def compute_boxmap(binary,scale,threshold=(.5,4),dtype='i'):
objects = binary_objects(binary)
bysize = sorted(objects,key=sl.area)
boxmap = zeros(binary.shape,dtype)
for o in bysize:
if sl.area(o)**.5<threshold[0]*scale: continue
if sl.area(o)**.5>threshold[1]*scale: continue
boxmap[o] = 1
return boxmap
def compute_boxmap(binary, scale, threshold=(.5, 4), dtype='i'):
objects = binary_objects(binary)
bysize = sorted(objects, key=sl.area)
boxmap = zeros(binary.shape, dtype)
for o in bysize:
if sl.area(o)**.5 < threshold[0] * scale: continue
if sl.area(o)**.5 > threshold[1] * scale: continue
boxmap[o] = 1
return boxmap
def estimate_scale(binary):
objects = binary_objects(binary)
bysize = sorted(objects,key=sl.area)
scalemap = np.zeros(binary.shape)
for o in bysize:
if np.amax(scalemap[o])>0: continue
scalemap[o] = sl.area(o)**0.5
scale = np.median(scalemap[(scalemap>3)&(scalemap<100)])
return scale
def estimate_scale(binary):
objects = binary_objects(binary)
bysize = sorted(objects,key=sl.area)
scalemap = zeros(binary.shape)
for o in bysize:
if amax(scalemap[o])>0: continue
scalemap[o] = sl.area(o)**0.5
scale = median(scalemap[(scalemap>3)&(scalemap<100)])
return scale