def on_touch_up(self, touch):
global letter1
global letter2
global turn
if touch.is_double_tap:
if turn:
letter1.downsampleShape(150)
self.canvas.clear()
else:
letter2.downsampleShape(150)
self.canvas.clear()
# distance (letter1, letter2)
#strokes1 = letter1.split_non_differentiable_points(1.2)
#strokes2 = letter2.split_non_differentiable_points(1.2)
#print len(strokes1)
#print len(strokes2)
#dist = stroke.compare(strokes1, strokes2)
letter1.normalize_wrt_max()
letter2.normalize_wrt_max()
dist = stroke.cloud_dist(letter1,letter2)
print dist
letter1.reset()
letter2.reset()
turn = not turn
def on_touch_up(self, touch):
global letter1
global letter2
global turn
if touch.is_double_tap:
if turn:
letter1.downsampleShape(150)
self.canvas.clear()
else:
letter2.downsampleShape(150)
self.canvas.clear()
# distance (letter1, letter2)
#strokes1 = letter1.split_non_differentiable_points(1.2)
#strokes2 = letter2.split_non_differentiable_points(1.2)
#print len(strokes1)
#print len(strokes2)
#dist = stroke.compare(strokes1, strokes2)
letter1.normalize_wrt_max()
letter2.normalize_wrt_max()
dist = stroke.cloud_dist(letter1, letter2)
print dist
letter1.reset()
letter2.reset()
turn = not turn
def minScore(data, draw, name):
try:
score_min = stroke.cloud_dist(data[name][0],draw)
except IndexError:
draw.downsampleShape(70)
score_min = stroke.cloud_dist(data[name][0],draw)
#print data[name][0].get_len()
#print draw.get_len()
for ref in data[name]:
try:
score_new = stroke.cloud_dist(ref,draw)
except IndexError:
draw.downsampleShape(70)
score_new = stroke.cloud_dist(ref,draw)
#print ref.get_len()
#print draw.get_len()
if score_new<score_min:
score_min = score_new
return score_min
def minScore(data, draw, name):
try:
score_min = stroke.cloud_dist(data[name][0], draw)
except IndexError:
draw.downsampleShape(70)
score_min = stroke.cloud_dist(data[name][0], draw)
#print data[name][0].get_len()
#print draw.get_len()
for ref in data[name]:
try:
score_new = stroke.cloud_dist(ref, draw)
except IndexError:
draw.downsampleShape(70)
score_new = stroke.cloud_dist(ref, draw)
#print ref.get_len()
#print draw.get_len()
if score_new < score_min:
score_min = score_new
return score_min
