pil example dreamy (snippet)

Python pil example 'dreamy'

Functions in program:

• def deepdream(net, base_img, iter_n=10, octave_n=4, octave_scale=1.4, end='inception_4c/output', clip=True, **step_params):
• def make_step(net, step_size=1.5, end='inception_4c/output', jitter=32, clip=True):
• def deprocess(net, img):
• def preprocess(net, img):

Modules used in program:

• import caffe
• import sys
• import PIL.Image
• import scipy.ndimage as nd
• import numpy as np

python dreamy

Python pil example: dreamy

# 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 sys
sys.path.insert(0, '/home/ricecake/Projects/caffe/python')
import caffe

#Nature
model_path = '../caffe/models/bvlc_googlenet/' # substitute your path here
net_fn   = model_path + 'deploy.prototxt'
#Had to be downloaded seperately.
param_fn = model_path + 'bvlc_googlenet.caffemodel'

#Structure
#model_path = './googlenet_places205/' # substitute your path here
#net_fn   = model_path + 'deploy_places205.protxt'
#param_fn = model_path + 'googlelet_places205_train_iter_2400000.caffemodel'

#Broken
#model_path = './hybridCNN/' # substitute your path here
#net_fn   = model_path + 'hybridCNN_deploy_upgraded.prototxt'
#param_fn = model_path + 'hybridCNN_iter_700000_upgraded.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])

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)
            print("lap %i of %i!" % ((i + octave*iter_n)+1, iter_n*octave_n))

        # extract details produced on the current octave
        detail = src.data[0]-octave_base
    # returning the resulting image
    return deprocess(net, src.data[0])

name = 'flower'
img = np.float32(PIL.Image.open("%s.ppm"%name))

frame = img
frame_i = 0
h, w = frame.shape[:2]
s = 0.05 # scale coefficient
for i in xrange(5):
    frame = deepdream(net, frame)
    PIL.Image.fromarray(np.uint8(frame)).save("frames/%s-%04d.ppm"%(name, frame_i))
    frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)
    frame_i += 1

Python links

• Learn Python: https://pythonbasics.org/
• Python Tutorial: https://pythonprogramminglanguage.com