import parse_haar
import sys
if __name__ == '__main__':
if len(sys.argv) < 3:
print 'usage: %s cascade.xml outfile.txt'
exit(1)
in_filename, out_filename = sys.argv[1:3]
cascade = parse_haar.parse_haar_xml(in_filename)
with open(out_filename, 'w') as outf:
for i, stage in enumerate(cascade.stages):
outf.write('stage %i\n'%i)
for j, feature in enumerate(stage.features):
outf.write(' feature %i\n'%j)
for k, shape in enumerate(feature.shapes):
outf.write(' %s\n'%str(shape[0]))
parser.add_option('-m', '--multi_scale', action='store_true', dest='multi_scale', default=False, help='multiscale detection')
(options, args) = parser.parse_args()
(options, args) = parser.parse_args()
if options.do_test:
test()
exit(0)
cascade_filename = options.cascade
src_dir = options.src_dir
output_dir = options.output_dir
use_multiscale = options.multi_scale
haar_classifier = parse_haar.parse_haar_xml(cascade_filename)
print str(haar_classifier)
# get all images in dir
image_filenames = [src_dir + os.path.sep + x for x in os.listdir(src_dir) if '.png' in x.lower()]
# parameters
scale_factor = 1.2
min_size = (40, 40)
if not os.path.isdir(output_dir):
print 'warning: creating outputdir %s'%output_dir
os.mkdir(output_dir)
for i, image_filename in enumerate(image_filenames):
image = imageio.read(image_filename)
