def main(args):
image = cv.LoadImage( args.input_image)
featuremap = FeatureMap()
featuremap.read_from_file( args.input_features )
classifier = load_classifier_from_file( args.classifier )
if not classifier.is_trained():
print "Warning: using an untrained classifier. This will probably break."
label_set = LabelSet()
label_set.read_from_file( args.label_set )
window = ClickWindow( image, args.zoom_out)
while(True):
cont = update_all_windows()
if not cont:
break
if window.update_label:
click_pt = window.click_pt
closest_pt = min( featuremap.get_feature_points(),
key = lambda pt: l2_dist(pt,click_pt) )
feature = featuremap.get_feature( closest_pt )
shape = featuremap.get_shape( closest_pt )
size = featuremap.get_size( closest_pt )
label = classifier.predict_label( feature )
label_name = "None" if label==0 else label_set.get_label_name(label)
label_color = cv.RGB(0,0,0) if label==0 else label_set.get_label_color(label)
print "Predicted label: %d (%s)"%(label, label_name);
window.set_patch( closest_pt, shape, size, label_color )
window.update_label = False
def main(args):
if not (args.input_classifier or args.classifier_type):
print "Must specify input classifier or type!"
return 1
if args.input_classifier:
args.classifier_type = get_classifier_name(args.input_classifier)
output_file = args.input_classifier
else:
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_features)
print "No output directory specified. Defaulting to %s" % directory
if not os.path.exists(directory):
os.makedirs(directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix, extension = os.path.splitext(
os.path.basename(args.input_features))
print "No output prefix selected. Defaulting to %s" % prefix
output_file = "%s/%s.cls" % (directory, prefix)
if args.classifier_type in CPP_CLASSIFIERS:
call_cpp(args)
return
#Now onto just the python implementation
if args.input_classifier:
classifier = load_classifier_from_file(args.input_classifier)
else:
classifier = instantiate_classifier_by_name(
args.classifier_type, include_unlabeled=args.include_unlabeled)
fm = FeatureMap()
if (args.input_features):
fm.read_from_file(args.input_features)
classifier.add_featuremap(fm)
if args.train:
classifier.train()
classifier.save_to_file(output_file)
print "Saved classifier to %s" % output_file
def main(args):
if not ( args.input_classifier or args.classifier_type ):
print "Must specify input classifier or type!"
return 1
if args.input_classifier:
args.classifier_type = get_classifier_name( args.input_classifier )
output_file = args.input_classifier
else:
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_features)
print "No output directory specified. Defaulting to %s"%directory
if not os.path.exists(directory):
os.makedirs(directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix, extension = os.path.splitext(os.path.basename(args.input_features))
print "No output prefix selected. Defaulting to %s"%prefix
output_file = "%s/%s.cls"%(directory,prefix)
if args.classifier_type in CPP_CLASSIFIERS:
call_cpp(args)
return
#Now onto just the python implementation
if args.input_classifier:
classifier = load_classifier_from_file( args.input_classifier )
else:
classifier = instantiate_classifier_by_name( args.classifier_type, include_unlabeled=args.include_unlabeled )
fm = FeatureMap()
if( args.input_features):
fm.read_from_file( args.input_features )
classifier.add_featuremap( fm )
if args.train:
classifier.train()
classifier.save_to_file( output_file )
print "Saved classifier to %s"%output_file
def main(args):
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_features)
if directory == "":
directory = "."
print "No output directory specified. Defaulting to %s" % directory
if not os.path.exists(directory):
os.makedirs(directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix, extension = os.path.splitext(
os.path.basename(args.input_features))
print "No output prefix selected. Defaulting to %s" % prefix
output_file = "%s/%s.libsvm" % (directory, prefix)
fm = FeatureMap()
fm.read_from_file(args.input_features)
labeled_features = []
for pt in fm.get_feature_points():
feature = fm.get_feature(pt)
label = fm.get_label(pt)
if label < 0:
continue
if (not args.include_unlabeled) and label == 0:
continue
labeled_features.append((label, feature))
f = open(output_file, 'w')
print len(labeled_features)
for (label, feature) in labeled_features:
f.write("%d\t" % label)
for i, val in enumerate(feature):
if val == 0:
continue
f.write("%d:%f " % (i, val))
f.write("\n")
f.close()
def main(args):
if args.output_directory:
directory = args.output_directory
else:
directory = os.path.dirname(args.input_features)
if directory == "":
directory = "."
print "No output directory specified. Defaulting to %s"%directory
if not os.path.exists(directory):
os.makedirs(directory)
if args.output_prefix:
prefix = args.output_prefix
else:
prefix, extension = os.path.splitext(os.path.basename(args.input_features))
print "No output prefix selected. Defaulting to %s"%prefix
output_file = "%s/%s.libsvm"%(directory,prefix)
fm = FeatureMap()
fm.read_from_file( args.input_features )
labeled_features = []
for pt in fm.get_feature_points():
feature = fm.get_feature(pt)
label = fm.get_label(pt)
if label < 0:
continue
if (not args.include_unlabeled) and label == 0:
continue
labeled_features.append( (label, feature) )
f = open(output_file,'w')
print len(labeled_features)
for (label, feature) in labeled_features:
f.write("%d\t"%label)
for i,val in enumerate(feature):
if val == 0:
continue
f.write("%d:%f "%(i,val))
f.write("\n")
f.close()
def main(args):
compared_image = cv.LoadImage( args.compared_image)
reference_image = cv.LoadImage( args.reference_image )
compared_featuremap = FeatureMap()
compared_featuremap.read_from_file( args.compared_features )
reference_featuremap = FeatureMap()
reference_featuremap.read_from_file( args.reference_features )
compare_window = ClickWindow( compared_image, args.compare_zoom_out)
reference_window = ReferenceWindow( reference_image, args.reference_zoom_out)
reference_window.move( compare_window.size()[0], 0 )
#nn_solver = pyflann.FLANN()
while(True):
cont = update_all_windows()
if not cont:
break
if compare_window.update_nn:
click_pt = compare_window.click_pt
closest_pt = min( compared_featuremap.get_feature_points(),
key = lambda pt: l2_dist(pt,click_pt) )
compared_feature = compared_featuremap.get_feature( closest_pt )
distance_map = {}
shape_map = {}
size_map = {}
for pt in reference_featuremap.get_feature_points():
distance_map[pt] = l2_dist( compared_feature,
reference_featuremap.get_feature(pt) )
shape_map[pt] = reference_featuremap.get_shape(pt)
size_map[pt] = reference_featuremap.get_size(pt)
knn = compute_knn( distance_map.keys(), lambda pt: distance_map[pt], 20 )
reference_window.set_knn( knn )
reference_window.set_distance_map( distance_map )
reference_window.set_shape_map( shape_map )
reference_window.set_size_map( size_map )
compare_window.update_nn = False
def main(args):
n_views = len(args.images)
assert len(args.features) == len(args.zoom_outs) == n_views
view_windows = []
for i in range(n_views):
image = cv.LoadImage( args.images[i] )
featuremap = FeatureMap()
featuremap.read_from_file( args.features[i] )
zoom_out = args.zoom_outs[i]
view_window = ViewWindow( image, zoom_out, featuremap, i)
view_windows.append(view_window)
#Tile
x = 0
y = 0
for win in view_windows:
if x + win.size()[0] >= SCREEN_WIDTH:
x = 0
y += win.size()[1]
win.move(x,y)
x += win.size()[0]
while(True):
cont = update_all_windows()
if not cont:
break
