├── CODE_LICENSE.txt ├── README.md ├── example.ipynb ├── hpatches_extract_pytorchsift.py ├── img ├── fox.png ├── hpatches-results.png ├── mp_kernel.png ├── total.png └── vlfeat_kernel.png ├── pytorch_sift.py └── weighting windows.ipynb /CODE_LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (C) 2017, Dmytro Mishkin 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are 6 | met: 7 | 1. Redistributions of source code must retain the above copyright 8 | notice, this list of conditions and the following disclaimer. 9 | 2. Redistributions in binary form must reproduce the above copyright 10 | notice, this list of conditions and the following disclaimer in the 11 | documentation and/or other materials provided with the 12 | distribution. 13 | 14 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 15 | "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 16 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 17 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 18 | HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 19 | SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 20 | LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 21 | DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 22 | THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 23 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 24 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This is an differentiable [pytorch](https://github.com/pytorch/pytorch) implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can be used for descriptop-based learning shape of affine feature. 2 | 3 | **UPD 08/2019** : pytorch-sift is added to [kornia](https://github.com/arraiyopensource/kornia) and available by `kornia.features.SIFTDescriptor` 4 | 5 | There are different implementations of the SIFT on the web. I tried to match [Michal Perdoch implementation](https://github.com/perdoch/hesaff/blob/master/siftdesc.cpp), which gives high quality features for image retrieval [CVPR2009](http://cmp.felk.cvut.cz/~chum/papers/perdoch-cvpr09.pdf). However, on planar datasets, it is inferior to [vlfeat implementation](http://www.vlfeat.org/sandbox/api/sift.html). 6 | The main difference is gaussian weighting window parameters, so I have made a vlfeat-like version too. MP version weights patch center much more (see image below, left) and additionally crops everything outside the circular region. Right is vlfeat version 7 | 8 | 9 | ![Michal Perdoch kernel](/img/mp_kernel.png) 10 | ![vlfeat kernel](/img/vlfeat_kernel.png) 11 | 12 | 13 | 14 | ```python 15 | 16 | descriptor_mp_mode = SIFTNet(patch_size = 65, 17 | sigma_type= 'hesamp', 18 | masktype='CircularGauss') 19 | 20 | descriptor_vlfeat_mode = SIFTNet(patch_size = 65, 21 | sigma_type= 'vlfeat', 22 | masktype='Gauss') 23 | 24 | ``` 25 | Results: 26 | 27 | ![hpatches mathing results](/img/hpatches-results.png) 28 | 29 | 30 | ``` 31 | OPENCV-SIFT - mAP 32 | Easy Hard Tough mean 33 | ------- ------- --------- ------- 34 | 0.47788 0.20997 0.0967711 0.26154 35 | 36 | VLFeat-SIFT - mAP 37 | Easy Hard Tough mean 38 | -------- -------- --------- -------- 39 | 0.466584 0.203966 0.0935743 0.254708 40 | 41 | PYTORCH-SIFT-VLFEAT-65 - mAP 42 | Easy Hard Tough mean 43 | -------- -------- --------- -------- 44 | 0.472563 0.202458 0.0910371 0.255353 45 | 46 | NUMPY-SIFT-VLFEAT-65 - mAP 47 | Easy Hard Tough mean 48 | -------- -------- --------- -------- 49 | 0.449431 0.197918 0.0905395 0.245963 50 | 51 | PYTORCH-SIFT-MP-65 - mAP 52 | Easy Hard Tough mean 53 | -------- -------- --------- -------- 54 | 0.430887 0.184834 0.0832707 0.232997 55 | 56 | NUMPY-SIFT-MP-65 - mAP 57 | Easy Hard Tough mean 58 | -------- ------- --------- -------- 59 | 0.417296 0.18114 0.0820582 0.226832 60 | 61 | 62 | ``` 63 | 64 | Speed: 65 | - 0.00246 s per 65x65 patch - [numpy SIFT](https://github.com/ducha-aiki/numpy-sift) 66 | - 0.00028 s per 65x65 patch - [C++ SIFT](https://github.com/perdoch/hesaff/blob/master/siftdesc.cpp) 67 | - 0.00074 s per 65x65 patch - CPU, 256 patches per batch 68 | - 0.00038 s per 65x65 patch - GPU (GM940, mobile), 256 patches per batch 69 | - 0.00038 s per 65x65 patch - GPU (GM940, mobile), 256 patches per batch 70 | 71 | 72 | 73 | 74 | If you use this code for academic purposes, please cite the following paper: 75 | 76 | ``` 77 | @InProceedings{AffNet2018, 78 | title = {Repeatability Is Not Enough: Learning Affine Regions via Discriminability}, 79 | author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas}, 80 | booktitle = {Proceedings of ECCV}, 81 | year = 2018, 82 | month = sep 83 | } 84 | 85 | ``` 86 | 87 | -------------------------------------------------------------------------------- /example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "data": { 12 | "text/plain": [ 13 | "" 14 | ] 15 | }, 16 | "execution_count": 3, 17 | "metadata": {}, 18 | "output_type": "execute_result" 19 | }, 20 | { 21 | "data": { 22 | "image/png": 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H54herFJLXD8cr55TozLSe2jT/I5So0rr6dpTI0AZ8N78u77oSSMRzU/wOvX2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ETtRIWwt9zpUUTa4XDYcHHw4MPDQ4MGmAK3sLCgRCJhsJvndQafMLuaZi8kAlZXVzUzM9PFN//4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LPNzj0B4nGnt5qFP7k7t3KMV+UnkAR44caQSpOkyWllsKshTV8fIM/Jy5uWq7Yk9KLS4uNvsI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\n", 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d6saAT548WbL3GtS9vTfXTndTahzIBB4faAKuU/4ODFzk4vdNCqj2+vx0d69u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33 | "text/plain": [ 34 | "" 35 | ] 36 | }, 37 | "metadata": {}, 38 | "output_type": "display_data" 39 | } 40 | ], 41 | "source": [ 42 | "%matplotlib inline\n", 43 | "import matplotlib.pyplot as plt\n", 44 | "import torch\n", 45 | "import time\n", 46 | "import numpy as np\n", 47 | "import os\n", 48 | "from PIL import Image\n", 49 | "\n", 50 | "patch_size = 65\n", 51 | "image = np.array(Image.open('img/fox.png').convert('L'))\n", 52 | "plt.figure()\n", 53 | "plt.imshow(image, cmap=\"gray\")\n", 54 | "\n", 55 | "h,w = image.shape\n", 56 | "patch = image[h/4: h/4 + patch_size,w/3 : w/3 + patch_size]\n", 57 | "plt.figure()\n", 58 | "plt.imshow(patch, cmap=\"gray\")\n" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 4, 64 | "metadata": { 65 | "collapsed": false 66 | }, 67 | "outputs": [ 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "('time', 0.0208740234375)\n", 73 | "[[ 4 6 7 9 8 19 22 13 3 7 9 14 2 4 4 3 5 9\n", 74 | " 11 8 17 57 51 22 11 29 23 18 3 7 9 9 18 11 13 12\n", 75 | " 77 93 91 99 119 119 119 73 30 73 98 79 17 9 7 3 60 47\n", 76 | " 57 26 56 86 76 42 15 39 67 50 6 7 7 2 19 20 27 11\n", 77 | " 5 7 12 18 5 7 5 4 8 13 12 5 20 26 17 16 14 17\n", 78 | " 15 20 5 15 3 4 24 49 71 57 85 56 31 40 104 119 119 119\n", 79 | " 12 48 29 25 10 21 28 34 34 36 31 18 42 69 84 60 5 20\n", 80 | " 22 14]]\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "from pytorch_sift import SIFTNet\n", 86 | "\n", 87 | "SIFT = SIFTNet(patch_size = patch_size)\n", 88 | "SIFT.eval()\n", 89 | "\n", 90 | "# It takes n_patches x 1 x patch_size x patch_size input == standard pytorch batch format\n", 91 | "patches = np.ndarray((1, 1, patch_size, patch_size), dtype=np.float32)\n", 92 | "patches[0,0,:,:] = patch\n", 93 | "t = time.time()\n", 94 | "with torch.no_grad():\n", 95 | " torch_patches = torch.from_numpy(patches)\n", 96 | " res = SIFT(torch_patches)\n", 97 | " sift = np.round(512. * res.data.cpu().numpy()).astype(np.int32)\n", 98 | "print ('time', time.time() - t)\n", 99 | "print (sift) \n" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 5, 105 | "metadata": { 106 | "collapsed": false 107 | }, 108 | "outputs": [ 109 | { 110 | "name": "stdout", 111 | "output_type": "stream", 112 | "text": [ 113 | "[[ 4 6 7 9 8 19 22 13 3 7 9 14 2 4 4 3 5 9\n", 114 | " 11 8 17 57 51 22 11 29 23 18 3 7 9 9 18 11 13 12\n", 115 | " 77 93 91 99 119 119 119 73 30 73 98 79 17 9 7 3 60 47\n", 116 | " 57 26 56 86 76 42 15 39 67 50 6 7 7 2 19 20 27 11\n", 117 | " 5 7 12 18 5 7 5 4 8 13 12 5 20 26 17 16 14 17\n", 118 | " 15 20 5 15 3 4 24 49 71 57 85 56 31 40 104 119 119 119\n", 119 | " 12 48 29 25 10 21 28 34 34 36 31 18 42 69 84 60 5 20\n", 120 | " 22 14]]\n" 121 | ] 122 | } 123 | ], 124 | "source": [ 125 | "#Now on GPU:\n", 126 | "\n", 127 | "SIFT = SIFT.cuda()\n", 128 | "with torch.no_grad():\n", 129 | " torch_patches = torch.from_numpy(patches).cuda()\n", 130 | " res = SIFT(torch_patches)\n", 131 | " sift = np.round(512. * res.data.cpu().numpy()).astype(np.int32)\n", 132 | "print (sift) \n" 133 | ] 134 | } 135 | ], 136 | "metadata": { 137 | "kernelspec": { 138 | "display_name": "Python 2", 139 | "language": "python", 140 | "name": "python2" 141 | }, 142 | "language_info": { 143 | "codemirror_mode": { 144 | "name": "ipython", 145 | "version": 2 146 | }, 147 | "file_extension": ".py", 148 | "mimetype": "text/x-python", 149 | "name": "python", 150 | "nbconvert_exporter": "python", 151 | "pygments_lexer": "ipython2", 152 | "version": "2.7.12" 153 | } 154 | }, 155 | "nbformat": 4, 156 | "nbformat_minor": 0 157 | } 158 | -------------------------------------------------------------------------------- /hpatches_extract_pytorchsift.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import argparse 3 | import torch 4 | import time 5 | import os 6 | import sys 7 | import cv2 8 | import math 9 | import numpy as np 10 | from tqdm import tqdm 11 | from pytorch_sift import SIFTNet 12 | from copy import deepcopy 13 | import random 14 | import time 15 | import numpy as np 16 | import glob 17 | import os 18 | 19 | assert len(sys.argv)==3, "Usage python hpatches_extract_pytorchsift.py hpatches_db_root_folder 64" 20 | OUT_W = int(sys.argv[2]) 21 | # all types of patches 22 | tps = ['ref','e1','e2','e3','e4','e5','h1','h2','h3','h4','h5',\ 23 | 't1','t2','t3','t4','t5'] 24 | 25 | class hpatches_sequence: 26 | """Class for loading an HPatches sequence from a sequence folder""" 27 | itr = tps 28 | def __init__(self,base): 29 | name = base.split('/') 30 | self.name = name[-1] 31 | self.base = base 32 | for t in self.itr: 33 | im_path = os.path.join(base, t+'.png') 34 | im = cv2.imread(im_path,0) 35 | self.N = im.shape[0]/65 36 | setattr(self, t, np.split(im, self.N)) 37 | 38 | 39 | seqs = glob.glob(sys.argv[1]+'/*') 40 | seqs = [os.path.abspath(p) for p in seqs] 41 | 42 | descr_name_vl = 'pytorch-sift-vlfeat-'+str(OUT_W) 43 | descr_name_mp = 'pytorch-sift-mp-'+str(OUT_W) 44 | 45 | descr_names = [descr_name_vl, descr_name_mp] 46 | 47 | model_vl = SIFTNet(OUT_W, mask_type = 'Gauss', sigma_type='vlfeat') 48 | model_vl.cuda() 49 | print(model_vl) 50 | 51 | model_mp = SIFTNet(OUT_W, mask_type = 'CircularGauss', sigma_type='hesamp') 52 | model_mp.cuda() 53 | print(model_mp) 54 | 55 | models = [model_vl, model_mp] 56 | 57 | for seq_path in seqs: 58 | seq = hpatches_sequence(seq_path) 59 | for descr_id, descr_name in enumerate(descr_names): 60 | model = models[descr_id] 61 | path = os.path.join(descr_name,seq.name) 62 | if not os.path.exists(path): 63 | os.makedirs(path) 64 | descr = np.zeros((int(seq.N),128)) # trivial (mi,sigma) descriptor 65 | for tp in tps: 66 | print(seq.name+'/'+tp) 67 | if os.path.isfile(os.path.join(path,tp+'.csv')): 68 | continue 69 | n_patches = 0 70 | for i,patch in enumerate(getattr(seq, tp)): 71 | n_patches+=1 72 | t = time.time() 73 | patches_for_net = np.zeros((n_patches, 1, OUT_W, OUT_W)) 74 | uuu = 0 75 | if OUT_W != 65: 76 | for i,patch in enumerate(getattr(seq, tp)): 77 | patches_for_net[i,0,:,:] = cv2.resize(patch,(OUT_W,OUT_W)) 78 | else: 79 | for i,patch in enumerate(getattr(seq, tp)): 80 | patches_for_net[i,0,:,:] = patch 81 | ### 82 | model.eval() 83 | outs = [] 84 | bs = 128; 85 | n_batches = int(n_patches / bs + 1) 86 | for batch_idx in range(n_batches): 87 | if batch_idx == n_batches - 1: 88 | if (batch_idx + 1) * bs > n_patches: 89 | end = n_patches 90 | else: 91 | end = (batch_idx + 1) * bs 92 | else: 93 | end = (batch_idx + 1) * bs 94 | data_a = patches_for_net[batch_idx * bs: end, :, :, :].astype(np.float32) 95 | data_a = torch.from_numpy(data_a) 96 | data_a = data_a.cuda() 97 | with torch.no_grad(): 98 | out_a = model(data_a) 99 | outs.append(out_a.data.cpu().numpy().reshape(-1, 128)) 100 | res_desc = np.concatenate(outs) 101 | res_desc = np.reshape(res_desc, (n_patches, -1)) 102 | out = np.reshape(res_desc, (n_patches,-1)) 103 | outs = np.clip(512*out, 0, 255) 104 | np.savetxt(os.path.join(path,tp+'.csv'), outs.astype(np.uint8), delimiter=',', fmt='%d') 105 | -------------------------------------------------------------------------------- /img/fox.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ducha-aiki/pytorch-sift/00e726f8d777fdd87064627c6cc770966d12b5eb/img/fox.png -------------------------------------------------------------------------------- /img/hpatches-results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ducha-aiki/pytorch-sift/00e726f8d777fdd87064627c6cc770966d12b5eb/img/hpatches-results.png -------------------------------------------------------------------------------- /img/mp_kernel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ducha-aiki/pytorch-sift/00e726f8d777fdd87064627c6cc770966d12b5eb/img/mp_kernel.png -------------------------------------------------------------------------------- /img/total.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ducha-aiki/pytorch-sift/00e726f8d777fdd87064627c6cc770966d12b5eb/img/total.png -------------------------------------------------------------------------------- /img/vlfeat_kernel.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ducha-aiki/pytorch-sift/00e726f8d777fdd87064627c6cc770966d12b5eb/img/vlfeat_kernel.png -------------------------------------------------------------------------------- /pytorch_sift.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | def getPoolingKernel(kernel_size = 25): 8 | half_size = float(kernel_size)/2.0 9 | xc2 = [] 10 | for i in range(kernel_size): 11 | xc2.append(half_size - abs(float(i)+0.5-half_size)) 12 | xc2 = np.array(xc2) 13 | kernel = np.outer(xc2.T,xc2) 14 | kernel = kernel/(half_size**2) 15 | return kernel 16 | 17 | def get_bin_weight_kernel_size_and_stride(patch_size, num_spatial_bins): 18 | ks = 2*int(patch_size / (num_spatial_bins+1)); 19 | stride= patch_size // num_spatial_bins 20 | pad = ks //4 21 | return ks, stride,pad 22 | 23 | class SIFTNet(nn.Module): 24 | def CircularGaussKernel(self,kernlen=21, circ = True, sigma_type = 'hesamp'): 25 | halfSize = float(kernlen) / 2.; 26 | r2 = float(halfSize**2); 27 | if sigma_type == 'hesamp': 28 | sigma_mul_2 = 0.9 * r2; 29 | elif sigma_type == 'vlfeat': 30 | sigma_mul_2 = kernlen**2 31 | else: 32 | raise ValueError('Unknown sigma_type', sigma_type, 'try hesamp or vlfeat') 33 | disq = 0; 34 | kernel = np.zeros((kernlen,kernlen)) 35 | for y in range(kernlen): 36 | for x in range(kernlen): 37 | disq = (y - halfSize+0.5)**2 + (x - halfSize+0.5)**2; 38 | kernel[y,x] = math.exp(-disq / sigma_mul_2) 39 | if circ and (disq >= r2): 40 | kernel[y,x] = 0. 41 | return kernel 42 | def __repr__(self): 43 | return self.__class__.__name__ + '(' + 'num_ang_bins=' + str(self.num_ang_bins) +\ 44 | ', ' + 'num_spatial_bins=' + str(self.num_spatial_bins) +\ 45 | ', ' + 'patch_size=' + str(self.patch_size) +\ 46 | ', ' + 'rootsift=' + str(self.rootsift) +\ 47 | ', ' + 'sigma_type=' + str(self.sigma_type) +\ 48 | ', ' + 'mask_type=' + str(self.mask_type) +\ 49 | ', ' + 'clipval=' + str(self.clipval) + ')' 50 | def __init__(self, 51 | patch_size = 65, 52 | num_ang_bins = 8, 53 | num_spatial_bins = 4, 54 | clipval = 0.2, 55 | rootsift = False, 56 | mask_type = 'CircularGauss', 57 | sigma_type = 'hesamp'): 58 | super(SIFTNet, self).__init__() 59 | self.eps = 1e-10 60 | self.num_ang_bins = num_ang_bins 61 | self.num_spatial_bins = num_spatial_bins 62 | self.clipval = clipval 63 | self.rootsift = rootsift 64 | self.mask_type = mask_type 65 | self.patch_size = patch_size 66 | self.sigma_type = sigma_type 67 | 68 | if self.mask_type == 'CircularGauss': 69 | self.gk = torch.from_numpy(self.CircularGaussKernel(kernlen=patch_size, circ=True, sigma_type=sigma_type).astype(np.float32)) 70 | elif self.mask_type == 'Gauss': 71 | self.gk = torch.from_numpy(self.CircularGaussKernel(kernlen=patch_size, circ=False, sigma_type=sigma_type).astype(np.float32)) 72 | elif self.mask_type == 'Uniform': 73 | self.gk = torch.ones(patch_size,patch_size).float() / float(patch_size*patch_size) 74 | else: 75 | raise ValueError(masktype, 'is unknown mask type') 76 | 77 | self.bin_weight_kernel_size, self.bin_weight_stride, self.pad = get_bin_weight_kernel_size_and_stride(patch_size, num_spatial_bins) 78 | self.gx = nn.Conv2d(1, 1, kernel_size=(1,3), bias = False) 79 | self.gx.weight.data = torch.tensor(np.array([[[[-1, 0, 1]]]], dtype=np.float32)) 80 | 81 | self.gy = nn.Conv2d(1, 1, kernel_size=(3,1), bias = False) 82 | self.gy.weight.data = torch.from_numpy(np.array([[[[-1], [0], [1]]]], dtype=np.float32)) 83 | nw = getPoolingKernel(kernel_size = self.bin_weight_kernel_size) 84 | 85 | self.pk = nn.Conv2d(1, 1, kernel_size=(nw.shape[0], nw.shape[1]), 86 | stride = (self.bin_weight_stride, self.bin_weight_stride), 87 | padding = (self.pad , self.pad ), 88 | bias = False) 89 | new_weights = np.array(nw.reshape((1, 1, nw.shape[0],nw.shape[1]))) 90 | self.pk.weight.data = torch.from_numpy(new_weights.astype(np.float32)) 91 | return 92 | 93 | def forward(self, x): 94 | gx = self.gx(F.pad(x, (1, 1, 0, 0), 'replicate')) 95 | gy = self.gy(F.pad(x, (0, 0, 1, 1), 'replicate')) 96 | mag = torch.sqrt(gx * gx + gy * gy + self.eps) 97 | ori = torch.atan2(gy, gx + self.eps) 98 | mag = mag * self.gk.expand_as(mag).to(mag.device) 99 | o_big = (ori + 2.0 * math.pi )/ (2.0 * math.pi) * float(self.num_ang_bins) 100 | bo0_big_ = torch.floor(o_big) 101 | wo1_big_ = o_big - bo0_big_ 102 | bo0_big = bo0_big_ % self.num_ang_bins 103 | bo1_big = (bo0_big + 1) % self.num_ang_bins 104 | wo0_big = (1.0 - wo1_big_) * mag 105 | wo1_big = wo1_big_ * mag 106 | ang_bins = [] 107 | for i in range(0, self.num_ang_bins): 108 | out = self.pk((bo0_big == i).float() * wo0_big + (bo1_big == i).float() * wo1_big) 109 | ang_bins.append(out) 110 | ang_bins = torch.cat(ang_bins,1) 111 | ang_bins = ang_bins.view(ang_bins.size(0), -1) 112 | ang_bins = F.normalize(ang_bins, p=2) 113 | ang_bins = torch.clamp(ang_bins, 0., float(self.clipval)) 114 | ang_bins = F.normalize(ang_bins, p=2) 115 | if self.rootsift: 116 | ang_bins = torch.sqrt(F.normalize(ang_bins,p=1) + 1e-10) 117 | return ang_bins -------------------------------------------------------------------------------- /weighting windows.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "%matplotlib inline\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "import torch\n", 14 | "import numpy as np\n", 15 | "from PIL import Image\n", 16 | "import seaborn as sns" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 3, 22 | "metadata": { 23 | "collapsed": false 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "from pytorch_sift import SIFTNet\n", 28 | "sift_desc = SIFTNet(65)" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 6, 34 | "metadata": { 35 | "collapsed": false 36 | }, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/plain": [ 41 | "" 42 | ] 43 | }, 44 | "execution_count": 6, 45 | "metadata": {}, 46 | "output_type": "execute_result" 47 | }, 48 | { 49 | "data": { 50 | "image/png": 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51 | "text/plain": [ 52 | "" 53 | ] 54 | }, 55 | "metadata": {}, 56 | "output_type": "display_data" 57 | }, 58 | { 59 | "data": { 60 | "image/png": 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61 | "text/plain": [ 62 | "" 63 | ] 64 | }, 65 | "metadata": {}, 66 | "output_type": "display_data" 67 | } 68 | ], 69 | "source": [ 70 | "mp_gauss = sift_desc.CircularGaussKernel(41, circ = True, sigma_type = 'hesamp')\n", 71 | "plt.figure()\n", 72 | "plt.grid(False)\n", 73 | "plt.imshow(255-mp_gauss, interpolation='None')\n", 74 | "\n", 75 | "vlfeat_gauss = sift_desc.CircularGaussKernel(41, circ = False, sigma_type ='vlfeat')\n", 76 | "plt.figure()\n", 77 | "plt.grid(False)\n", 78 | "plt.imshow(255-vlfeat_gauss, interpolation='None')\n" 79 | ] 80 | } 81 | ], 82 | "metadata": { 83 | "kernelspec": { 84 | "display_name": "Python 2", 85 | "language": "python", 86 | "name": "python2" 87 | }, 88 | "language_info": { 89 | "codemirror_mode": { 90 | "name": "ipython", 91 | "version": 2 92 | }, 93 | "file_extension": ".py", 94 | "mimetype": "text/x-python", 95 | "name": "python", 96 | "nbconvert_exporter": "python", 97 | "pygments_lexer": "ipython2", 98 | "version": "2.7.12" 99 | } 100 | }, 101 | "nbformat": 4, 102 | "nbformat_minor": 0 103 | } 104 | --------------------------------------------------------------------------------