# coding: utf-8

# # Deep Dreams (with Caffe)
#
# This notebook demonstrates how to use the [Caffe](http://caffe.berkeleyvision.org/) neural network framework to produce "dream" visuals shown in the [Google Research blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html).
#
# It'll be interesting to see what imagery people are able to generate using the described technique. If you post images to Google+, Facebook, or Twitter, be sure to tag them with **#deepdream** so other researchers can check them out too.
#
# ##Dependencies
# This notebook is designed to have as few dependencies as possible:
# * Standard Python scientific stack: [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/), [PIL](http://www.pythonware.com/products/pil/), [IPython](http://ipython.org/). Those libraries can also be installed as a part of one of the scientific packages for Python, such as [Anaconda](http://continuum.io/downloads) or [Canopy](https://store.enthought.com/).
# * [Caffe](http://caffe.berkeleyvision.org/) deep learning framework ([installation instructions](http://caffe.berkeleyvision.org/installation.html)).
# * Google [protobuf](https://developers.google.com/protocol-buffers/) library that is used for Caffe model manipulation.

# In[12]:

# 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 caffe

# If your GPU supports CUDA and Caffe was built with CUDA support,
# uncomment the following to run Caffe operations on the GPU.
caffe.set_mode_gpu()
caffe.set_device(0) # select GPU device if multiple devices exist

def showarray(a, fmt='jpeg'):
    a = np.uint8(np.clip(a, 0, 255))
    f = StringIO()
    PIL.Image.fromarray(a).save(f, fmt)



# ## Loading DNN model
# In this notebook we are going to use a [GoogLeNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) model trained on [ImageNet](http://www.image-net.org/) dataset.
# Feel free to experiment with other models from Caffe [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). One particularly interesting [model](http://places.csail.mit.edu/downloadCNN.html) was trained in [MIT Places](http://places.csail.mit.edu/) dataset. It produced many visuals from the [original blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html).

# In[21]:

model_path = '/home/nolan/caffe/models/bvlc_googlenet/' # substitute your path here
net_fn   = model_path + 'deploy.prototxt'
param_fn = model_path + 'bvlc_googlenet.caffemodel'

# 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))

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

# 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])


# ##  Producing dreams

# Making the "dream" images is very simple. Essentially it is just a gradient ascent process that tries to maximize the L2 norm of activations of a particular DNN layer. Here are a few simple tricks that we found useful for getting good images:
# * offset image by a random jitter
# * normalize the magnitude of gradient ascent steps
# * apply ascent across multiple scales (octaves)
#
# First we implement a basic gradient ascent step function, applying the first two tricks:

# In[3]:

def objective_L2(dst):
    dst.diff[:] = dst.data

def make_step(net, step_size=1.5, end='inception_4a/output',
              jitter=32, clip=True, objective=objective_L2):
    '''Basic gradient ascent step.'''

    src = net.blobs['data']
    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)
    objective(dst)  # 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)


# Next we implement an ascent through different scales. We call these scales "octaves".

# In[4]:

def deepdream(net, base_img, iter_n=16, octave_n=4, octave_scale=1.4,
              end='inception_3a/3x3', 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])


# Now we are ready to let the neural network reveal its dreams! Let's take a [cloud image](https://commons.wikimedia.org/wiki/File:Appearance_of_sky_for_weather_forecast,_Dhaka,_Bangladesh.JPG) as a starting point:

# In[5]:

img = np.float32(PIL.Image.open('fractal.jpg'))



net.blobs.keys()


# What if we feed the `deepdream` function its own output, after applying a little zoom to it? It turns out that this leads to an endless stream of impressions of the things that the network saw during training. Some patterns fire more often than others, suggestive of basins of attraction.
#
# We will start the process from the same sky image as above, but after some iteration the original image becomes irrelevant; even random noise can be used as the starting point.

# In[7]:

frame = img
frame_i = 0


# In[22]:

h, w = frame.shape[:2]
s = 0.02 # scale coefficient
for i in xrange(500):
    frame = deepdream(net, frame)
    PIL.Image.fromarray(np.uint8(frame)).save("frames/%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


# Be careful running the code above, it can bring you into very strange realms!