def goodness(self, arg):#particle, image):
particle, image = arg
mask = wimage(GWhiskerLayer(particle, self.renderer_dl, self.renderer_length, self.renderer_width, translate=self.current_translation), width=IMAGE_WIDTH, height=IMAGE_HEIGHT)
mask_sum = mask.sum()
if mask_sum == 0:
return 0
result = ((mask*image).sum()/(255.0*mask_sum))**self.goodness_power
if DEBUG:
from common import make_run_path
import os
wim = wimage((mask*image).data/255.0)
imsurf=cairo.ImageSurface(cairo.FORMAT_ARGB32, IMAGE_WIDTH*3,IMAGE_HEIGHT)
ctx = cairo.Context(imsurf)
for im in (image, mask, wim):
ctx.save()
im.render(ctx)
ctx.restore()
ctx.translate(IMAGE_WIDTH,0)
imsurf.write_to_png(make_run_path(os.path.join("debug","%s%i.png"%("frame", self.debug_i))))
self.debug_i += 1
print "sum, result:", mask_sum, result
return result
def goodness(self, arg):#particle, image):
particle, image = arg
mask = wimage(SquareLayer(particle))
processed_image = wimage(image)
mask_sum = mask.sum()
if mask_sum == 0:
return 0
return (mask*processed_image).sum()/(255*mask_sum)
def goodness(self, arg):#particle, image):
# return 1
particle, image = arg
mask = wimage(PendulumLayer(particle))
processed_image = wimage(image)
mask_sum = mask.sum()
if mask_sum == 0:
return 0
# mask = wimage(PendulumLayer(particle)).transform(abs_edge).blur(3)
# processed_image = wimage(image).transform(abs_edge).blur(3)
return (mask*processed_image).sum()/(255*mask_sum)
testrange=range(-tries,tries+1)
testalpha=0.3
pos=(272.,126.)
w=3
whisker3=wvideo(map(lambda b:
GWhiskerLayer((float(b*(16/float(tries)))/(512**2),0,0,0),rotation=0,width=w,translate=pos),testrange),alpha=testalpha)
whisker2=wvideo(map(lambda b:
GWhiskerLayer((0,float(b*(16/float(tries)))/512,0,0),rotation=0,width=w,translate=pos),testrange),alpha=testalpha)
whisker1=wvideo(map(lambda b:
GWhiskerLayer((0,0,float(b*(16/float(tries)))/8,0),rotation=0,width=w,translate=pos),testrange),alpha=testalpha)
testimage=wimage("/misc/projects/whisker/video/m8_multi-frames/preprocessed_whiskers/frame-0488.png")
zero=wvideo([testimage]*len(testrange),alpha=testalpha)
x1=numpy.array(map(lambda b:float(b*(16/float(tries)))/(512**2),testrange))
x2=numpy.array(map(lambda b:float(b*(16/float(tries)))/512,testrange))
x3=numpy.array(map(lambda b:float(b*(16/float(tries)))/8,testrange))
#phi
zerot=zero
whisker1t=whisker1
whisker2t=whisker2
whisker3t=whisker3
#trans = scipy.ndimage.laplace
#zerot=zero.transform(trans)
def filter_image(img, f=None):
"""
@param img the image to filter
@param f function f:img->img
"""
return wimage(f(img))
