def __init__(self, image, training_filename=None, classifier=None):
""" Setup a wrapped image with music methods around
If a classifier or training_filename is set, the classifier will be used
to match objects after text and "in-staff" objects have been removed.
If both training_filename and classifier is set, classier wins.
Keyword Arguments:
image - Gamera Image or image filename
training_filename - Filename to use for classifier,(optional)
classifier - Classifier to use. If both training_filename and
classifier is set, classifier wins.
"""
self.l = logging.getLogger(self.__class__.__name__)
if isinstance(image, basestring):
image = load_image(image)
self._ccs = None
self._orig = image
self._image = image
self._image = self._image.to_onebit()
self._ms = None
self._noinside = None
self._text_obj = None
self.classifier = None
if not training_filename is None:
self.classifier = Classifier_with_remove(training_filename)
if not classifier is None:
self.classifier = classifier
def __init__(self,filename):
self.filename = filename
self._l = logging.getLogger(self.__class__.__name__)
self.c = Classifier_with_remove(training_filename="../preomr_edited_cnn.xml")
self.c.set_k(1)
"""
seg = self.ccs_overall()
if classify:
classified = seg['classified']
else:
classified = []
return seg['text'], seg['inside'], seg['outside'], classified
if __name__ == '__main__':
from gamera.core import *
from class_dynamic import Classifier_with_remove
from ill_music import IllMusicImage
import sys
#LOG_FILENAME = '/tmp/logging_example.out'
FORMAT = "%(asctime)-15s %(levelname)s [%(name)s.%(funcName)s] %(message)s"
logging.basicConfig(level=logging.DEBUG, format=FORMAT)
init_gamera()
c = Classifier_with_remove(training_filename="preomr_edited_cnn.xml")
c.set_k(1)
filename = sys.argv[-1]
#c.classifier.load_settings("gasettings.txt")
mi = IllMusicImage(load_image(filename), classifier=c)
ret = mi.without()
ret.save_PNG("%s_Removed.png" % filename)
logging.debug("Done with %s" % filename)
ret = mi.color_segment(classified_box=True)
ret.save_PNG("%s_ColorSegment.png" % filename)
logging.debug("Done with %s" % filename)
def test_e_fp(filename, expected_count=10):
init_gamera()
c = Classifier_with_remove()
c.set_k(1)
c.change_features(["volume64regions"])
ci = c.classify_image(filename)
#files = ["mergedyn2.xml", "mergedyn.xml","only-dynamics.xml",
# "newtrain-dynamic.xml", "preomr.xml"]
files = ["preomr.xml", "preomr_edited.xml", "preomr_edited_cnn.xml"]
import os.path
# try to match with different trainingsets.
for dynamic in ([d for d in files if os.path.isfile(d)]):
ci.load_new_training_data(dynamic)
print "%s - count_of_training=%d, k=%d" % (dynamic, len(c.stats), c.k)
result = {} # Push into buckets based on the count of found glyphs.
csv = {}
sys.stdout.flush()
# Try with different epsilon for false_positives: e_fp
for e_fp in arange(0.01, 1.01, 0.01):
c.e_fp = e_fp
count = len(ci.classified_glyphs())
# Init bucket.
if not result.has_key(count):
result[count] = []
result[count].append((e_fp, c.d_t()))
csv[e_fp] = count
# Find the best match to the wanted result.
k, res, diff = find_nearest(result, expected_count)
confid = [(len(v), key, v[0][0], v[0][1])
for key, v in result.iteritems()]
confid2 = [(key, len(v)) for key, v in result.iteritems()]
confid.sort(reverse=True)
confid2.sort()
print "efp,count"
for e_fp, c in sorted(csv.iteritems()):
print "%s,%s" % (e_fp, c)
print
print "count,spansize"
for count, spansize in confid2:
print "%s,%s" % (count, spansize)
return
ret = []
for i in range(0, min(10, len(confid)) + 1):
ret.append(confid[i])
if not result.has_key(expected_count):
print "Never found the desired amount with %s" % dynamic
print "Found in %d(%d): %s" % (k, diff, [r for r in res])
rgbimg = ci.image.to_rgb()
cg = ci.classified_glyphs(res[0].d_t)
[outline(rgbimg, g, 3.0, RGBPixel(255, 0, 0)) for g in cg]
rgbimg.save_PNG("class_%s_%s.png" % (filename, dynamic))
print
