labelTrain.py

#!/bin/python

# Generate labels.txt and train.txt files for caffee trainig dataset

import os
import sys

targetDir = sys.argv[1]

root, dirs, files = os.walk(targetDir).next()
print('Root: %s' % root)
print('DIRs: %s' % dirs)
print('Files: %s' % files)

dirs.sort()
files = []
labels = {}

with open('%s/_labels.txt' % root, 'w') as L:
  with open('%s/_train.txt' % root, 'w') as T:
    for dir in dirs:
      L.write("%s\n" % dir)
      for R, D, F in os.walk('%s/%s' % (root, dir)):
        for f in F:
          T.write("%s/%s %s\n" % (R, f, dir))

print('Done!!!')

multyCrop.py

#!/bin/python

# Cut out images to create trainig set

import sys
from PIL import Image

size = 16
fileName = sys.argv[1]
if len(sys.argv) > 2:
	size = int(sys.argv[2])

im = Image.open('%s.png' % fileName)
w = 0
h = 0

while h + size < im.size[1]:
  w = 0
  while w + size < im.size[0]:
    im.crop((w, h, w+size, h+size)).save('%s-%s-%s.png' % (fileName, h, w))
    w += 1
  h += 1