def __call__(self, im):
tmp = im.asPIL()
tmp = tmp.resize((160, 120))
tmp = Image(tmp)
points = self.dog.detect(tmp)
for score, pt, radius in points:
pt = Point(pt.X() * 4, pt.Y() * 4)
im.annotateCircle(pt, radius * 4)
return im
def __call__(self, im):
tmp = im.asPIL()
tmp = tmp.resize((160, 120))
tmp = Image(tmp)
points = self.surf.detect(tmp)
for score, pt, radius in points:
score = score - 500.0
if score > 500.0:
score = 500.0
score = int(255 * score / 500.0)
color = "#%02x0000" % score
pt = Point(pt.X() * 4, pt.Y() * 4)
im.annotateCircle(pt, radius, color=color)
return im
def AffineNormalizePoints(points):
'''
Create a transform that centers a set of points such that there mean is (0,0)
and then scale such that there average distance from (0,0) is 1.0
@param points: list of link.Point to normalize
@returns: an AffineTransform object
'''
# compute the center
mean = Point(0,0)
count = 0
for point in points:
mean += point
count += 1
mean = (1.0/count)*mean
# mean center the points
center = AffineTranslate(-mean.X(),-mean.Y(),(0,0))
points = center.transformPoints(points)
# Compute the mean distance
mean_dist = 0.0
count = 0
for point in points:
x,y = point.X(),point.Y()
dist = sqrt(x*x+y*y)
mean_dist += dist
count += 1
mean_dist = (1.0/count)*mean_dist
# Rescale the points
scale = AffineScale(1.0/mean_dist,(0,0))
points = scale.transformPoints(points)
# compute the composite transform
norm = scale*center
return norm