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 handleEventsUnzoomed(self, event, x, y, flags, param):
if event == cv.CV_EVENT_LBUTTONDOWN:
if mode == SET or self.id >= 0:
self.click_pt = (x, y)
elif mode == SELECT:
pts = self.get_pts()
global selected_index
selected_index = min(pts.keys(), key=lambda i: l2_dist(pts[i], (x, y)))
def handleEventsUnzoomed(self,event,x,y,flags,param):
if event == cv.CV_EVENT_LBUTTONDOWN:
if mode == SET or self.id >= 0:
self.click_pt = (x,y)
elif mode == SELECT:
pts = self.get_pts()
global selected_index
selected_index = min( pts.keys(),
key = lambda i: l2_dist(pts[i], (x,y) ) )
def handleEventsUnzoomed(self, event, x, y, flags, param):
if event == cv.CV_EVENT_LBUTTONDOWN:
if self.mode == SET:
points.append((x, y))
elif self.mode == DELETE:
i = min(range(len(points)),
key=lambda i: l2_dist(points[i], (x, y)))
deleted.append(points.pop(i))
elif self.mode == LINE_TILE:
if len(self.tile_params) < 3:
self.tile_params.append((x, y))
elif self.mode == BLOCK_TILE:
if len(self.tile_params) < 5:
self.tile_params.append((x, y))
def handleEventsUnzoomed(self,event,x,y,flags,param):
if event == cv.CV_EVENT_LBUTTONDOWN:
if self.mode == SET:
points.append( (x,y) )
elif self.mode == DELETE:
i = min( range(len(points)),
key = lambda i: l2_dist(points[i], (x,y) ) )
deleted.append(points.pop(i))
elif self.mode == LINE_TILE:
if len(self.tile_params) < 3:
self.tile_params.append( (x,y) )
elif self.mode == BLOCK_TILE:
if len(self.tile_params) < 5:
self.tile_params.append( (x,y) )
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 dist(self, v1, v2):
return l2_dist(v1, v2)
def dist(self, v1, v2):
#Hard coded for now, make dist fxn variable later
return l2_dist(v1, v2)
def dist( self, v1, v2 ):
return l2_dist( v1, v2 )
def dist( self, v1, v2 ):
#Hard coded for now, make dist fxn variable later
return l2_dist( v1, v2 )
