# input image are mostly retained in the output image.
sigma = (i * 4.0) / num_iterations + 0.5
grad_smooth1 = gaussian_filter(grad, sigma=sigma)
grad_smooth2 = gaussian_filter(grad, sigma=sigma*2)
grad_smooth3 = gaussian_filter(grad, sigma=sigma*0.5)
grad = (grad_smooth1 + grad_smooth2 + grad_smooth3)
# Scale the step-size according to the gradient-values.# This may not be necessary because the tiled-gradient# is already normalized.
step_size_scaled = step_size / (np.std(grad) + 1e-8)
# Update the image by following the gradient.
img += grad * step_size_scaled
if show_gradient:
# Print statistics for the gradient.
msg = "Gradient min: {0:>9.6f}, max: {1:>9.6f}, stepsize: {2:>9.2f}"
print(msg.format(grad.min(), grad.max(), step_size_scaled))
# Plot the gradient.
plot_gradient(grad)
else:
# Otherwise show a little progress-indicator.
print(". ", end="")
print()
print("Image after:")
plot_image(img)
return img複製代碼
defrecursive_optimize(layer_tensor, image, num_repeats=4, rescale_factor=0.7, blend=0.2, num_iterations=10, step_size=3.0, tile_size=400):""" Recursively blur and downscale the input image. Each downscaled image is run through the optimize_image() function to amplify the patterns that the Inception model sees. Parameters: image: Input image used as the starting point. rescale_factor: Downscaling factor for the image. num_repeats: Number of times to downscale the image. blend: Factor for blending the original and processed images. Parameters passed to optimize_image(): layer_tensor: Reference to a tensor that will be maximized. num_iterations: Number of optimization iterations to perform. step_size: Scale for each step of the gradient ascent. tile_size: Size of the tiles when calculating the gradient. """# Do a recursive step?if num_repeats>0:
# Blur the input image to prevent artifacts when downscaling.# The blur amount is controlled by sigma. Note that the# colour-channel is not blurred as it would make the image gray.
sigma = 0.5
img_blur = gaussian_filter(image, sigma=(sigma, sigma, 0.0))
# Downscale the image.
img_downscaled = resize_image(image=img_blur,
factor=rescale_factor)
# Recursive call to this function.# Subtract one from num_repeats and use the downscaled image.
img_result = recursive_optimize(layer_tensor=layer_tensor,
image=img_downscaled,
num_repeats=num_repeats-1,
rescale_factor=rescale_factor,
blend=blend,
num_iterations=num_iterations,
step_size=step_size,
tile_size=tile_size)
# Upscale the resulting image back to its original size.
img_upscaled = resize_image(image=img_result, size=image.shape)
# Blend the original and processed images.
image = blend * image + (1.0 - blend) * img_upscaled
print("Recursive level:", num_repeats)
# Process the image using the DeepDream algorithm.
img_result = optimize_image(layer_tensor=layer_tensor,
image=image,
num_iterations=num_iterations,
step_size=step_size,
tile_size=tile_size)
return img_result複製代碼
