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frame[-1,:] = True
frame[:,-1] = True
# dilate non-brush segments
outside_brush_mask = np.copy(~brush_mask)
outside_brush_mask = mh.morph.dilate(outside_brush_mask, np.ones((brush_size, brush_size)))
# compute end points of line
end_points = np.zeros(brush_mask.shape,dtype=bool)
first_point,last_point = (i_js[0],i_js[-1])
bind = lambda x: tuple(min(x[i] - bb[2*i], brush_mask.shape[1-i]-1) for i in range(1,-1,-1))
first_points, last_points = (bind(x) for x in [first_point, last_point])
end_points[first_points],end_points[last_points] = (True, True)
end_points = mh.morph.dilate(end_points, np.ones((2*brush_size, 2*brush_size)))
# compute seeds
seed_mask = np.zeros(brush_mask.shape,dtype=bool)
# seed_mask[outside_brush_mask & brush_mask] = True
seed_mask[outside_brush_mask] = True
seed_mask[frame] = True
# seed_mask[corners] = False
seed_mask[end_points] = False
seeds,n = mh.label(seed_mask)
# remove small regions
sizes = mh.labeled.labeled_size(seeds)
min_seed_size = 5
too_small = np.where(sizes < min_seed_size)
seeds = mh.labeled.remove_regions(seeds, too_small).astype(np.uint8)
# add linear interpolation to brush stroke
dense_brush += zip(ys,xs)
width = self.__dojoserver.get_image()._width
height = self.__dojoserver.get_image()._height
# add dense brush stroke to mask image
brush_mask = np.zeros((height, width),dtype=bool)
# for c in i_js:
for c in dense_brush:
brush_mask[c[1],c[0]] = True
# crop
brush_mask = brush_mask[bb[2]:bb[3],bb[0]:bb[1]]
brush_mask = mh.morph.dilate(brush_mask, np.ones((2*brush_size, 2*brush_size)))
brush_image = np.copy(sub_tile)
brush_image[~brush_mask] = 0
# compute frame
frame = np.zeros(brush_mask.shape,dtype=bool)
frame[0,:] = True
frame[:,0] = True
frame[-1,:] = True
frame[:,-1] = True
# dilate non-brush segments
outside_brush_mask = np.copy(~brush_mask)
outside_brush_mask = mh.morph.dilate(outside_brush_mask, np.ones((brush_size, brush_size)))
# compute end points of line
sub_tile = tile[bbox[2]:bbox[3],bbox[0]:bbox[1]]
mh.imsave('/tmp/box10.tif', sub_tile)
#
# create mask
#
mask = np.zeros((1024,1024),dtype=np.uint8)
bs = input['brush_size']
i_js = input['i_js']
for d in i_js:
mask[d[1], d[0]] = 255
for i in range(bs):
mask = mh.morph.dilate(mask)
mask = np.invert(mask)
mask = mask[bbox[2]:bbox[3],bbox[0]:bbox[1]]
# mh.imsave('/tmp/mask.tif', mask)
grad_x = np.gradient(sub_tile)[0]
grad_y = np.gradient(sub_tile)[1]
grad = np.add(np.square(grad_x), np.square(grad_y))
#grad = np.add(np.abs(grad_x), np.abs(grad_y))
grad -= grad.min()
grad /= grad.max()
grad *= 255
grad = grad.astype(np.uint8)
# corners[0:n,-n:-1] = True
# corners[0:n,-1] = True
# # lower left
# corners[-n:-1,0:n] = True
# corners[-1,0:n] = True
# # lower right
# corners[-n:-1,-n:-1] = True
# corners[-1,-n:-1] = True
# corners[-n:-1,-1] = True
# corners[-1,-1] = True
# dilate non-brush segments
outside_brush_mask = np.copy(~brush_mask)
outside_brush_mask = mh.morph.dilate(outside_brush_mask, np.ones((brush_size, brush_size)))
# # compute brush boundary
# for y in range(outside_brush_mask.shape[0]-1):
# for x in range(outside_brush_mask.shape[1]-1):
# if brush_mask[y,x] != brush_mask[y,x+1]: #and seg_sub_tile[y,x] == label_id:
# outside_brush_mask[y,x] = 1
# outside_brush_mask[y,x+1] = 1
# if brush_mask[y,x] != brush_mask[y+1,x]: #and seg_sub_tile[y,x] == label_id:
# outside_brush_mask[y,x] = 1
# outside_brush_mask[y+1,x] = 1
# for y in range(1,outside_brush_mask.shape[0]):
# for x in range(1,outside_brush_mask.shape[1]):
brush_mask = brush_mask[bb[2]:bb[3],bb[0]:bb[1]]
brush_mask = mh.morph.dilate(brush_mask, np.ones((2*brush_size, 2*brush_size)))
brush_image = np.copy(sub_tile)
brush_image[~brush_mask] = 0
# compute frame
frame = np.zeros(brush_mask.shape,dtype=bool)
frame[0,:] = True
frame[:,0] = True
frame[-1,:] = True
frame[:,-1] = True
# dilate non-brush segments
outside_brush_mask = np.copy(~brush_mask)
outside_brush_mask = mh.morph.dilate(outside_brush_mask, np.ones((brush_size, brush_size)))
# compute end points of line
end_points = np.zeros(brush_mask.shape,dtype=bool)
first_point,last_point = (i_js[0],i_js[-1])
bind = lambda x: tuple(min(x[i] - bb[2*i], brush_mask.shape[1-i]-1) for i in range(1,-1,-1))
first_points, last_points = (bind(x) for x in [first_point, last_point])
end_points[first_points],end_points[last_points] = (True, True)
end_points = mh.morph.dilate(end_points, np.ones((2*brush_size, 2*brush_size)))
# compute seeds
seed_mask = np.zeros(brush_mask.shape,dtype=bool)
# seed_mask[outside_brush_mask & brush_mask] = True
seed_mask[outside_brush_mask] = True
seed_mask[frame] = True
# add linear interpolation to brush stroke
dense_brush += zip(ys,xs)
# dense_brush = list(set(dense_brush))
# add dense brush stroke to mask image
brush_mask = np.zeros((1024,1024),dtype=bool)
# for c in i_js:
for c in dense_brush:
brush_mask[c[1],c[0]] = True
# crop
brush_mask = brush_mask[bbox[2]:bbox[3],bbox[0]:bbox[1]]
brush_mask = mh.morph.dilate(brush_mask, np.ones((2*brush_size, 2*brush_size)))
brush_image = np.copy(sub_tile)
brush_image[~brush_mask] = 0
# compute frame
frame = np.zeros(brush_mask.shape,dtype=bool)
frame[0,:] = True
frame[:,0] = True
frame[-1,:] = True
frame[:,-1] = True
# # compute corners
# corners = np.zeros(brush_mask.shape,dtype=bool)
frame[-1,:] = True
frame[:,-1] = True
# dilate non-brush segments
outside_brush_mask = np.copy(~brush_mask)
outside_brush_mask = mh.morph.dilate(outside_brush_mask, np.ones((brush_size, brush_size)))
# compute end points of line
end_points = np.zeros(brush_mask.shape,dtype=bool)
first_point,last_point = (i_js[0],i_js[-1])
bind = lambda x: tuple(min(x[i] - bb[2*i], brush_mask.shape[1-i]-1) for i in range(1,-1,-1))
first_points, last_points = (bind(x) for x in [first_point, last_point])
end_points[first_points],end_points[last_points] = (True, True)
end_points = mh.morph.dilate(end_points, np.ones((2*brush_size, 2*brush_size)))
# compute seeds
seed_mask = np.zeros(brush_mask.shape,dtype=bool)
# seed_mask[outside_brush_mask & brush_mask] = True
seed_mask[outside_brush_mask] = True
seed_mask[frame] = True
# seed_mask[corners] = False
seed_mask[end_points] = False
seeds,n = mh.label(seed_mask)
# remove small regions
sizes = mh.labeled.labeled_size(seeds)
min_seed_size = 5
too_small = np.where(sizes < min_seed_size)
seeds = mh.labeled.remove_regions(seeds, too_small).astype(np.uint8)