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dreamscript.py
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dreamscript.py
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# imports and basic notebook setup
from cStringIO import StringIO
import numpy as np
import scipy.ndimage as nd
import PIL.Image
from IPython.display import clear_output, Image, display
from google.protobuf import text_format
import os
import caffe
directory = "frames"
if not os.path.exists(directory):
os.mkdir(directory)
def showarray(a, fmt='jpeg'):
a = np.uint8(np.clip(a, 0, 255))
f = StringIO()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
def load_model(model_path, net_fn, param_fn, swap=True):
_net_fn = model_path + net_fn
_param_fn = model_path + param_fn
# Patching model to be able to compute gradients.
# Note that you can also manually add "force_backward: true" line to "deploy.prototxt".
model = caffe.io.caffe_pb2.NetParameter()
text_format.Merge(open(_net_fn).read(), model)
model.force_backward = True
open('tmp.prototxt', 'w').write(str(model))
if(swap):
net = caffe.Classifier('tmp.prototxt', _param_fn,
mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent
channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB
else:
net = caffe.Classifier('tmp.prototxt', _param_fn,
mean = np.float32([104.0, 116.0, 122.0]))
return net
# a couple of utility functions for converting to and from Caffe's input image layout
def preprocess(net, img):
return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']
def deprocess(net, img):
return np.dstack((img + net.transformer.mean['data'])[::-1])
def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True):
'''Basic gradient ascent step.'''
src = net.blobs['data'] # input image is stored in Net's 'data' blob
dst = net.blobs[end]
ox, oy = np.random.randint(-jitter, jitter+1, 2)
src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) # apply jitter shift
net.forward(end=end)
dst.diff[:] = dst.data # specify the optimization objective
net.backward(start=end)
g = src.diff[0]
# apply normalized ascent step to the input image
src.data[:] += step_size/np.abs(g).mean() * g
src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) # unshift image
if clip:
bias = net.transformer.mean['data']
src.data[:] = np.clip(src.data, -bias, 255-bias)
def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_4c/output', clip=True, **step_params):
# prepare base images for all octaves
octaves = [preprocess(net, base_img)]
for i in xrange(octave_n-1):
octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))
src = net.blobs['data']
detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
for octave, octave_base in enumerate(octaves[::-1]):
h, w = octave_base.shape[-2:]
if octave > 0:
# upscale details from the previous octave
h1, w1 = detail.shape[-2:]
detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
src.reshape(1,3,h,w) # resize the network's input image size
src.data[0] = octave_base+detail
for i in xrange(iter_n):
make_step(net, end=end, clip=clip, **step_params)
# visualization
vis = deprocess(net, src.data[0])
if not clip: # adjust image contrast if clipping is disabled
vis = vis*(255.0/np.percentile(vis, 99.98))
showarray(vis)
print octave, i, end, vis.shape
clear_output(wait=True)
# extract details produced on the current octave
detail = src.data[0]-octave_base
# returning the resulting image
return deprocess(net, src.data[0])
frame = np.float32(PIL.Image.open('sky.jpg'))
frame_i = 1
h, w = frame.shape[:2]
s = 0.02 # scale coefficient
def dream_cycle(net, range_=100, iter_n=10, octave_n=4, octave_scale=1.4,end='inception_4c/output'):
global frame
global frame_i
for i in xrange(range_):
frame = deepdream(net, frame, iter_n, octave_n, octave_scale, end)
PIL.Image.fromarray(np.uint8(frame)).save(directory+"/%04d.jpg"%frame_i)
frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)
frame_i += 1
for i in xrange(30):
net = load_model('../caffe/models/googlenet_imagenet/', 'deploy.prototxt', 'bvlc_googlenet.caffemodel')
dream_cycle(net, 20, 10, 6, 1.4, 'inception_4c/output')
dream_cycle(net, 2, 10, 6, 1.4, 'inception_3b/5x5_reduce')
net = load_model('../caffe/models/googlenet_places205/', 'deploy.prototxt', 'net.caffemodel', False)
dream_cycle(net, 2, 10, 6, 1.4, 'inception_4c/output')
net = load_model('../caffe/models/cars/', 'deploy.prototxt', 'net.caffemodel', False)
dream_cycle(net, 2, 10, 6, 1.4, 'inception_3b_output')