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@naoyashiga
Created May 22, 2016
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image reg
import numpy
import matplotlib.pyplot as plt
import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links as L
from chainer import optimizers
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import io
from IPython.display import clear_output, Image, display
from scipy.misc import imresize
import PIL.Image
def showImageArray(array, fmt='jpeg'):
array = numpy.uint8(numpy.clip(array, 0, 255))
f = io.BytesIO()
PIL.Image.fromarray(array).save(f, fmt, quality=100, optimize=True)
display(Image(data=f.getvalue()))
# pilImg = PIL.Image.fromarray(array)
# pilImg.show()
# display(Image(data=array))
def to_np(imgpath):
return numpy.float32(PIL.Image.open(imgpath))
def getTrainingData(img):
print(img.shape)
rgbs = []
indices = []
width = img.shape[0]
height = img.shape[1]
for i in range(width):
for j in range(height):
_s = float(width)
x = (i - _s / 2.0) / _s
y = (j - _s / 2.0) / _s
rgb = img[i, j]
rgbs.append(rgb)
indices.append([x, y])
rgbs = numpy.array(rgbs, dtype=numpy.float32)
indices = numpy.array(indices, dtype=numpy.float32)
x_data = Variable(indices)
y_data = Variable(rgbs / 255.0 - 0.5)
# y_data = Variable(rgbs)
return x_data, y_data
size = 50
img = imresize(to_np("./cat.jpeg"), (size, size))
# print(img)
showImageArray(img)
x_data, y_data = getTrainingData(img)
model = chainer.Chain(l1=L.Linear(2, 20, wscale=2),
l2 = L.Linear(20, 20, wscale=1),
l3=L.Linear(20, 20, wscale=2),
l4=L.Linear(20, 20, wscale=1),
l5=L.Linear(20, 20, wscale=2),
l6=L.Linear(20, 20, wscale=1),
l7=L.Linear(20, 20, wscale=1),
l8=L.Linear(20, 20, wscale=1),
l13=L.Linear(20, 3, wscale=2))
def forward(model, x, t):
hidden = F.relu(model.l1(x))
hidden = F.relu(model.l2(hidden))
hidden = F.relu(model.l3(hidden))
hidden = F.relu(model.l4(hidden))
hidden = F.relu(model.l5(hidden))
hidden = F.relu(model.l6(hidden))
hidden = F.relu(model.l7(hidden))
hidden = F.relu(model.l8(hidden))
hidden = model.l13(hidden)
return F.mean_squared_error(hidden, t), hidden
optimizer = optimizers.SGD(lr=0.01)
optimizer.setup(model)
for i in range(1000000):
optimizer.zero_grads()
loss, hidden = forward(model, x_data, y_data)
# print(i)
loss.backward()
optimizer.update()
if i % 100 == 0:
# print((hidden.data + 0.5) * 255.)
img2 = hidden.data.reshape((50,50,3))
# print((img2 + 0.5) * 255.)
showImageArray((img2 + 0.5) * 255.)
# showImageArray((hidden.data + 0.5) * 255.)
clear_output(wait=True)
print("done")
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