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SparseImgRepresenter.py
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SparseImgRepresenter.py
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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
import time
class ScaleSpaceAffinePatchExtractor(nn.Module):
def __init__(self,
border = 16,
num_features = 500,
patch_size = 32,
mrSize = 3.0,
nlevels = 3,
num_Baum_iters = 0,
init_sigma = 1.6,
th = None,
RespNet = None, OriNet = None, AffNet = None):
super(ScaleSpaceAffinePatchExtractor, self).__init__()
self.mrSize = mrSize
self.PS = patch_size
self.b = border;
self.num = num_features
self.nlevels = nlevels
self.num_Baum_iters = num_Baum_iters
self.init_sigma = init_sigma
self.th = th;
if th is not None:
self.num = -1
else:
self.th = 0
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 multiScaleDetector(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 = NMS3dAndComposeA(w = octave[0].size(3),
h = octave[0].size(2),
border = self.b, mrSize = self.mrSize)
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])
if top_resp is None:
continue
octaveMap = octaveMap_current
aff_matrices.append(aff_matrix), top_responces.append(top_resp)
pyr_id = Variable(oct_idx * torch.ones(aff_matrix.size(0)))
lev_id = Variable((level_idx - 1) * torch.ones(aff_matrix.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 getAffineShape(self, final_resp, LAFs, final_pyr_idxs, final_level_idxs, num_features = 0):
pe_time = 0
affnet_time = 0
pyr_inv_idxs = get_inverted_pyr_index(self.scale_pyr, final_pyr_idxs, final_level_idxs)
t = time.time()
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, LAFs, PS = self.AffNet.PS)
pe_time+=time.time() - t
t = time.time()
base_A = torch.eye(2).unsqueeze(0).expand(final_pyr_idxs.size(0),2,2)
if final_resp.is_cuda:
base_A = base_A.cuda()
base_A = Variable(base_A)
is_good = None
n_patches = patches_small.size(0)
for i in range(self.num_Baum_iters):
t = time.time()
A = batched_forward(self.AffNet, patches_small, 256)
is_good_current = 1
affnet_time += time.time() - t
if is_good is None:
is_good = is_good_current
else:
is_good = is_good * is_good_current
base_A = torch.bmm(A, base_A);
new_LAFs = torch.cat([torch.bmm(base_A,LAFs[:,:,0:2]), LAFs[:,:,2:] ], dim =2)
#print torch.sqrt(new_LAFs[0,0,0]*new_LAFs[0,1,1] - new_LAFs[0,1,0] *new_LAFs[0,0,1]) * scale_pyr[0][0].size(2)
if i != self.num_Baum_iters - 1:
pe_time+=time.time() - t
t = time.time()
patches_small = extract_patches_from_pyramid_with_inv_index(self.scale_pyr, pyr_inv_idxs, new_LAFs, PS = self.AffNet.PS)
pe_time+= time.time() - t
l1,l2 = batch_eig2x2(A)
ratio1 = torch.abs(l1 / (l2 + 1e-8))
converged_mask = (ratio1 <= 1.2) * (ratio1 >= (0.8))
l1,l2 = batch_eig2x2(base_A)
ratio = torch.abs(l1 / (l2 + 1e-8))
idxs_mask = ((ratio < 6.0) * (ratio > (1./6.))) * checkTouchBoundary(new_LAFs)
num_survived = idxs_mask.float().sum()
if (num_features > 0) and (num_survived.data.item() > num_features):
final_resp = final_resp * idxs_mask.float() #zero bad points
final_resp, idxs = torch.topk(final_resp, k = num_features);
else:
idxs = Variable(torch.nonzero(idxs_mask.data).view(-1).long())
final_resp = final_resp[idxs]
final_pyr_idxs = final_pyr_idxs[idxs]
final_level_idxs = final_level_idxs[idxs]
base_A = torch.index_select(base_A, 0, idxs)
LAFs = torch.index_select(LAFs, 0, idxs)
new_LAFs = torch.cat([torch.bmm(base_A, LAFs[:,:,0:2]),
LAFs[:,:,2:]], dim =2)
print ('affnet_time',affnet_time)
print ('pe_time', pe_time)
return final_resp, new_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:
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 = False):
### Detection
t = time.time()
num_features_prefilter = self.num
if self.num_Baum_iters > 0:
num_features_prefilter = int(1.5 * self.num);
responses, LAFs, final_pyr_idxs, final_level_idxs = self.multiScaleDetector(x,num_features_prefilter)
print (time.time() - t, 'detection multiscale')
t = time.time()
LAFs[:,0:2,0:2] = self.mrSize * LAFs[:,:,0:2]
if self.num_Baum_iters > 0:
responses, LAFs, final_pyr_idxs, final_level_idxs = self.getAffineShape(responses, LAFs, final_pyr_idxs, final_level_idxs, self.num)
print (time.time() - t, 'affine shape iters')
t = time.time()
if do_ori:
LAFs = self.getOrientation(LAFs, final_pyr_idxs, final_level_idxs)
#pyr_inv_idxs = get_inverted_pyr_index(self.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