def ransac_voting_layer(mask,
vertex,
round_hyp_num,
inlier_thresh=0.999,
confidence=0.99,
max_iter=20,
min_num=5,
max_num=30000):
'''
:param mask: [b,h,w]
:param vertex: [b,h,w,vn,2]
:param round_hyp_num:
:param inlier_thresh:
:return: [b,vn,2]
'''
b, h, w, vn, _ = vertex.shape
batch_win_pts = []
for bi in range(b):
hyp_num = 0
cur_mask = (mask[bi]).byte()
foreground_num = torch.sum(cur_mask)
# if too few points, just skip it
if foreground_num < min_num:
win_pts = torch.zeros([1, vn, 2],
dtype=torch.float32,
device=mask.device)
batch_win_pts.append(win_pts) # [1,vn,2]
continue
# if too many inliers, we randomly down sample it
if foreground_num > max_num:
selection = torch.zeros(cur_mask.shape,
dtype=torch.float32,
device=mask.device).uniform_(0, 1)
selected_mask = (selection < (max_num / foreground_num.float()))
cur_mask *= selected_mask
coords = torch.nonzero(cur_mask).float() # [tn,2]
coords = coords[:, [1, 0]]
direct = vertex[bi].masked_select(
torch.unsqueeze(torch.unsqueeze(cur_mask, 2), 3)) # [tn,vn,2]
direct = direct.view([coords.shape[0], vn, 2])
tn = coords.shape[0]
idxs = torch.zeros([round_hyp_num, vn, 2],
dtype=torch.int32,
device=mask.device).random_(0, direct.shape[0])
all_win_ratio = torch.zeros([vn],
dtype=torch.float32,
device=mask.device)
all_win_pts = torch.zeros([vn, 2],
dtype=torch.float32,
device=mask.device)
cur_iter = 0
while True:
# generate hypothesis
cur_hyp_pts = ransac_voting.generate_hypothesis(
direct, coords, idxs) # [hn,vn,2]
# voting for hypothesis
cur_inlier = torch.zeros([round_hyp_num, vn, tn],
dtype=torch.uint8,
device=mask.device)
ransac_voting.voting_for_hypothesis(direct, coords, cur_hyp_pts,
cur_inlier,
inlier_thresh) # [hn,vn,tn]
# find max
cur_inlier_counts = torch.sum(cur_inlier, 2) # [hn,vn]
cur_win_counts, cur_win_idx = torch.max(cur_inlier_counts,
0) # [vn]
cur_win_pts = cur_hyp_pts[cur_win_idx, torch.arange(vn)]
cur_win_ratio = cur_win_counts.float() / tn
# update best point
larger_mask = all_win_ratio < cur_win_ratio
all_win_pts[larger_mask, :] = cur_win_pts[larger_mask, :]
all_win_ratio[larger_mask] = cur_win_ratio[larger_mask]
# check confidence
hyp_num += round_hyp_num
cur_iter += 1
cur_min_ratio = torch.min(all_win_ratio)
if (1 - (1 - cur_min_ratio**2)**
hyp_num) > confidence or cur_iter > max_iter:
break
# compute mean intersection again
normal = torch.zeros_like(direct) # [tn,vn,2]
normal[:, :, 0] = direct[:, :, 1]
normal[:, :, 1] = -direct[:, :, 0]
all_inlier = torch.zeros([1, vn, tn],
dtype=torch.uint8,
device=mask.device)
all_win_pts = torch.unsqueeze(all_win_pts, 0) # [1,vn,2]
ransac_voting.voting_for_hypothesis(direct, coords, all_win_pts,
all_inlier,
inlier_thresh) # [1,vn,tn]
# coords [tn,2] normal [vn,tn,2]
all_inlier = torch.squeeze(all_inlier.float(), 0) # [vn,tn]
normal = normal.permute(1, 0, 2) # [vn,tn,2]
normal = normal * torch.unsqueeze(all_inlier,
2) # [vn,tn,2] outlier is all zero
b = torch.sum(normal * torch.unsqueeze(coords, 0), 2) # [vn,tn]
ATA = torch.matmul(normal.permute(0, 2, 1), normal) # [vn,2,2]
ATb = torch.sum(normal * torch.unsqueeze(b, 2), 1) # [vn,2]
try:
all_win_pts = torch.matmul(torch.inverse(ATA),
torch.unsqueeze(ATb, 2)) # [vn,2,1]
batch_win_pts.append(all_win_pts[None, :, :, 0])
except:
all_win_pts = torch.zeros([1, ATA.size(0), 2]).to(ATA.device)
batch_win_pts.append(all_win_pts)
batch_win_pts = torch.cat(batch_win_pts)
return batch_win_pts
def estimate_voting_distribution_with_mean(mask, vertex, mean, round_hyp_num=256, min_hyp_num=4096, topk=128, inlier_thresh=0.99, min_num=5, max_num=30000, output_hyp=False):
b, h, w, vn, _ = vertex.shape
all_hyp_pts, all_inlier_ratio = [], []
for bi in range(b):
k = 0
cur_mask = mask[bi] == k + 1
foreground = torch.sum(cur_mask)
# if too few points, just skip it
if foreground < min_num:
cur_hyp_pts = torch.zeros([1, min_hyp_num, vn, 2], dtype=torch.float32, device=mask.device).float()
all_hyp_pts.append(cur_hyp_pts) # [1,vn,2]
cur_inlier_ratio = torch.ones([1, min_hyp_num, vn], dtype=torch.int64, device=mask.device).float()
all_inlier_ratio.append(cur_inlier_ratio)
continue
# if too many inliers, we randomly down sample it
if foreground > max_num:
selection = torch.zeros(cur_mask.shape, dtype=torch.float32, device=mask.device).uniform_(0, 1)
selected_mask = (selection < (max_num / foreground.float()))
cur_mask *= selected_mask
foreground = torch.sum(cur_mask)
coords = torch.nonzero(cur_mask).float() # [tn,2]
coords = coords[:, [1, 0]]
direct = vertex[bi].masked_select(torch.unsqueeze(torch.unsqueeze(cur_mask, 2), 3)) # [tn,vn,2]
direct = direct.view([coords.shape[0], vn, 2])
tn = coords.shape[0]
round_num = np.ceil(min_hyp_num/round_hyp_num)
cur_hyp_pts = []
cur_inlier_ratio = []
for round_idx in range(int(round_num)):
idxs = torch.zeros([round_hyp_num, vn, 2], dtype=torch.int32, device=mask.device).random_(0, direct.shape[0])
# generate hypothesis
hyp_pts = ransac_voting.generate_hypothesis(direct, coords, idxs) # [hn,vn,2]
# voting for hypothesis
inlier = torch.zeros([round_hyp_num, vn, tn], dtype=torch.uint8, device=mask.device)
ransac_voting.voting_for_hypothesis(direct, coords, hyp_pts, inlier, inlier_thresh) # [hn,vn,tn]
inlier_ratio = torch.sum(inlier, 2) # [hn,vn]
inlier_ratio = inlier_ratio.float()/foreground.float() # ratio
cur_hyp_pts.append(hyp_pts)
cur_inlier_ratio.append(inlier_ratio)
cur_hyp_pts = torch.cat(cur_hyp_pts, 0)
cur_inlier_ratio = torch.cat(cur_inlier_ratio, 0)
all_hyp_pts.append(torch.unsqueeze(cur_hyp_pts, 0))
all_inlier_ratio.append(torch.unsqueeze(cur_inlier_ratio, 0))
all_hyp_pts = torch.cat(all_hyp_pts, 0) # b,hn,vn,2
all_inlier_ratio = torch.cat(all_inlier_ratio, 0) # b,hn,vn
# raw_hyp_pts=all_hyp_pts.permute(0,2,1,3).clone()
# raw_hyp_ratio=all_inlier_ratio.permute(0,2,1).clone()
all_hyp_pts = all_hyp_pts.permute(0, 2, 1, 3) # b,vn,hn,2
all_inlier_ratio = all_inlier_ratio.permute(0, 2, 1) # b,vn,hn
thresh = torch.max(all_inlier_ratio, 2)[0]-0.1 # b,vn
all_inlier_ratio[all_inlier_ratio < torch.unsqueeze(thresh, 2)] = 0.0
diff_pts = all_hyp_pts-torch.unsqueeze(mean, 2) # b,vn,hn,2
weighted_diff_pts = diff_pts * torch.unsqueeze(all_inlier_ratio, 3)
cov = torch.matmul(diff_pts.transpose(2, 3), weighted_diff_pts) # b,vn,2,2
cov /= torch.unsqueeze(torch.unsqueeze(torch.sum(all_inlier_ratio, 2), 2), 3)+1e-3 # b,vn,2,2
# if output_hyp:
# return mean,cov,all_hyp_pts,all_inlier_ratio,raw_hyp_pts,raw_hyp_ratio
return mean, cov
