-
Notifications
You must be signed in to change notification settings - Fork 47
/
OnePassSIR.py
153 lines (150 loc) · 7.77 KB
/
OnePassSIR.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import torch
import torch.nn as nn
import numpy as np
import math
import torch.nn.functional as F
from torch.autograd import Variable
from copy import deepcopy
from Utils import GaussianBlur, batch_eig2x2, line_prepender, batched_forward
from LAF import LAFs2ell,abc2A, angles2A, generate_patch_grid_from_normalized_LAFs, extract_patches, get_inverted_pyr_index, denormalizeLAFs, extract_patches_from_pyramid_with_inv_index, rectifyAffineTransformationUpIsUp
from LAF import get_pyramid_and_level_index_for_LAFs, normalizeLAFs, checkTouchBoundary
from HandCraftedModules import HessianResp, AffineShapeEstimator, OrientationDetector, ScalePyramid, NMS3dAndComposeA,NMS3dAndComposeAAff
import time
class OnePassSIR(nn.Module):
def __init__(self,
border = 16,
num_features = 500,
patch_size = 32,
mrSize = 3.0,
nlevels = 3,
th = None,#16.0/ 3.0,
num_Baum_iters = 0,
init_sigma = 1.6,
RespNet = None, OriNet = None, AffNet = None):
super(OnePassSIR, self).__init__()
self.mrSize = mrSize
self.PS = patch_size
self.b = border;
self.num = num_features
self.th = th;
if th is not None:
self.num = -1
else:
self.th = 0
self.nlevels = nlevels
self.num_Baum_iters = num_Baum_iters
self.init_sigma = init_sigma
if RespNet is not None:
self.RespNet = RespNet
else:
self.RespNet = HessianResp()
if OriNet is not None:
self.OriNet = OriNet
else:
self.OriNet= OrientationDetector(patch_size = 19);
if AffNet is not None:
self.AffNet = AffNet
else:
self.AffNet = AffineShapeEstimator(patch_size = 19)
self.ScalePyrGen = ScalePyramid(nLevels = self.nlevels, init_sigma = self.init_sigma, border = self.b)
return
def multiScaleDetectorAff(self,x, num_features = 0):
t = time.time()
self.scale_pyr, self.sigmas, self.pix_dists = self.ScalePyrGen(x)
### Detect keypoints in scale space
aff_matrices = []
top_responces = []
pyr_idxs = []
level_idxs = []
det_t = 0
nmst = 0
for oct_idx in range(len(self.sigmas)):
#print oct_idx
octave = self.scale_pyr[oct_idx]
sigmas_oct = self.sigmas[oct_idx]
pix_dists_oct = self.pix_dists[oct_idx]
low = None
cur = None
high = None
octaveMap = (self.scale_pyr[oct_idx][0] * 0).byte()
nms_f = NMS3dAndComposeAAff(w = octave[0].size(3),
h = octave[0].size(2),
border = self.b, mrSize = self.mrSize)
#oct_aff_map = F.upsample(self.AffNet(octave[0]), (octave[0].size(2), octave[0].size(3)),mode='bilinear')
oct_aff_map = self.AffNet(octave[0])
for level_idx in range(1, len(octave)-1):
if cur is None:
low = torch.clamp(self.RespNet(octave[level_idx - 1], (sigmas_oct[level_idx - 1 ])) - self.th, min = 0)
else:
low = cur
if high is None:
cur = torch.clamp(self.RespNet(octave[level_idx ], (sigmas_oct[level_idx ])) - self.th, min = 0)
else:
cur = high
high = torch.clamp(self.RespNet(octave[level_idx + 1], (sigmas_oct[level_idx + 1 ])) - self.th, min = 0)
top_resp, aff_matrix, octaveMap_current = nms_f(low, cur, high,
num_features = num_features,
octaveMap = octaveMap,
scales = sigmas_oct[level_idx - 1:level_idx + 2],
aff_resp = oct_aff_map)
if top_resp is None:
continue
octaveMap = octaveMap_current
not_touch_boundary_idx = checkTouchBoundary(torch.cat([aff_matrix[:,:2,:2] *3.0, aff_matrix[:,:,2:]], dim =2))
aff_matrices.append(aff_matrix[not_touch_boundary_idx.byte()]), top_responces.append(top_resp[not_touch_boundary_idx.byte()])
pyr_id = Variable(oct_idx * torch.ones(aff_matrices[-1].size(0)))
lev_id = Variable((level_idx - 1) * torch.ones(aff_matrices[-1].size(0))) #prevBlur
if x.is_cuda:
pyr_id = pyr_id.cuda()
lev_id = lev_id.cuda()
pyr_idxs.append(pyr_id)
level_idxs.append(lev_id)
all_responses = torch.cat(top_responces, dim = 0)
aff_m_scales = torch.cat(aff_matrices,dim = 0)
pyr_idxs_scales = torch.cat(pyr_idxs,dim = 0)
level_idxs_scale = torch.cat(level_idxs, dim = 0)
if (num_features > 0) and (num_features < all_responses.size(0)):
all_responses, idxs = torch.topk(all_responses, k = num_features);
LAFs = torch.index_select(aff_m_scales, 0, idxs)
final_pyr_idxs = pyr_idxs_scales[idxs]
final_level_idxs = level_idxs_scale[idxs]
else:
return all_responses, aff_m_scales, pyr_idxs_scales , level_idxs_scale
return all_responses, LAFs, final_pyr_idxs, final_level_idxs,
def getOrientation(self, LAFs, final_pyr_idxs, final_level_idxs):
pyr_inv_idxs = get_inverted_pyr_index(self.scale_pyr, final_pyr_idxs, final_level_idxs)
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, LAFs, PS = self.OriNet.PS)
max_iters = 1
### Detect orientation
for i in range(max_iters):
angles = self.OriNet(patches_small)
if len(angles.size()) > 2:
LAFs = torch.cat([torch.bmm( LAFs[:,:,:2], angles), LAFs[:,:,2:]], dim = 2)
else:
LAFs = torch.cat([torch.bmm( LAFs[:,:,:2], angles2A(angles).view(-1,2,2)), LAFs[:,:,2:]], dim = 2)
if i != max_iters-1:
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, LAFs, PS = self.OriNet.PS)
return LAFs
def extract_patches_from_pyr(self, dLAFs, PS = 41):
pyr_idxs, level_idxs = get_pyramid_and_level_index_for_LAFs(dLAFs, self.sigmas, self.pix_dists, PS)
pyr_inv_idxs = get_inverted_pyr_index(self.scale_pyr, pyr_idxs, level_idxs)
patches = extract_patches_from_pyramid_with_inv_index(self.scale_pyr,
pyr_inv_idxs,
normalizeLAFs(dLAFs, self.scale_pyr[0][0].size(3), self.scale_pyr[0][0].size(2)),
PS = PS)
return patches
def forward(self,x, do_ori = True):
### Detection
t = time.time()
num_features_prefilter = self.num
responses, LAFs, final_pyr_idxs, final_level_idxs = self.multiScaleDetectorAff(x,num_features_prefilter)
print time.time() - t, 'detection multiscale'
t = time.time()
LAFs[:,0:2,0:2] = self.mrSize * LAFs[:,:,0:2]
if do_ori:
LAFs = self.getOrientation(LAFs, final_pyr_idxs, final_level_idxs)
#pyr_inv_idxs = get_inverted_pyr_index(scale_pyr, final_pyr_idxs, final_level_idxs)
#patches = extract_patches_from_pyramid_with_inv_index(scale_pyr, pyr_inv_idxs, LAFs, PS = self.PS)
#patches = extract_patches(x, LAFs, PS = self.PS)
#print time.time() - t, len(LAFs), ' patches extraction'
return denormalizeLAFs(LAFs, x.size(3), x.size(2)), responses