def draw_circles(image,cands,origin,spacing):
#make empty matrix, which will be filled with the mask
image_mask = np.zeros(image.shape, dtype=np.int16)
#run over all the nodules in the lungs
for ca in cands.values:
#get middel x-,y-, and z-worldcoordinate of the nodule
#radius = np.ceil(ca[4])/2 ## original: replaced the ceil with a very minor increase of 1% ....
radius = (ca[4])/2 + 0.51 * spacing[0] # increasing by circa half of distance in z direction .... (trying to capture wider region/border for learning ... and adress the rough net .
coord_x = ca[1]
coord_y = ca[2]
coord_z = ca[3]
image_coord = np.array((coord_z,coord_y,coord_x))
#determine voxel coordinate given the worldcoordinate
image_coord = world_2_voxel(image_coord,origin,spacing)
#determine the range of the nodule
#noduleRange = seq(-radius, radius, RESIZE_SPACING[0]) # original, uniform spacing
noduleRange_z = seq(-radius, radius, spacing[0])
noduleRange_y = seq(-radius, radius, spacing[1])
noduleRange_x = seq(-radius, radius, spacing[2])
#x = y = z = -2
#create the mask
for x in noduleRange_x:
for y in noduleRange_y:
for z in noduleRange_z:
coords = world_2_voxel(np.array((coord_z+z,coord_y+y,coord_x+x)),origin,spacing)
#if (np.linalg.norm(image_coord-coords) * RESIZE_SPACING[0]) < radius: ### original (contrained to a uniofrm RESIZE)
if (np.linalg.norm((image_coord-coords) * spacing)) < radius:
image_mask[int(np.round(coords[0])),int(np.round(coords[1])),int(np.round(coords[2]))] = int(1)
return image_mask
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