|
总结:SSD预测输出后的解码过程:
confidence_thresh=0.6,
iou_threshold=0.45,
top_k=100,
1、拿到模型预测后输出的矩阵
2、一系列变换:边界框boxes坐标由相对值变为绝对值,坐标形式centroids2corners,反归一化
3、一张图片一张图片的处理 一种类别一种类别的处理
4、同一个类别中,预测概率值经过给定置信度阈值的过滤后取概率最大的box
5、用剩下的box分别与概率最大的box计算iou值
6、小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)
7、循环往复,直到剩下的box个数为0,最终NMS我们需要的结果是:每一次的选出的概率最大的box
8、在0维上加上对应的类别id,即可得到所有检测到的物体的类别id和定位坐标box
9、如果数量比较多,则需要经过top-k过滤
- 原始预测值 y_pred: (6, 17538, 18)
- 维度截断前10,预测boxes的相对值变绝对值,坐标(centroids)中心点变成角形式(xmin,ymin,xmax,ymax),反归一化变成实际像素值 shape= (6, 17538, 10)
- 正在处理的图片编号:0
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(11, 5)
- threshold_met: [[ 0.96580559 33.61340046 150.98859072 136.08298302 210.48692465]
- [ 0.97073776 35.04751325 153.48874927 141.03665829 205.3978622 ]
- [ 0.95259297 39.73779559 149.55267012 138.34213257 206.87343478]
- [ 0.89510643 39.29663658 151.50745511 132.8565073 211.78327203]
- [ 0.94443005 37.43476868 151.41758323 134.86936569 205.93265891]
- [ 0.79319638 40.46634436 147.42987156 128.49414825 202.48596668]
- [ 0.96809524 27.92355537 155.43519258 136.28113747 214.9014473 ]
- [ 0.98907334 24.69376087 150.93723536 132.63394833 204.56310511]
- [ 0.95623392 22.58502245 147.32278883 131.53581619 208.34618211]
- [ 0.9804498 22.16494203 152.472049 132.7502346 209.70026851]
- [ 0.94763917 29.06545758 149.00491834 134.20828342 210.49881577]]
- 先得到同一个类别中,概率值最大的box: [ 0.98907334 24.69376087 150.93723536 132.63394833 204.56310511]
- 用10个剩下的box分别与概率最大的box计算iou值:similarities.shape=(10,)
- iou的具体值(mode=element-wise): [0.8062058 0.79038136 0.77129147 0.7647146 0.83756321 0.74423871
- 0.72578245 0.85420101 0.86579716 0.82893815]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (1, 5) [[ 0.98907334 24.69376087 150.93723536 132.63394833 204.56310511]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 1. 0.98907334 24.69376087 150.93723536 132.63394833
- 204.56310511]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(1, 5)
- threshold_met: [[ 0.61496943 215.41189671 111.31170094 378.15725327 217.35234261]]
- 先得到同一个类别中,概率值最大的box: [ 0.61496943 215.41189671 111.31170094 378.15725327 217.35234261]
- maxima(经过NMS后的结果): (1, 5) [[ 0.61496943 215.41189671 111.31170094 378.15725327 217.35234261]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 2. 0.61496943 215.41189671 111.31170094 378.15725327
- 217.35234261]]
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (2, 6) [[ 1. 0.98907334 24.69376087 150.93723536 132.63394833
- 204.56310511]
- [ 2. 0.61496943 215.41189671 111.31170094 378.15725327
- 217.35234261]]
- 正在处理的图片编号:1
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(77, 5)
- threshold_met: [[ 0.72249418 219.75242615 153.1521678 241.30391121 172.38067389]
- [ 0.63175714 224.0515995 153.88339162 247.74650574 172.52631783]
- [ 0.78431815 49.97917414 156.60817623 95.96534014 191.85233116]
- [ 0.97783732 52.20789671 158.34558606 100.47534227 190.98113179]
- [ 0.66519636 60.76698303 157.64154196 105.52818775 185.77827215]
- [ 0.83306515 75.56817055 156.78489804 113.13670635 181.3839376 ]
- [ 0.76062477 82.64760017 158.90575647 114.19803143 183.16969872]
- [ 0.9681372 81.52966976 158.95491242 118.49089622 181.38511777]
- [ 0.72419327 75.96833467 157.8263998 119.00913477 182.48233795]
- [ 0.88565707 87.54989862 158.64579678 124.78005409 181.21948242]
- [ 0.76149821 85.91345787 157.34673142 129.07217503 181.88368678]
- [ 0.91055995 105.39467812 159.93198752 130.98977566 179.53603864]
- [ 0.93923956 101.22835636 158.29587579 136.55478001 178.74550223]
- [ 0.77576023 97.51484871 157.45096207 137.86335468 177.55844593]
- [ 0.9875946 113.33487511 158.25378299 137.9722023 176.57663226]
- [ 0.99766099 112.83602715 157.94686675 139.12671089 176.06701255]
- [ 0.98388278 108.79235744 156.75501823 139.50529575 176.702106 ]
- [ 0.61978006 124.07536983 158.51563811 138.35942745 175.7309854 ]
- [ 0.99592662 121.07721806 158.33714604 142.34191418 174.59809184]
- [ 0.99863428 119.32604313 158.375144 143.32727909 174.77560043]
- [ 0.98871082 118.16326618 156.49112463 145.52780628 174.63476658]
- [ 0.96136111 130.29853821 158.1648767 150.66467285 172.93472886]
- [ 0.9956851 129.40013409 157.70614743 152.63318539 173.20198417]
- [ 0.97767442 127.52254486 156.90146685 153.92612457 172.85417318]
- [ 0.84387225 136.15815639 158.35917592 153.13718319 171.19165063]
- [ 0.89749861 133.18143368 158.85887146 153.54702473 171.89326286]
- [ 0.70893997 133.25424671 158.37911367 154.75643635 171.28146887]
- [ 0.61332923 218.48110199 154.73957062 240.66309929 179.40852642]
- [ 0.9830699 221.70513153 156.12831116 242.03518867 175.49525499]
- [ 0.90813005 222.41485119 155.08598685 244.20275688 174.52545762]
- [ 0.62235928 226.32760048 156.82033896 245.16139984 179.26374078]
- [ 0.98147362 224.30598736 157.73985386 244.48144913 176.41382217]
- [ 0.74661607 224.83150005 155.18934131 250.34960747 178.35654616]
- [ 0.9671545 222.86640644 155.90136051 245.54406166 177.41868496]
- [ 0.61027837 234.94965076 156.99382424 256.05279922 185.51986814]
- [ 0.81136084 232.95317173 157.17038512 260.53679466 184.45406556]
- [ 0.8977809 237.0672369 156.65000081 269.07259941 187.75847554]
- [ 0.6113261 52.17610359 157.48654604 88.91575813 194.47270632]
- [ 0.69243836 53.53913784 160.01324058 94.62131023 190.34877419]
- [ 0.99926549 50.32630205 160.6002152 99.16625261 190.06789327]
- [ 0.63045138 57.20763803 161.08161807 93.30059052 188.86508346]
- [ 0.94717294 46.96068048 159.42901969 104.41734552 191.21856093]
- [ 0.99991632 48.14987183 162.33640909 99.23004627 189.70298767]
- [ 0.9107694 48.7369895 160.88706851 100.56310415 190.16273618]
- [ 0.66317242 47.47363329 156.22842908 105.59125185 189.37366605]
- [ 0.98607868 50.74757695 159.10068154 98.67894173 185.75114608]
- [ 0.82275349 66.52825356 157.45414495 107.58130074 182.43995905]
- [ 0.83345973 79.20886517 157.92124271 117.09488869 180.25091887]
- [ 0.65916258 85.87562799 158.82571936 123.02241325 181.4479351 ]
- [ 0.68752068 233.41815948 155.51054478 261.57938004 188.57127428]
- [ 0.88525099 235.58012009 156.85658455 264.50099945 190.96859694]
- [ 0.78732401 244.17614937 157.38059878 275.00201225 193.05340648]
- [ 0.98235148 50.27100563 160.94330549 95.57601929 192.66947508]
- [ 0.99753368 49.24220324 159.10289884 93.05137396 187.49179244]
- [ 0.66598719 43.60121369 159.54807401 96.89675331 191.14750028]
- [ 0.99362344 53.05385113 162.62837648 100.8002615 189.69715834]
- [ 0.99742162 53.54989529 165.17379284 103.67035389 189.95858431]
- [ 0.98410797 53.69304657 163.722682 101.04702473 190.5049324 ]
- [ 0.85896868 58.69515181 159.42880511 101.96716547 185.66706777]
- [ 0.99778509 49.81727958 162.8572762 97.03897476 186.05598807]
- [ 0.99320203 46.62781477 162.64715195 91.62993193 188.89600039]
- [ 0.91097564 242.31081963 153.76625061 287.04068184 194.19704676]
- [ 0.67423779 242.34140396 153.26884389 287.0425415 195.27474046]
- [ 0.79372537 252.5521946 150.29686689 302.56052971 202.82721519]
- [ 0.67557091 251.07719421 152.27222443 301.91019058 198.49802256]
- [ 0.60347539 262.34584808 140.18645883 330.25497437 212.12155223]
- [ 0.93310475 278.59125137 133.46484303 406.14732742 240.08486867]
- [ 0.88905698 285.05822182 125.94563663 414.32135582 232.7352047 ]
- [ 0.94346797 273.73829842 140.49496651 405.59214592 228.25380564]
- [ 0.70535594 282.78608322 144.89312768 411.64209366 227.63848901]
- [ 0.65507102 287.64707565 141.68227315 432.68651962 241.3729012 ]
- [ 0.88083708 285.42766571 138.64716589 441.9786644 246.06478214]
- [ 0.85064954 283.10666084 145.36702037 434.47663307 231.66444898]
- [ 0.73932117 291.0688591 145.39954662 454.62867737 230.73506355]
- [ 0.69014585 296.09524727 130.34936786 417.02490807 237.44975924]
- [ 0.86166078 302.17955589 138.09015155 450.4754734 251.24793649]
- [ 0.71307981 311.71751976 139.75530267 475.39721489 269.67009902]]
- 先得到同一个类别中,概率值最大的box: [ 0.99991632 48.14987183 162.33640909 99.23004627 189.70298767]
- 用76个剩下的box分别与概率最大的box计算iou值:similarities.shape=(76,)
- iou的具体值(mode=element-wise): [0. 0. 0.71498715 0.76311688 0.51356002 0.24084369
- 0.19002992 0.17841835 0.235436 0.10930819 0.11850432 0.
- 0. 0.01195957 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0.5741067 0.73972468 0.8907537 0.66346473 0.76532905
- 0.90120122 0.71073877 0.72267417 0.37222167 0.19025522 0.12870508
- 0. 0. 0. 0.77713582 0.71581866 0.7631944
- 0.86777219 0.73735489 0.79771335 0.59569531 0.78366882 0.79376011
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (56, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.99863428 119.32604313 158.375144 143.32727909 174.77560043]
- 用55个剩下的box分别与概率最大的box计算iou值:similarities.shape=(55,)
- iou的具体值(mode=element-wise): [0. 0. 0. 0. 0. 0.
- 0.07675909 0.12364191 0.23970631 0.33900959 0.33745629 0.56709233
- 0.59554475 0.49006536 0.57026954 0.87459872 0.78070099 0.3758211
- 0.37740123 0.39050547 0.17682375 0.25101563 0.24019076 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.05017515
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (49, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.9956851 129.40013409 157.70614743 152.63318539 173.20198417]
- 用48个剩下的box分别与概率最大的box计算iou值:similarities.shape=(48,)
- iou的具体值(mode=element-wise): [0. 0. 0. 0. 0. 0.
- 0. 0. 0.02509198 0.10929508 0.1260791 0.83553275
- 0.8198082 0.57687623 0.68169777 0.64539796 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (43, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.9830699 221.70513153 156.12831116 242.03518867 175.49525499]
- 用42个剩下的box分别与概率最大的box计算iou值:similarities.shape=(42,)
- iou的具体值(mode=element-wise): [0.65057521 0.54551752 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.63989328
- 0.79102305 0.56078146 0.69078454 0.51122726 0.72727727 0.15161736
- 0.16984282 0.07225356 0. 0. 0. 0.14413073
- 0.09548975 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (34, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.9681372 81.52966976 158.95491242 118.49089622 181.38511777]
- 用33个剩下的box分别与概率最大的box计算iou值:similarities.shape=(33,)
- iou的具体值(mode=element-wise): [0.6788535 0.79792557 0.78122792 0.70252993 0.63139252 0.23903224
- 0.2823847 0.31215136 0. 0. 0. 0.45995059
- 0.82538095 0.78006928 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (25, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.94346797 273.73829842 140.49496651 405.59214592 228.25380564]
- 用24个剩下的box分别与概率最大的box计算iou值:similarities.shape=(24,)
- iou的具体值(mode=element-wise): [0. 0. 0. 0. 0. 0.
- 0. 0. 0.00357045 0.0418803 0.04335517 0.11936389
- 0.10320008 0.3262412 0.79463235 0.7148508 0.84175594 0.64544
- 0.59104297 0.70104972 0.59156524 0.64433127 0.47079207 0.33492643]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (15, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.93923956 101.22835636 158.29587579 136.55478001 178.74550223]
- 用14个剩下的box分别与概率最大的box计算iou值:similarities.shape=(14,)
- iou的具体值(mode=element-wise): [0.64840278 0.79752075 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (12, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.91097564 242.31081963 153.76625061 287.04068184 194.19704676]
- 用11个剩下的box分别与概率最大的box计算iou值:similarities.shape=(11,)
- iou的具体值(mode=element-wise): [0.19421066 0.24095247 0.42225857 0.3029937 0.37140765 0.60805474
- 0.96182943 0.45852904 0.53768868 0.17531475 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (7, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.8977809 237.0672369 156.65000081 269.07259941 187.75847554]
- 用6个剩下的box分别与概率最大的box计算iou值:similarities.shape=(6,)
- iou的具体值(mode=element-wise): [0.51284113 0.57798057 0.65502422 0.74729463 0.03689705 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (2, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.71307981 311.71751976 139.75530267 475.39721489 269.67009902]
- 用1个剩下的box分别与概率最大的box计算iou值:similarities.shape=(1,)
- iou的具体值(mode=element-wise): [0.0537353]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (1, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.60347539 262.34584808 140.18645883 330.25497437 212.12155223]
- maxima(经过NMS后的结果): (11, 5) [[ 0.99991632 48.14987183 162.33640909 99.23004627 189.70298767]
- [ 0.99863428 119.32604313 158.375144 143.32727909 174.77560043]
- [ 0.9956851 129.40013409 157.70614743 152.63318539 173.20198417]
- [ 0.9830699 221.70513153 156.12831116 242.03518867 175.49525499]
- [ 0.9681372 81.52966976 158.95491242 118.49089622 181.38511777]
- [ 0.94346797 273.73829842 140.49496651 405.59214592 228.25380564]
- [ 0.93923956 101.22835636 158.29587579 136.55478001 178.74550223]
- [ 0.91097564 242.31081963 153.76625061 287.04068184 194.19704676]
- [ 0.8977809 237.0672369 156.65000081 269.07259941 187.75847554]
- [ 0.71307981 311.71751976 139.75530267 475.39721489 269.67009902]
- [ 0.60347539 262.34584808 140.18645883 330.25497437 212.12155223]]
- maxima_output(在0维上加上对应的类别id): (11, 6) [[ 1. 0.99991632 48.14987183 162.33640909 99.23004627
- 189.70298767]
- [ 1. 0.99863428 119.32604313 158.375144 143.32727909
- 174.77560043]
- [ 1. 0.9956851 129.40013409 157.70614743 152.63318539
- 173.20198417]
- [ 1. 0.9830699 221.70513153 156.12831116 242.03518867
- 175.49525499]
- [ 1. 0.9681372 81.52966976 158.95491242 118.49089622
- 181.38511777]
- [ 1. 0.94346797 273.73829842 140.49496651 405.59214592
- 228.25380564]
- [ 1. 0.93923956 101.22835636 158.29587579 136.55478001
- 178.74550223]
- [ 1. 0.91097564 242.31081963 153.76625061 287.04068184
- 194.19704676]
- [ 1. 0.8977809 237.0672369 156.65000081 269.07259941
- 187.75847554]
- [ 1. 0.71307981 311.71751976 139.75530267 475.39721489
- 269.67009902]
- [ 1. 0.60347539 262.34584808 140.18645883 330.25497437
- 212.12155223]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (11, 6) [[ 1. 0.99991632 48.14987183 162.33640909 99.23004627
- 189.70298767]
- [ 1. 0.99863428 119.32604313 158.375144 143.32727909
- 174.77560043]
- [ 1. 0.9956851 129.40013409 157.70614743 152.63318539
- 173.20198417]
- [ 1. 0.9830699 221.70513153 156.12831116 242.03518867
- 175.49525499]
- [ 1. 0.9681372 81.52966976 158.95491242 118.49089622
- 181.38511777]
- [ 1. 0.94346797 273.73829842 140.49496651 405.59214592
- 228.25380564]
- [ 1. 0.93923956 101.22835636 158.29587579 136.55478001
- 178.74550223]
- [ 1. 0.91097564 242.31081963 153.76625061 287.04068184
- 194.19704676]
- [ 1. 0.8977809 237.0672369 156.65000081 269.07259941
- 187.75847554]
- [ 1. 0.71307981 311.71751976 139.75530267 475.39721489
- 269.67009902]
- [ 1. 0.60347539 262.34584808 140.18645883 330.25497437
- 212.12155223]]
- 正在处理的图片编号:2
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(9, 5)
- threshold_met: [[ 0.75215954 126.81917667 150.52061677 166.79785252 174.54941869]
- [ 0.72137636 118.82426262 156.49695396 148.21206093 173.96403551]
- [ 0.62573338 127.13476181 155.17378449 155.91001511 172.95268178]
- [ 0.8579843 122.28881836 155.35283089 159.26702499 174.06102419]
- [ 0.77935374 117.39601851 153.4496069 161.96522713 173.70997667]
- [ 0.69708437 132.20377922 154.79691625 160.48350334 173.55682254]
- [ 0.76109886 125.37506104 152.20177174 166.21584892 176.1164546 ]
- [ 0.84214813 128.04242134 155.31349182 162.44550705 174.37398434]
- [ 0.71698338 120.72495461 152.64616013 164.87989426 174.33693409]]
- 先得到同一个类别中,概率值最大的box: [ 0.8579843 122.28881836 155.35283089 159.26702499 174.06102419]
- 用8个剩下的box分别与概率最大的box计算iou值:similarities.shape=(8,)
- iou的具体值(mode=element-wise): [0.5806809 0.60188316 0.72665464 0.74108815 0.67518874 0.61295194
- 0.76522371 0.72230954]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (1, 5) [[ 0.8579843 122.28881836 155.35283089 159.26702499 174.06102419]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 1. 0.8579843 122.28881836 155.35283089 159.26702499
- 174.06102419]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (1, 6) [[ 1. 0.8579843 122.28881836 155.35283089 159.26702499
- 174.06102419]]
- 正在处理的图片编号:3
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(16, 5)
- threshold_met: [[ 0.83615065 31.17763281 131.31062686 181.47391319 253.48488092]
- [ 0.98970848 37.51424789 125.80279112 209.18792725 267.2634244 ]
- [ 0.93533438 46.49530649 115.87157249 225.3269577 268.55342388]
- [ 0.8587783 27.37287283 136.90109253 173.07815552 266.76249504]
- [ 0.99867141 34.14845467 136.68976128 206.80286407 271.14822865]
- [ 0.99361199 44.92258072 126.22027695 224.30296898 275.00020266]
- [ 0.65652001 31.03308678 154.25248146 203.0613327 273.74925613]
- [ 0.83881658 46.58774614 151.75019503 206.27194405 295.52804232]
- [ 0.99856502 38.07931423 133.35420191 218.76410007 267.36681461]
- [ 0.95636439 35.37297964 135.80897748 214.43787575 274.3303299 ]
- [ 0.99495357 28.24527025 141.43128991 220.69158554 269.15100217]
- [ 0.88248748 18.37405443 126.62755251 231.09898567 268.17290783]
- [ 0.83220071 39.43939447 118.30216348 220.38265228 269.59469318]
- [ 0.98419178 25.70425987 124.55210388 225.48322678 264.49656487]
- [ 0.93223453 -6.6250205 126.96025372 240.31868935 273.29034805]
- [ 0.6840108 26.47614956 143.79932284 220.59578419 280.51208854]]
- 先得到同一个类别中,概率值最大的box: [ 0.99867141 34.14845467 136.68976128 206.80286407 271.14822865]
- 用15个剩下的box分别与概率最大的box计算iou值:similarities.shape=(15,)
- iou的具体值(mode=element-wise): [0.70606035 0.87042296 0.71948317 0.74877797 0.77358186 0.82181013
- 0.70333616 0.86870696 0.92315419 0.85660799 0.74128782 0.78468985
- 0.75812911 0.64244242 0.79187667]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (1, 5) [[ 0.99867141 34.14845467 136.68976128 206.80286407 271.14822865]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 1. 0.99867141 34.14845467 136.68976128 206.80286407
- 271.14822865]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(2, 5)
- threshold_met: [[ 0.62705225 276.77527428 123.70916605 323.44805717 211.40727997]
- [ 0.77580345 273.19664955 136.21562719 321.30354881 213.64070177]]
- 先得到同一个类别中,概率值最大的box: [ 0.77580345 273.19664955 136.21562719 321.30354881 213.64070177]
- 用1个剩下的box分别与概率最大的box计算iou值:similarities.shape=(1,)
- iou的具体值(mode=element-wise): [0.74908807]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (1, 5) [[ 0.77580345 273.19664955 136.21562719 321.30354881 213.64070177]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 3. 0.77580345 273.19664955 136.21562719 321.30354881
- 213.64070177]]
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (2, 6) [[ 1. 0.99867141 34.14845467 136.68976128 206.80286407
- 271.14822865]
- [ 3. 0.77580345 273.19664955 136.21562719 321.30354881
- 213.64070177]]
- 正在处理的图片编号:4
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(9, 5)
- threshold_met: [[ 0.89631104 159.06984329 141.68805778 187.76189804 165.12234807]
- [ 0.90466708 155.8009243 141.27936065 188.0476284 165.78345895]
- [ 0.81128764 158.84270668 141.43401682 188.05990219 164.74363804]
- [ 0.95014256 159.748106 146.04595006 186.57394409 166.58906937]
- [ 0.78325415 156.25057697 143.61928403 190.64013004 167.08084345]
- [ 0.95104986 156.92095757 146.14132941 187.00819016 166.46257639]
- [ 0.76541919 153.07462692 146.18838429 187.50088692 166.10714793]
- [ 0.69924593 161.54718876 144.94376779 186.80349827 166.49824977]
- [ 0.75742286 159.24526691 146.20946646 184.93296146 166.13563299]]
- 先得到同一个类别中,概率值最大的box: [ 0.95104986 156.92095757 146.14132941 187.00819016 166.46257639]
- 用8个剩下的box分别与概率最大的box计算iou值:similarities.shape=(8,)
- iou的具体值(mode=element-wise): [0.70378965 0.72905334 0.68176577 0.88300622 0.75779056 0.85879581
- 0.79874574 0.83717507]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (1, 5) [[ 0.95104986 156.92095757 146.14132941 187.00819016 166.46257639]]
- maxima_output(在0维上加上对应的类别id): (1, 6) [[ 1. 0.95104986 156.92095757 146.14132941 187.00819016
- 166.46257639]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (1, 6) [[ 1. 0.95104986 156.92095757 146.14132941 187.00819016
- 166.46257639]]
- 正在处理的图片编号:5
- 在17538个box中(先都看同一个类别)类别为:1的概率值大于给定阈值的box个数:(33, 5)
- threshold_met: [[ 0.79548252 141.32047176 150.02050996 162.78303623 167.98333526]
- [ 0.85634446 141.29078865 150.84210634 164.34156418 167.43335724]
- [ 0.86653131 141.7171669 150.04974604 165.22047043 167.55713224]
- [ 0.87877679 140.23023605 150.45549273 165.6980896 167.42046475]
- [ 0.6583057 140.80884933 150.55536032 167.6718235 166.83247089]
- [ 0.78633976 177.22223282 149.77755547 204.15175438 172.92995453]
- [ 0.938685 178.00370693 151.63866878 209.14591312 176.80408359]
- [ 0.64307755 175.72311401 149.79396164 205.59270859 171.76904082]
- [ 0.95129114 179.99699593 150.00847578 211.26763344 175.18122196]
- [ 0.99564457 179.13518429 151.96942091 212.69395351 176.72950029]
- [ 0.90695029 179.63258743 150.6311059 210.85103989 174.56452847]
- [ 0.88417858 269.14887428 150.45765638 306.57797813 176.9205451 ]
- [ 0.78744322 177.78748512 152.63578892 202.94514656 178.33904028]
- [ 0.99593568 177.95637131 152.51301527 207.7253437 176.57525539]
- [ 0.99452776 175.70822239 152.81785727 207.99086094 179.46288586]
- [ 0.99065512 178.68970871 152.64905691 207.49136925 176.89304352]
- [ 0.83494103 176.19021893 151.80852413 208.4480238 177.04886198]
- [ 0.93428355 179.28159714 150.00302196 208.25065613 178.76277566]
- [ 0.99940383 177.28039742 151.95738673 210.60796738 176.8543303 ]
- [ 0.99863523 178.41635227 151.37447119 211.8632555 177.7857542 ]
- [ 0.99869847 176.68629169 151.8278718 210.20323277 177.48749256]
- [ 0.94521946 171.44990444 149.40937757 213.64281178 177.55147219]
- [ 0.71909463 263.66294861 152.94653177 301.93222046 180.97071648]
- [ 0.96401632 269.98397827 153.22329998 300.86242676 177.0947814 ]
- [ 0.9752388 266.84000015 152.23551393 305.36424637 179.28866744]
- [ 0.94146687 267.47202873 153.76878977 301.30379677 177.80191898]
- [ 0.6493988 259.9168396 153.13982964 304.83364105 178.02529335]
- [ 0.64769369 271.7250824 151.85735822 304.10453796 176.82393193]
- [ 0.87841833 269.64998245 149.27762747 307.32902527 177.65954733]
- [ 0.86343527 176.38857365 151.91116333 210.36762714 179.45466042]
- [ 0.76632452 265.59811592 154.0679276 302.19964027 179.14815545]
- [ 0.80384678 347.57612228 137.46753931 464.33950424 223.73067141]
- [ 0.71599853 341.07370377 138.16979527 463.92436981 217.09472537]]
- 先得到同一个类别中,概率值最大的box: [ 0.99940383 177.28039742 151.95738673 210.60796738 176.8543303 ]
- 用32个剩下的box分别与概率最大的box计算iou值:similarities.shape=(32,)
- iou的具体值(mode=element-wise): [0. 0. 0. 0. 0. 0.63344539
- 0.92151863 0.60624402 0.78464658 0.88412212 0.79879247 0.
- 0.70266097 0.86327709 0.77558936 0.83906312 0.8940115 0.76592786
- 0.87913815 0.94204094 0.69880148 0. 0. 0.
- 0. 0. 0. 0. 0.87459752 0.
- 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (16, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.9752388 266.84000015 152.23551393 305.36424637 179.28866744]
- 用15个剩下的box分别与概率最大的box计算iou值:similarities.shape=(15,)
- iou的具体值(mode=element-wise): [0. 0. 0. 0. 0. 0.78508134
- 0.77663816 0.70726602 0.78015872 0.77850835 0.75504954 0.75440068
- 0.82622498 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (7, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.87877679 140.23023605 150.45549273 165.6980896 167.42046475]
- 用6个剩下的box分别与概率最大的box计算iou值:similarities.shape=(6,)
- iou的具体值(mode=element-wise): [0.80293173 0.88385894 0.8964118 0.87276022 0. 0. ]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (2, 5)
- 先得到同一个类别中,概率值最大的box: [ 0.80384678 347.57612228 137.46753931 464.33950424 223.73067141]
- 用1个剩下的box分别与概率最大的box计算iou值:similarities.shape=(1,)
- iou的具体值(mode=element-wise): [0.86748001]
- 小于iou阈值的则保留(大于的表示与概率最大的box重合度非常高 需要丢弃)boxes: (0, 5)
- maxima(经过NMS后的结果): (4, 5) [[ 0.99940383 177.28039742 151.95738673 210.60796738 176.8543303 ]
- [ 0.9752388 266.84000015 152.23551393 305.36424637 179.28866744]
- [ 0.87877679 140.23023605 150.45549273 165.6980896 167.42046475]
- [ 0.80384678 347.57612228 137.46753931 464.33950424 223.73067141]]
- maxima_output(在0维上加上对应的类别id): (4, 6) [[ 1. 0.99940383 177.28039742 151.95738673 210.60796738
- 176.8543303 ]
- [ 1. 0.9752388 266.84000015 152.23551393 305.36424637
- 179.28866744]
- [ 1. 0.87877679 140.23023605 150.45549273 165.6980896
- 167.42046475]
- [ 1. 0.80384678 347.57612228 137.46753931 464.33950424
- 223.73067141]]
- 在17538个box中(先都看同一个类别)类别为:2的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:3的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:4的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 在17538个box中(先都看同一个类别)类别为:5的概率值大于给定阈值的box个数:(0, 5)
- threshold_met: []
- 所有正样本类别的就处理完了pred= (4, 6) [[ 1. 0.99940383 177.28039742 151.95738673 210.60796738
- 176.8543303 ]
- [ 1. 0.9752388 266.84000015 152.23551393 305.36424637
- 179.28866744]
- [ 1. 0.87877679 140.23023605 150.45549273 165.6980896
- 167.42046475]
- [ 1. 0.80384678 347.57612228 137.46753931 464.33950424
- 223.73067141]]
复制代码
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