In [2]:

    

# texture dataset
import glob as gl
classes = {'canvas1' : 0, 'cushion1' : 1, 'linseeds1' : 2, 'sand1' : 3, 'seat2' : 4, 'stone1' : 5}
f = open('datasets\\texture\\samples.csv', 'w')
f.write('Id,Class\n')
for image in gl.glob('datasets\\texture\\*.jpg'):
    name = image.split('\\')[-1].split('.')[0]
    labelstr = image.split('\\')[-1].split('-')[0]
    label = classes[labelstr]
    f.write('%s,%d\n' % (name, label))
f.close()


    

In [5]:

    

# mnist dataset
import glob as gl
f = open('datasets\\mnist\\samples.csv', 'w')
f.write('Id,Class\n')
for image in gl.glob('datasets\\mnist\\*.jpg'):
    name = image.split('\\')[-1].split('.')[0]
    label = int(image.split('\\')[-1].split('_')[1])
    f.write('%s,%d\n' % (name, label))
f.close()


    

In [ ]:

    

# emotion dataset
import glob as gl
classes = {'anger' : 0, 'disgust' : 1, 'fear' : 2, 'sad' : 3, 'amusement' : 4, 'awe' : 5, 'contentment' : 6, 'excitement' : 7}
f = open('datasets/emotion/samples.csv', 'w')
f.write('Id,Class\n')
for image in gl.glob('datasets/emotion/*.jpg'):
    name = image.split('/')[-1].split('.')[0]
    labelstr = image.split('/')[-1].split('_')[0]
    label = classes[labelstr]
    f.write('%s,%d\n' % (name, label))
f.close()