pil example demosine (snippet)

Python pil example 'demosine'

Functions in program:

• def shiftfunc(n):
• 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):
• def showarray(a, fmt='jpeg'):

Modules used in program:

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

python demosine

Python pil example: demosine

# 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 sys
import caffe
import itertools

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()))

# 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 storred 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 optimiation objective
    net.backward(start=end)
    g = src.diff[0]
    # apply normaized 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])

model_path = '../caffe/models/bvlc_googlenet/'
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

try:
    inputfile = sys.argv[1]
except IndexError:
    inputfile = "sky1024px.jpg"

framedir = inputfile.split(".")[0]+"_frames"
try:
    os.mkdir(framedir)
except OSError: # assume errno=17 (directory already exists)
    pass

print("Opening", inputfile)
img = np.float32(PIL.Image.open(inputfile))

frame = img
h, w = frame.shape[:2]
s = 0.017
theta = 0.1

def shiftfunc(n):
    return int(17 * np.sin(n/17.))/5

for i in itertools.count(0):
    print("\n\n===Saving the results after %i rounds===" % (i,))
    PIL.Image.fromarray(np.uint8(frame)).save(framedir+"/%04d.jpg"%i)

    # since this is kind of a big shift, we only apply it every other iteration
    if i % 2 == 0:
        for n in range(frame.shape[0]):
            frame[:, n] = np.roll(frame[:, n], 3*shiftfunc(n))

    frame = deepdream(net, frame, iter_n=9, octave_scale=1.35)

Python links

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