In [1]:

    

from numpy import *
from PIL import Image
import pickle
from pylab import *
import os
from scipy.misc import *
from matplotlib.pyplot import *


    

In [4]:

    

path = 'sudoku_images/ocr_data/training/'
imlist = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.jpg')]


    

In [76]:

    

im = array(Image.open(imlist[3]))
im = imresize(im, (300, 300))
im = im[30:-30, 30:-30]
figure()
gray()
imshow(im)
axis('off')
show()


    

In [77]:

    

from scipy.ndimage import measurements, morphology


    

In [297]:

    

im = array(Image.open(imlist[33]).convert('L'))
im = imresize(im, (300, 300))
im = im[30:-30, 30:-30]
im = 1*(im<128)
im_open = morphology.binary_opening(im, ones((9, 9)), iterations=2)
im_open = 1*(im_open>0.5)

col = np.sum(im_open, axis=0)
row = np.sum(im_open, axis=1)
xmin = max(0, col.nonzero()[0][0]-10)
xmax = min(299, col.nonzero()[0][-1]+10)
ymin = max(0, row.nonzero()[0][0]-10)
ymax = min(299, row.nonzero()[0][-1]+10)

im_crop = im_open[ymin:ymax, xmin:xymax]
im_crop = imresize(im_crop, (24, 24))
im_crop = 1*(im_crop>128)


    

In [298]:

    

figure()
gray()
subplot(1, 3, 1)
imshow(im)
axis('off')
subplot(1, 3, 2)
imshow(im_open)
axis('off')
subplot(1, 3, 3)
imshow(im_crop)
axis('off')
show()


    

In [299]:

    

pixel_sum = sum(im_crop)
print pixel_sum
print im_crop.shape[0]*im_crop.shape[1]
print 1.0*pixel_sum/(im_crop.shape[0]*im_crop.shape[1])


    

    




212
576
0.368055555556

In [300]:

    

col_sum = np.sum(im_crop, axis=0)
row_sum = np.sum(im_crop, axis=1)
print col_sum
print row_sum


    

    




[ 0  0 12 15 17 19 19 16 13 14 15 18 18 16 13  7  0  0  0  0  0  0  0  0]
[ 0  6  9 10 11 11  6  5  5 11 12 13 14 12 10  8 10 10 12 12 11  9  5  0]

In [301]:

    

def count_alternative_lines(l, a):
    cnt = 0
    fp = l[0]-a
    for i in l[1:]:
        fc = i-a
        if (fp*fc)<0:
            cnt += 1
        if not fc==0:
            fp = fc
    return cnt


    

In [302]:

    

nbr_alt_cols = 1.0*count_alternative_lines(col_sum, len(row_sum)/2)/len(col_sum)
nbr_alt_rows = 1.0*count_alternative_lines(row_sum, len(col_sum)/2)/len(row_sum)


    

In [303]:

    

print nbr_alt_cols
print nbr_alt_rows


    

    




0.0833333333333
0.0833333333333

In [304]:

    

max_col_sum = max(col_sum)
max_row_sum = max(row_sum)


    

In [305]:

    

im_inv = 1-im_open
labels, nbr_of_objects = measurements.label(im_inv)
nbr_of_holes = nbr_of_objects-1


    

In [306]:

    

imm = im_crop

xx = sum([x*x*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
xx = 1.0*xx/sum([x*x for y in range(imm.shape[0]) for x in range(imm.shape[1])])

yy = sum([y*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
yy = 1.0*yy/sum([y*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])

xy = sum([x*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
xy = 1.0*xy/sum([x*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])

xxx = sum([x*x*x*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
xxx = 1.0*xxx/sum([x*x*x for y in range(imm.shape[0]) for x in range(imm.shape[1])])

yyy = sum([y*y*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
yyy = 1.0*yyy/sum([y*y*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])


    

In [307]:

    

print xx, yy, xy, xxx, yyy


    

    




0.168256629664 0.359341273512 0.274325246797 0.106622055065 0.337642980291

In [326]:

    

def get_moments(imm):
    xx = sum([x*x*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    xx = 1.0*xx/(sum([x*x for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)

    yy = sum([y*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    yy = 1.0*yy/(sum([y*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)

    xy = sum([x*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    xy = 1.0*xy/(sum([x*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)

    xxx = sum([x*x*x*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    xxx = 1.0*xxx/(sum([x*x*x for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)

    yyy = sum([y*y*y*imm[y, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    yyy = 1.0*yyy/(sum([y*y*y for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)
    
    return (xx, yy, xy, xxx, yyy)


    

In [327]:

    

print get_moments(imm)


    

    




(0.16825646752961423, 0.35934092724625416, 0.27432488667705485, 0.10662204923346104, 0.33764296182289688)

In [328]:

    

xs = sum([imm[y, x]*imm[y, imm.shape[1]-x-1] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
xs = 1.0*xs/sum([imm[y, x]**2 for y in range(imm.shape[0]) for x in range(imm.shape[1])])

ys = sum([imm[y, x]*imm[imm.shape[0]-y-1, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
ys = 1.0*ys/sum([imm[y, x]**2 for y in range(imm.shape[0]) for x in range(imm.shape[1])])


    

In [329]:

    

print xs, ys


    

    




0.358490566038 0.830188679245

In [332]:

    

def get_symmetry(imm):
    xs = sum([imm[y, x]*imm[y, imm.shape[1]-x-1] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    xs = 1.0*xs/(sum([imm[y, x]**2 for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)

    ys = sum([imm[y, x]*imm[imm.shape[0]-y-1, x] for y in range(imm.shape[0]) for x in range(imm.shape[1])])
    ys = 1.0*ys/(sum([imm[y, x]**2 for y in range(imm.shape[0]) for x in range(imm.shape[1])])+0.1)
    return (xs, ys)


    

In [333]:

    

print get_symmetry(imm)


    

    




(0.35832154644035835, 0.82979726544082977)

In [334]:

    

def compute_feature(im):
    """ Returns a feature vector for an
    ocr image patch. """
    
    im = imresize(im, (300, 300))
    im = im[30:-30, 30:-30]
    im = 1*(im<128)
    im_open = morphology.binary_opening(im, ones((9, 9)), iterations=2)
    im_open = 1*(im_open>0.5)
    
    col = np.sum(im_open, axis=0)
    row = np.sum(im_open, axis=1)
    try:
        xmin = max(0, col.nonzero()[0][0]-10)
        xmax = min(299, col.nonzero()[0][-1]+10)
        ymin = max(0, row.nonzero()[0][0]-10)
        ymax = min(299, row.nonzero()[0][-1]+10)
    except:
        xmin = 0
        xmax = 299
        ymin = 0
        ymax = 299

    im_crop = im_open[ymin:ymax, xmin:xymax]
    im_crop = imresize(im_crop, (24, 24))
    im_crop = 1*(im_crop>128)

    black_proportion = 1.0*sum(im_crop)/(im_crop.shape[0]*im_crop.shape[1])

    col_sum = np.sum(im_crop, axis=0)
    row_sum = np.sum(im_crop, axis=1)

    max_col_sum = 1.0*max(col_sum)/im_crop.shape[0]
    max_row_sum = 1.0*max(row_sum)/im_crop.shape[1]

    nbr_alt_cols = 1.0*count_alternative_lines(col_sum, len(row_sum)/2)/len(col_sum)
    nbr_alt_rows = 1.0*count_alternative_lines(row_sum, len(col_sum)/2)/len(row_sum)
    
    labels, nbr_of_objects = measurements.label(1-im_crop)
    nbr_of_holes = nbr_of_objects-1

    moments = get_moments(im_crop)
    
    symmetry = get_symmetry(im_crop)
    
    return [black_proportion, max_col_sum, max_row_sum, nbr_alt_cols, nbr_alt_rows,
           nbr_of_holes, moments[0], moments[1], moments[2], moments[3], moments[4],
           symmetry[0], symmetry[1]]


    

In [346]:

    

compute_feature(array(Image.open(imlist[6]).convert('L')))


    

    
Out[346]:





[0.3802083333333333,
 0.8333333333333334,
 0.625,
 0.08333333333333333,
 0.16666666666666666,
 1,
 0.20482558122727679,
 0.34412547783160091,
 0.29507942779953289,
 0.13905625683415943,
 0.32079655880261065,
 0.40164308534915566,
 0.83067092651757191]

In [336]:

    

def load_ocr_data(path):
    """ Return labels and ocr features for all images in path. """
    
    # create list of all files ending in .jpg
    imlist = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.jpg')]
    
    labels = [int(imfile.split('/')[-1][0]) for imfile in imlist]
    features = []
    for imname in imlist:
        print imname
        im = array(Image.open(imname).convert('L'))
        features.append(compute_feature(im))
    return array(features), labels


    

In [337]:

    

features, labels = load_ocr_data('sudoku_images/ocr_data/training/')
test_features, test_labels = load_ocr_data('sudoku_images/ocr_data/testing/')


    

    




sudoku_images/ocr_data/training/5_126.jpg
sudoku_images/ocr_data/training/3_21.jpg
sudoku_images/ocr_data/training/3_249.jpg
sudoku_images/ocr_data/training/8_116.jpg
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In [338]:

    

features = map(list, features)
test_features = map(list, test_features)


    

In [342]:

    

from svmutil import *


    

In [343]:

    

prob = svm_problem(labels, features)
param = svm_parameter('-t 0')
m = svm_train(prob, param)


    

In [344]:

    

res = svm_predict(labels, features, m)


    

    




Accuracy = 81.0504% (1142/1409) (classification)

In [345]:

    

res = svm_predict(test_labels, test_features, m)


    

    




Accuracy = 77.7332% (775/997) (classification)

In [ ]: