├── .gitmodules ├── PR_bench ├── GardensPointWalking │ ├── day_left │ │ ├── Image000.jpg │ │ ├── Image001.jpg │ │ ├── Image002.jpg │ │ ├── Image003.jpg │ │ ├── Image004.jpg │ │ ├── Image005.jpg │ │ ├── Image006.jpg │ │ ├── Image007.jpg │ │ ├── Image008.jpg │ │ ├── Image009.jpg │ │ ├── Image010.jpg │ │ ├── Image011.jpg │ │ ├── Image012.jpg │ │ ├── Image013.jpg │ │ ├── Image014.jpg │ │ ├── Image015.jpg │ │ ├── Image016.jpg │ │ ├── Image017.jpg │ │ ├── Image018.jpg │ │ ├── Image019.jpg │ │ ├── Image020.jpg │ │ ├── Image021.jpg │ │ ├── Image022.jpg │ │ ├── Image023.jpg │ │ ├── Image024.jpg │ │ ├── Image025.jpg │ │ ├── Image026.jpg │ │ ├── Image027.jpg │ │ ├── Image028.jpg │ │ ├── Image029.jpg │ │ ├── Image030.jpg │ │ ├── Image031.jpg │ │ ├── Image032.jpg │ │ ├── Image033.jpg │ │ ├── Image034.jpg │ │ ├── Image035.jpg │ │ ├── Image036.jpg │ │ ├── Image037.jpg │ │ ├── Image038.jpg │ │ ├── Image039.jpg │ │ ├── Image040.jpg │ │ ├── Image041.jpg │ │ ├── Image042.jpg │ │ ├── Image043.jpg │ │ ├── Image044.jpg │ │ ├── Image045.jpg │ │ ├── Image046.jpg │ │ ├── Image047.jpg │ │ ├── Image048.jpg │ │ ├── Image049.jpg │ │ ├── Image050.jpg │ │ ├── Image051.jpg │ │ ├── Image052.jpg │ │ ├── Image053.jpg │ │ ├── Image054.jpg │ │ ├── Image055.jpg │ │ ├── Image056.jpg │ │ ├── Image057.jpg │ │ ├── Image058.jpg │ │ ├── Image059.jpg │ │ ├── Image060.jpg │ │ ├── Image061.jpg │ │ ├── Image062.jpg │ │ ├── Image063.jpg │ │ ├── Image064.jpg │ │ ├── Image065.jpg │ │ ├── Image066.jpg │ │ ├── Image067.jpg │ │ ├── Image068.jpg │ │ ├── Image069.jpg │ │ ├── Image070.jpg │ │ ├── Image071.jpg │ │ ├── Image072.jpg │ │ ├── Image073.jpg │ │ ├── Image074.jpg │ │ ├── Image075.jpg │ │ ├── Image076.jpg │ │ ├── Image077.jpg │ │ ├── Image078.jpg │ │ ├── Image079.jpg │ │ ├── Image080.jpg │ │ ├── Image081.jpg │ │ ├── Image082.jpg │ │ ├── Image083.jpg │ │ ├── Image084.jpg │ │ ├── Image085.jpg │ │ ├── Image086.jpg │ │ ├── Image087.jpg │ │ ├── Image088.jpg │ │ ├── Image089.jpg │ │ ├── Image090.jpg │ │ ├── Image091.jpg │ │ ├── Image092.jpg │ │ ├── Image093.jpg │ │ ├── Image094.jpg │ │ ├── Image095.jpg │ │ ├── Image096.jpg │ │ ├── Image097.jpg │ │ ├── Image098.jpg │ │ ├── Image099.jpg │ │ ├── Image100.jpg │ │ ├── Image101.jpg │ │ ├── Image102.jpg │ │ ├── Image103.jpg │ │ ├── Image104.jpg │ │ ├── Image105.jpg │ │ ├── Image106.jpg │ │ ├── Image107.jpg │ │ ├── Image108.jpg │ │ ├── Image109.jpg │ │ ├── Image110.jpg │ │ ├── Image111.jpg │ │ ├── Image112.jpg │ │ ├── Image113.jpg │ │ ├── Image114.jpg │ │ ├── Image115.jpg │ │ ├── Image116.jpg │ │ ├── Image117.jpg │ │ ├── Image118.jpg │ │ ├── Image119.jpg │ │ ├── Image120.jpg │ │ ├── Image121.jpg │ │ ├── Image122.jpg │ │ ├── Image123.jpg │ │ ├── Image124.jpg │ │ ├── Image125.jpg │ │ ├── Image126.jpg │ │ ├── Image127.jpg │ │ ├── Image128.jpg │ │ ├── Image129.jpg │ │ ├── Image130.jpg │ │ ├── Image131.jpg │ │ ├── Image132.jpg │ │ ├── Image133.jpg │ │ ├── Image134.jpg │ │ ├── Image135.jpg │ │ ├── Image136.jpg │ │ ├── Image137.jpg │ │ ├── Image138.jpg │ │ ├── Image139.jpg │ │ ├── Image140.jpg │ │ ├── Image141.jpg │ │ ├── Image142.jpg │ │ ├── Image143.jpg │ │ ├── Image144.jpg │ │ ├── Image145.jpg │ │ ├── Image146.jpg │ │ ├── Image147.jpg │ │ ├── Image148.jpg │ │ ├── Image149.jpg │ │ ├── Image150.jpg │ │ ├── Image151.jpg │ │ ├── Image152.jpg │ │ ├── Image153.jpg │ │ ├── Image154.jpg │ │ ├── Image155.jpg │ │ ├── Image156.jpg │ │ ├── Image157.jpg │ │ ├── Image158.jpg │ │ ├── Image159.jpg │ │ ├── Image160.jpg │ │ ├── Image161.jpg │ │ ├── Image162.jpg │ │ ├── Image163.jpg │ │ ├── Image164.jpg │ │ ├── Image165.jpg │ │ ├── Image166.jpg │ │ ├── Image167.jpg │ │ ├── Image168.jpg │ │ ├── Image169.jpg │ │ ├── Image170.jpg │ │ ├── Image171.jpg │ │ ├── Image172.jpg │ │ ├── Image173.jpg │ │ ├── Image174.jpg │ │ ├── Image175.jpg │ │ ├── Image176.jpg │ │ ├── Image177.jpg │ │ ├── Image178.jpg │ │ ├── Image179.jpg │ │ ├── Image180.jpg │ │ ├── Image181.jpg │ │ ├── Image182.jpg │ │ ├── Image183.jpg │ │ ├── Image184.jpg │ │ ├── Image185.jpg │ │ ├── Image186.jpg │ │ ├── Image187.jpg │ │ ├── Image188.jpg │ │ ├── Image189.jpg │ │ ├── Image190.jpg │ │ ├── Image191.jpg │ │ ├── Image192.jpg │ │ ├── Image193.jpg │ │ ├── Image194.jpg │ │ ├── Image195.jpg │ │ ├── Image196.jpg │ │ ├── Image197.jpg │ │ ├── Image198.jpg │ │ └── Image199.jpg │ ├── day_right │ │ ├── Image000.jpg │ │ ├── Image001.jpg │ │ ├── Image002.jpg │ │ ├── Image003.jpg │ │ ├── Image004.jpg │ │ ├── Image005.jpg │ │ ├── Image006.jpg │ │ ├── Image007.jpg │ │ ├── Image008.jpg │ │ ├── Image009.jpg │ │ ├── Image010.jpg │ │ ├── Image011.jpg │ │ ├── Image012.jpg │ │ ├── Image013.jpg │ │ ├── Image014.jpg │ │ ├── Image015.jpg │ │ ├── Image016.jpg │ │ ├── Image017.jpg │ │ ├── Image018.jpg │ │ ├── Image019.jpg │ │ ├── Image020.jpg │ │ ├── Image021.jpg │ │ ├── Image022.jpg │ │ ├── Image023.jpg │ │ ├── Image024.jpg │ │ ├── Image025.jpg │ │ ├── Image026.jpg │ │ ├── Image027.jpg │ │ ├── Image028.jpg │ │ ├── Image029.jpg │ │ ├── Image030.jpg │ │ ├── Image031.jpg │ │ ├── Image032.jpg │ │ ├── Image033.jpg │ │ ├── Image034.jpg │ │ ├── Image035.jpg │ │ ├── Image036.jpg │ │ ├── Image037.jpg │ │ ├── Image038.jpg │ │ ├── Image039.jpg │ │ ├── Image040.jpg │ │ ├── Image041.jpg │ │ ├── Image042.jpg │ │ ├── Image043.jpg │ │ ├── Image044.jpg │ │ ├── Image045.jpg │ │ ├── Image046.jpg │ │ ├── Image047.jpg │ │ ├── Image048.jpg │ │ ├── Image049.jpg │ │ ├── Image050.jpg │ │ ├── Image051.jpg │ │ ├── Image052.jpg │ │ ├── Image053.jpg │ │ ├── Image054.jpg │ │ ├── Image055.jpg │ │ ├── Image056.jpg │ │ ├── Image057.jpg │ │ ├── Image058.jpg │ │ ├── Image059.jpg │ │ ├── Image060.jpg │ │ ├── Image061.jpg │ │ ├── Image062.jpg │ │ ├── Image063.jpg │ │ ├── Image064.jpg │ │ ├── Image065.jpg │ │ ├── Image066.jpg │ │ ├── Image067.jpg │ │ ├── Image068.jpg │ │ ├── Image069.jpg │ │ ├── Image070.jpg │ │ ├── Image071.jpg │ │ ├── Image072.jpg │ │ ├── Image073.jpg │ │ ├── Image074.jpg │ │ ├── Image075.jpg │ │ ├── Image076.jpg │ │ ├── Image077.jpg │ │ ├── Image078.jpg │ │ ├── Image079.jpg │ │ ├── Image080.jpg │ │ ├── Image081.jpg │ │ ├── Image082.jpg │ │ ├── Image083.jpg │ │ ├── Image084.jpg │ │ ├── Image085.jpg │ │ ├── Image086.jpg │ │ ├── Image087.jpg │ │ ├── Image088.jpg │ │ ├── Image089.jpg │ │ ├── Image090.jpg │ │ ├── Image091.jpg │ │ ├── Image092.jpg │ │ ├── Image093.jpg │ │ ├── Image094.jpg │ │ ├── Image095.jpg │ │ ├── Image096.jpg │ │ ├── Image097.jpg │ │ ├── Image098.jpg │ │ ├── Image099.jpg │ │ ├── Image100.jpg │ │ ├── Image101.jpg │ │ ├── Image102.jpg │ │ ├── Image103.jpg │ │ ├── Image104.jpg │ │ ├── Image105.jpg │ │ ├── Image106.jpg │ │ ├── Image107.jpg │ │ ├── Image108.jpg │ │ ├── Image109.jpg │ │ ├── Image110.jpg │ │ ├── Image111.jpg │ │ ├── Image112.jpg │ │ ├── Image113.jpg │ │ ├── Image114.jpg │ │ ├── Image115.jpg │ │ ├── Image116.jpg │ │ ├── Image117.jpg │ │ ├── Image118.jpg │ │ ├── Image119.jpg │ │ ├── Image120.jpg │ │ ├── Image121.jpg │ │ ├── Image122.jpg │ │ ├── Image123.jpg │ │ ├── Image124.jpg │ │ ├── Image125.jpg │ │ ├── Image126.jpg │ │ ├── Image127.jpg │ │ ├── Image128.jpg │ │ ├── Image129.jpg │ │ ├── Image130.jpg │ │ ├── Image131.jpg │ │ ├── Image132.jpg │ │ ├── Image133.jpg │ │ ├── Image134.jpg │ │ ├── Image135.jpg │ │ ├── Image136.jpg │ │ ├── Image137.jpg │ │ ├── Image138.jpg │ │ ├── Image139.jpg │ │ ├── Image140.jpg │ │ ├── Image141.jpg │ │ ├── Image142.jpg │ │ ├── Image143.jpg │ │ ├── Image144.jpg │ │ ├── Image145.jpg │ │ ├── Image146.jpg │ │ ├── Image147.jpg │ │ ├── Image148.jpg │ │ ├── Image149.jpg │ │ ├── Image150.jpg │ │ ├── Image151.jpg │ │ ├── Image152.jpg │ │ ├── Image153.jpg │ │ ├── Image154.jpg │ │ ├── Image155.jpg │ │ ├── Image156.jpg │ │ ├── Image157.jpg │ │ ├── Image158.jpg │ │ ├── Image159.jpg │ │ ├── Image160.jpg │ │ ├── Image161.jpg │ │ ├── Image162.jpg │ │ ├── Image163.jpg │ │ ├── Image164.jpg │ │ ├── Image165.jpg │ │ ├── Image166.jpg │ │ ├── Image167.jpg │ │ ├── Image168.jpg │ │ ├── Image169.jpg │ │ ├── Image170.jpg │ │ ├── Image171.jpg │ │ ├── Image172.jpg │ │ ├── Image173.jpg │ │ ├── Image174.jpg │ │ ├── Image175.jpg │ │ ├── Image176.jpg │ │ ├── Image177.jpg │ │ ├── Image178.jpg │ │ ├── Image179.jpg │ │ ├── Image180.jpg │ │ ├── Image181.jpg │ │ ├── Image182.jpg │ │ ├── Image183.jpg │ │ ├── Image184.jpg │ │ ├── Image185.jpg │ │ ├── Image186.jpg │ │ ├── Image187.jpg │ │ ├── Image188.jpg │ │ ├── Image189.jpg │ │ ├── Image190.jpg │ │ ├── Image191.jpg │ │ ├── Image192.jpg │ │ ├── Image193.jpg │ │ ├── Image194.jpg │ │ ├── Image195.jpg │ │ ├── Image196.jpg │ │ ├── Image197.jpg │ │ ├── Image198.jpg │ │ └── Image199.jpg │ └── night_right │ │ ├── Image000.jpg │ │ ├── Image001.jpg │ │ ├── Image002.jpg │ │ ├── Image003.jpg │ │ ├── Image004.jpg │ │ ├── Image005.jpg │ │ ├── Image006.jpg │ │ ├── Image007.jpg │ │ ├── Image008.jpg │ │ ├── Image009.jpg │ │ ├── Image010.jpg │ │ ├── Image011.jpg │ │ ├── Image012.jpg │ │ ├── Image013.jpg │ │ ├── Image014.jpg │ │ ├── Image015.jpg │ │ ├── Image016.jpg │ │ ├── Image017.jpg │ │ ├── Image018.jpg │ │ ├── Image019.jpg │ │ ├── Image020.jpg │ │ ├── Image021.jpg │ │ ├── Image022.jpg │ │ ├── Image023.jpg │ │ ├── Image024.jpg │ │ ├── Image025.jpg │ │ ├── Image026.jpg │ │ ├── Image027.jpg │ │ ├── Image028.jpg │ │ ├── Image029.jpg │ │ ├── Image030.jpg │ │ ├── Image031.jpg │ │ ├── Image032.jpg │ │ ├── Image033.jpg │ │ ├── Image034.jpg │ │ ├── Image035.jpg │ │ ├── Image036.jpg │ │ ├── Image037.jpg │ │ ├── Image038.jpg │ │ ├── Image039.jpg │ │ ├── Image040.jpg │ │ ├── Image041.jpg │ │ ├── Image042.jpg │ │ ├── Image043.jpg │ │ ├── Image044.jpg │ │ ├── Image045.jpg │ │ ├── Image046.jpg │ │ ├── Image047.jpg │ │ ├── Image048.jpg │ │ ├── Image049.jpg │ │ ├── Image050.jpg │ │ ├── Image051.jpg │ │ ├── Image052.jpg │ │ ├── Image053.jpg │ │ ├── Image054.jpg │ │ ├── Image055.jpg │ │ ├── Image056.jpg │ │ ├── Image057.jpg │ │ ├── Image058.jpg │ │ ├── Image059.jpg │ │ ├── Image060.jpg │ │ ├── Image061.jpg │ │ ├── Image062.jpg │ │ ├── Image063.jpg │ │ ├── Image064.jpg │ │ ├── Image065.jpg │ │ ├── Image066.jpg │ │ ├── Image067.jpg │ │ ├── Image068.jpg │ │ ├── Image069.jpg │ │ ├── Image070.jpg │ │ ├── Image071.jpg │ │ ├── Image072.jpg │ │ ├── Image073.jpg │ │ ├── Image074.jpg │ │ ├── Image075.jpg │ │ ├── Image076.jpg │ │ ├── Image077.jpg │ │ ├── Image078.jpg │ │ ├── Image079.jpg │ │ ├── Image080.jpg │ │ ├── Image081.jpg │ │ ├── Image082.jpg │ │ ├── Image083.jpg │ │ ├── Image084.jpg │ │ ├── Image085.jpg │ │ ├── Image086.jpg │ │ ├── Image087.jpg │ │ ├── Image088.jpg │ │ ├── Image089.jpg │ │ ├── Image090.jpg │ │ ├── Image091.jpg │ │ ├── Image092.jpg │ │ ├── Image093.jpg │ │ ├── Image094.jpg │ │ ├── Image095.jpg │ │ ├── Image096.jpg │ │ ├── Image097.jpg │ │ ├── Image098.jpg │ │ ├── Image099.jpg │ │ ├── Image100.jpg │ │ ├── Image101.jpg │ │ ├── Image102.jpg │ │ ├── Image103.jpg │ │ ├── Image104.jpg │ │ ├── Image105.jpg │ │ ├── Image106.jpg │ │ ├── Image107.jpg │ │ ├── Image108.jpg │ │ ├── Image109.jpg │ │ ├── Image110.jpg │ │ ├── Image111.jpg │ │ ├── Image112.jpg │ │ ├── Image113.jpg │ │ ├── Image114.jpg │ │ ├── Image115.jpg │ │ ├── Image116.jpg │ │ ├── Image117.jpg │ │ ├── Image118.jpg │ │ ├── Image119.jpg │ │ ├── Image120.jpg │ │ ├── Image121.jpg │ │ ├── Image122.jpg │ │ ├── Image123.jpg │ │ ├── Image124.jpg │ │ ├── Image125.jpg │ │ ├── Image126.jpg │ │ ├── Image127.jpg │ │ ├── Image128.jpg │ │ ├── Image129.jpg │ │ ├── Image130.jpg │ │ ├── Image131.jpg │ │ ├── Image132.jpg │ │ ├── Image133.jpg │ │ ├── Image134.jpg │ │ ├── Image135.jpg │ │ ├── Image136.jpg │ │ ├── Image137.jpg │ │ ├── Image138.jpg │ │ ├── Image139.jpg │ │ ├── Image140.jpg │ │ ├── Image141.jpg │ │ ├── Image142.jpg │ │ ├── Image143.jpg │ │ ├── Image144.jpg │ │ ├── Image145.jpg │ │ ├── Image146.jpg │ │ ├── Image147.jpg │ │ ├── Image148.jpg │ │ ├── Image149.jpg │ │ ├── Image150.jpg │ │ ├── Image151.jpg │ │ ├── Image152.jpg │ │ ├── Image153.jpg │ │ ├── Image154.jpg │ │ ├── Image155.jpg │ │ ├── Image156.jpg │ │ ├── Image157.jpg │ │ ├── Image158.jpg │ │ ├── Image159.jpg │ │ ├── Image160.jpg │ │ ├── Image161.jpg │ │ ├── Image162.jpg │ │ ├── Image163.jpg │ │ ├── Image164.jpg │ │ ├── Image165.jpg │ │ ├── Image166.jpg │ │ ├── Image167.jpg │ │ ├── Image168.jpg │ │ ├── Image169.jpg │ │ ├── Image170.jpg │ │ ├── Image171.jpg │ │ ├── Image172.jpg │ │ ├── Image173.jpg │ │ ├── Image174.jpg │ │ ├── Image175.jpg │ │ ├── Image176.jpg │ │ ├── Image177.jpg │ │ ├── Image178.jpg │ │ ├── Image179.jpg │ │ ├── Image180.jpg │ │ ├── Image181.jpg │ │ ├── Image182.jpg │ │ ├── Image183.jpg │ │ ├── Image184.jpg │ │ ├── Image185.jpg │ │ ├── Image186.jpg │ │ ├── Image187.jpg │ │ ├── Image188.jpg │ │ ├── Image189.jpg │ │ ├── Image190.jpg │ │ ├── Image191.jpg │ │ ├── Image192.jpg │ │ ├── Image193.jpg │ │ ├── Image194.jpg │ │ ├── Image195.jpg │ │ ├── Image196.jpg │ │ ├── Image197.jpg │ │ ├── Image198.jpg │ │ └── Image199.jpg ├── PR_curves │ ├── PR_conv3.png │ ├── PR_conv4.png │ ├── PR_conv5.png │ ├── PR_fc6.png │ ├── PR_fc7.png │ ├── PR_pool1.png │ └── PR_pool2.png ├── README.md ├── distance_matrix_generator.py ├── feature_extractor.py ├── matcher_frame2frame.py └── plot_PR_curves.py ├── README.md ├── loop-detection ├── README.md ├── deep_feature_extractor.py ├── loop_detector.py ├── matcher_visual.py ├── myconf.py ├── plot_gps.py └── similarity_matrix_generator.py └── place-recognition ├── GPX.py ├── deep_feature_extractor.py ├── deep_feature_lm.py ├── matcher_visual.py ├── myconf.py └── similarity_matrix_generator.py /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "caffe"] 2 | path = caffe 3 | url = https://github.com/aghagol/caffe.git 4 | -------------------------------------------------------------------------------- /PR_bench/GardensPointWalking/day_left/Image000.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aghagol/loop-detection/2efbb768787235a4a020d6b27ca0a1761beb515d/PR_bench/GardensPointWalking/day_left/Image000.jpg -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | To get the PR curves shown in the paper [On the performance of convnet features for place recognition](https://arxiv.org/abs/1501.04158), download the [Gardens Point dataset](https://wiki.qut.edu.au/display/cyphy/Day+and+Night+with+Lateral+Pose+Change+Datasets) and run the python scripts in the following order: 2 | 3 | - `feature_extractor.py` 4 | - `distance_matrix_generator.py` 5 | - `plot_PR_curves.py` 6 | -------------------------------------------------------------------------------- /PR_bench/distance_matrix_generator.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | plt.rcParams['figure.figsize'] = (10, 10) 5 | plt.rcParams['image.interpolation'] = 'nearest' 6 | 7 | # layers = ['data','pool1','pool2','conv3','conv4','conv5','fc6','fc7'] 8 | layers = ['data'] 9 | for layer in layers: 10 | 11 | with open('net_day_left_'+layer) as fr: 12 | Xdl = pickle.load(fr).reshape((200,-1)) 13 | 14 | with open('net_day_right_'+layer) as fr: 15 | Xdr = pickle.load(fr).reshape((200,-1)) 16 | 17 | with open('net_night_right_'+layer) as fr: 18 | Xnr = pickle.load(fr).reshape((200,-1)) 19 | 20 | Xdl /= np.sqrt(np.sum(np.power(Xdl,2),axis=1)).reshape(200,1) 21 | Xdr /= np.sqrt(np.sum(np.power(Xdr,2),axis=1)).reshape(200,1) 22 | Xnr /= np.sqrt(np.sum(np.power(Xnr,2),axis=1)).reshape(200,1) 23 | 24 | D_nr_dr = Xnr.dot(Xdr.T) 25 | with open('D_nr_dr_'+layer, 'w') as fw: 26 | pickle.dump(D_nr_dr,fw) 27 | # plt.imshow(D_nr_dr) 28 | # plt.colorbar() 29 | # plt.title('appearance changes (night right vs day right)') 30 | # plt.savefig('conv3_cosine_nr_dr.png', bbox_inches='tight') 31 | # plt.show() 32 | 33 | D_dl_dr = Xdl.dot(Xdr.T) 34 | with open('D_dl_dr_'+layer, 'w') as fw: 35 | pickle.dump(D_dl_dr,fw) 36 | # plt.imshow(D_dl_dr) 37 | # plt.colorbar() 38 | # plt.title('viewpoint changes (day left vs day right)') 39 | # plt.savefig('conv3_cosine_dl_dr.png', bbox_inches='tight') 40 | # plt.show() 41 | 42 | D_nr_dl = Xnr.dot(Xdl.T) 43 | with open('D_nr_dl_'+layer, 'w') as fw: 44 | pickle.dump(D_nr_dl,fw) 45 | # plt.imshow(D_nr_dl) 46 | # plt.colorbar() 47 | # plt.title('appearance+view changes (night right vs day left)') 48 | # plt.savefig('conv3_cosine_nr_dl.png', bbox_inches='tight') 49 | # plt.show() 50 | 51 | 52 | 53 | -------------------------------------------------------------------------------- /PR_bench/feature_extractor.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os, sys, pickle 3 | import matplotlib.pyplot as plt 4 | plt.rcParams['image.interpolation'] = 'nearest' 5 | plt.rcParams['image.cmap'] = 'gray' 6 | os.environ['GLOG_minloglevel'] = '2' 7 | import caffe 8 | caffe_root = '/home/mo/caffe/' 9 | caffe.set_mode_gpu() 10 | 11 | # dataset = 'day_left' 12 | dataset = sys.argv[1] 13 | prepath = 'data/'+dataset+'/' 14 | images = [f for f in os.listdir(prepath) if f.endswith('.jpg')] 15 | images.sort() 16 | 17 | """ load net weights and allocate memory """ 18 | model_def = '/home/mo/caffe/models/places/places205CNN_deploy.prototxt' 19 | W = '/home/mo/caffe/models/places/places205CNN_iter_300000.caffemodel' 20 | net = caffe.Net(model_def, W, caffe.TEST) 21 | net.blobs['data'].reshape(len(images), 3, 227, 227) 22 | 23 | """ pre-processing for input """ 24 | blob = caffe.proto.caffe_pb2.BlobProto() 25 | mu_f = open(caffe_root+'models/places/places205CNN_mean.binaryproto','rb') 26 | blob.ParseFromString(mu_f.read()) 27 | mu = np.array(caffe.io.blobproto_to_array(blob))[0] 28 | BGRmean = mu.mean(1).mean(1) 29 | transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) 30 | transformer.set_transpose('data', (2,0,1)) 31 | transformer.set_mean('data', BGRmean) 32 | transformer.set_raw_scale('data', 255) 33 | transformer.set_channel_swap('data', (2,1,0)) 34 | 35 | """ forward step """ 36 | for i in range(len(images)): 37 | image = caffe.io.load_image(prepath+images[i]) 38 | net.blobs['data'].data[i] = transformer.preprocess('data', image) 39 | print "finished loading the net... moving forward" 40 | output = net.forward() 41 | print "writing features to output file..." 42 | 43 | """ write stuff """ 44 | with open('net_'+dataset+'_data','w') as fw: 45 | pickle.dump(net.blobs['data'].data,fw) 46 | 47 | # with open('net_'+dataset+'_pool1','w') as fw: 48 | # pickle.dump(net.blobs['pool1'].data,fw) 49 | 50 | # with open('net_'+dataset+'_pool2','w') as fw: 51 | # pickle.dump(net.blobs['pool2'].data,fw) 52 | 53 | # with open('net_'+dataset+'_conv3','w') as fw: 54 | # pickle.dump(net.blobs['conv3'].data,fw) 55 | 56 | # with open('net_'+dataset+'_conv4','w') as fw: 57 | # pickle.dump(net.blobs['conv4'].data,fw) 58 | 59 | # with open('net_'+dataset+'_conv5','w') as fw: 60 | # pickle.dump(net.blobs['conv5'].data,fw) 61 | 62 | # with open('net_'+dataset+'_fc6','w') as fw: 63 | # pickle.dump(net.blobs['fc6'].data,fw) 64 | 65 | # with open('net_'+dataset+'_fc7','w') as fw: 66 | # pickle.dump(net.blobs['fc7'].data,fw) 67 | -------------------------------------------------------------------------------- /PR_bench/matcher_frame2frame.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | plt.rcParams['figure.figsize'] = (10, 10) 5 | plt.rcParams['image.interpolation'] = 'nearest' 6 | 7 | with open('D_nr_dr') as f: 8 | D_nr_dr = pickle.load(f) 9 | 10 | with open('D_dl_dr') as f: 11 | D_dl_dr = pickle.load(f) 12 | 13 | with open('D_nr_dl') as f: 14 | D_nr_dl = pickle.load(f) 15 | 16 | with open('matching_with_appearance_changes.txt','w') as f: 17 | f.write('night right --> day right\n') 18 | for row in range(D_nr_dr.shape[0]): 19 | col = np.argsort(D_nr_dr[row])[-1] 20 | if row in np.argsort(D_nr_dr.T[col])[-3:]: 21 | f.write('frame %03d --> frame %03d\n' % (row,col)) 22 | else: 23 | f.write('frame %03d --> no match\n' % (row)) 24 | 25 | with open('matching_with_view_changes.txt','w') as f: 26 | f.write('day left --> day right\n') 27 | for row in range(D_dl_dr.shape[0]): 28 | col = np.argsort(D_dl_dr[row])[-1] 29 | if row in np.argsort(D_dl_dr.T[col])[-3:]: 30 | f.write('frame %03d --> frame %03d\n' % (row,col)) 31 | else: 32 | f.write('frame %03d --> no match\n' % (row)) 33 | 34 | with open('matching_with_appearance_and_view_changes.txt','w') as f: 35 | f.write('night right --> day left\n') 36 | for row in range(D_nr_dl.shape[0]): 37 | col = np.argsort(D_nr_dl[row])[-1] 38 | if row in np.argsort(D_nr_dl.T[col])[-3:]: 39 | f.write('frame %03d --> frame %03d\n' % (row,col)) 40 | else: 41 | f.write('frame %03d --> no match\n' % (row)) 42 | -------------------------------------------------------------------------------- /PR_bench/plot_PR_curves.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | plt.rcParams['figure.figsize'] = (10, 10) 5 | plt.rcParams['image.interpolation'] = 'nearest' 6 | 7 | e = 3 #true-positive region: +/-e frames 8 | r_range = np.arange(1,2,.001) #threshold for PR curve 9 | 10 | # layers = ['data','pool1','pool2','conv3','conv4','conv5','fc6','fc7'] 11 | layers = ['data'] 12 | for layer in layers: 13 | 14 | with open('D_nr_dr_'+layer) as f: 15 | D_nr_dr = pickle.load(f) 16 | 17 | with open('D_dl_dr_'+layer) as f: 18 | D_dl_dr = pickle.load(f) 19 | 20 | with open('D_nr_dl_'+layer) as f: 21 | D_nr_dl = pickle.load(f) 22 | 23 | n = D_nr_dr.shape[0] 24 | AUC = [] 25 | for D in [D_nr_dr,D_dl_dr,D_nr_dl]: 26 | TP = np.zeros_like(r_range) 27 | FP = np.zeros_like(r_range) 28 | FN = np.zeros_like(r_range) 29 | for i in range(len(r_range)): 30 | r = r_range[i] 31 | for row in range(n): 32 | col2, col1 = np.argsort(D[row])[-2:] 33 | if ( D[row,col1] / D[row,col2] ) >= r: 34 | if (row <= col1+e) and (row >= col1-e): 35 | TP[i] +=1 36 | else: 37 | FP[i] +=1 38 | else: 39 | FN[i] +=1 40 | IX = (TP+FP) > 0 #retain only the valid indices 41 | R = TP[IX] / ( TP[IX] + FN[IX] ) 42 | P = TP[IX] / ( TP[IX] + FP[IX] ) 43 | AUC.append(np.trapz(P[::-1],R[::-1])) 44 | plt.plot(R, P) 45 | # F1 = 2 * (P*R) / (P+R) 46 | 47 | labels = [ 'AUC=%.3f (night-right vs day-right)' % AUC[0], 48 | 'AUC=%.3f (day-left vs day-right)' % AUC[1], 49 | 'AUC=%.3f (night-right vs day-left)' % AUC[2] ] 50 | 51 | p = np.arange(.001,1,.001) 52 | for f in np.arange(.1,1,.1): 53 | r = np.array([f*v/(2*v-f) if 2*v!=f else -1 for v in p]) 54 | p_cut = p[np.logical_and(r>=0,r<=1)] 55 | r_cut = r[np.logical_and(r>=0,r<=1)] 56 | plt.plot(r_cut, p_cut, "--", color='gray') 57 | plt.annotate(r"$F_1=%.1f$" % f, xy=(r_cut[0], p_cut[0]), 58 | xytext=(.9, p_cut[0]), size="small", color="gray") 59 | 60 | plt.legend(labels, loc='lower left') 61 | plt.xlim([-0.01,1.01]) 62 | plt.xlabel('Recall') 63 | plt.ylim([-0.01,1.01]) 64 | plt.ylabel('Precision') 65 | plt.title('Precision-Recall curve for layer: {0}'.format(layer)) 66 | plt.savefig('PR_'+layer+'.png', bbox_inches='tight') 67 | plt.show() 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | # plt.plot(FP.flatten()/n,TP.flatten()/n) 84 | # plt.xlim([-0.01,1.01]) 85 | # plt.xlabel('False Positive') 86 | # plt.ylim([-0.01,1.01]) 87 | # plt.ylabel('True Positive') 88 | # plt.title('ROC curve') 89 | # plt.show() 90 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Loop detection using deep net features 2 | [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) feature vectors for loop closing in SLAM 3 | 4 | It has been proposed that feature vectors from AlexNet are robust with respect to appearance changes: 5 | 6 | N. Sunderhauf, F. Dayoub, S. Shirazi, B. Upcroft, and M. Milford, [On the performance of convnet features for place recognition](https://arxiv.org/abs/1501.04158), arXiv preprint arXiv:1501.04158, 2015. 7 | 8 | Look in the folder `PR_bench` for our implementation of the above paper over the [Gardens Point dataset](https://wiki.qut.edu.au/display/raq/Day+and+Night+with+Lateral+Pose+Change+Datasets). 9 | 10 | #### Demo: 11 | 12 | We use feature vectors from the `conv3` layer of AlexNet to match each frame of the testing video with a frame in the reference video that is captured on a different day. Videos were captured using a Google Nexus 5 phone. GPS is recorded simultanously using a dashcam Andriod app. I put a cheap ($5) fisheye lens on Nexus 5 to increase its field of view. 13 | 14 | - Matching video frames from a cloudy day to video frames of a sunny day and predicting the location. On the map, big blue circle corresponds to the GPS location that is recorded over each test video frame (ground-truth) and the green star corresponds to the GPS location of the reference video frame (prediction). 15 | 16 |

17 | IMAGE ALT TEXT HERE 18 |

19 | -------------------------------------------------------------------------------- /loop-detection/README.md: -------------------------------------------------------------------------------- 1 | Python scripts must be run in the following order: 2 | 3 | - `deep_feature_eextractor.py` 4 | - `similarity_matrix_generator.py` 5 | - `matcher_visual` or `loop_detector.py` 6 | -------------------------------------------------------------------------------- /loop-detection/deep_feature_extractor.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from PIL import Image 3 | import os, sys, pickle, re, time 4 | import matplotlib.pyplot as plt 5 | os.environ['GLOG_minloglevel'] = '2' 6 | caffe_root = '/home/mo/caffe/' 7 | sys.path.append(caffe_root+'python') 8 | import caffe 9 | caffe.set_mode_gpu() 10 | from myconf import * 11 | 12 | def bboxes(imsize): 13 | bbs = [] 14 | # global scales 15 | for scale in scales: 16 | rows_l = (0,int(scale*imsize[0])) 17 | rows_c = (int((1-scale)*imsize[0]/2),int((1+scale)*imsize[0]/2)) 18 | rows_r = (int((1-scale)*imsize[0]),imsize[0]) 19 | cols_l = (0,int(scale*imsize[1])) 20 | cols_c = (int((1-scale)*imsize[1]/2),int((1+scale)*imsize[1]/2)) 21 | cols_r = (int((1-scale)*imsize[1]),imsize[1]) 22 | bbs.append( ( rows_l , cols_l ) ) 23 | bbs.append( ( rows_l , cols_c ) ) 24 | bbs.append( ( rows_l , cols_r ) ) 25 | bbs.append( ( rows_c , cols_l ) ) 26 | bbs.append( ( rows_c , cols_c ) ) 27 | bbs.append( ( rows_c , cols_r ) ) 28 | bbs.append( ( rows_r , cols_l ) ) 29 | bbs.append( ( rows_r , cols_c ) ) 30 | bbs.append( ( rows_r , cols_r ) ) 31 | bbs.append( ( (0,imsize[0]) , (0,imsize[1]) ) ) 32 | return bbs 33 | 34 | if not os.path.exists(featpath): os.mkdir(featpath) 35 | # layers = ['data','pool1','pool2','conv3','conv4','conv5','fc6','fc7'] 36 | layers = ['conv3','fc6'] 37 | K = len(bboxes((100,100))) #number of patches per image 38 | batch_size = K /K 39 | 40 | images = [f for f in os.listdir(campath) if f.endswith('.png')] 41 | images.sort() 42 | N = len(images) 43 | 44 | """ load net weights and allocate memory """ 45 | model_def = '/home/mo/caffe/models/places/places205CNN_deploy.prototxt' 46 | W = '/home/mo/caffe/models/places/places205CNN_iter_300000.caffemodel' 47 | net = caffe.Net(model_def, W, caffe.TEST) 48 | net.blobs['data'].reshape(batch_size*K, 3, 227, 227) 49 | 50 | """ pre-processing for input """ 51 | blob = caffe.proto.caffe_pb2.BlobProto() 52 | mu_f = open(caffe_root+'models/places/places205CNN_mean.binaryproto','rb') 53 | blob.ParseFromString(mu_f.read()) 54 | 55 | """ forward step """ 56 | for i in range(N / batch_size + 1): 57 | for j in range(batch_size): 58 | n = batch_size*i + j 59 | if (n>=N): break 60 | print 'CPU: working on %s (%.2f%%)'%(images[n],(n+1.)/N*100) 61 | image = Image.open(campath+images[n]) 62 | bbs = bboxes(image.size[::-1]) 63 | with open(featpath+images[n][:-4]+'.meta','wb') as fbb: 64 | pickle.dump(bbs,fbb) 65 | for k, bb in enumerate(bbs): 66 | print '...patch number %d'%(k+j*K) 67 | patch = image.crop((bb[1][0],bb[0][0],bb[1][1],bb[0][1])) 68 | patch = patch.resize((227, 227), Image.ANTIALIAS) 69 | patch = np.asarray(patch) 70 | patch = patch-patch.mean() 71 | patch = patch.reshape(1,227,227) 72 | patch = np.concatenate((patch,)*3,axis=0) 73 | net.blobs['data'].data[k+j*K] = patch 74 | print 'Titan enters' 75 | t1 = time.time() 76 | output = net.forward() 77 | t2 = time.time() 78 | print 'Titan leaves (%f ms / patch)' %((t2-t1)*1000/batch_size/K) 79 | print 'CPU: writing to disk' 80 | for j in range(batch_size): 81 | n = batch_size*i + j 82 | if (n>=N): break 83 | for layer in layers: 84 | with open(featpath+images[n][:-4]+'.'+layer,'wb') as fw: 85 | pickle.dump(net.blobs[layer].data[range(j*K,j*K+K)],fw) 86 | -------------------------------------------------------------------------------- /loop-detection/myconf.py: -------------------------------------------------------------------------------- 1 | #path to landmarks directory 2 | lmpath = '../Soonhac/left/' 3 | 4 | #path to background map directory 5 | mappath = '../localize/' 6 | 7 | #path to camera data directory 8 | campath = '../Soonhac/left/' 9 | 10 | #path to features directory for test data 11 | featpath = './features/' 12 | 13 | #path to features directory for landmarks 14 | lmfeatpath = './' 15 | 16 | #output directory 17 | outpath = './out/' 18 | 19 | # #fish-eye crop ( ( row1 , row2 ) , ( col1 , col2 ) ) 20 | febb_stl = ( ( 250 , 2250 ) , ( 600 , 2850 ) ) 21 | febb_vid = ( ( 50 , 450 ) , ( 100 , 550 ) ) 22 | 23 | #scales at which patches are extracted 24 | scales = [.8] 25 | -------------------------------------------------------------------------------- /loop-detection/plot_gps.py: -------------------------------------------------------------------------------- 1 | import os 2 | import matplotlib.pyplot as plt 3 | 4 | def make_tree(C,T, parent_depth): 5 | if not C: return (T,C) 6 | elif C[0]['depth'] <= parent_depth: 7 | return (T,C) 8 | elif len(C)==1: #last entry 9 | T[C[0]['key']] = C[0]['val'] 10 | return (T,C[1:]) 11 | elif C[0]['depth'] > C[1]['depth']: #step out 12 | T[C[0]['key']] = C[0]['val'] 13 | return (T,C[1:]) 14 | elif C[0]['depth'] == C[1]['depth']: #stay 15 | T[C[0]['key']] = C[0]['val'] 16 | return make_tree(C[1:],T, parent_depth) 17 | elif C[0]['depth'] < C[1]['depth']: #step in 18 | T[C[0]['key']],C = make_tree(C[1:],{},C[0]['depth']) 19 | T,C = make_tree(C,T, parent_depth) 20 | return (T,C) 21 | 22 | datapath = '../Soonhac/left' 23 | gps_files = [f for f in os.listdir(datapath) if f.endswith('.gps')] 24 | gps_files.sort() 25 | 26 | T = {} 27 | for gpsf in gps_files: 28 | C = [] 29 | with open(datapath+'/'+gpsf) as f: 30 | for ln in f: 31 | s = dict(zip(['key','val'],[i.strip() for i in ln.split(':')])) 32 | s['depth'] = len(ln)-len(ln.lstrip()) 33 | C.append(s) 34 | T[gpsf[:-4]] = make_tree(C,{},-1)[0] 35 | 36 | fig, ax = plt.subplots(1,1) 37 | ax.set_xlim([-20,70]) 38 | ax.set_ylim([-20,70]) 39 | for gpsf in gps_files: 40 | x = float(T[gpsf[:-4]]['pose']['pose']['position']['x']) 41 | y = float(T[gpsf[:-4]]['pose']['pose']['position']['y']) 42 | ax.plot(x,y,'go') 43 | plt.pause(.1) 44 | 45 | 46 | -------------------------------------------------------------------------------- /loop-detection/similarity_matrix_generator.py: -------------------------------------------------------------------------------- 1 | import pickle, os 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from myconf import * 5 | 6 | layers = ['fc6'] 7 | 8 | images = [f for f in os.listdir(campath) if f.endswith('.png')] 9 | images.sort() 10 | N = len(images) 11 | 12 | for layer in layers: 13 | with open(featpath+images[0][:-4]+'.'+layer,'rb') as fr: 14 | X = pickle.load(fr) 15 | X = X.reshape((X.shape[0],-1)).T 16 | X /= np.sqrt(np.power(X,2).sum(axis=0)) 17 | for i in range(len(images)): 18 | print 'working on image %s (%2.2f%%)' %(images[i],i*100./len(images)) 19 | pd = [] 20 | with open(featpath+images[i][:-4]+'.'+layer,'rb') as fr: 21 | Y = pickle.load(fr) 22 | Y = Y.reshape((Y.shape[0],-1)).T 23 | Y /= np.sqrt(np.power(Y,2).sum(axis=0)) 24 | # for j in range(i): 25 | # with open(featpath+images[j][:-4]+'.'+layer,'rb') as fr: 26 | # X = pickle.load(fr) 27 | # X = X.reshape((X.shape[0],-1)).T 28 | # X /= np.sqrt(np.power(X,2).sum(axis=0)) 29 | D = (Y.T).dot(X) 30 | # pd.append((D.max(),D.argmax())) 31 | with open(featpath+images[i][:-4]+'.'+layer+'.D','wb') as fw: 32 | pickle.dump(D,fw) 33 | -------------------------------------------------------------------------------- /place-recognition/GPX.py: -------------------------------------------------------------------------------- 1 | class point: 2 | """Holds points generated by the GPX logger of dailyroads""" 3 | def __init__(self,idx): 4 | self.index = idx 5 | def set_date(self,date): 6 | self.date = date 7 | def set_time(self,t): 8 | h,m,s = t.split(':') 9 | self.h = int(h) 10 | self.m = int(m) 11 | self.s = int(s) 12 | self.t = t 13 | def set_latitude(self,lat): 14 | self.lat = lat 15 | def set_longitude(self,lon): 16 | self.lon = lon 17 | def set_altitude(self,alt): 18 | self.alt = alt 19 | def set_speed(self,spd): 20 | self.spd = spd 21 | def get_time(self): 22 | return self.h * 3600 + self.m * 60 + self.s 23 | 24 | def read_from_srt(filename): 25 | gps = [] 26 | with open(filename) as myfile: 27 | for line in myfile: 28 | if len(line.split())==0: 29 | continue 30 | if line.rstrip().isdigit(): 31 | gp = point(int(line)) 32 | continue 33 | if line.split(':')[0].isdigit(): 34 | continue 35 | if line.split()[-1]=='m': 36 | ss = line.split() 37 | if ss[0].isdigit(): 38 | gp.set_speed(float(ss[0])) 39 | gp.set_date(ss[2]) 40 | gp.set_time(ss[3]) 41 | if ss[4].isdigit(): 42 | gp.set_altitude(float(ss[4])) 43 | continue 44 | if line.split()[0]=='lat:': 45 | ss = line.split() 46 | gp.set_latitude(float(ss[1])) 47 | gp.set_longitude(float(ss[3])) 48 | gps.append(gp) 49 | return gps -------------------------------------------------------------------------------- /place-recognition/myconf.py: -------------------------------------------------------------------------------- 1 | #path to landmarks directory 2 | lmpath = '../cam/vid_3_28/' 3 | 4 | #path to background map directory 5 | mappath = '../localize/' 6 | 7 | #path to camera data directory 8 | campath = '../cam/still_3_28/' 9 | 10 | #path to features directory for test data 11 | featpath = './features/' 12 | 13 | #path to features directory for landmarks 14 | lmfeatpath = './' 15 | 16 | #output directory 17 | outpath = './out/' 18 | 19 | #fish-eye crop ( ( row1 , row2 ) , ( col1 , col2 ) ) 20 | febb_stl = ( ( 250 , 2250 ) , ( 600 , 2850 ) ) 21 | febb_vid = ( ( 50 , 450 ) , ( 100 , 550 ) ) 22 | 23 | #scales at which patches are extracted 24 | scales = [.5, .65, .8, .9] 25 | -------------------------------------------------------------------------------- /place-recognition/similarity_matrix_generator.py: -------------------------------------------------------------------------------- 1 | import pickle, os 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | plt.rcParams['figure.figsize'] = (10, 10) 5 | plt.rcParams['image.interpolation'] = 'nearest' 6 | from myconf import * 7 | 8 | camsub = 'up' #subpath for lm data 9 | # layers = ['data','pool1','pool2','conv3','conv4','conv5','fc6','fc7'] 10 | layers = ['conv3'] 11 | 12 | images = [f for f in os.listdir(campath) if f.endswith('.jpg')] 13 | images.sort() 14 | N = len(images) 15 | 16 | for layer in layers: 17 | 18 | with open(lmfeatpath+'lmfeat.'+camsub+'.'+layer) as fr: 19 | X = pickle.load(fr) 20 | X = X.reshape((X.shape[0],-1)).T 21 | X /= np.sqrt(np.power(X,2).sum(axis=0)) 22 | 23 | for image in images: 24 | with open(featpath+image[:-4]+'.'+layer,'rb') as fr: 25 | Y = pickle.load(fr) 26 | Y = Y.reshape((Y.shape[0],-1)).T 27 | Y /= np.sqrt(np.power(Y,2).sum(axis=0)) 28 | D = (Y.T).dot(X) 29 | with open(featpath+image[:-4]+'.'+layer+'.'+camsub,'wb') as fw: 30 | pickle.dump(D,fw) 31 | --------------------------------------------------------------------------------