Julia (julialang) Kaggle Tutorial : Reading in the Images

#!/usr/bin/julia

# Our next task is to read in all the training images and make them
# into a big matrix.

# The code on the kaggle website doesn't seem to work for me, but after
# a bit of hunting around, I came up with this little program

using DataFrames
using Images # lots of warnings, but it's ok

# Paths to our data files
labelsfile="./trainLabels.csv"
imagedirectory="./trainResized/"

@printf("Reading %s\n", labelsfile)
labels=readtable(labelsfile)
no_of_images=size(labels)[1]
@printf("Read in %d labels from %s\n", no_of_images, labelsfile)
@printf("Image %i is of an %s\n",labels[1,1],labels[1,2])


@printf("reading %s images\n", no_of_images)

# All the images have been resized to 20x20 in the trainResized directory
imageSize=20*20

# So let's try and get the desired effect on the first image
image="$(imagedirectory)1.Bmp"
img=imread(image)
# turn our colour image into a greyscale image
img_gs=convert(Image{Gray},img)
# turn the specialized image format into an array of floats
img_floats=reinterpret(Float32,float32(img_gs))
# turn the 20x20 array into a 1x400 vector
img_vec=reshape(img_floats,1,imageSize)

# After all that, I feel the need to check I haven't buggered it up
# There's a julia package for looking at images
Pkg.add("ImageView")
require("ImageView")
# Should pop up a little grey 'n'
ImageView.view(grayim(reshape(img_vec,20,20)))

# Now we want to use that process to convert all the images into one
# big array of image vectors

# Create a gigantic array to put the images in
x=zeros(no_of_images,imageSize)

# We can iterate over a dataframe's columns by name (it takes a while!)
for (a,b) in enumerate(labels[:ID]);
    image="$(imagedirectory)$(b).Bmp"
    img=imread(image)
    assert(size(img)==(20,20)) # paranoia
    img_gs=convert(Image{Gray},img)
    assert(size(img_gs)==(20,20))
    img_floats=reinterpret(Float32,float32(img_gs))
    assert(size(img_floats)==(20,20))
    img_vec=reshape(img_floats,1,imageSize)
    assert(size(img_vec)==(1,400))

    @printf("%s %s\n",a,image)
    x[a,:]=img_vec
end

# and one final paranoid check
ImageView.view(grayim(reshape(x[6200,:],20,20)))
labels[6200,:]


# So that's how to turn 6000 or so images into one big matrix.