Licensed under a 2 clause BSD license, see COPYING for details
'''
import sys
import os.path
import argparse
import pr0ntools.layer.parser
from PIL import Image
from opencv.cv import *
from PIL import Image
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Nuke 'em")
parser.add_argument('files', metavar='files', type=str, nargs='+',
help='Input training folders and files to classify')
args = parser.parse_args()
dirs = list()
files = list()
for f in args.files:
if not os.path.exists(f):
raise ValueError('No file %s' % f)
if os.path.isdir(f):
dirs.append(f)
else:
files.append(f)
print '%d training dirs' % len(dirs)
print '%d things to classify' % len(files)
gray[h][w] = im.getpixel((w,h))
else:
gray[h][w] = im.getpixel((w,h))[0]
return gray
@staticmethod
def from_file(fn):
parser = pr0ntools.layer.parser.MultilayerSVGParser(fn)
parser.run()
if len(parser.images) != 1:
raise Exception('Test vector must have exactly one image')
return SVGTestVector(list(parser.images)[0], parser.layers)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Manipulate .pto files')
parser.add_argument('image_in', metavar='image_in', type=str, nargs=1,
help='Image to process')
parser.add_argument('svg_out', metavar='svg_out', type=str, nargs=1,
help='Output file')
parser.add_argument('training_in', metavar='training_in', type=str, nargs='+',
help='Tagged SVGs')
#parser.add_argument('--allow-missing', action="store_true", dest="allow_missing", default=True, help='Allow missing images')
args = parser.parse_args()
image_in_fn = args.image_in[0]
svg_out_fn = args.svg_out[0]
training_svgs = args.training_in
if svg_out_fn.find('svg') < 0:
raise Exception("Output must be SVG")
for f in training_svgs:
if f.find('.svg') < 0:
raise Exception('Training must be SVGs')
import sys
import os.path
import argparse
import pr0ntools.layer.parser
from PIL import Image
from opencv.cv import *
from PIL import Image
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Nuke 'em")
parser.add_argument('files',
metavar='files',
type=str,
nargs='+',
help='Input training folders and files to classify')
args = parser.parse_args()
dirs = list()
files = list()
for f in args.files:
if not os.path.exists(f):
raise ValueError('No file %s' % f)
if os.path.isdir(f):
dirs.append(f)
else:
files.append(f)
print '%d training dirs' % len(dirs)