def salScore2votes(salImSeq, sp):
"""
Convert saliency score to votes
Input:
salImSeq: (n, h, w) where n > 1: float. FG>0, BG=0. score in [0,1].
sp: (n,h,w): 0-indexed regions, #regions <= numsp
Output:
votes: (k,) where k < numsp*n
"""
n, h, w = salImSeq.shape
numsp = np.max(sp) + 1
votes = np.zeros((numsp * n, ), dtype=np.float)
startInd = 0
for i in range(n):
sp1 = sp[i].reshape(-1)
val1 = salImSeq[i].reshape(-1)
sizeOut = np.max(sp1) + 1
# assign average score of pixels to a superpixel
sumScore = utils.my_accumarray(sp1, val1, sizeOut, 'plus')
count = utils.my_accumarray(sp1, np.ones(sp1.shape), sizeOut, 'plus')
votes[startInd:startInd + sizeOut] = sumScore / count
startInd += sizeOut
votes = votes[:startInd]
return votes
def remove_low_energy_blobs(maskSeq,
binTh,
relSize=0.6,
relEnergy=None,
target=None):
"""
Input:
maskSeq: (n, h, w) where n > 1: float. FG>0, BG=0. Not thresholded.
binTh: binary threshold for maskSeq for finding blobs: [0, max(maskSeq)]
relSize: [0,1]: size of FG blobs to keep compared to largest one
Only used if relEnergy is None.
relEnergy: Ideally it should be <= binTh. Kill blobs whose:
(total energy <= relEnergy * numPixlesInBlob)
If relEnergy is given, relSize is not used.
target: value to which set the low energy blobs to.
Default: binTh-epsilon. Must be less than binTh.
Output:
maskSeq: (n, h, w) where n > 1: float. FG>0, BG=0. Not thresholded. It
has same values as input, except the low energy blobs where its
value is target.
"""
sTime = time.time()
if target is None:
target = binTh - 1e-5
for i in range(maskSeq.shape[0]):
mask = (maskSeq[i] > binTh).astype(np.uint8)
if np.sum(mask) == 0:
continue
sp1, num = ndimage.label(mask) # 0 in sp1 is same as 0 in mask i.e. BG
count = utils.my_accumarray(sp1, np.ones(sp1.shape), num + 1, 'plus')
if relEnergy is not None:
sumScore = utils.my_accumarray(sp1, maskSeq[i], num + 1, 'plus')
destroyFG = sumScore[1:] < relEnergy * count[1:]
else:
sizeLargestBlob = np.max(count[1:])
destroyFG = count[1:] < relSize * sizeLargestBlob
destroyFG = np.concatenate(([False], destroyFG))
maskSeq[i][destroyFG[sp1]] = target
eTime = time.time()
print('Removing low energy blobs finished: %.2f s' % (eTime - sTime))
return maskSeq
def get_region_boxes(sp):
"""
Get bounding boxes for each superpixel image
Input: sp: (h,w): 0-indexed regions, #regions <= numsp
Output: boxes: (numsp, 4) : (xmin, ymin, xmax, ymax)
"""
x = np.arange(0, sp.shape[1])
y = np.arange(0, sp.shape[0])
xv, yv = np.meshgrid(x, y)
sizeOut = np.max(sp) + 1
sp1 = sp.reshape(-1)
xv = xv.reshape(-1)
yv = yv.reshape(-1)
spxmin = utils.my_accumarray(sp1, xv, sizeOut, 'min')
spymin = utils.my_accumarray(sp1, yv, sizeOut, 'min')
spxmax = utils.my_accumarray(sp1, xv, sizeOut, 'max')
spymax = utils.my_accumarray(sp1, yv, sizeOut, 'max')
boxes = np.hstack((spxmin.reshape(-1, 1), spymin.reshape(-1, 1),
spxmax.reshape(-1, 1), spymax.reshape(-1, 1)))
return boxes
