sample.py from overfeat library translated into julia

overfeat_sample.jl

using PyPlot
using PyCall
@pyimport overfeat
@pyimport scipy.ndimage as pyndimage
@pyimport scipy.misc as pymisc

# read image
image = pyndimage.imread("../../samples/bee.jpg")

# resize and crop into a 231x231 image
h0 = size(image,1) #.shape[0]
w0 = size(image,2) #.shape[1]
d0 = float(min(h0, w0))
image = image[int(round((h0-d0)/2.))+1:int(round((h0-d0)/2.)+d0),
              int(round((w0-d0)/2.))+1:int(round((w0-d0)/2.)+d0), :]
image = pymisc.imresize(image, (231, 231)) #.astype(numpy.float32)
image = convert(Array{Float32}, image)
im2 = copy(image)

# numpy loads image with colors as last dimension, transpose tensor
h = size(image,1) #.shape[0]
w = size(image,2) #.shape[1]
c = size(image,3) #.shape[2]
image = reshape(image, w*h, c)
image = image' #.transpose()
image = reshape(image, c, h, w)
println( "Image size :", size(image))

figure()
imshow(reshape(reshape(image, c, w*h)', w,h,c))


# initialize overfeat. Note that this takes time, so do it only once if possible
overfeat.init("../../data/default/net_weight_0", 0)

# run overfeat on the image
b = overfeat.fprop(image)

# display top 5 classes
b = b[:]
top = [(b[i], i-1) for i in 1:length(b)]
sort!(top) #.sort()
println("\nTop classes :")
for i=0:4
    println(overfeat.get_class_name(top[end-i][2]))
end