├── .gitignore ├── .vscode ├── c_cpp_properties.json ├── launch.json ├── settings.json └── tasks.json ├── CMakeLists.txt ├── README.md ├── data └── TUD-Stadtmitte │ ├── det │ ├── 000001-acf.jpg │ └── det.txt │ ├── gt │ └── gt.txt │ └── img1 │ ├── 000001.jpg │ ├── 000002.jpg │ ├── 000003.jpg │ ├── 000004.jpg │ ├── 000005.jpg │ ├── 000006.jpg │ ├── 000007.jpg │ ├── 000008.jpg │ ├── 000009.jpg │ ├── 000010.jpg │ ├── 000011.jpg │ ├── 000012.jpg │ ├── 000013.jpg │ ├── 000014.jpg │ ├── 000015.jpg │ ├── 000016.jpg │ ├── 000017.jpg │ ├── 000018.jpg │ ├── 000019.jpg │ ├── 000020.jpg │ ├── 000021.jpg │ ├── 000022.jpg │ ├── 000023.jpg │ ├── 000024.jpg │ ├── 000025.jpg │ ├── 000026.jpg │ ├── 000027.jpg │ ├── 000028.jpg │ ├── 000029.jpg │ ├── 000030.jpg │ ├── 000031.jpg │ ├── 000032.jpg │ ├── 000033.jpg │ ├── 000034.jpg │ ├── 000035.jpg │ ├── 000036.jpg │ ├── 000037.jpg │ ├── 000038.jpg │ ├── 000039.jpg │ ├── 000040.jpg │ ├── 000041.jpg │ ├── 000042.jpg │ ├── 000043.jpg │ ├── 000044.jpg │ ├── 000045.jpg │ ├── 000046.jpg │ ├── 000047.jpg │ ├── 000048.jpg │ ├── 000049.jpg │ ├── 000050.jpg │ ├── 000051.jpg │ ├── 000052.jpg │ ├── 000053.jpg │ ├── 000054.jpg │ ├── 000055.jpg │ ├── 000056.jpg │ ├── 000057.jpg │ ├── 000058.jpg │ ├── 000059.jpg │ ├── 000060.jpg │ ├── 000061.jpg │ ├── 000062.jpg │ ├── 000063.jpg │ ├── 000064.jpg │ ├── 000065.jpg │ ├── 000066.jpg │ ├── 000067.jpg │ ├── 000068.jpg │ ├── 000069.jpg │ ├── 000070.jpg │ ├── 000071.jpg │ ├── 000072.jpg │ ├── 000073.jpg │ ├── 000074.jpg │ ├── 000075.jpg │ ├── 000076.jpg │ ├── 000077.jpg │ ├── 000078.jpg │ ├── 000079.jpg │ ├── 000080.jpg │ ├── 000081.jpg │ ├── 000082.jpg │ ├── 000083.jpg │ ├── 000084.jpg │ ├── 000085.jpg │ ├── 000086.jpg │ ├── 000087.jpg │ ├── 000088.jpg │ ├── 000089.jpg │ ├── 000090.jpg │ ├── 000091.jpg │ ├── 000092.jpg │ ├── 000093.jpg │ ├── 000094.jpg │ ├── 000095.jpg │ ├── 000096.jpg │ ├── 000097.jpg │ ├── 000098.jpg │ ├── 000099.jpg │ ├── 000100.jpg │ ├── 000101.jpg │ ├── 000102.jpg │ ├── 000103.jpg │ ├── 000104.jpg │ ├── 000105.jpg │ ├── 000106.jpg │ ├── 000107.jpg │ ├── 000108.jpg │ ├── 000109.jpg │ ├── 000110.jpg │ ├── 000111.jpg │ ├── 000112.jpg │ ├── 000113.jpg │ ├── 000114.jpg │ ├── 000115.jpg │ ├── 000116.jpg │ ├── 000117.jpg │ ├── 000118.jpg │ ├── 000119.jpg │ ├── 000120.jpg │ ├── 000121.jpg │ ├── 000122.jpg │ ├── 000123.jpg │ ├── 000124.jpg │ ├── 000125.jpg │ ├── 000126.jpg │ ├── 000127.jpg │ ├── 000128.jpg │ ├── 000129.jpg │ ├── 000130.jpg │ ├── 000131.jpg │ ├── 000132.jpg │ ├── 000133.jpg │ ├── 000134.jpg │ ├── 000135.jpg │ ├── 000136.jpg │ ├── 000137.jpg │ ├── 000138.jpg │ ├── 000139.jpg │ ├── 000140.jpg │ ├── 000141.jpg │ ├── 000142.jpg │ ├── 000143.jpg │ ├── 000144.jpg │ ├── 000145.jpg │ ├── 000146.jpg │ ├── 000147.jpg │ ├── 000148.jpg │ ├── 000149.jpg │ ├── 000150.jpg │ ├── 000151.jpg │ ├── 000152.jpg │ ├── 000153.jpg │ ├── 000154.jpg │ ├── 000155.jpg │ ├── 000156.jpg │ ├── 000157.jpg │ ├── 000158.jpg │ ├── 000159.jpg │ ├── 000160.jpg │ ├── 000161.jpg │ ├── 000162.jpg │ ├── 000163.jpg │ ├── 000164.jpg │ ├── 000165.jpg │ ├── 000166.jpg │ ├── 000167.jpg │ ├── 000168.jpg │ ├── 000169.jpg │ ├── 000170.jpg │ ├── 000171.jpg │ ├── 000172.jpg │ ├── 000173.jpg │ ├── 000174.jpg │ ├── 000175.jpg │ ├── 000176.jpg │ ├── 000177.jpg │ ├── 000178.jpg │ └── 000179.jpg ├── include ├── Hungarian.h ├── KalmanTracker.h ├── datatrans.h ├── track.h └── utils.h ├── run.sh └── src ├── Hungarian.cpp ├── KalmanTracker.cpp ├── main.cpp ├── track.cpp └── utils.cpp /.gitignore: -------------------------------------------------------------------------------- 1 | build/* 2 | # data/* 3 | output/* 4 | results_1/* 5 | results/* 6 | results_2/* -------------------------------------------------------------------------------- /.vscode/c_cpp_properties.json: -------------------------------------------------------------------------------- 1 | { 2 | "configurations": [ 3 | { 4 | "name": "Linux", 5 | "includePath": [ 6 | "${workspaceFolder}/**", 7 | "/usr/include/" 8 | ], 9 | "defines": [], 10 | "compilerPath": "/usr/bin/clang", 11 | "cStandard": "c11", 12 | "cppStandard": "c++11", 13 | "intelliSenseMode": "linux-clang-x64" 14 | } 15 | ], 16 | "version": 4 17 | } -------------------------------------------------------------------------------- /.vscode/launch.json: -------------------------------------------------------------------------------- 1 | { 2 | // Use IntelliSense to learn about possible attributes. 3 | // Hover to view descriptions of existing attributes. 4 | // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 5 | "version": "0.2.0", 6 | "configurations": [ 7 | { 8 | "name": "clang++ - Build and debug active file", 9 | "type": "cppdbg", 10 | "request": "launch", 11 | "program": "${workspaceFolder}/build/main", // 指定调试时的可执行文件名 12 | "args": [], 13 | "stopAtEntry": false, 14 | "cwd": "${fileDirname}", 15 | "environment": [], 16 | "externalConsole": false, 17 | "MIMode": "gdb", 18 | "setupCommands": [ 19 | { 20 | "description": "Enable pretty-printing for gdb", 21 | "text": "-enable-pretty-printing", 22 | "ignoreFailures": true 23 | } 24 | ], 25 | "preLaunchTask": "Build", 26 | "miDebuggerPath": "/usr/bin/gdb" 27 | } 28 | ] 29 | } -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "files.associations": { 3 | "*.ipp": "cpp", 4 | "cctype": "cpp", 5 | "clocale": "cpp", 6 | "cmath": "cpp", 7 | "csetjmp": "cpp", 8 | "csignal": "cpp", 9 | "cstdarg": "cpp", 10 | "cstddef": "cpp", 11 | "cstdio": "cpp", 12 | "cstdlib": "cpp", 13 | "cstring": "cpp", 14 | "ctime": "cpp", 15 | "cwchar": "cpp", 16 | "cwctype": "cpp", 17 | "array": "cpp", 18 | "atomic": "cpp", 19 | "hash_map": "cpp", 20 | "hash_set": "cpp", 21 | "strstream": "cpp", 22 | "*.tcc": "cpp", 23 | "bitset": "cpp", 24 | "chrono": "cpp", 25 | "complex": "cpp", 26 | "condition_variable": "cpp", 27 | "cstdint": "cpp", 28 | "deque": "cpp", 29 | "list": "cpp", 30 | "unordered_map": "cpp", 31 | "unordered_set": "cpp", 32 | "vector": "cpp", 33 | "exception": "cpp", 34 | "rope": "cpp", 35 | "slist": "cpp", 36 | "fstream": "cpp", 37 | "functional": "cpp", 38 | "future": "cpp", 39 | "initializer_list": "cpp", 40 | "iomanip": "cpp", 41 | "iosfwd": "cpp", 42 | "iostream": "cpp", 43 | "istream": "cpp", 44 | "limits": "cpp", 45 | "mutex": "cpp", 46 | "new": "cpp", 47 | "ostream": "cpp", 48 | "numeric": "cpp", 49 | "ratio": "cpp", 50 | "sstream": "cpp", 51 | "stdexcept": "cpp", 52 | "streambuf": "cpp", 53 | "system_error": "cpp", 54 | "thread": "cpp", 55 | "cfenv": "cpp", 56 | "cinttypes": "cpp", 57 | "regex": "cpp", 58 | "tuple": "cpp", 59 | "type_traits": "cpp", 60 | "utility": "cpp", 61 | "typeindex": "cpp", 62 | "typeinfo": "cpp", 63 | "valarray": "cpp", 64 | "codecvt": "cpp", 65 | "algorithm": "cpp", 66 | "filesystem": "cpp", 67 | "iterator": "cpp", 68 | "map": "cpp", 69 | "memory": "cpp", 70 | "memory_resource": "cpp", 71 | "optional": "cpp", 72 | "random": "cpp", 73 | "set": "cpp", 74 | "string": "cpp", 75 | "string_view": "cpp", 76 | "variant": "cpp" 77 | } 78 | } -------------------------------------------------------------------------------- /.vscode/tasks.json: -------------------------------------------------------------------------------- 1 | { 2 | "options": { 3 | "cwd": "${workspaceFolder}/build" 4 | }, 5 | "tasks": [ 6 | { 7 | "type": "shell", 8 | "label": "cmake", 9 | "command": "cmake", 10 | "args": [ 11 | ".." 12 | ] 13 | }, 14 | { 15 | "label": "make", 16 | "group": { 17 | "kind": "build", 18 | "isDefault": true 19 | }, 20 | "command":"make", 21 | "args": ["-j8"] 22 | }, 23 | { 24 | "label": "Build", 25 | "dependsOrder": "sequence", // 按列出的顺序执行任务依赖项 26 | "dependsOn":[ 27 | "cmake", 28 | "make" 29 | ] 30 | } 31 | ], 32 | "version": "2.0.0" 33 | } -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.0.2) 2 | project(sort_cpp) 3 | 4 | ## 设定编译参数 5 | set(CMAKE_BUILD_TYPE "Debug") # Debug or Release 6 | set(CMAKE_CXX_FLAGS "-std=c++14") 7 | 8 | # OpenCV 9 | find_package(OpenCV REQUIRED) 10 | if(NOT OpenCV_FOUND) 11 | message(FATAL_ERROR "OpenCV not found") 12 | endif() 13 | 14 | # 设定头文件路径 15 | include_directories( 16 | ${CMAKE_CURRENT_SOURCE_DIR}/include 17 | ) 18 | 19 | # 设定源码列表,查找指定目录(都放在./src/中)中的所有源文件,并将名称保存到 DIR_SRCS 变量中 20 | aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/src/ DIR_SRC) 21 | 22 | ## Declare a C++ executable 23 | add_executable(main ${DIR_SRC}) 24 | target_link_libraries(main ${OpenCV_LIBS}) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # sort-track-cpp 2 | 3 | 基于匈牙利匹配和卡尔曼滤波的SORT多目标跟踪算法。 4 | 5 | SORT (object tracking) and detector implemented in C++. 6 | 7 | ```bash 8 | # 在ubuntu18.04中经过了测试 9 | $ ./run.sh 10 | ``` 11 | 12 | References: 13 | - [代码注释基本一致](https://blog.csdn.net/zimiao552147572/article/details/106009225) 14 | - [opencv中的KalmanFilter详解](https://blog.csdn.net/gdfsg/article/details/50904811) 15 | - [sort-cpp-ubuntu](https://github.com/engcang/ros-yolo-sort) 16 | - [mtcnn-light-with-tracking](https://github.com/anhducle98/mtcnn-light-with-tracking) -------------------------------------------------------------------------------- /data/TUD-Stadtmitte/det/000001-acf.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ganleiboy/sort-track-cpp/0ce3bccd8f3174edfbdf7871af362f6c78078205/data/TUD-Stadtmitte/det/000001-acf.jpg -------------------------------------------------------------------------------- /data/TUD-Stadtmitte/det/det.txt: -------------------------------------------------------------------------------- 1 | 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1,6,513,111,36.501,122.15,1,16.592,8.209,0 7 | 1,7,576,84,61.632,195.56,1,9.2619,3.381,0 8 | 2,1,84,99,61.08,218.56,1,4.4717,5.5332,0 9 | 2,2,184,95,75.63,226.62,1,4.4935,4.4599,0 10 | 2,3,184,96,35.532,154.5,1,12.621,10.628,0 11 | 2,4,360,82,72.831,245.79,1,4.4652,2.802,0 12 | 2,5,460,89,64.89,236.19,1,4.7833,2.1314,0 13 | 2,6,512,111,36.48,122.15,1,16.59,8.2237,0 14 | 2,7,574,84,61.596,195.37,1,9.2594,3.4022,0 15 | 3,1,80,100,61.08,218.56,1,4.3797,5.5027,0 16 | 3,2,188,95,75.412,226.14,1,4.5047,4.4288,0 17 | 3,3,184,96,35.636,154.51,1,12.621,10.628,0 18 | 3,4,362,82,72.718,245.31,1,4.4692,2.7865,0 19 | 3,5,462,89,65.005,235.71,1,4.861,2.1575,0 20 | 3,6,511,111,36.453,122.14,1,16.589,8.2383,0 21 | 3,7,572,84,61.552,195.14,1,9.257,3.4233,0 22 | 4,1,75,100,61.08,218.56,1,4.3626,5.542,0 23 | 4,2,192,95,75.173,225.62,1,4.5926,4.4526,0 24 | 4,3,184,96,35.751,154.52,1,12.621,10.628,0 25 | 4,4,365,82,72.593,244.78,1,4.5481,2.8082,0 26 | 4,5,465,89,65.131,235.18,1,4.8657,2.1333,0 27 | 4,6,510,111,36.425,122.13,1,16.588,8.2529,0 28 | 4,7,569,84,61.504,194.89,1,9.3727,3.5155,0 29 | 5,1,69,101,61.08,218.56,1,4.2642,5.527,0 30 | 5,2,196,95,74.925,225.07,1,4.6036,4.4212,0 31 | 5,3,184,96,35.87,154.53,1,12.621,10.628,0 32 | 5,4,368,82,72.464,244.24,1,4.554,2.7847,0 33 | 5,5,468,89,65.262,234.63,1,4.9457,2.1514,0 34 | 5,6,508,111,36.395,122.13,1,16.585,8.2822,0 35 | 5,7,566,84,61.455,194.63,1,9.3689,3.5473,0 36 | 6,1,64,101,61.08,218.56,1,4.2469,5.5661,0 37 | 6,2,200,95,74.675,224.53,1,4.6921,4.445,0 38 | 6,3,184,96,35.99,154.53,1,12.621,10.628,0 39 | 6,4,371,82,72.332,243.69,1,4.6335,2.8064,0 40 | 6,5,471,89,65.394,234.07,1,4.9504,2.127,0 41 | 6,6,507,111,36.365,122.12,1,16.584,8.2968,0 42 | 6,7,564,84,61.404,194.37,1,9.3664,3.5685,0 43 | 6,8,616,108,23.994,126.99,1,16.481,6.6959,0 44 | 7,1,59,101,61.08,218.56,1,4.2294,5.6051,0 45 | 7,2,204,95,74.425,223.98,1,4.7812,4.4689,0 46 | 7,3,184,96,36.11,154.54,1,12.624,10.615,0 47 | 7,4,374,82,72.2,243.13,1,4.6393,2.7828,0 48 | 7,5,473,90,65.525,233.52,1,4.9535,2.1108,0 49 | 7,6,506,112,36.335,122.11,1,16.365,8.1919,0 50 | 7,7,561,84,61.352,194.1,1,9.3627,3.6003,0 51 | 7,8,615,108,25.355,127.03,1,16.267,6.5908,0 52 | 8,1,53,102,61.08,218.56,1,4.1314,5.5897,0 53 | 8,2,208,95,74.175,223.43,1,4.7921,4.4372,0 54 | 8,3,184,96,36.23,154.55,1,12.624,10.615,0 55 | 8,4,377,82,72.068,242.59,1,4.7194,2.8046,0 56 | 8,5,476,90,65.655,232.97,1,5.034,2.1287,0 57 | 8,6,504,112,36.305,122.11,1,16.362,8.2209,0 58 | 8,7,558,85,61.3,193.84,1,9.3589,3.6321,0 59 | 8,8,613,108,27.016,127.08,1,16.266,6.6053,0 60 | 9,1,48,102,61.08,218.56,1,4.1136,5.6285,0 61 | 9,2,212,95,73.925,222.88,1,4.8791,4.469,0 62 | 9,3,183,96,36.35,154.56,1,12.621,10.628,0 63 | 9,4,380,82,71.936,242.03,1,4.7232,2.7887,0 64 | 9,5,479,90,65.786,232.41,1,5.0386,2.1042,0 65 | 9,6,503,112,36.275,122.1,1,16.361,8.2354,0 66 | 9,7,555,85,61.248,193.58,1,9.355,3.6639,0 67 | 9,8,611,108,28.839,127.13,1,16.265,6.6197,0 68 | 10,1,43,103,61.08,218.56,1,4.0197,5.6051,0 69 | 10,2,216,95,73.675,222.33,1,4.8899,4.4371,0 70 | 10,3,183,96,36.47,154.57,1,12.621,10.628,0 71 | 10,4,383,82,71.805,241.49,1,4.804,2.8105,0 72 | 10,5,482,90,65.918,231.85,1,5.1198,2.1221,0 73 | 10,6,502,112,36.245,122.1,1,16.36,8.25,0 74 | 10,7,553,85,61.196,193.31,1,9.3524,3.685,0 75 | 10,8,609,108,30.725,127.19,1,16.264,6.6341,0 76 | 11,1,37,103,61.08,218.56,1,3.998,5.6514,0 77 | 11,2,220,95,73.425,221.78,1,4.9803,4.461,0 78 | 11,3,183,96,36.59,154.57,1,12.621,10.628,0 79 | 11,4,386,82,71.675,240.94,1,4.8855,2.8325,0 80 | 11,5,485,90,66.05,231.3,1,5.1258,2.0892,0 81 | 11,6,500,112,36.216,122.09,1,16.357,8.279,0 82 | 11,7,550,85,61.145,193.05,1,9.3486,3.7168,0 83 | 11,8,607,107,32.63,127.24,1,16.477,6.7542,0 84 | 12,1,32,103,61.238,218.55,1,3.9798,5.69,0 85 | 12,2,224,95,73.176,221.23,1,4.9909,4.4288,0 86 | 12,3,183,96,36.71,154.58,1,12.621,10.628,0 87 | 12,4,389,82,71.545,240.38,1,4.8911,2.8085,0 88 | 12,5,487,90,66.182,230.75,1,5.2062,2.1153,0 89 | 12,6,499,112,36.188,122.08,1,16.356,8.2934,0 90 | 12,7,547,85,61.095,192.79,1,9.4654,3.8112,0 91 | 12,8,606,107,34.535,127.3,1,16.477,6.7542,0 92 | 13,1,26,104,61.782,218.53,1,3.8826,5.6739,0 93 | 13,2,228,95,72.928,220.68,1,5.082,4.4526,0 94 | 13,3,183,96,36.83,154.59,1,12.621,10.628,0 95 | 13,4,393,82,71.414,239.84,1,4.9751,2.8224,0 96 | 13,5,490,90,66.314,230.19,1,5.2107,2.0905,0 97 | 13,6,498,112,36.16,122.08,1,16.355,8.308,0 98 | 13,7,545,85,61.045,192.53,1,9.4628,3.8325,0 99 | 13,8,604,107,36.421,127.35,1,16.476,6.7687,0 100 | 14,1,20,104,63.155,218.48,1,3.8641,5.7122,0 101 | 14,2,232,95,72.68,220.13,1,5.0925,4.4202,0 102 | 14,3,183,96,36.95,154.6,1,12.621,10.628,0 103 | 14,4,396,82,71.282,239.28,1,4.9806,2.7982,0 104 | 14,5,493,90,66.445,229.63,1,5.2932,2.1084,0 105 | 14,6,496,112,36.132,122.07,1,16.352,8.337,0 106 | 14,7,542,85,60.995,192.26,1,9.4588,3.8644,0 107 | 14,8,602,107,38.244,127.4,1,16.475,6.7833,0 108 | 15,1,14,105,65.7,218.39,1,3.7712,5.6883,0 109 | 15,2,237,95,72.432,219.59,1,5.1868,4.4359,0 110 | 15,3,183,96,37.07,154.61,1,12.621,10.628,0 111 | 15,4,399,82,71.15,238.74,1,5.0634,2.8202,0 112 | 15,5,496,90,66.575,229.08,1,5.2976,2.0834,0 113 | 15,6,495,112,36.104,122.07,1,16.351,8.3515,0 114 | 15,7,539,85,60.945,192,1,9.4548,3.8963,0 115 | 15,8,600,107,39.905,127.45,1,16.473,6.8124,0 116 | 16,1,7,105,69.165,218.26,1,3.7524,5.7263,0 117 | 16,2,241,95,72.184,219.04,1,5.1971,4.4033,0 118 | 16,3,183,96,37.19,154.61,1,12.621,10.628,0 119 | 16,4,402,82,71.018,238.19,1,5.0689,2.7958,0 120 | 16,5,499,90,66.706,228.53,1,5.3808,2.1013,0 121 | 16,6,493,112,36.076,122.06,1,16.348,8.3805,0 122 | 16,7,536,85,60.895,191.74,1,9.5729,3.9922,0 123 | 16,8,598,107,41.266,127.49,1,16.472,6.8269,0 124 | 17,1,0,105,72.378,218.15,1,3.7336,5.7644,0 125 | 17,2,245,95,71.935,218.49,1,5.2869,4.4352,0 126 | 17,3,183,96,37.309,154.62,1,12.621,10.628,0 127 | 17,4,405,83,70.886,237.64,1,5.0743,2.7714,0 128 | 17,5,501,90,66.838,227.97,1,5.4633,2.1278,0 129 | 17,6,492,113,36.051,122.07,1,16.132,8.2756,0 130 | 17,7,534,86,60.845,191.47,1,9.448,3.9494,0 131 | 17,8,597,108,42.219,127.52,1,16.258,6.7208,0 132 | 18,1,1,106,74.057,218.12,1,3.6675,5.6872,0 133 | 18,2,249,95,71.685,217.94,1,5.3801,4.4591,0 134 | 18,3,183,96,37.427,154.63,1,12.621,10.628,0 135 | 18,4,408,83,70.755,237.09,1,5.0798,2.747,0 136 | 18,5,504,90,66.97,227.41,1,5.4676,2.1025,0 137 | 18,6,491,113,36.037,122.1,1,16.13,8.2899,0 138 | 18,7,531,86,60.795,191.21,1,9.4439,3.9812,0 139 | 18,8,595,108,42.763,127.53,1,16.256,6.7496,0 140 | 19,1,-4,106,73.244,218.22,1,3.6448,5.7326,0 141 | 19,2,253,95,71.435,217.39,1,5.3902,4.4261,0 142 | 19,3,183,96,37.545,154.64,1,12.621,10.628,0 143 | 19,4,411,83,70.625,236.54,1,5.1631,2.7687,0 144 | 19,5,507,90,67.102,226.86,1,5.5523,2.1205,0 145 | 19,6,489,113,36.041,122.18,1,16.128,8.3187,0 146 | 19,7,528,86,60.745,190.95,1,9.562,4.0777,0 147 | 19,8,594,108,43.004,127.54,1,16.255,6.7641,0 148 | 20,1,-7,106,69.735,218.48,1,3.6257,5.7704,0 149 | 20,2,257,95,71.185,216.84,1,5.4841,4.4499,0 150 | 20,3,183,96,37.663,154.65,1,12.621,10.628,0 151 | 20,4,414,83,70.495,235.99,1,5.2472,2.7906,0 152 | 20,5,510,90,67.234,226.31,1,5.5565,2.0951,0 153 | 20,6,487,113,36.071,122.33,1,16.125,8.3475,0 154 | 20,7,526,86,60.695,190.69,1,9.5592,4.099,0 155 | 20,8,592,109,43.075,127.56,1,16.042,6.6878,0 156 | 21,1,-9,106,64.375,218.86,1,3.6104,5.8007,0 157 | 21,2,261,95,70.935,216.29,1,5.494,4.4167,0 158 | 21,3,183,96,37.781,154.65,1,12.621,10.628,0 159 | 21,4,417,83,70.364,235.44,1,5.2525,2.7659,0 160 | 21,5,513,90,67.365,225.75,1,5.6419,2.1131,0 161 | 21,6,485,112,36.127,122.53,1,16.337,8.4965,0 162 | 21,7,523,86,60.645,190.43,1,9.555,4.131,0 163 | 21,8,591,109,43.068,127.59,1,16.041,6.7021,0 164 | 22,1,0,106,58.857,219.25,1,3.5605,5.6935,0 165 | 22,2,265,95,70.685,215.74,1,5.5886,4.4405,0 166 | 22,3,183,96,37.9,154.66,1,12.621,10.628,0 167 | 22,4,420,83,70.232,234.89,1,5.3372,2.7878,0 168 | 22,5,515,90,67.495,225.19,1,5.6447,2.096,0 169 | 22,6,483,112,36.201,122.79,1,16.335,8.5255,0 170 | 22,7,520,86,60.595,190.16,1,9.5509,4.163,0 171 | 22,8,590,109,43.022,127.65,1,16.04,6.7164,0 172 | 23,2,269,95,70.435,215.19,1,5.5984,4.407,0 173 | 23,3,183,96,38.02,154.67,1,12.624,10.615,0 174 | 23,4,423,83,70.1,234.34,1,5.3425,2.7629,0 175 | 23,5,518,90,67.626,224.64,1,5.7308,2.1141,0 176 | 23,6,481,112,36.286,123.07,1,16.117,8.4337,0 177 | 23,7,517,86,60.545,189.9,1,9.6702,4.2612,0 178 | 23,8,590,109,42.956,127.74,1,16.04,6.7164,0 179 | 24,2,273,95,70.185,214.64,1,5.6937,4.4308,0 180 | 24,3,183,96,38.14,154.68,1,12.624,10.615,0 181 | 24,4,426,83,69.968,233.79,1,5.4262,2.7932,0 182 | 24,5,521,90,67.758,224.09,1,5.7349,2.0883,0 183 | 24,6,479,112,36.375,123.36,1,16.114,8.4625,0 184 | 24,7,515,86,60.495,189.64,1,9.6674,4.2826,0 185 | 24,8,590,109,42.875,127.84,1,16.04,6.7164,0 186 | 25,2,277,95,69.935,214.1,1,5.701,4.4055,0 187 | 25,3,183,96,38.26,154.69,1,12.624,10.615,0 188 | 25,4,429,83,69.836,233.25,1,5.4314,2.7682,0 189 | 25,5,524,90,67.89,223.53,1,5.8217,2.1064,0 190 | 25,6,477,112,36.465,123.66,1,16.111,8.4912,0 191 | 25,7,512,86,60.445,189.38,1,9.6631,4.3148,0 192 | 25,8,590,109,42.786,127.95,1,16.04,6.7164,0 193 | 26,2,282,95,69.685,213.55,1,5.7994,4.4208,0 194 | 26,3,183,96,38.38,154.69,1,12.624,10.615,0 195 | 26,4,432,83,69.705,232.69,1,5.5176,2.7902,0 196 | 26,5,527,90,68.022,222.97,1,5.9107,2.1159,0 197 | 26,6,475,111,36.556,123.95,1,16.324,8.6416,0 198 | 26,7,509,86,60.395,189.11,1,9.6587,4.3469,0 199 | 26,8,589,109,42.695,128.06,1,15.832,6.6268,0 200 | 27,2,286,95,69.435,213,1,5.8089,4.3868,0 201 | 27,3,183,96,38.5,154.7,1,12.624,10.615,0 202 | 27,4,435,83,69.575,232.15,1,5.5227,2.765,0 203 | 27,5,529,90,68.154,222.42,1,5.9134,2.0985,0 204 | 27,6,473,111,36.648,124.24,1,16.106,8.5488,0 205 | 27,7,507,87,60.345,188.85,1,9.6558,4.3683,0 206 | 27,8,589,109,42.602,128.17,1,15.832,6.6268,0 207 | 28,2,290,95,69.185,212.45,1,5.9057,4.4105,0 208 | 28,3,183,96,38.62,154.71,1,12.624,10.615,0 209 | 28,4,438,83,69.444,231.59,1,5.6096,2.787,0 210 | 28,5,532,90,68.285,221.87,1,6.0017,2.1167,0 211 | 28,6,471,111,36.74,124.54,1,16.103,8.5775,0 212 | 28,7,504,87,60.295,188.59,1,9.6515,4.4004,0 213 | 28,8,589,109,42.51,128.29,1,15.832,6.6268,0 214 | 29,2,294,95,68.935,211.9,1,6.0033,4.4344,0 215 | 29,3,183,96,38.74,154.72,1,12.624,10.615,0 216 | 29,4,441,83,69.312,231.04,1,5.6146,2.7616,0 217 | 29,5,535,90,68.415,221.31,1,6.0057,2.0904,0 218 | 29,6,469,111,36.832,124.83,1,16.1,8.6063,0 219 | 29,7,501,87,60.245,188.32,1,9.6471,4.4325,0 220 | 29,8,589,109,42.417,128.4,1,15.832,6.6268,0 221 | 30,2,298,95,68.685,211.35,1,6.0126,4.3999,0 222 | 30,3,183,96,38.86,154.73,1,12.624,10.615,0 223 | 30,4,444,83,69.18,230.5,1,5.7023,2.7837,0 224 | 30,5,538,91,68.546,220.75,1,6.0097,2.0641,0 225 | 30,6,466,110,36.924,125.12,1,16.096,8.6494,0 226 | 30,7,498,87,60.195,188.06,1,9.6426,4.4646,0 227 | 30,8,589,109,42.323,128.51,1,15.832,6.6268,0 228 | 31,2,302,95,68.436,210.8,1,6.111,4.4237,0 229 | 31,3,183,96,38.98,154.73,1,12.624,10.615,0 230 | 31,4,447,83,69.048,229.94,1,5.7906,2.8059,0 231 | 31,5,541,91,68.678,220.2,1,6.0136,2.0378,0 232 | 31,6,464,110,37.015,125.42,1,16.093,8.6782,0 233 | 31,7,496,87,60.145,187.8,1,9.7647,4.5544,0 234 | 31,8,589,109,42.23,128.63,1,15.832,6.6268,0 235 | 32,2,306,95,68.188,210.25,1,6.1201,4.3891,0 236 | 32,3,183,96,39.1,154.74,1,12.624,10.615,0 237 | 32,4,450,84,68.916,229.4,1,5.7122,2.7326,0 238 | 32,5,543,91,68.81,219.65,1,6.1013,2.0645,0 239 | 32,6,462,110,37.105,125.71,1,16.091,8.707,0 240 | 32,7,493,87,60.095,187.54,1,9.7602,4.5867,0 241 | 32,8,589,109,42.137,128.74,1,15.832,6.6268,0 242 | 33,2,310,95,67.94,209.7,1,6.217,4.4215,0 243 | 33,3,183,96,39.22,154.75,1,12.624,10.615,0 244 | 33,4,453,84,68.785,228.84,1,5.8005,2.7545,0 245 | 33,5,546,91,68.942,219.09,1,6.1052,2.038,0 246 | 33,6,460,110,37.195,126.01,1,15.876,8.6142,0 247 | 33,7,490,87,60.045,187.27,1,9.7557,4.6189,0 248 | 33,8,589,109,42.043,128.86,1,15.832,6.6268,0 249 | 34,2,314,95,67.692,209.16,1,6.2261,4.3866,0 250 | 34,3,182,96,39.34,154.76,1,12.621,10.628,0 251 | 34,4,456,84,68.655,228.29,1,5.8054,2.7288,0 252 | 34,5,549,91,69.074,218.54,1,6.195,2.0559,0 253 | 34,6,458,109,37.285,126.3,1,16.085,8.7645,0 254 | 34,7,488,87,59.995,187.01,1,9.7511,4.6512,0 255 | 34,8,589,109,41.95,128.97,1,15.83,6.641,0 256 | 35,2,318,95,67.444,208.61,1,6.326,4.4104,0 257 | 35,3,182,96,39.46,154.77,1,12.621,10.628,0 258 | 35,4,459,84,68.525,227.75,1,5.8945,2.7508,0 259 | 35,5,552,91,69.205,217.98,1,6.2857,2.0739,0 260 | 35,6,456,109,37.375,126.59,1,16.082,8.7933,0 261 | 35,7,485,87,59.945,186.75,1,9.8731,4.7534,0 262 | 35,8,589,109,41.858,129.09,1,15.626,6.5386,0 263 | 36,2,322,95,67.195,208.06,1,6.3349,4.3753,0 264 | 36,3,182,96,39.58,154.77,1,12.621,10.628,0 265 | 36,4,463,84,68.394,227.2,1,5.9009,2.7163,0 266 | 36,5,555,91,69.335,217.43,1,6.2895,2.0471,0 267 | 36,6,454,109,37.465,126.89,1,16.079,8.8221,0 268 | 36,7,482,87,59.895,186.49,1,9.8685,4.7859,0 269 | 36,8,589,109,41.765,129.2,1,15.626,6.5386,0 270 | 37,2,327,95,66.945,207.51,1,6.4379,4.3901,0 271 | 37,3,182,96,39.7,154.78,1,12.621,10.628,0 272 | 37,4,466,84,68.262,226.65,1,5.9907,2.7382,0 273 | 37,5,557,91,69.466,216.87,1,6.3797,2.0741,0 274 | 37,6,451,109,37.554,127.18,1,15.863,8.7425,0 275 | 37,7,479,88,59.845,186.22,1,9.7372,4.7478,0 276 | 37,8,589,109,41.672,129.31,1,15.626,6.5386,0 277 | 38,2,331,95,66.695,206.96,1,6.5395,4.4139,0 278 | 38,3,182,96,39.82,154.79,1,12.621,10.628,0 279 | 38,4,469,84,68.13,226.1,1,5.9954,2.7121,0 280 | 38,5,560,91,69.57,216.32,1,6.3834,2.0471,0 281 | 38,6,449,108,37.642,127.47,1,16.072,8.894,0 282 | 38,7,477,88,59.795,185.96,1,9.8607,4.8399,0 283 | 38,8,589,109,41.58,129.43,1,15.626,6.5386,0 284 | 39,2,335,95,66.445,206.41,1,6.5482,4.3783,0 285 | 39,3,182,96,39.94,154.8,1,12.621,10.628,0 286 | 39,4,472,84,67.998,225.55,1,6.0845,2.7428,0 287 | 39,5,563,91,69.578,215.77,1,6.4757,2.0652,0 288 | 39,6,447,108,37.725,127.74,1,16.069,8.9228,0 289 | 39,7,474,88,59.745,185.7,1,9.8559,4.8723,0 290 | 39,8,588,109,41.487,129.54,1,15.625,6.5526,0 291 | 40,2,339,95,66.195,205.86,1,6.6506,4.402,0 292 | 40,3,182,96,40.06,154.81,1,12.624,10.615,0 293 | 40,4,475,84,67.866,225,1,6.0892,2.7165,0 294 | 40,5,566,91,69.344,215.23,1,6.4794,2.038,0 295 | 40,6,445,108,37.8,128,1,15.854,8.8281,0 296 | 40,7,471,88,59.695,185.44,1,9.8512,4.9047,0 297 | 40,8,588,108,41.393,129.66,1,15.829,6.6552,0 298 | 41,2,343,95,65.945,205.31,1,6.657,4.3751,0 299 | 41,3,182,96,40.18,154.81,1,12.624,10.615,0 300 | 41,4,478,84,67.735,224.45,1,6.1806,2.7385,0 301 | 41,5,569,91,68.659,214.71,1,6.5725,2.0561,0 302 | 41,6,444,108,37.862,128.22,1,15.852,8.8424,0 303 | 41,7,469,88,59.645,185.17,1,9.848,4.9262,0 304 | 41,8,588,108,41.3,129.77,1,15.829,6.6552,0 305 | 42,2,347,95,65.695,204.76,1,6.7604,4.3989,0 306 | 42,3,182,96,40.3,154.82,1,12.624,10.615,0 307 | 42,4,481,84,67.605,223.9,1,6.2728,2.7607,0 308 | 42,5,571,91,67.362,214.2,1,6.5737,2.047,0 309 | 42,6,442,108,37.913,128.41,1,15.849,8.8709,0 310 | 42,7,466,88,59.595,184.91,1,9.9713,5.0309,0 311 | 42,8,588,108,41.208,129.88,1,15.829,6.6552,0 312 | 43,2,351,95,65.445,204.21,1,6.7688,4.3628,0 313 | 43,3,182,96,40.42,154.83,1,12.624,10.615,0 314 | 43,4,484,84,67.475,223.35,1,6.2774,2.7341,0 315 | 43,5,574,91,65.454,213.72,1,6.6665,2.0743,0 316 | 43,6,440,107,37.954,128.58,1,16.059,9.0236,0 317 | 43,7,463,88,59.545,184.65,1,9.9665,5.0634,0 318 | 43,8,588,108,41.115,130,1,15.625,6.5526,0 319 | 44,2,355,95,65.195,203.67,1,6.873,4.3864,0 320 | 44,3,182,96,40.54,154.84,1,12.624,10.615,0 321 | 44,4,487,84,67.344,222.8,1,6.3705,2.7563,0 322 | 44,5,577,91,63.096,213.26,1,6.6689,2.0559,0 323 | 44,6,439,107,37.99,128.74,1,16.057,9.038,0 324 | 44,7,461,88,59.495,184.38,1,9.9632,5.0851,0 325 | 44,8,588,108,41.023,130.11,1,15.625,6.5526,0 326 | 45,2,359,95,64.945,203.12,1,6.8813,4.3501,0 327 | 45,3,182,96,40.66,154.85,1,12.624,10.615,0 328 | 45,4,490,84,67.212,222.25,1,6.375,2.7296,0 329 | 45,5,580,91,60.496,212.81,1,6.7625,2.0834,0 330 | 45,6,437,107,38.025,128.89,1,16.056,9.0524,0 331 | 45,7,458,88,59.445,184.12,1,9.9583,5.1177,0 332 | 45,8,588,108,40.93,130.22,1,15.625,6.5526,0 333 | 46,2,363,95,64.695,202.57,1,6.9863,4.3737,0 334 | 46,3,182,96,40.78,154.85,1,12.624,10.615,0 335 | 46,4,493,84,67.08,221.71,1,6.4689,2.7518,0 336 | 46,5,582,91,57.8,212.36,1,6.7625,2.0834,0 337 | 46,6,436,107,38.059,129.04,1,15.842,8.9423,0 338 | 46,7,455,88,59.395,183.86,1,10.083,5.2241,0 339 | 46,8,588,108,40.837,130.34,1,15.625,6.5526,0 340 | 47,2,367,95,64.445,202.02,1,6.9945,4.3371,0 341 | 47,3,182,96,40.9,154.86,1,12.624,10.615,0 342 | 47,4,496,84,66.948,221.16,1,6.4733,2.7249,0 343 | 47,5,585,91,55.075,211.91,1,6.8571,2.1111,0 344 | 47,6,434,107,38.093,129.2,1,15.839,8.9708,0 345 | 47,7,452,89,59.345,183.6,1,9.9484,5.1827,0 346 | 47,8,588,108,40.743,130.45,1,15.625,6.5526,0 347 | 48,2,372,95,64.195,201.47,1,7.1024,4.3514,0 348 | 48,3,182,96,41.02,154.87,1,12.624,10.615,0 349 | 48,4,499,85,66.816,220.6,1,6.4777,2.698,0 350 | 48,5,588,91,52.35,211.46,1,6.8595,2.0925,0 351 | 48,6,433,107,38.127,129.35,1,15.837,8.9851,0 352 | 48,7,450,89,59.294,183.34,1,9.945,5.2044,0 353 | 48,8,588,108,40.65,130.57,1,15.625,6.5526,0 354 | 49,2,376,95,63.945,200.92,1,7.2074,4.3844,0 355 | 49,3,182,96,41.14,154.88,1,12.624,10.615,0 356 | 49,4,502,85,66.685,220.06,1,6.4821,2.6711,0 357 | 49,5,590,91,49.625,211.01,1,6.8595,2.0925,0 358 | 49,6,431,107,38.16,129.5,1,15.834,9.0136,0 359 | 49,7,447,89,59.242,183.07,1,9.94,5.2369,0 360 | 49,8,588,108,40.557,130.68,1,15.625,6.5526,0 361 | 50,2,380,95,63.695,200.37,1,7.2153,4.3472,0 362 | 50,3,182,96,41.26,154.89,1,12.624,10.615,0 363 | 50,4,505,85,66.555,219.5,1,6.5768,2.6929,0 364 | 50,5,593,91,46.9,210.56,1,6.955,2.1204,0 365 | 50,6,430,107,38.193,129.65,1,15.833,9.0279,0 366 | 50,7,444,89,59.19,182.81,1,10.065,5.3441,0 367 | 50,8,588,108,40.463,130.8,1,15.625,6.5526,0 368 | 51,2,384,95,63.446,199.82,1,7.3232,4.3709,0 369 | 51,3,182,96,41.38,154.89,1,12.624,10.615,0 370 | 51,4,508,85,66.425,218.96,1,6.6723,2.715,0 371 | 51,5,596,91,44.176,210.11,1,6.9573,2.1017,0 372 | 51,6,428,107,38.227,129.8,1,15.83,9.0565,0 373 | 51,7,442,89,59.138,182.55,1,10.061,5.3659,0 374 | 51,8,587,108,40.37,130.91,1,15.624,6.5667,0 375 | 52,2,388,95,63.198,199.27,1,7.3309,4.3335,0 376 | 52,3,182,96,41.5,154.9,1,12.624,10.615,0 377 | 52,4,511,85,66.294,218.41,1,6.6766,2.6877,0 378 | 52,5,599,91,41.452,209.66,1,7.0526,2.1391,0 379 | 52,6,427,107,38.26,129.95,1,15.828,9.0707,0 380 | 52,7,439,89,59.086,182.28,1,10.056,5.3986,0 381 | 52,8,587,108,40.278,131.02,1,15.422,6.4656,0 382 | 53,2,392,95,62.95,198.72,1,7.4398,4.3571,0 383 | 53,3,182,96,41.62,154.91,1,12.624,10.615,0 384 | 53,4,514,85,66.162,217.85,1,6.773,2.7098,0 385 | 53,5,601,91,38.728,209.21,1,7.0537,2.1297,0 386 | 53,6,425,107,38.293,130.1,1,15.616,8.9753,0 387 | 53,7,436,89,59.035,182.02,1,10.051,5.4313,0 388 | 53,8,587,108,40.185,131.14,1,15.422,6.4656,0 389 | 54,2,396,95,62.702,198.18,1,7.4474,4.3194,0 390 | 54,3,182,96,41.74,154.92,1,12.624,10.615,0 391 | 54,4,517,85,66.03,217.31,1,6.7772,2.6824,0 392 | 54,5,604,91,36.004,208.76,1,7.1511,2.158,0 393 | 54,6,424,107,38.327,130.26,1,15.614,8.9895,0 394 | 54,7,433,89,58.985,181.76,1,10.177,5.5404,0 395 | 54,8,587,108,40.093,131.25,1,15.422,6.4656,0 396 | 55,2,399,95,62.454,197.63,1,7.5553,4.3525,0 397 | 55,3,182,96,41.86,154.93,1,12.624,10.615,0 398 | 55,4,520,85,65.898,216.75,1,6.8731,2.7136,0 399 | 55,5,607,91,33.28,208.32,1,7.1522,2.1485,0 400 | 55,6,422,107,38.361,130.41,1,15.611,9.0178,0 401 | 55,7,431,89,58.935,181.5,1,10.174,5.5623,0 402 | 55,8,587,108,40.003,131.37,1,15.422,6.4656,0 403 | 56,2,403,95,62.205,197.08,1,7.5628,4.3145,0 404 | 56,3,182,96,41.98,154.93,1,12.624,10.615,0 405 | 56,4,523,85,65.766,216.21,1,6.8773,2.686,0 406 | 56,5,610,91,30.555,207.87,1,7.2506,2.177,0 407 | 56,6,421,106,38.395,130.56,1,15.819,9.1564,0 408 | 56,7,428,89,58.885,181.24,1,10.168,5.5952,0 409 | 56,8,587,108,39.926,131.48,1,15.421,6.4795,0 410 | 57,2,407,95,61.955,196.53,1,7.6717,4.3476,0 411 | 57,3,182,96,42.1,154.94,1,12.627,10.602,0 412 | 57,4,526,85,65.635,215.66,1,6.9755,2.7082,0 413 | 57,5,612,91,27.83,207.42,1,7.2506,2.177,0 414 | 57,6,419,106,38.429,130.71,1,15.816,9.185,0 415 | 57,7,425,90,58.835,180.97,1,10.163,5.6281,0 416 | 57,8,587,107,39.88,131.59,1,15.623,6.5808,0 417 | 58,2,410,95,61.705,195.98,1,7.7817,4.381,0 418 | 58,3,182,96,42.22,154.95,1,12.627,10.602,0 419 | 58,4,529,85,65.505,215.11,1,6.9796,2.6804,0 420 | 58,5,615,91,25.132,206.97,1,7.3499,2.2058,0 421 | 58,6,418,106,38.463,130.86,1,15.814,9.1993,0 422 | 58,7,423,90,58.785,180.71,1,10.16,5.65,0 423 | 58,8,586,107,39.893,131.71,1,15.622,6.5949,0 424 | 59,2,414,95,61.455,195.43,1,7.789,4.3426,0 425 | 59,3,182,96,42.34,154.96,1,12.627,10.602,0 426 | 59,4,533,85,65.374,214.56,1,7.08,2.6932,0 427 | 59,5,618,91,22.525,206.54,1,7.3522,2.1866,0 428 | 59,6,416,106,38.497,131.01,1,15.601,9.1027,0 429 | 59,7,420,90,58.735,180.45,1,10.154,5.6829,0 430 | 59,8,586,108,39.984,131.82,1,15.42,6.4935,0 431 | 60,2,417,95,61.205,194.88,1,7.9001,4.376,0 432 | 60,3,181,96,42.46,154.97,1,12.624,10.615,0 433 | 60,4,536,85,65.242,214.01,1,7.0841,2.6652,0 434 | 60,5,620,91,20.15,206.15,1,7.3533,2.1771,0 435 | 60,6,415,106,38.53,131.16,1,15.6,9.1169,0 436 | 60,7,417,90,58.685,180.18,1,10.149,5.7157,0 437 | 60,8,585,108,40.156,131.94,1,15.42,6.4935,0 438 | 61,2,421,95,60.955,194.33,1,7.9073,4.3373,0 439 | 61,3,181,96,42.579,154.97,1,12.624,10.615,0 440 | 61,4,539,85,65.11,213.46,1,7.1841,2.6874,0 441 | 61,5,622,91,18.204,205.83,1,7.4525,2.2155,0 442 | 61,6,413,106,38.563,131.32,1,15.597,9.1452,0 443 | 61,7,414,90,58.635,179.92,1,10.276,5.8274,0 444 | 61,8,584,108,40.385,132.05,1,15.221,6.4074,0 445 | 62,2,425,95,60.705,193.78,1,8.0212,4.361,0 446 | 62,3,181,96,42.697,154.98,1,12.624,10.615,0 447 | 62,4,542,85,64.978,212.91,1,7.285,2.7097,0 448 | 62,5,623,91,16.842,205.61,1,7.4525,2.2155,0 449 | 62,6,412,106,38.597,131.47,1,15.595,9.1594,0 450 | 62,7,412,90,58.585,179.66,1,10.273,5.8494,0 451 | 62,8,582,108,40.646,132.16,1,15.219,6.4351,0 452 | 63,2,428,95,60.455,193.24,1,8.0266,4.3318,0 453 | 63,3,181,96,42.815,154.99,1,12.624,10.615,0 454 | 63,4,545,86,64.846,212.36,1,7.192,2.6309,0 455 | 63,6,410,106,38.63,131.62,1,15.592,9.1877,0 456 | 63,7,409,90,58.535,179.4,1,10.267,5.8825,0 457 | 63,8,581,108,40.92,132.28,1,15.217,6.449,0 458 | 64,2,432,95,60.205,192.69,1,8.1415,4.3556,0 459 | 64,3,181,96,42.933,155,1,12.45,10.488,0 460 | 64,4,548,86,64.715,211.81,1,7.2929,2.6529,0 461 | 64,6,409,106,38.663,131.77,1,15.59,9.2019,0 462 | 64,7,406,90,58.485,179.13,1,10.262,5.9155,0 463 | 64,8,580,108,41.198,132.39,1,15.216,6.4628,0 464 | 65,2,435,95,59.955,192.14,1,8.145,4.3359,0 465 | 65,3,181,96,43.051,155.01,1,12.45,10.488,0 466 | 65,4,551,86,64.585,211.26,1,7.2968,2.6245,0 467 | 65,6,407,106,38.697,131.92,1,15.587,9.2302,0 468 | 65,7,404,90,58.435,178.87,1,10.393,6.0181,0 469 | 65,8,579,108,41.475,132.51,1,15.215,6.4767,0 470 | 66,2,439,96,59.705,191.59,1,8.152,4.2966,0 471 | 66,3,181,96,43.17,155.01,1,12.45,10.488,0 472 | 66,4,554,86,64.455,210.71,1,7.3986,2.6465,0 473 | 66,6,406,106,38.731,132.07,1,15.379,9.1199,0 474 | 66,7,401,91,58.385,178.61,1,10.252,5.9706,0 475 | 66,8,577,108,41.752,132.62,1,15.213,6.5043,0 476 | 67,2,442,96,59.455,191.04,1,8.1572,4.2671,0 477 | 67,3,181,96,43.29,155.02,1,12.45,10.488,0 478 | 67,4,557,86,64.324,210.16,1,7.4024,2.6179,0 479 | 67,6,404,106,38.765,132.22,1,15.376,9.148,0 480 | 67,7,398,91,58.335,178.34,1,10.247,6.0036,0 481 | 67,8,576,109,42.03,132.74,1,15.017,6.4051,0 482 | 68,2,446,96,59.205,190.49,1,8.2731,4.2905,0 483 | 68,3,181,96,43.41,155.03,1,12.45,10.488,0 484 | 68,4,560,86,64.192,209.62,1,7.5053,2.6399,0 485 | 68,6,403,105,38.799,132.38,1,15.581,9.2869,0 486 | 68,7,395,91,58.285,178.08,1,10.241,6.0366,0 487 | 68,8,575,109,42.307,132.85,1,15.016,6.4188,0 488 | 69,2,450,96,58.955,189.94,1,8.3902,4.314,0 489 | 69,3,181,96,43.53,155.04,1,12.45,10.488,0 490 | 69,4,563,86,64.06,209.06,1,7.509,2.6111,0 491 | 69,6,401,105,38.833,132.53,1,15.577,9.3152,0 492 | 69,7,393,91,58.235,177.82,1,10.372,6.14,0 493 | 69,8,573,109,42.585,132.96,1,15.014,6.4462,0 494 | 70,2,453,96,58.706,189.39,1,8.3953,4.2841,0 495 | 70,3,181,96,43.65,155.04,1,12.45,10.488,0 496 | 70,4,566,86,63.904,208.52,1,7.6116,2.6428,0 497 | 70,6,400,105,38.867,132.68,1,15.576,9.3294,0 498 | 70,7,390,91,58.185,177.56,1,10.367,6.1733,0 499 | 70,8,572,109,42.862,133.08,1,14.82,6.3617,0 500 | 71,2,457,96,58.458,188.84,1,8.5135,4.3076,0 501 | 71,3,181,96,43.77,155.05,1,12.45,10.488,0 502 | 71,4,569,86,63.666,207.98,1,7.7165,2.6651,0 503 | 71,6,399,105,38.9,132.83,1,15.574,9.3436,0 504 | 71,7,387,91,58.135,177.29,1,10.361,6.2065,0 505 | 71,8,571,109,43.138,133.19,1,14.819,6.3753,0 506 | 72,2,460,96,58.21,188.29,1,8.5184,4.2775,0 507 | 72,3,181,96,43.889,155.06,1,12.45,10.488,0 508 | 72,4,572,86,63.221,207.46,1,7.7202,2.6359,0 509 | 72,6,397,105,38.933,132.98,1,15.571,9.3719,0 510 | 72,7,385,91,58.085,177.03,1,10.357,6.2287,0 511 | 72,8,570,109,43.415,133.31,1,14.818,6.3889,0 512 | 73,2,464,96,57.962,187.75,1,8.6362,4.3112,0 513 | 73,3,181,96,44.002,155.07,1,12.453,10.475,0 514 | 73,4,575,86,62.392,206.98,1,7.8261,2.6582,0 515 | 73,6,396,105,38.967,133.13,1,15.362,9.2604,0 516 | 73,7,382,91,58.035,176.77,1,10.488,6.3452,0 517 | 73,8,568,109,43.693,133.42,1,14.816,6.4161,0 518 | 74,2,467,96,57.714,187.2,1,8.641,4.2808,0 519 | 74,3,181,96,44.103,155.07,1,12.453,10.475,0 520 | 74,4,578,86,61.039,206.56,1,7.8285,2.6387,0 521 | 74,6,395,105,39,133.28,1,15.361,9.2745,0 522 | 74,7,379,91,57.985,176.51,1,10.48,6.3898,0 523 | 74,8,567,110,43.97,133.53,1,14.624,6.3324,0 524 | 74,9,604,108,34.383,160.6,1,10.673,3.9121,0 525 | 75,2,471,96,57.465,186.65,1,8.7616,4.3044,0 526 | 75,3,181,96,44.18,155.08,1,12.453,10.475,0 527 | 75,4,580,86,59.162,206.19,1,7.8297,2.6289,0 528 | 75,6,395,105,39.033,133.44,1,15.361,9.2745,0 529 | 75,7,376,91,57.935,176.25,1,10.474,6.4233,0 530 | 75,8,566,110,44.248,133.65,1,14.624,6.3324,0 531 | 75,9,601,109,36.779,160.24,1,10.536,3.8681,0 532 | 76,2,475,96,57.215,186.1,1,8.768,4.2637,0 533 | 76,3,181,96,44.225,155.07,1,12.453,10.475,0 534 | 76,4,583,86,56.901,205.85,1,7.9354,2.661,0 535 | 76,6,395,105,39.067,133.59,1,15.361,9.2745,0 536 | 76,7,374,92,57.885,175.98,1,10.47,6.4455,0 537 | 76,8,565,110,44.525,133.76,1,14.623,6.3459,0 538 | 76,9,596,109,39.702,159.8,1,10.666,3.9802,0 539 | 77,2,478,96,56.965,185.55,1,8.8881,4.2974,0 540 | 77,3,182,96,44.239,155.07,1,12.456,10.462,0 541 | 77,4,586,86,54.433,205.54,1,7.9378,2.6413,0 542 | 77,6,394,105,39.101,133.74,1,15.359,9.2885,0 543 | 77,7,371,92,57.835,175.72,1,10.464,6.479,0 544 | 77,8,563,110,44.802,133.88,1,14.621,6.3729,0 545 | 77,9,592,110,42.91,159.33,1,10.529,3.9357,0 546 | 78,2,482,96,56.715,185,1,8.8944,4.2564,0 547 | 78,3,182,96,44.23,155.06,1,12.456,10.462,0 548 | 78,4,588,86,51.883,205.24,1,7.9378,2.6413,0 549 | 78,6,394,105,39.135,133.89,1,15.359,9.2885,0 550 | 78,7,368,92,57.785,175.46,1,10.458,6.5124,0 551 | 78,8,562,110,45.078,133.99,1,14.62,6.3864,0 552 | 78,9,587,110,46.196,158.83,1,10.66,4.0368,0 553 | 79,2,485,97,56.465,184.45,1,8.8991,4.2256,0 554 | 79,3,182,96,44.209,155.05,1,12.456,10.462,0 555 | 79,4,591,86,49.309,204.94,1,8.0446,2.6737,0 556 | 79,6,394,105,39.169,134.04,1,15.155,9.1645,0 557 | 79,7,366,92,57.735,175.19,1,10.454,6.5347,0 558 | 79,8,561,110,45.353,134.1,1,14.431,6.3034,0 559 | 79,9,582,111,49.393,158.34,1,10.521,4.0145,0 560 | 80,2,489,97,56.215,183.9,1,9.0219,4.2489,0 561 | 80,3,183,96,44.182,155.04,1,12.459,10.449,0 562 | 80,4,593,86,46.714,204.63,1,8.0458,2.6638,0 563 | 80,6,394,105,39.203,134.19,1,15.155,9.1645,0 564 | 80,7,363,92,57.685,174.93,1,10.586,6.6541,0 565 | 80,8,559,110,45.621,134.22,1,14.429,6.3301,0 566 | 80,9,577,111,52.285,157.88,1,10.651,4.1161,0 567 | 81,2,493,97,55.965,183.35,1,9.0265,4.2178,0 568 | 81,3,183,96,44.154,155.03,1,12.459,10.449,0 569 | 81,4,596,86,44.047,204.31,1,8.0481,2.6439,0 570 | 81,6,394,105,39.237,134.34,1,15.155,9.1645,0 571 | 81,7,360,92,57.635,174.67,1,10.58,6.6878,0 572 | 81,8,558,111,45.87,134.33,1,14.243,6.2481,0 573 | 81,9,573,112,54.61,157.46,1,10.513,4.082,0 574 | 82,2,496,97,55.715,182.81,1,9.1491,4.2515,0 575 | 82,3,184,96,44.125,155.01,1,12.462,10.436,0 576 | 82,4,599,86,41.201,203.93,1,8.1549,2.6864,0 577 | 82,6,394,105,39.27,134.5,1,15.155,9.1645,0 578 | 82,7,357,92,57.585,174.41,1,10.574,6.7214,0 579 | 82,8,557,111,46.083,134.45,1,14.243,6.2481,0 580 | 82,9,569,112,56.162,157.1,1,10.51,4.1157,0 581 | 83,2,500,97,55.465,182.26,1,9.1551,4.2098,0 582 | 83,3,184,96,44.095,155,1,12.462,10.436,0 583 | 83,4,602,86,38.021,203.44,1,8.1572,2.6665,0 584 | 83,6,394,105,39.303,134.65,1,15.155,9.1645,0 585 | 83,7,355,92,57.535,174.15,1,10.57,6.7439,0 586 | 83,8,555,111,46.248,134.56,1,14.241,6.2747,0 587 | 83,9,565,112,56.941,156.8,1,10.639,4.2291,0 588 | 84,2,503,97,55.215,181.71,1,9.279,4.2436,0 589 | 84,3,185,96,44.065,154.99,1,12.638,10.55,0 590 | 84,4,606,86,34.389,202.82,1,8.2663,2.6993,0 591 | 84,6,394,105,39.337,134.8,1,15.155,9.1645,0 592 | 84,7,352,92,57.485,173.88,1,10.704,6.8655,0 593 | 84,8,554,111,46.364,134.68,1,14.239,6.2879,0 594 | 84,9,561,112,57.154,156.55,1,10.634,4.2743,0 595 | 85,2,507,97,54.965,181.16,1,9.2849,4.2016,0 596 | 85,3,185,96,44.035,154.98,1,12.638,10.55,0 597 | 85,4,610,86,30.35,202.06,1,8.2686,2.6791,0 598 | 85,6,394,105,39.371,134.95,1,15.155,9.1645,0 599 | 85,7,349,92,57.435,173.62,1,10.697,6.8994,0 600 | 85,8,553,111,46.446,134.79,1,14.238,6.3012,0 601 | 85,9,558,112,57.061,156.32,1,10.63,4.3082,0 602 | 86,2,510,97,54.715,180.61,1,9.41,4.2354,0 603 | 86,3,186,96,44.005,154.97,1,12.64,10.537,0 604 | 86,4,614,86,26.137,201.23,1,8.3788,2.7121,0 605 | 86,6,394,105,39.405,135.1,1,14.955,9.0423,0 606 | 86,7,347,93,57.385,173.36,1,10.553,6.8336,0 607 | 86,8,551,111,46.508,134.9,1,14.236,6.3278,0 608 | 86,9,555,113,56.848,156.1,1,10.492,4.2727,0 609 | 87,2,514,97,54.465,180.06,1,9.4158,4.1931,0 610 | 87,3,186,96,43.976,154.96,1,12.638,10.55,0 611 | 87,4,618,86,22.143,200.43,1,8.4901,2.7455,0 612 | 87,6,393,105,39.439,135.25,1,14.953,9.0561,0 613 | 87,7,344,93,57.335,173.09,1,10.547,6.8673,0 614 | 87,8,550,111,46.563,135.02,1,14.053,6.2463,0 615 | 87,9,551,113,56.6,155.89,1,10.621,4.3871,0 616 | 88,2,518,97,54.215,179.51,1,9.5438,4.2163,0 617 | 88,3,186,95,43.948,154.94,1,12.813,10.678,0 618 | 88,4,621,86,18.815,199.75,1,8.6016,2.7894,0 619 | 88,6,393,105,39.473,135.4,1,14.953,9.0561,0 620 | 88,7,341,93,57.285,172.83,1,10.681,6.9898,0 621 | 88,8,548,111,46.617,135.13,1,14.05,6.2726,0 622 | 88,9,548,113,56.352,155.67,1,10.617,4.421,0 623 | 89,2,521,97,53.966,178.96,1,9.6702,4.2612,0 624 | 89,3,187,95,43.92,154.93,1,12.816,10.665,0 625 | 89,4,624,86,16.471,199.28,1,8.6038,2.7689,0 626 | 89,6,393,105,39.507,135.56,1,14.953,9.0561,0 627 | 89,7,339,93,57.235,172.57,1,10.676,7.0124,0 628 | 89,8,547,111,46.67,135.25,1,14.049,6.2858,0 629 | 89,9,545,113,56.104,155.46,1,10.613,4.4547,0 630 | 90,2,525,97,53.718,178.41,1,9.6759,4.2183,0 631 | 90,3,187,95,43.892,154.92,1,12.816,10.665,0 632 | 90,6,393,105,39.54,135.71,1,14.953,9.0561,0 633 | 90,7,337,93,57.185,172.31,1,10.672,7.035,0 634 | 90,8,545,111,46.722,135.36,1,14.046,6.3121,0 635 | 90,9,541,113,55.855,155.24,1,10.607,4.5111,0 636 | 91,2,528,97,53.47,177.87,1,9.8051,4.2525,0 637 | 91,3,188,95,43.864,154.91,1,12.819,10.652,0 638 | 91,6,393,105,39.573,135.86,1,14.953,9.0561,0 639 | 91,7,335,93,57.134,172.04,1,10.668,7.0576,0 640 | 91,8,544,111,46.775,135.47,1,14.045,6.3253,0 641 | 91,9,538,113,55.605,155.03,1,10.603,4.5448,0 642 | 92,2,532,98,53.222,177.32,1,9.6856,4.1431,0 643 | 92,3,188,95,43.835,154.9,1,12.819,10.652,0 644 | 92,6,393,105,39.607,136.01,1,14.755,8.9357,0 645 | 92,7,334,93,57.082,171.78,1,10.808,7.1595,0 646 | 92,8,542,111,46.827,135.59,1,14.043,6.3516,0 647 | 92,9,535,113,55.355,154.81,1,10.735,4.6503,0 648 | 93,2,536,98,52.974,176.77,1,9.8161,4.1661,0 649 | 93,3,189,95,43.805,154.88,1,12.822,10.639,0 650 | 93,6,393,106,39.64,136.16,1,14.561,8.817,0 651 | 93,7,333,93,57.03,171.52,1,10.806,7.1709,0 652 | 93,8,541,111,46.88,135.7,1,14.041,6.3648,0 653 | 93,9,532,113,55.106,154.59,1,10.731,4.6842,0 654 | 94,2,539,98,52.725,176.22,1,9.8202,4.1336,0 655 | 94,3,189,95,43.775,154.87,1,12.822,10.639,0 656 | 94,6,393,106,39.673,136.31,1,14.561,8.817,0 657 | 94,7,331,93,56.978,171.25,1,10.801,7.1937,0 658 | 94,8,539,111,46.933,135.81,1,14.039,6.3911,0 659 | 94,9,528,113,54.858,154.38,1,10.725,4.7295,0 660 | 95,2,543,98,52.475,175.67,1,9.9521,4.1565,0 661 | 95,3,190,95,43.745,154.86,1,12.824,10.626,0 662 | 95,6,393,106,39.707,136.46,1,14.561,8.817,0 663 | 95,7,330,93,56.926,170.99,1,10.943,7.2972,0 664 | 95,8,538,111,46.985,135.93,1,14.037,6.4043,0 665 | 95,9,525,113,54.61,154.16,1,10.721,4.7634,0 666 | 96,2,546,98,52.225,175.12,1,9.9561,4.1238,0 667 | 96,3,190,95,43.715,154.85,1,12.824,10.626,0 668 | 96,6,393,106,39.741,136.62,1,14.561,8.817,0 669 | 96,7,329,93,56.875,170.73,1,10.941,7.3087,0 670 | 96,8,536,111,47.037,136.04,1,13.855,6.3357,0 671 | 96,9,522,113,54.362,153.95,1,10.855,4.8709,0 672 | 97,2,550,98,51.975,174.57,1,10.088,4.1577,0 673 | 97,3,190,95,43.685,154.84,1,12.824,10.626,0 674 | 97,6,392,106,39.775,136.77,1,14.559,8.8306,0 675 | 97,7,328,93,56.825,170.47,1,10.939,7.3201,0 676 | 97,8,535,111,47.09,136.16,1,13.854,6.3488,0 677 | 97,9,518,113,54.114,153.73,1,10.849,4.9164,0 678 | 98,2,553,98,51.725,174.02,1,10.092,4.1248,0 679 | 98,3,191,95,43.656,154.83,1,12.827,10.613,0 680 | 98,6,392,106,39.809,136.92,1,14.559,8.8306,0 681 | 98,7,326,93,56.775,170.21,1,10.934,7.3431,0 682 | 98,8,533,111,47.143,136.27,1,13.851,6.3749,0 683 | 98,9,515,113,53.865,153.51,1,10.843,4.9619,0 684 | 99,2,557,98,51.475,173.47,1,10.227,4.1477,0 685 | 99,3,191,95,43.628,154.81,1,12.827,10.613,0 686 | 99,6,392,106,39.843,137.07,1,14.367,8.7136,0 687 | 99,7,325,93,56.725,169.94,1,11.078,7.4483,0 688 | 99,8,532,111,47.195,136.38,1,13.85,6.388,0 689 | 99,9,512,114,53.615,153.3,1,10.701,4.9215,0 690 | 100,2,561,98,51.225,172.92,1,10.363,4.1709,0 691 | 100,3,192,95,43.6,154.8,1,12.83,10.6,0 692 | 100,6,392,106,39.877,137.22,1,14.367,8.7136,0 693 | 100,7,324,93,56.675,169.68,1,11.076,7.4599,0 694 | 100,8,530,111,47.248,136.5,1,13.848,6.4141,0 695 | 100,9,508,114,53.365,153.08,1,10.695,4.9667,0 696 | 101,2,564,98,50.975,172.38,1,10.367,4.1375,0 697 | 101,3,192,95,43.572,154.79,1,12.83,10.6,0 698 | 101,6,392,106,39.91,137.37,1,14.367,8.7136,0 699 | 101,7,322,93,56.625,169.42,1,11.071,7.483,0 700 | 101,8,529,111,47.3,136.61,1,13.846,6.4271,0 701 | 101,9,505,114,53.115,152.87,1,10.829,5.0755,0 702 | 102,2,568,98,50.725,171.83,1,10.505,4.1606,0 703 | 102,3,193,94,43.544,154.78,1,13.011,10.716,0 704 | 102,6,392,106,39.943,137.52,1,14.367,8.7136,0 705 | 102,7,321,93,56.575,169.16,1,11.069,7.4945,0 706 | 102,8,527,111,47.353,136.73,1,13.844,6.4532,0 707 | 102,9,502,114,52.866,152.65,1,10.825,5.1095,0 708 | 103,2,571,98,50.475,171.28,1,10.508,4.1269,0 709 | 103,3,193,94,43.515,154.77,1,13.011,10.716,0 710 | 103,6,392,106,39.977,137.67,1,14.367,8.7136,0 711 | 103,7,320,93,56.525,168.89,1,11.215,7.6016,0 712 | 103,8,526,111,47.407,136.84,1,13.842,6.4663,0 713 | 103,9,498,114,52.618,152.44,1,10.819,5.1549,0 714 | 104,2,575,98,50.225,170.73,1,10.648,4.15,0 715 | 104,3,194,94,43.485,154.76,1,13.013,10.703,0 716 | 104,6,392,106,40.01,137.83,1,14.369,8.7001,0 717 | 104,7,319,93,56.475,168.63,1,11.213,7.6132,0 718 | 104,8,524,111,47.46,136.96,1,13.84,6.4924,0 719 | 104,9,495,114,52.37,152.22,1,10.814,5.1889,0 720 | 105,2,578,99,49.951,170.18,1,10.516,4.0595,0 721 | 105,3,194,94,43.455,154.75,1,13.013,10.703,0 722 | 105,6,392,106,40.043,137.98,1,14.369,8.7001,0 723 | 105,7,317,93,56.425,168.37,1,11.208,7.6365,0 724 | 105,8,523,111,47.513,137.07,1,13.661,6.4106,0 725 | 105,9,492,114,52.122,152,1,10.81,5.2229,0 726 | 106,2,582,99,49.594,169.65,1,10.655,4.0821,0 727 | 106,3,194,94,43.425,154.73,1,13.013,10.703,0 728 | 106,6,392,106,40.077,138.12,1,14.18,8.5849,0 729 | 106,7,316,93,56.375,168.1,1,11.206,7.6481,0 730 | 106,8,521,111,47.565,137.18,1,13.659,6.4365,0 731 | 106,9,489,114,51.874,151.79,1,10.943,5.3455,0 732 | 107,2,585,99,49.027,169.14,1,10.659,4.0481,0 733 | 107,3,195,94,43.395,154.72,1,13.016,10.69,0 734 | 107,6,392,106,40.111,138.27,1,14.18,8.5849,0 735 | 107,7,315,93,56.325,167.84,1,11.353,7.757,0 736 | 107,8,520,111,47.617,137.3,1,13.657,6.4494,0 737 | 107,9,485,114,51.625,151.57,1,10.937,5.3911,0 738 | 108,2,589,99,48.071,168.69,1,10.799,4.0707,0 739 | 108,3,195,94,43.365,154.71,1,13.016,10.69,0 740 | 108,6,392,106,40.145,138.4,1,14.18,8.5849,0 741 | 108,7,313,93,56.275,167.58,1,11.349,7.7804,0 742 | 108,8,518,111,47.67,137.41,1,13.655,6.4753,0 743 | 108,9,482,114,51.377,151.36,1,10.932,5.4253,0 744 | 109,2,592,99,46.586,168.31,1,10.802,4.0479,0 745 | 109,3,196,94,43.336,154.7,1,13.019,10.677,0 746 | 109,6,392,106,40.179,138.51,1,14.18,8.5849,0 747 | 109,7,312,93,56.225,167.32,1,11.347,7.7922,0 748 | 109,8,517,111,47.723,137.53,1,13.653,6.4883,0 749 | 109,9,479,114,51.138,151.14,1,10.928,5.4595,0 750 | 110,2,595,99,44.571,167.99,1,10.942,4.0935,0 751 | 110,3,196,94,43.308,154.69,1,13.019,10.677,0 752 | 110,6,392,106,40.213,138.61,1,14.18,8.5849,0 753 | 110,7,311,93,56.175,167.06,1,11.345,7.8039,0 754 | 110,8,515,111,47.775,137.64,1,13.65,6.5141,0 755 | 110,9,475,114,50.92,150.93,1,11.063,5.5844,0 756 | 111,2,598,99,42.167,167.73,1,10.944,4.0705,0 757 | 111,3,197,94,43.28,154.68,1,13.021,10.664,0 758 | 111,6,393,106,40.247,138.7,1,14.182,8.5716,0 759 | 111,7,310,93,56.125,166.79,1,11.494,7.9146,0 760 | 111,8,514,111,47.827,137.75,1,13.649,6.5271,0 761 | 111,9,472,114,50.742,150.73,1,11.058,5.6188,0 762 | 112,2,601,99,39.553,167.5,1,10.945,4.0591,0 763 | 112,3,197,94,43.252,154.66,1,13.021,10.664,0 764 | 112,6,393,106,40.28,138.78,1,14.182,8.5716,0 765 | 112,7,308,93,56.075,166.53,1,11.489,7.9383,0 766 | 112,8,512,111,47.88,137.87,1,13.646,6.553,0 767 | 112,9,469,114,50.617,150.53,1,11.053,5.6532,0 768 | 113,2,603,99,36.855,167.28,1,10.946,4.0476,0 769 | 113,3,197,94,43.224,154.65,1,13.021,10.664,0 770 | 113,6,394,107,40.313,138.86,1,13.998,8.4449,0 771 | 113,7,307,93,56.025,166.27,1,11.487,7.9501,0 772 | 113,8,511,111,47.933,137.98,1,13.645,6.5659,0 773 | 113,9,466,114,50.545,150.35,1,11.049,5.6876,0 774 | 114,2,606,99,34.132,167.06,1,10.948,4.0246,0 775 | 114,3,198,94,43.195,154.64,1,13.024,10.65,0 776 | 114,6,394,107,40.347,138.94,1,13.998,8.4449,0 777 | 114,7,306,93,55.975,166,1,11.483,7.9738,0 778 | 114,8,509,111,47.985,138.1,1,13.467,6.4969,0 779 | 114,9,463,114,50.513,150.17,1,11.044,5.722,0 780 | 115,2,609,99,31.41,166.84,1,11.089,4.0818,0 781 | 115,3,198,94,43.165,154.63,1,13.024,10.65,0 782 | 115,6,395,107,40.38,139.02,1,13.816,8.3202,0 783 | 115,7,304,93,55.925,165.74,1,11.632,8.0983,0 784 | 115,8,508,111,48.038,138.21,1,13.467,6.4969,0 785 | 115,9,459,114,50.502,149.99,1,11.181,5.8494,0 786 | 116,2,611,99,28.714,166.63,1,11.09,4.0702,0 787 | 116,3,198,93,43.135,154.62,1,13.205,10.781,0 788 | 116,6,395,107,40.413,139.09,1,13.816,8.3202,0 789 | 116,7,303,93,55.875,165.48,1,11.63,8.1103,0 790 | 116,8,506,111,48.09,138.32,1,13.465,6.5225,0 791 | 116,9,456,114,50.5,149.82,1,11.176,5.884,0 792 | 117,2,614,99,26.11,166.42,1,11.092,4.0471,0 793 | 117,3,198,93,43.105,154.61,1,13.205,10.781,0 794 | 117,6,396,107,40.447,139.17,1,13.818,8.3071,0 795 | 117,7,302,93,55.825,165.22,1,11.628,8.1222,0 796 | 117,8,505,111,48.143,138.44,1,13.463,6.5354,0 797 | 117,9,453,114,50.5,149.65,1,11.171,5.9186,0 798 | 118,2,616,99,23.737,166.23,1,11.092,4.0471,0 799 | 118,3,198,93,43.075,154.59,1,13.205,10.781,0 800 | 118,6,396,107,40.481,139.25,1,13.818,8.3071,0 801 | 118,7,301,93,55.775,164.95,1,11.781,8.2367,0 802 | 118,8,503,111,48.197,138.55,1,13.46,6.561,0 803 | 118,9,450,114,50.5,149.47,1,11.166,5.9532,0 804 | 119,2,618,99,21.792,166.07,1,11.094,4.0355,0 805 | 119,3,198,93,43.045,154.58,1,13.205,10.781,0 806 | 119,6,397,107,40.515,139.33,1,13.819,8.2939,0 807 | 119,7,299,93,55.725,164.69,1,11.777,8.2608,0 808 | 119,8,502,111,48.25,138.67,1,13.459,6.5739,0 809 | 119,9,447,114,50.5,149.3,1,11.161,5.9878,0 810 | 120,2,620,99,20.431,165.96,1,11.237,4.0815,0 811 | 120,3,198,93,43.016,154.57,1,13.205,10.781,0 812 | 120,6,397,108,40.549,139.41,1,13.639,8.1842,0 813 | 120,7,298,93,55.675,164.43,1,11.774,8.2728,0 814 | 120,8,500,111,48.303,138.78,1,13.456,6.5995,0 815 | 120,9,444,114,50.5,149.12,1,11.156,6.0223,0 816 | 121,3,198,93,42.988,154.56,1,13.205,10.781,0 817 | 121,6,398,108,40.583,139.49,1,13.64,8.1712,0 818 | 121,7,297,93,55.625,164.16,1,11.772,8.2848,0 819 | 121,8,499,111,48.355,138.9,1,13.455,6.6123,0 820 | 121,9,441,114,50.5,148.95,1,11.296,6.141,0 821 | 122,3,198,93,42.96,154.55,1,13.205,10.781,0 822 | 122,6,398,108,40.617,139.56,1,13.64,8.1712,0 823 | 122,7,295,93,55.575,163.9,1,11.926,8.4134,0 824 | 122,8,497,111,48.407,139.01,1,13.28,6.5433,0 825 | 122,9,437,114,50.5,148.78,1,11.289,6.1874,0 826 | 123,3,198,93,42.932,154.54,1,13.205,10.781,0 827 | 123,6,398,108,40.65,139.64,1,13.64,8.1712,0 828 | 123,7,294,93,55.525,163.64,1,11.924,8.4256,0 829 | 123,8,496,111,48.46,139.12,1,13.278,6.556,0 830 | 123,9,434,114,50.5,148.6,1,11.284,6.2222,0 831 | 124,3,198,93,42.904,154.53,1,13.205,10.781,0 832 | 124,6,399,108,40.683,139.72,1,13.642,8.1582,0 833 | 124,7,293,93,55.475,163.38,1,11.921,8.4377,0 834 | 124,8,494,111,48.513,139.24,1,13.275,6.5815,0 835 | 124,9,431,114,50.5,148.43,1,11.279,6.257,0 836 | 125,3,198,93,42.875,154.51,1,13.205,10.781,0 837 | 125,6,399,108,40.717,139.8,1,13.642,8.1582,0 838 | 125,7,292,94,55.425,163.12,1,11.761,8.345,0 839 | 125,8,493,111,48.565,139.35,1,13.274,6.5942,0 840 | 125,9,428,114,50.494,148.26,1,11.273,6.2918,0 841 | 126,3,198,93,42.845,154.5,1,13.205,10.781,0 842 | 126,6,400,108,40.751,139.88,1,13.644,8.1452,0 843 | 126,7,290,94,55.375,162.85,1,11.914,8.4741,0 844 | 126,8,491,111,48.617,139.47,1,13.271,6.6197,0 845 | 126,9,425,114,50.47,148.08,1,11.268,6.3265,0 846 | 127,3,198,93,42.815,154.49,1,13.205,10.781,0 847 | 127,6,400,109,40.785,139.96,1,13.466,8.0373,0 848 | 127,7,289,94,55.323,162.59,1,11.912,8.4862,0 849 | 127,8,490,111,48.666,139.59,1,13.27,6.6324,0 850 | 127,9,422,114,50.397,147.91,1,11.41,6.4482,0 851 | 128,3,198,93,42.785,154.48,1,13.205,10.781,0 852 | 128,6,401,109,40.819,140.04,1,13.292,7.9182,0 853 | 128,7,288,94,55.262,162.34,1,11.91,8.4984,0 854 | 128,8,488,111,48.709,139.72,1,13.267,6.6578,0 855 | 128,9,419,114,50.234,147.74,1,11.405,6.4832,0 856 | 129,3,198,93,42.755,154.47,1,13.205,10.781,0 857 | 129,6,401,109,40.853,140.12,1,13.292,7.9182,0 858 | 129,7,287,94,55.182,162.11,1,11.908,8.5105,0 859 | 129,8,487,111,48.741,139.88,1,13.265,6.6706,0 860 | 129,9,416,114,49.949,147.57,1,11.398,6.5298,0 861 | 130,3,198,93,42.726,154.46,1,13.205,10.781,0 862 | 130,6,402,109,40.887,140.19,1,13.294,7.9054,0 863 | 130,7,285,94,55.064,161.91,1,12.063,8.6418,0 864 | 130,8,485,111,48.758,140.08,1,13.092,6.6014,0 865 | 130,9,414,114,49.541,147.4,1,11.394,6.5531,0 866 | 131,3,198,93,42.698,154.44,1,13.205,10.781,0 867 | 131,6,402,109,40.92,140.27,1,13.294,7.9054,0 868 | 131,7,284,93,54.896,161.76,1,12.223,8.7626,0 869 | 131,8,484,111,48.76,140.3,1,13.091,6.6141,0 870 | 131,9,411,114,49.044,147.23,1,11.389,6.5881,0 871 | 132,3,198,93,42.67,154.43,1,13.205,10.781,0 872 | 132,6,403,109,40.953,140.35,1,13.296,7.8926,0 873 | 132,7,284,93,54.678,161.67,1,12.223,8.7626,0 874 | 132,8,483,111,48.751,140.56,1,13.089,6.6267,0 875 | 132,9,409,114,48.498,147.06,1,11.385,6.6115,0 876 | 133,3,198,93,42.642,154.42,1,13.205,10.781,0 877 | 133,6,403,109,40.987,140.43,1,13.296,7.8926,0 878 | 133,7,283,93,54.424,161.61,1,12.221,8.7749,0 879 | 133,8,482,111,48.736,140.82,1,13.088,6.6393,0 880 | 133,9,407,114,47.932,146.88,1,11.528,6.7359,0 881 | 134,3,198,93,42.614,154.41,1,13.205,10.781,0 882 | 134,6,404,110,41.02,140.51,1,13.124,7.7754,0 883 | 134,7,282,93,54.15,161.57,1,12.219,8.7873,0 884 | 134,8,480,111,48.719,141.1,1,12.917,6.5708,0 885 | 134,9,404,113,47.36,146.72,1,11.674,6.862,0 886 | 134,10,0,119,29.818,172.01,1,6.4463,8.3377,0 887 | 135,3,198,93,42.585,154.4,1,13.205,10.781,0 888 | 135,6,404,110,41.053,140.59,1,13.124,7.7754,0 889 | 135,7,281,93,53.868,161.54,1,12.214,8.812,0 890 | 135,8,479,111,48.7,141.37,1,12.915,6.5834,0 891 | 135,9,402,113,46.788,146.54,1,11.671,6.8857,0 892 | 135,10,0,118,32.385,173.01,1,6.4544,8.3186,0 893 | 136,3,198,92,42.555,154.38,1,13.389,10.914,0 894 | 136,6,405,110,41.087,140.66,1,13.126,7.7627,0 895 | 136,7,281,93,53.585,161.51,1,12.214,8.812,0 896 | 136,8,478,111,48.681,141.65,1,12.914,6.5959,0 897 | 136,9,400,113,46.216,146.37,1,11.667,6.9094,0 898 | 136,10,0,117,35.485,174.21,1,6.4585,8.3091,0 899 | 137,3,198,92,42.525,154.37,1,13.389,10.914,0 900 | 137,6,405,110,41.121,140.74,1,13.126,7.7627,0 901 | 137,7,280,93,53.302,161.48,1,12.212,8.8243,0 902 | 137,8,477,111,48.663,141.92,1,12.912,6.6084,0 903 | 137,9,397,113,45.645,146.2,1,11.659,6.9567,0 904 | 137,10,0,115,38.777,175.46,1,6.5707,8.3793,0 905 | 138,3,198,92,42.495,154.36,1,13.389,10.914,0 906 | 138,6,406,110,41.155,140.82,1,13.128,7.75,0 907 | 138,7,279,93,53.018,161.45,1,12.21,8.8367,0 908 | 138,8,476,111,48.645,142.19,1,12.745,6.5281,0 909 | 138,9,395,113,45.075,146.03,1,11.656,6.9803,0 910 | 138,10,0,114,41.906,176.59,1,6.5747,8.3697,0 911 | 139,3,198,92,42.465,154.35,1,13.389,10.914,0 912 | 139,6,406,110,41.189,140.9,1,13.128,7.75,0 913 | 139,7,279,93,52.734,161.43,1,12.21,8.8367,0 914 | 139,8,474,111,48.627,142.47,1,12.742,6.5529,0 915 | 139,9,393,113,44.505,145.86,1,11.805,7.0972,0 916 | 139,10,0,113,44.543,177.42,1,6.5827,8.3505,0 917 | 140,3,198,92,42.435,154.34,1,13.389,10.914,0 918 | 140,6,407,111,41.223,140.98,1,12.959,7.6347,0 919 | 140,7,278,92,52.45,161.4,1,12.372,8.9598,0 920 | 140,8,473,111,48.609,142.74,1,12.741,6.5653,0 921 | 140,9,390,113,43.935,145.69,1,11.797,7.1449,0 922 | 140,10,0,112,46.463,177.82,1,6.6921,8.4308,0 923 | 141,3,197,92,42.406,154.33,1,13.386,10.928,0 924 | 141,6,407,111,41.257,141.06,1,12.79,7.5334,0 925 | 141,7,277,92,52.166,161.37,1,12.37,8.9723,0 926 | 141,8,472,111,48.59,143.01,1,12.576,6.4858,0 927 | 141,9,388,113,43.365,145.52,1,11.794,7.1687,0 928 | 141,10,8,112,47.665,177.81,1,6.7239,8.3536,0 929 | 142,3,197,92,42.378,154.31,1,13.386,10.928,0 930 | 142,6,408,111,41.29,141.13,1,12.792,7.5209,0 931 | 142,7,277,92,51.882,161.34,1,12.367,8.9848,0 932 | 142,8,471,110,48.571,143.29,1,12.738,6.5902,0 933 | 142,9,386,113,42.794,145.35,1,11.79,7.1925,0 934 | 142,10,11,112,48.341,177.48,1,6.7397,8.315,0 935 | 143,3,197,92,42.35,154.3,1,13.386,10.928,0 936 | 143,6,408,111,41.323,141.21,1,12.792,7.5209,0 937 | 143,7,276,92,51.598,161.31,1,12.365,8.9972,0 938 | 143,8,469,110,48.553,143.56,1,12.735,6.615,0 939 | 143,9,384,112,42.222,145.18,1,11.941,7.3117,0 940 | 143,10,15,112,48.732,176.98,1,6.862,8.3662,0 941 | 144,3,197,92,42.322,154.29,1,13.386,10.928,0 942 | 144,6,409,111,41.357,141.29,1,12.794,7.5085,0 943 | 144,7,275,92,51.314,161.28,1,12.363,9.0097,0 944 | 144,8,468,110,48.535,143.84,1,12.733,6.6274,0 945 | 144,9,381,112,41.65,145.01,1,11.934,7.3597,0 946 | 144,10,19,112,49.01,176.41,1,6.8775,8.3273,0 947 | 145,3,197,92,42.294,154.28,1,13.386,10.928,0 948 | 145,6,409,111,41.39,141.37,1,12.794,7.5085,0 949 | 145,7,275,92,51.03,161.25,1,12.363,9.0097,0 950 | 145,8,467,110,48.517,144.11,1,12.568,6.5475,0 951 | 145,9,379,112,41.078,144.84,1,12.087,7.4809,0 952 | 145,10,23,112,49.255,175.83,1,7.0009,8.3788,0 953 | 146,3,197,92,42.265,154.27,1,13.386,10.928,0 954 | 146,6,410,111,41.423,141.45,1,12.796,7.496,0 955 | 146,7,274,92,50.746,161.22,1,12.361,9.0221,0 956 | 146,8,466,110,48.499,144.38,1,12.567,6.5598,0 957 | 146,9,377,112,40.506,144.66,1,12.083,7.5051,0 958 | 146,10,27,112,49.5,175.25,1,7.0163,8.3397,0 959 | 147,3,197,92,42.235,154.26,1,13.386,10.928,0 960 | 147,6,410,112,41.457,141.54,1,12.63,7.3964,0 961 | 147,7,273,92,50.462,161.19,1,12.358,9.0346,0 962 | 147,8,464,110,48.48,144.66,1,12.563,6.5844,0 963 | 147,9,374,112,39.935,144.5,1,12.075,7.5536,0 964 | 147,10,31,112,49.745,174.66,1,7.1408,8.3913,0 965 | 148,3,197,92,42.205,154.25,1,13.386,10.928,0 966 | 148,6,411,112,41.491,141.66,1,12.632,7.384,0 967 | 148,7,273,92,50.178,161.16,1,12.358,9.0346,0 968 | 148,8,463,110,48.461,144.93,1,12.562,6.5968,0 969 | 148,9,372,112,39.371,144.32,1,12.072,7.5777,0 970 | 148,10,35,112,49.991,174.08,1,7.156,8.352,0 971 | 149,3,197,92,42.175,154.23,1,13.386,10.928,0 972 | 149,6,411,112,41.525,141.82,1,12.632,7.384,0 973 | 149,7,272,91,49.894,161.14,1,12.521,9.1727,0 974 | 149,8,462,110,48.443,145.21,1,12.399,6.5174,0 975 | 149,9,370,112,38.83,144.15,1,12.068,7.602,0 976 | 149,10,39,112,50.237,173.5,1,7.2853,8.3939,0 977 | 150,3,197,92,42.145,154.22,1,13.386,10.928,0 978 | 150,6,411,112,41.559,142.04,1,12.468,7.2859,0 979 | 150,7,271,91,49.61,161.11,1,12.518,9.1853,0 980 | 150,8,461,110,48.425,145.48,1,12.398,6.5297,0 981 | 150,9,367,112,38.343,143.98,1,12.221,7.738,0 982 | 150,10,43,113,50.483,172.91,1,7.3003,8.3543,0 983 | 151,3,197,92,42.115,154.21,1,13.386,10.928,0 984 | 151,6,411,112,41.593,142.32,1,12.468,7.2859,0 985 | 151,7,270,91,49.326,161.08,1,12.516,9.1979,0 986 | 151,8,460,110,48.407,145.75,1,12.396,6.5419,0 987 | 151,9,365,112,37.959,143.79,1,12.216,7.7746,0 988 | 151,10,47,113,50.729,172.33,1,7.3152,8.3147,0 989 | 152,3,197,92,42.086,154.2,1,13.386,10.928,0 990 | 152,6,410,112,41.627,142.65,1,12.466,7.2982,0 991 | 152,7,270,91,49.042,161.05,1,12.516,9.1979,0 992 | 152,8,458,110,48.389,146.03,1,12.234,6.4756,0 993 | 152,9,363,111,37.714,143.6,1,12.373,7.9006,0 994 | 152,10,51,113,50.975,171.75,1,7.4418,8.3664,0 995 | 153,3,197,92,42.058,154.19,1,13.386,10.928,0 996 | 153,6,410,112,41.66,143,1,12.305,7.2013,0 997 | 153,7,269,91,48.758,161.02,1,12.514,9.2104,0 998 | 153,8,457,110,48.37,146.3,1,12.232,6.4877,0 999 | 153,9,362,111,37.608,143.39,1,12.371,7.9129,0 1000 | 153,10,56,113,51.22,171.16,1,7.4602,8.3166,0 1001 | 154,3,197,92,42.03,154.18,1,13.386,10.928,0 1002 | 154,6,409,112,41.693,143.37,1,12.303,7.2135,0 1003 | 154,7,268,91,48.475,160.99,1,12.681,9.3383,0 1004 | 154,8,456,110,48.351,146.58,1,12.231,6.4998,0 1005 | 154,9,360,111,37.605,143.17,1,12.367,7.9375,0 1006 | 154,10,60,113,51.465,170.58,1,7.5879,8.3684,0 1007 | 155,3,197,92,42.002,154.16,1,13.386,10.928,0 1008 | 155,6,409,112,41.727,143.73,1,12.303,7.2135,0 1009 | 155,7,268,91,48.192,160.96,1,12.681,9.3383,0 1010 | 155,8,455,110,48.333,146.85,1,12.229,6.5119,0 1011 | 155,9,359,111,37.656,142.95,1,12.53,8.0532,0 1012 | 155,10,64,113,51.71,170,1,7.6024,8.3283,0 1013 | 156,3,197,92,41.974,154.15,1,13.383,10.941,0 1014 | 156,6,408,112,41.76,144.09,1,12.142,7.1299,0 1015 | 156,7,267,91,47.908,160.93,1,12.676,9.3637,0 1016 | 156,8,453,110,48.315,147.12,1,12.069,6.4462,0 1017 | 156,9,357,111,37.73,142.73,1,12.526,8.078,0 1018 | 156,10,68,113,51.955,169.41,1,7.7312,8.3803,0 1019 | 157,3,197,91,41.945,154.14,1,13.57,11.076,0 1020 | 157,6,408,112,41.793,144.46,1,12.142,7.1299,0 1021 | 157,7,266,91,47.624,160.9,1,12.674,9.3764,0 1022 | 157,8,452,110,48.297,147.4,1,12.067,6.4582,0 1023 | 157,9,356,111,37.81,142.51,1,12.524,8.0904,0 1024 | 157,10,72,113,52.201,168.83,1,7.865,8.4227,0 1025 | 158,3,197,91,41.915,154.13,1,13.57,11.076,0 1026 | 158,6,407,112,41.827,144.82,1,12.14,7.1421,0 1027 | 158,7,266,90,47.34,160.88,1,12.846,9.4936,0 1028 | 158,8,451,110,48.279,147.67,1,12.066,6.4703,0 1029 | 158,9,354,111,37.89,142.28,1,12.52,8.1152,0 1030 | 158,10,76,113,52.447,168.25,1,7.8791,8.382,0 1031 | 159,3,197,91,41.885,154.12,1,13.57,11.076,0 1032 | 159,6,407,112,41.861,145.19,1,11.982,7.0475,0 1033 | 159,7,265,90,47.056,160.85,1,12.844,9.5064,0 1034 | 159,8,450,110,48.26,147.95,1,12.064,6.4823,0 1035 | 159,9,353,111,37.97,142.06,1,12.518,8.1276,0 1036 | 159,10,80,113,52.693,167.66,1,8.0105,8.4347,0 1037 | 160,3,197,91,41.855,154.11,1,13.57,11.076,0 1038 | 160,6,406,112,41.895,145.55,1,11.981,7.0595,0 1039 | 160,7,264,90,46.772,160.82,1,12.841,9.5192,0 1040 | 160,8,448,110,48.24,148.22,1,11.906,6.4171,0 1041 | 160,9,351,111,38.05,141.84,1,12.682,8.2455,0 1042 | 160,10,84,114,52.939,167.08,1,7.9073,8.3008,0 1043 | 161,3,197,91,41.825,154.09,1,13.57,11.076,0 1044 | 161,6,406,112,41.929,145.91,1,11.981,7.0595,0 1045 | 161,7,264,90,46.488,160.79,1,12.841,9.5192,0 1046 | 161,8,447,110,48.221,148.5,1,11.904,6.4291,0 1047 | 161,9,350,111,38.13,141.62,1,12.68,8.258,0 1048 | 161,10,88,114,53.185,166.5,1,8.0384,8.3529,0 1049 | 162,3,197,91,41.795,154.08,1,13.57,11.076,0 1050 | 162,6,405,112,41.963,146.25,1,11.824,6.978,0 1051 | 162,7,263,90,46.204,160.76,1,12.839,9.532,0 1052 | 162,8,446,110,48.203,148.77,1,11.902,6.441,0 1053 | 162,9,348,111,38.21,141.4,1,12.676,8.283,0 1054 | 162,10,93,114,53.43,165.91,1,8.1743,8.3952,0 1055 | 163,3,197,91,41.766,154.07,1,13.57,11.076,0 1056 | 163,6,404,112,41.997,146.55,1,11.822,6.9899,0 1057 | 163,7,262,90,45.922,160.73,1,12.834,9.5576,0 1058 | 163,8,445,110,48.185,149.04,1,11.748,6.3646,0 1059 | 163,9,347,111,38.29,141.17,1,12.674,8.2955,0 1060 | 163,10,97,114,53.675,165.33,1,8.188,8.354,0 1061 | 164,3,196,91,41.738,154.06,1,13.567,11.09,0 1062 | 164,6,403,112,42.03,146.81,1,11.822,6.9899,0 1063 | 164,7,262,90,45.646,160.69,1,12.834,9.5576,0 1064 | 164,8,444,110,48.167,149.32,1,11.746,6.3764,0 1065 | 164,9,345,111,38.369,140.95,1,12.839,8.4282,0 1066 | 164,10,101,114,53.92,164.75,1,8.3217,8.4068,0 1067 | 165,3,196,91,41.71,154.05,1,13.567,11.09,0 1068 | 165,6,401,112,42.063,147.03,1,11.665,6.9212,0 1069 | 165,7,261,90,45.386,160.64,1,12.832,9.5704,0 1070 | 165,8,442,110,48.149,149.59,1,11.743,6.4001,0 1071 | 165,9,344,111,38.447,140.73,1,12.837,8.4408,0 1072 | 165,10,105,114,54.165,164.16,1,8.3385,8.3549,0 1073 | 166,3,196,91,41.682,154.04,1,13.567,11.09,0 1074 | 166,6,399,113,42.097,147.2,1,11.51,6.8533,0 1075 | 166,7,260,90,45.154,160.56,1,12.83,9.5832,0 1076 | 166,8,441,110,48.13,149.86,1,11.741,6.412,0 1077 | 166,9,342,110,38.525,140.51,1,13.004,8.5755,0 1078 | 166,10,109,114,54.411,163.58,1,8.4735,8.4078,0 1079 | 167,3,196,91,41.654,154.03,1,13.567,11.09,0 1080 | 167,6,397,113,42.131,147.36,1,11.506,6.8768,0 1081 | 167,7,260,90,44.962,160.44,1,12.83,9.5832,0 1082 | 167,8,440,110,48.111,150.14,1,11.588,6.3362,0 1083 | 167,9,341,110,38.603,140.28,1,13.002,8.5882,0 1084 | 167,10,113,114,54.657,163,1,8.4868,8.3661,0 1085 | 168,3,196,91,41.625,154.01,1,13.567,11.09,0 1086 | 168,6,395,113,42.165,147.52,1,11.502,6.9003,0 1087 | 168,7,260,89,44.808,160.28,1,13.004,9.703,0 1088 | 168,8,439,110,48.093,150.41,1,11.587,6.3479,0 1089 | 168,9,339,110,38.681,140.06,1,12.998,8.6136,0 1090 | 168,10,117,114,54.903,162.41,1,8.623,8.4192,0 1091 | 169,3,196,91,41.595,154,1,13.567,11.09,0 1092 | 169,6,393,113,42.199,147.67,1,11.499,6.9238,0 1093 | 169,7,260,90,44.682,160.09,1,12.83,9.5832,0 1094 | 169,8,437,110,48.075,150.69,1,11.583,6.3715,0 1095 | 169,9,338,110,38.76,139.84,1,13.17,8.7378,0 1096 | 169,10,121,115,55.149,161.83,1,8.6361,8.3771,0 1097 | 170,3,196,91,41.565,153.99,1,13.756,11.227,0 1098 | 170,6,391,113,42.233,147.82,1,11.495,6.9473,0 1099 | 170,7,260,90,44.573,159.88,1,13.004,9.703,0 1100 | 170,8,436,110,48.057,150.96,1,11.582,6.3833,0 1101 | 170,9,336,110,38.84,139.62,1,13.165,8.7634,0 1102 | 170,10,125,115,55.395,161.25,1,8.6491,8.3351,0 1103 | 171,3,196,91,41.535,153.98,1,13.756,11.227,0 1104 | 171,6,389,113,42.267,147.97,1,11.491,6.9707,0 1105 | 171,7,260,90,44.47,159.68,1,13.004,9.703,0 1106 | 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/////////////////////////////////////////////////////////////////////////////// 2 | // Hungarian.h: Header file for Class HungarianAlgorithm. 3 | // 4 | // This is a C++ wrapper with slight modification of a hungarian algorithm implementation by Markus Buehren. 5 | // The original implementation is a few mex-functions for use in MATLAB, found here: 6 | // http://www.mathworks.com/matlabcentral/fileexchange/6543-functions-for-the-rectangular-assignment-problem 7 | // 8 | // Both this code and the orignal code are published under the BSD license. 9 | // by Cong Ma, 2016 10 | // 11 | 12 | #ifndef HUNGARIAN_H 13 | #define HUNGARIAN_H 14 | 15 | #include 16 | #include 17 | 18 | using namespace std; 19 | 20 | class HungarianAlgorithm 21 | { 22 | public: 23 | HungarianAlgorithm(); 24 | ~HungarianAlgorithm(); 25 | double Solve(vector> &DistMatrix, vector &Assignment); 26 | 27 | private: 28 | void assignmentoptimal(int *assignment, double *cost, double *distMatrix, int nOfRows, int nOfColumns); 29 | void buildassignmentvector(int *assignment, bool *starMatrix, int nOfRows, int nOfColumns); 30 | void computeassignmentcost(int *assignment, double *cost, double *distMatrix, int nOfRows); 31 | void step2a(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim); 32 | void step2b(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim); 33 | void step3(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim); 34 | void step4(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim, int row, int col); 35 | void step5(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim); 36 | }; 37 | 38 | #endif -------------------------------------------------------------------------------- /include/KalmanTracker.h: -------------------------------------------------------------------------------- 1 | /////////////////////////////////////////////////////////////////////////////// 2 | // KalmanTracker.h: KalmanTracker Class Declaration 3 | 4 | #ifndef KALMAN_H 5 | #define KALMAN_H 2 6 | 7 | #include "datatrans.h" 8 | #include "opencv2/video/tracking.hpp" 9 | #include "opencv2/highgui/highgui.hpp" 10 | 11 | using namespace std; 12 | using namespace cv; 13 | 14 | #define StateType Rect_ 15 | 16 | // This class represents the internel state of individual tracked objects observed as bounding box. 17 | class KalmanTracker 18 | { 19 | public: 20 | KalmanTracker(TrackingBox init_track_box) 21 | { 22 | init_kf(init_track_box.box); // 卡尔曼滤波器只需要bbox即可 23 | m_time_since_update = 0; // // 创建新tracker时不会调用update函数，初始化为0 24 | m_observed_num = 1; // 首次被检测模型观测到 25 | m_hit_streak = 0; 26 | m_age = 1; 27 | m_id = kf_count; 28 | kf_count++; 29 | 30 | obj_conf = init_track_box.obj_conf; 31 | class_id = init_track_box.class_id; 32 | } 33 | 34 | KalmanTracker(){} 35 | 36 | ~KalmanTracker() 37 | { 38 | m_history.clear(); 39 | } 40 | 41 | StateType predict(); 42 | void update(TrackingBox track_box); 43 | 44 | StateType get_state(); 45 | StateType get_rect_xysr(float cx, float cy, float s, float r); 46 | 47 | StateType latestRect; // 当前跟踪序列最新的bbox坐标。在对外输出时：如果有观测值则使用观测值;如果没有观测值就使用预测值 48 | static int kf_count; // 每次调用构造函数kf_count就会加1 49 | 50 | int m_max_missing_observed_num = 2; // 允许连续跟丢的最大次数，比如101有效、1001有效、10001则无效 51 | int m_time_since_update; // 距离上一次被观测到间隔的帧数 52 | int m_observed_num; // 该跟踪序列被检测模型观测到的总次数，如果某帧中没有检测到则值不会发生改变。每执行update一次，便hits+=1 53 | int m_hit_streak; // 被连续观测到的次数。判断当前是否做了更新，大于等于1的说明做了更新，只要连续帧中没有做连续更新，hit_streak就会清零 54 | int m_age; // 该目标框的年龄，初始化为1。每执行predict一次，便age+=1。可用于区别对待新生tracker和稳定tracker 55 | int m_id; // track_id。每次调用构造函数kf_count就会加1 56 | 57 | float obj_conf; 58 | int class_id; 59 | std::vector kps_in_pic; 60 | std::vector kps_in_robot; 61 | 62 | private: 63 | void init_kf(StateType stateMat); 64 | 65 | cv::KalmanFilter kf; 66 | cv::Mat measurement; // 观测值 67 | 68 | std::vector m_history; // 保存单个目标框连续预测的多个结果到history列表中，一旦执行update就会清空 69 | }; 70 | 71 | #endif -------------------------------------------------------------------------------- /include/datatrans.h: -------------------------------------------------------------------------------- 1 | #ifndef __DATATRANS_H 2 | #define __DATATRANS_H 3 | 4 | #include 5 | 6 | using namespace std; 7 | 8 | struct Bbox 9 | { 10 | float score; 11 | int class_id; // 类别ID 12 | int bbox_id; // bbox id in current frame 13 | cv::Rect_ rect; 14 | }; 15 | 16 | 17 | struct TrackingBox 18 | { 19 | int frame_id; 20 | int track_id; 21 | int class_id; 22 | float obj_conf; // 是否为前景的置信度 23 | cv::Rect_ box; 24 | 25 | // 构造函数 26 | TrackingBox(){} 27 | // 重载构造函数 28 | TrackingBox(Bbox obj){ 29 | box = obj.rect; 30 | obj_conf = obj.score; 31 | class_id = obj.class_id; 32 | track_id = -1; // 初始化为-1 33 | } 34 | }; 35 | 36 | #endif // DATATRANS_H -------------------------------------------------------------------------------- /include/track.h: -------------------------------------------------------------------------------- 1 | #ifndef __TRACK_H__ 2 | #define __TRACK_H__ 3 | 4 | #include 5 | #include "Hungarian.h" 6 | #include "KalmanTracker.h" 7 | #include "datatrans.h" 8 | 9 | #include "opencv2/video/tracking.hpp" 10 | #include "opencv2/highgui/highgui.hpp" 11 | 12 | using namespace std; 13 | using namespace cv; 14 | 15 | 16 | class TRACKER 17 | { 18 | public: 19 | // int total_frames = 0; // 记录总帧数 20 | double total_time = 0.0; // 记录总耗时 21 | static const int max_num = 100; // max num of people per frame 22 | Scalar_ randColor[max_num]; // 颜色数组 23 | 24 | int frame_count = 0; // 记录处理了多少帧数据。由于刚调用update函数就会加一，所以实际是从1开始计数 25 | int max_lost_time = 3; // 连续预测的最大次数，即目标未被检测到的帧数，超过之后会被删 26 | int lower_max_lost_time = 2; // 对新生tracker和稳定tracker的容忍度不同，如果稳定tracker连续丢失3次就被移除，那新生tracker则是2次 27 | int min_hits = 3; // 目标命中的最小次数，小于该次数时getReport函数不返回该目标的KalmanTracker卡尔曼滤波对象 28 | double iouThreshold = 0.3; 29 | vector trackers; // 维护所有的跟踪序列，列表元素是KalmanTracker的对象 30 | 31 | // variables used in the for-loop 32 | vector> predictedBoxes; 33 | vector> iouMatrix; 34 | vector assignment; 35 | set unmatchedDetections; 36 | set unmatchedTrajectories; 37 | set allItems; 38 | set matchedItems; 39 | vector matchedPairs; 40 | vector frameTrackingResult; // 用于保存最新的对外输出结果 41 | unsigned int trkNum = 0; 42 | unsigned int detNum = 0; 43 | 44 | double cycle_time = 0.0; 45 | int64 start_time = 0; 46 | 47 | TRACKER() 48 | { 49 | KalmanTracker::kf_count = 0; // tracking id relies on this, so we have to reset it in each seq. 50 | RNG rng(0xFFFFFFFF); 51 | for (int i = 0; i < max_num; i++) 52 | rng.fill(randColor[i], RNG::UNIFORM, 0, 256); 53 | } 54 | 55 | // Computes IOU between two bounding boxes 56 | double getIOU(Rect_ bb_test, Rect_ bb_gt); 57 | // 更新全局跟踪器 58 | void update(const vector &detFrameData); 59 | // 对外输出跟踪结果 60 | vector getReport(); 61 | }; 62 | 63 | #endif -------------------------------------------------------------------------------- /include/utils.h: -------------------------------------------------------------------------------- 1 | #ifndef UTILS_H 2 | #define UTILS_H 3 | #include 4 | #include // 使用fstream读写文件 5 | #include 6 | #include "track.h" 7 | 8 | using namespace std; 9 | 10 | // 获取目录中所有文件的文件路径，可以指定文件类型 11 | void getFilePaths(string &folder, vector &filepaths, string postfix, bool sort_=true); 12 | // 从行人真值TXT文件中读取检测结果，返回以frame_id为key的字典 13 | void getDetectResults(string &detfile, std::map> &det_results); 14 | // 字符串分割，返回vector，sep:分割符号。可应对如下情况："3,4,5" | "3,4,5," | ",3,4,5," 15 | void splitString(string &str, vector &out, char sep); 16 | 17 | // 可视化检测结果 18 | void drawPic(cv::Mat &img, string &savepath, const std::vector &results); 19 | // 可视化跟踪结果 20 | void drawPic(cv::Mat &img, string savepath, const std::vector &results, TRACKER &tracker); 21 | 22 | #endif -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | # 自动化编译和运行的脚本 2 | rm -rf build 3 | mkdir build 4 | cd build/ 5 | cmake .. 6 | make -j8 7 | ./main -------------------------------------------------------------------------------- /src/Hungarian.cpp: -------------------------------------------------------------------------------- 1 | /////////////////////////////////////////////////////////////////////////////// 2 | // Hungarian.cpp: Implementation file for Class HungarianAlgorithm. 3 | // 4 | // This is a C++ wrapper with slight modification of a hungarian algorithm implementation by Markus Buehren. 5 | // The original implementation is a few mex-functions for use in MATLAB, found here: 6 | // http://www.mathworks.com/matlabcentral/fileexchange/6543-functions-for-the-rectangular-assignment-problem 7 | // 8 | // Both this code and the orignal code are published under the BSD license. 9 | // by Cong Ma, 2016 10 | // 11 | #include 12 | #include 13 | #include "Hungarian.h" 14 | 15 | HungarianAlgorithm::HungarianAlgorithm() {} 16 | HungarianAlgorithm::~HungarianAlgorithm() {} 17 | 18 | //********************************************************// 19 | // A single function wrapper for solving assignment problem. 20 | //********************************************************// 21 | double HungarianAlgorithm::Solve(vector> &DistMatrix, vector &Assignment) 22 | { 23 | unsigned int nRows = static_cast(DistMatrix.size()); 24 | unsigned int nCols = static_cast(DistMatrix[0].size()); 25 | 26 | // 异常值检查。此处增加异常值检查是因为由于在工程开发中遇到过异常报错。 27 | if (nRows > 10000 || nCols > 10000) 28 | { 29 | return -1.0; // 避免数据类型转换导致产生异常大的值，(int, unsigned int, size_t) 30 | } 31 | for (unsigned int i = 0; i < nRows; i++){ 32 | for (unsigned int j = 0; j < nCols; j++){ 33 | if (DistMatrix[i][j] < 0 || DistMatrix[i][j] > 1 || !std::isfinite(DistMatrix[i][j])) 34 | { 35 | return -1.0; 36 | } 37 | } 38 | } 39 | 40 | double *distMatrixIn = new double[nRows * nCols]; 41 | int *assignment = new int[nRows]; 42 | double cost = 0.0; 43 | 44 | // Fill in the distMatrixIn. Mind the index is "i + nRows * j". 45 | // Here the cost matrix of size MxN is defined as a double precision array of N*M elements. 46 | // In the solving functions matrices are seen to be saved MATLAB-internally in row-order. 47 | // (i.e. the matrix [1 2; 3 4] will be stored as a vector [1 3 2 4], NOT [1 2 3 4]). 48 | for (unsigned int i = 0; i < nRows; i++) 49 | for (unsigned int j = 0; j < nCols; j++) 50 | distMatrixIn[i + nRows * j] = DistMatrix[i][j]; 51 | 52 | // call solving function 53 | assignmentoptimal(assignment, &cost, distMatrixIn, nRows, nCols); 54 | 55 | Assignment.clear(); 56 | for (unsigned int r = 0; r < nRows; r++) 57 | Assignment.push_back(assignment[r]); 58 | 59 | delete[] distMatrixIn; 60 | delete[] assignment; 61 | return cost; 62 | } 63 | 64 | //********************************************************// 65 | // Solve optimal solution for assignment problem using Munkres algorithm, also known as Hungarian Algorithm. 66 | //********************************************************// 67 | void HungarianAlgorithm::assignmentoptimal(int *assignment, double *cost, double *distMatrixIn, int nOfRows, int nOfColumns) 68 | { 69 | double *distMatrix, *distMatrixTemp, *distMatrixEnd, *columnEnd, value, minValue; 70 | bool *coveredColumns, *coveredRows, *starMatrix, *newStarMatrix, *primeMatrix; 71 | int nOfElements, minDim, row, col; 72 | 73 | /* initialization */ 74 | *cost = 0; 75 | for (row = 0; row < nOfRows; row++) 76 | assignment[row] = -1; 77 | 78 | /* generate working copy of distance Matrix */ 79 | /* check if all matrix elements are positive */ 80 | nOfElements = nOfRows * nOfColumns; 81 | distMatrix = (double *)malloc(nOfElements * sizeof(double)); 82 | distMatrixEnd = distMatrix + nOfElements; 83 | 84 | for (row = 0; row < nOfElements; row++) 85 | { 86 | value = distMatrixIn[row]; 87 | if (value < 0) 88 | cerr << "All matrix elements have to be non-negative." << endl; 89 | distMatrix[row] = value; 90 | } 91 | 92 | /* memory allocation */ 93 | coveredColumns = (bool *)calloc(nOfColumns, sizeof(bool)); 94 | coveredRows = (bool *)calloc(nOfRows, sizeof(bool)); 95 | starMatrix = (bool *)calloc(nOfElements, sizeof(bool)); 96 | primeMatrix = (bool *)calloc(nOfElements, sizeof(bool)); 97 | newStarMatrix = (bool *)calloc(nOfElements, sizeof(bool)); /* used in step4 */ 98 | 99 | /* preliminary steps */ 100 | if (nOfRows <= nOfColumns) 101 | { 102 | minDim = nOfRows; 103 | 104 | for (row = 0; row < nOfRows; row++) 105 | { 106 | /* find the smallest element in the row */ 107 | distMatrixTemp = distMatrix + row; 108 | minValue = *distMatrixTemp; 109 | distMatrixTemp += nOfRows; 110 | while (distMatrixTemp < distMatrixEnd) 111 | { 112 | value = *distMatrixTemp; 113 | if (value < minValue) 114 | minValue = value; 115 | distMatrixTemp += nOfRows; 116 | } 117 | 118 | /* subtract the smallest element from each element of the row */ 119 | distMatrixTemp = distMatrix + row; 120 | while (distMatrixTemp < distMatrixEnd) 121 | { 122 | *distMatrixTemp -= minValue; 123 | distMatrixTemp += nOfRows; 124 | } 125 | } 126 | 127 | /* Steps 1 and 2a */ 128 | for (row = 0; row < nOfRows; row++) 129 | for (col = 0; col < nOfColumns; col++) 130 | if (fabs(distMatrix[row + nOfRows * col]) < DBL_EPSILON) 131 | if (!coveredColumns[col]) 132 | { 133 | starMatrix[row + nOfRows * col] = true; 134 | coveredColumns[col] = true; 135 | break; 136 | } 137 | } 138 | else /* if(nOfRows > nOfColumns) */ 139 | { 140 | minDim = nOfColumns; 141 | 142 | for (col = 0; col < nOfColumns; col++) 143 | { 144 | /* find the smallest element in the column */ 145 | distMatrixTemp = distMatrix + nOfRows * col; 146 | columnEnd = distMatrixTemp + nOfRows; 147 | 148 | minValue = *distMatrixTemp++; 149 | while (distMatrixTemp < columnEnd) 150 | { 151 | value = *distMatrixTemp++; 152 | if (value < minValue) 153 | minValue = value; 154 | } 155 | 156 | /* subtract the smallest element from each element of the column */ 157 | distMatrixTemp = distMatrix + nOfRows * col; 158 | while (distMatrixTemp < columnEnd) 159 | *distMatrixTemp++ -= minValue; 160 | } 161 | 162 | /* Steps 1 and 2a */ 163 | for (col = 0; col < nOfColumns; col++) 164 | for (row = 0; row < nOfRows; row++) 165 | if (fabs(distMatrix[row + nOfRows * col]) < DBL_EPSILON) 166 | if (!coveredRows[row]) 167 | { 168 | starMatrix[row + nOfRows * col] = true; 169 | coveredColumns[col] = true; 170 | coveredRows[row] = true; 171 | break; 172 | } 173 | for (row = 0; row < nOfRows; row++) 174 | coveredRows[row] = false; 175 | } 176 | 177 | /* move to step 2b */ 178 | step2b(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 179 | 180 | /* compute cost and remove invalid assignments */ 181 | computeassignmentcost(assignment, cost, distMatrixIn, nOfRows); 182 | 183 | /* free allocated memory */ 184 | free(distMatrix); 185 | free(coveredColumns); 186 | free(coveredRows); 187 | free(starMatrix); 188 | free(primeMatrix); 189 | free(newStarMatrix); 190 | 191 | return; 192 | } 193 | 194 | /********************************************************/ 195 | void HungarianAlgorithm::buildassignmentvector(int *assignment, bool *starMatrix, int nOfRows, int nOfColumns) 196 | { 197 | int row, col; 198 | 199 | for (row = 0; row < nOfRows; row++) 200 | for (col = 0; col < nOfColumns; col++) 201 | if (starMatrix[row + nOfRows * col]) 202 | { 203 | #ifdef ONE_INDEXING 204 | assignment[row] = col + 1; /* MATLAB-Indexing */ 205 | #else 206 | assignment[row] = col; 207 | #endif 208 | break; 209 | } 210 | } 211 | 212 | /********************************************************/ 213 | void HungarianAlgorithm::computeassignmentcost(int *assignment, double *cost, double *distMatrix, int nOfRows) 214 | { 215 | int row, col; 216 | 217 | for (row = 0; row < nOfRows; row++) 218 | { 219 | col = assignment[row]; 220 | if (col >= 0) 221 | *cost += distMatrix[row + nOfRows * col]; 222 | } 223 | } 224 | 225 | /********************************************************/ 226 | void HungarianAlgorithm::step2a(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim) 227 | { 228 | bool *starMatrixTemp, *columnEnd; 229 | int col; 230 | 231 | /* cover every column containing a starred zero */ 232 | for (col = 0; col < nOfColumns; col++) 233 | { 234 | starMatrixTemp = starMatrix + nOfRows * col; 235 | columnEnd = starMatrixTemp + nOfRows; 236 | while (starMatrixTemp < columnEnd) 237 | { 238 | if (*starMatrixTemp++) 239 | { 240 | coveredColumns[col] = true; 241 | break; 242 | } 243 | } 244 | } 245 | 246 | /* move to step 3 */ 247 | step2b(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 248 | } 249 | 250 | /********************************************************/ 251 | void HungarianAlgorithm::step2b(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim) 252 | { 253 | int col, nOfCoveredColumns; 254 | 255 | /* count covered columns */ 256 | nOfCoveredColumns = 0; 257 | for (col = 0; col < nOfColumns; col++) 258 | if (coveredColumns[col]) 259 | nOfCoveredColumns++; 260 | 261 | if (nOfCoveredColumns == minDim) 262 | { 263 | /* algorithm finished */ 264 | buildassignmentvector(assignment, starMatrix, nOfRows, nOfColumns); 265 | } 266 | else 267 | { 268 | /* move to step 3 */ 269 | step3(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 270 | } 271 | } 272 | 273 | /********************************************************/ 274 | void HungarianAlgorithm::step3(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim) 275 | { 276 | bool zerosFound; 277 | int row, col, starCol; 278 | 279 | zerosFound = true; 280 | while (zerosFound) 281 | { 282 | zerosFound = false; 283 | for (col = 0; col < nOfColumns; col++) 284 | if (!coveredColumns[col]) 285 | for (row = 0; row < nOfRows; row++) 286 | if ((!coveredRows[row]) && (fabs(distMatrix[row + nOfRows * col]) < DBL_EPSILON)) 287 | { 288 | /* prime zero */ 289 | primeMatrix[row + nOfRows * col] = true; 290 | 291 | /* find starred zero in current row */ 292 | for (starCol = 0; starCol < nOfColumns; starCol++) 293 | if (starMatrix[row + nOfRows * starCol]) 294 | break; 295 | 296 | if (starCol == nOfColumns) /* no starred zero found */ 297 | { 298 | /* move to step 4 */ 299 | step4(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim, row, col); 300 | return; 301 | } 302 | else 303 | { 304 | coveredRows[row] = true; 305 | coveredColumns[starCol] = false; 306 | zerosFound = true; 307 | break; 308 | } 309 | } 310 | } 311 | 312 | /* move to step 5 */ 313 | step5(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 314 | } 315 | 316 | /********************************************************/ 317 | void HungarianAlgorithm::step4(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim, int row, int col) 318 | { 319 | int n, starRow, starCol, primeRow, primeCol; 320 | int nOfElements = nOfRows * nOfColumns; 321 | 322 | /* generate temporary copy of starMatrix */ 323 | for (n = 0; n < nOfElements; n++) 324 | newStarMatrix[n] = starMatrix[n]; 325 | 326 | /* star current zero */ 327 | newStarMatrix[row + nOfRows * col] = true; 328 | 329 | /* find starred zero in current column */ 330 | starCol = col; 331 | for (starRow = 0; starRow < nOfRows; starRow++) 332 | if (starMatrix[starRow + nOfRows * starCol]) 333 | break; 334 | 335 | while (starRow < nOfRows) 336 | { 337 | /* unstar the starred zero */ 338 | newStarMatrix[starRow + nOfRows * starCol] = false; 339 | 340 | /* find primed zero in current row */ 341 | primeRow = starRow; 342 | for (primeCol = 0; primeCol < nOfColumns; primeCol++) 343 | if (primeMatrix[primeRow + nOfRows * primeCol]) 344 | break; 345 | 346 | /* star the primed zero */ 347 | newStarMatrix[primeRow + nOfRows * primeCol] = true; 348 | 349 | /* find starred zero in current column */ 350 | starCol = primeCol; 351 | for (starRow = 0; starRow < nOfRows; starRow++) 352 | if (starMatrix[starRow + nOfRows * starCol]) 353 | break; 354 | } 355 | 356 | /* use temporary copy as new starMatrix */ 357 | /* delete all primes, uncover all rows */ 358 | for (n = 0; n < nOfElements; n++) 359 | { 360 | primeMatrix[n] = false; 361 | starMatrix[n] = newStarMatrix[n]; 362 | } 363 | for (n = 0; n < nOfRows; n++) 364 | coveredRows[n] = false; 365 | 366 | /* move to step 2a */ 367 | step2a(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 368 | } 369 | 370 | /********************************************************/ 371 | void HungarianAlgorithm::step5(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim) 372 | { 373 | double h, value; 374 | int row, col; 375 | 376 | /* find smallest uncovered element h */ 377 | h = DBL_MAX; 378 | for (row = 0; row < nOfRows; row++) 379 | if (!coveredRows[row]) 380 | for (col = 0; col < nOfColumns; col++) 381 | if (!coveredColumns[col]) 382 | { 383 | value = distMatrix[row + nOfRows * col]; 384 | if (value < h) 385 | h = value; 386 | } 387 | 388 | /* add h to each covered row */ 389 | for (row = 0; row < nOfRows; row++) 390 | if (coveredRows[row]) 391 | for (col = 0; col < nOfColumns; col++) 392 | distMatrix[row + nOfRows * col] += h; 393 | 394 | /* subtract h from each uncovered column */ 395 | for (col = 0; col < nOfColumns; col++) 396 | if (!coveredColumns[col]) 397 | for (row = 0; row < nOfRows; row++) 398 | distMatrix[row + nOfRows * col] -= h; 399 | 400 | /* move to step 3 */ 401 | step3(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim); 402 | } -------------------------------------------------------------------------------- /src/KalmanTracker.cpp: -------------------------------------------------------------------------------- 1 | /////////////////////////////////////////////////////////////////////////////// 2 | // KalmanTracker.cpp: KalmanTracker Class Implementation Declaration 3 | 4 | #include "KalmanTracker.h" 5 | 6 | int KalmanTracker::kf_count = 0; 7 | 8 | // initialize Kalman filter 9 | void KalmanTracker::init_kf(StateType stateMat) 10 | { 11 | int stateNum = 7; // 一个7维的状态更新向量：[u,v,s,r,u^,v^,s^]T。Note：u^,v^,s^表示运动速度 12 | int measureNum = 4; // 一个4维的观测输入，即中心面积的形式[x,y,s,r]，即[检测框中心x坐标,y坐标,面积,宽高比]。 13 | kf = KalmanFilter(stateNum, measureNum, 0); 14 | 15 | // 状态转移矩阵(A)。默认两帧的时间间隔是1，无量纲 16 | // Note：如果要计算实际运动速度，则右上角的三个1需要更改为两帧的时间间隔dt，并在每次调用predict()函数之前进行重置 17 | kf.transitionMatrix = (Mat_(stateNum, stateNum) << 18 | 1, 0, 0, 0, 1, 0, 0, 19 | 0, 1, 0, 0, 0, 1, 0, 20 | 0, 0, 1, 0, 0, 0, 1, 21 | 0, 0, 0, 1, 0, 0, 0, 22 | 0, 0, 0, 0, 1, 0, 0, 23 | 0, 0, 0, 0, 0, 1, 0, 24 | 0, 0, 0, 0, 0, 0, 1); 25 | 26 | measurement = Mat::zeros(measureNum, 1, CV_32F); // 观测值，初始化为0 27 | 28 | setIdentity(kf.measurementMatrix); // 测量矩阵 H 29 | setIdentity(kf.processNoiseCov, Scalar::all(1e-2)); // 系统误差 Q 30 | setIdentity(kf.measurementNoiseCov, Scalar::all(1e-1)); // 测量误差 R 31 | setIdentity(kf.errorCovPost, Scalar::all(1)); // 最小均方误差 P'(k)) 32 | 33 | // initialize state vector with bounding box in [cx,cy,s,r] style 34 | kf.statePost.at(0, 0) = stateMat.x + stateMat.width / 2; // 检测框中心x坐标 35 | kf.statePost.at(1, 0) = stateMat.y + stateMat.height / 2; // 检测框中心y坐标 36 | kf.statePost.at(2, 0) = stateMat.area(); // 检测框面积 37 | kf.statePost.at(3, 0) = stateMat.width / stateMat.height; // 检测框宽高比 38 | } 39 | 40 | // Predict the estimated bounding box. 41 | StateType KalmanTracker::predict() 42 | { 43 | // predict 44 | Mat p = kf.predict(); // 计算预测的状态值，一个7维的状态更新向量，最后三个元素是运动速度 45 | m_age += 1; 46 | 47 | if (m_time_since_update > m_max_missing_observed_num) 48 | m_hit_streak = 0; 49 | m_time_since_update += 1; 50 | 51 | StateType predictBox = get_rect_xysr(p.at(0, 0), p.at(1, 0), p.at(2, 0), p.at(3, 0)); 52 | 53 | latestRect = predictBox; // 使用最近一次预测值更新latestRect变量 54 | 55 | m_history.push_back(predictBox); 56 | return m_history.back(); // 返回对vector最后一个元素的引用 57 | } 58 | 59 | // Update the state vector with observed bounding box. 60 | void KalmanTracker::update(TrackingBox track_box) 61 | { 62 | latestRect = track_box.box; // 使用最近一次观测值更新latestRect变量 63 | 64 | m_time_since_update = 0; // 每次观察到目标就重置为0 65 | m_history.clear(); 66 | m_observed_num += 1; 67 | m_hit_streak += 1; 68 | 69 | obj_conf = track_box.obj_conf; 70 | class_id = track_box.class_id; 71 | 72 | // measurement 73 | measurement.at(0, 0) = track_box.box.x + track_box.box.width / 2; 74 | measurement.at(1, 0) = track_box.box.y + track_box.box.height / 2; 75 | measurement.at(2, 0) = track_box.box.area(); 76 | measurement.at(3, 0) = track_box.box.width / track_box.box.height; 77 | 78 | // update 79 | kf.correct(measurement); // 根据测量值更新状态值 80 | } 81 | 82 | // Return the current state vector 83 | StateType KalmanTracker::get_state() 84 | { 85 | Mat s = kf.statePost; 86 | return get_rect_xysr(s.at(0, 0), s.at(1, 0), s.at(2, 0), s.at(3, 0)); 87 | } 88 | 89 | // Convert bounding box from [cx,cy,s,r] to [x,y,w,h] style. 90 | StateType KalmanTracker::get_rect_xysr(float cx, float cy, float s, float r) 91 | { 92 | float w = sqrt(s * r); 93 | float h = s / w; 94 | float x = (cx - w / 2); 95 | float y = (cy - h / 2); 96 | 97 | if (x < 0 && cx > 0) 98 | x = 0; 99 | if (y < 0 && cy > 0) 100 | y = 0; 101 | 102 | return StateType(x, y, w, h); 103 | } 104 | 105 | /* 106 | // -------------------------------------------------------------------- 107 | // Kalman Filter Demonstrating, a 2-d ball demo 108 | // -------------------------------------------------------------------- 109 | const int winHeight = 600; 110 | const int winWidth = 800; 111 | Point mousePosition = Point(winWidth >> 1, winHeight >> 1); 112 | // mouse event callback 113 | void mouseEvent(int event, int x, int y, int flags, void *param) 114 | { 115 | if (event == CV_EVENT_MOUSEMOVE) { 116 | mousePosition = Point(x, y); 117 | } 118 | } 119 | void TestKF(); 120 | void main() 121 | { 122 | TestKF(); 123 | } 124 | void TestKF() 125 | { 126 | int stateNum = 4; 127 | int measureNum = 2; 128 | KalmanFilter kf = KalmanFilter(stateNum, measureNum, 0); 129 | // initialization 130 | Mat processNoise(stateNum, 1, CV_32F); 131 | Mat measurement = Mat::zeros(measureNum, 1, CV_32F); 132 | kf.transitionMatrix = *(Mat_(stateNum, stateNum) << 133 | 1, 0, 1, 0, 134 | 0, 1, 0, 1, 135 | 0, 0, 1, 0, 136 | 0, 0, 0, 1); 137 | setIdentity(kf.measurementMatrix); 138 | setIdentity(kf.processNoiseCov, Scalar::all(1e-2)); 139 | setIdentity(kf.measurementNoiseCov, Scalar::all(1e-1)); 140 | setIdentity(kf.errorCovPost, Scalar::all(1)); 141 | randn(kf.statePost, Scalar::all(0), Scalar::all(winHeight)); 142 | namedWindow("Kalman"); 143 | setMouseCallback("Kalman", mouseEvent); 144 | Mat img(winHeight, winWidth, CV_8UC3); 145 | while (1) 146 | { 147 | // predict 148 | Mat prediction = kf.predict(); 149 | Point predictPt = Point(prediction.at(0, 0), prediction.at(1, 0)); 150 | // generate measurement 151 | Point statePt = mousePosition; 152 | measurement.at(0, 0) = statePt.x; 153 | measurement.at(1, 0) = statePt.y; 154 | // update 155 | kf.correct(measurement); 156 | // visualization 157 | img.setTo(Scalar(255, 255, 255)); 158 | circle(img, predictPt, 8, CV_RGB(0, 255, 0), -1); // predicted point as green 159 | circle(img, statePt, 8, CV_RGB(255, 0, 0), -1); // current position as red 160 | imshow("Kalman", img); 161 | char code = (char)waitKey(100); 162 | if (code == 27 || code == 'q' || code == 'Q') 163 | break; 164 | } 165 | destroyWindow("Kalman"); 166 | } 167 | */ -------------------------------------------------------------------------------- /src/main.cpp: -------------------------------------------------------------------------------- 1 | #include "opencv2/opencv.hpp" 2 | #include "track.h" 3 | #include "utils.h" 4 | 5 | int main(int argc, char **argv) 6 | { 7 | cout << "+++++++++++++++++ enter main +++++++++++++++++" << endl; 8 | // 逐帧读取检测结果跑跟踪，检测结果是所有帧保存在一个TXT文件中，每行一个bbox 9 | string imgfolder = "../data/TUD-Stadtmitte/img1/"; 10 | string detfile = "../data/TUD-Stadtmitte/gt/gt.txt"; // 直接使用真值进行测试 11 | string savefolder = "../results/"; // 保存可视化结果的目录 12 | 13 | vector imgpaths; 14 | getFilePaths(imgfolder, imgpaths, "jpg"); // 获取所有文件路径 15 | 16 | // 从TXT文件中读取检测结果 17 | std::map> det_results; // key是frame_id 18 | getDetectResults(detfile, det_results); 19 | 20 | TRACKER tracker; // 创建全局跟踪器 21 | int frame_id = 1; // frame_id是从1开始编号的 22 | // 逐帧遍历 23 | for (auto imgpath : imgpaths) 24 | { 25 | cout << "frame_id: " << frame_id << ", " << imgpath << endl; 26 | // 提取检测结果，将检测框封装成跟踪所需要的数据格式。可在TrackingBox类中增加其他属性。 27 | vector detFrameData; 28 | vector bboxes = det_results[frame_id]; 29 | for (int i = 0; i < bboxes.size(); ++i) 30 | { 31 | TrackingBox cur_box(bboxes[i]); 32 | cur_box.frame_id = frame_id; 33 | detFrameData.push_back(cur_box); 34 | } 35 | frame_id++; 36 | // 跟踪 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 37 | tracker.update(detFrameData); 38 | vector tracking_results = tracker.getReport(); 39 | // 可视化跟踪结果 40 | Mat frame = cv::imread(imgpath); 41 | string imgname = imgpath.substr(imgpath.find_last_of('/') + 1); // 提取文件名 42 | drawPic(frame, savefolder + imgname, tracking_results, tracker); 43 | } 44 | 45 | return 0; 46 | } -------------------------------------------------------------------------------- /src/track.cpp: -------------------------------------------------------------------------------- 1 | #include "track.h" 2 | 3 | double TRACKER::getIOU(Rect_ bb_test, Rect_ bb_gt) 4 | { 5 | float intersection = (bb_test & bb_gt).area(); 6 | float unionArea = bb_test.area() + bb_gt.area() - intersection; 7 | if (unionArea < DBL_EPSILON) 8 | return 0; 9 | return (double)(intersection / unionArea); 10 | } 11 | 12 | 13 | void TRACKER::update(const vector &detFrameData) 14 | { 15 | // total_frames++; 16 | frame_count++; 17 | 18 | // count running time using clock() 19 | start_time = getTickCount(); 20 | 21 | // 初始化，the first frame met 22 | if (trackers.size() == 0) 23 | { 24 | // initialize kalman trackers using first detections. 25 | for (unsigned int i = 0; i < detFrameData.size(); i++) 26 | { 27 | KalmanTracker trk = KalmanTracker(detFrameData[i]); 28 | trackers.push_back(trk); 29 | } 30 | return; 31 | } 32 | 33 | /////////////////////////////////////// 34 | // 3.1. get predicted locations from existing trackers. 35 | predictedBoxes.clear(); 36 | 37 | for (auto it = trackers.begin(); it != trackers.end();) 38 | { 39 | Rect_ pBox = (*it).predict(); 40 | if (pBox.x >= 0 && pBox.y >= 0) 41 | { 42 | predictedBoxes.push_back(pBox); 43 | it++; 44 | } 45 | else 46 | { 47 | it = trackers.erase(it); 48 | //cerr << "Box invalid at frame: " << frame_count << endl; 49 | } 50 | } 51 | 52 | /////////////////////////////////////// 53 | // 3.2. associate detections to tracked object (both represented as bounding boxes) 54 | // dets : detFrameData[fi] 55 | trkNum = predictedBoxes.size(); 56 | detNum = detFrameData.size(); 57 | 58 | iouMatrix.clear(); 59 | iouMatrix.resize(trkNum, vector(detNum, 0)); 60 | // compute iou matrix as a distance matrix 61 | for (unsigned int i = 0; i < trkNum; i++) 62 | { 63 | for (unsigned int j = 0; j < detNum; j++) 64 | { 65 | // use 1-iou because the hungarian algorithm computes a minimum-cost assignment. 66 | iouMatrix[i][j] = 1 - getIOU(predictedBoxes[i], detFrameData[j].box); 67 | } 68 | } 69 | 70 | // solve the assignment problem using hungarian algorithm. 71 | // the resulting assignment is [track(prediction) : detection], with len=preNum 72 | HungarianAlgorithm HungAlgo; 73 | assignment.clear(); 74 | double cost_ = HungAlgo.Solve(iouMatrix, assignment); 75 | if (cost_ == -1.0) 76 | { 77 | cout << "hungarian assignment error !" << endl; // 如果是因为异常值退出，则打印 78 | } 79 | 80 | // find matches, unmatched_detections and unmatched_predictions 81 | unmatchedTrajectories.clear(); 82 | unmatchedDetections.clear(); 83 | allItems.clear(); 84 | matchedItems.clear(); 85 | 86 | if (detNum > trkNum) // there are unmatched detections 87 | { 88 | for (unsigned int n = 0; n < detNum; n++) 89 | allItems.insert(n); 90 | 91 | for (unsigned int i = 0; i < trkNum; ++i) 92 | matchedItems.insert(assignment[i]); 93 | // 找到没有配对上的检测框 94 | set_difference(allItems.begin(), allItems.end(), 95 | matchedItems.begin(), matchedItems.end(), 96 | insert_iterator>(unmatchedDetections, unmatchedDetections.begin())); 97 | } 98 | else if (detNum < trkNum) // there are unmatched trajectory/predictions 99 | { 100 | for (unsigned int i = 0; i < trkNum; ++i) 101 | if (assignment[i] == -1) // unassigned label will be set as -1 in the assignment algorithm 102 | unmatchedTrajectories.insert(i); 103 | } 104 | 105 | // filter out matched with low IOU 106 | matchedPairs.clear(); 107 | for (unsigned int i = 0; i < trkNum; ++i) 108 | { 109 | if (assignment[i] == -1) // pass over invalid values 110 | continue; 111 | if (1 - iouMatrix[i][assignment[i]] < iouThreshold) 112 | { 113 | unmatchedTrajectories.insert(i); 114 | unmatchedDetections.insert(assignment[i]); 115 | } 116 | else 117 | matchedPairs.push_back(cv::Point(i, assignment[i])); 118 | } 119 | 120 | /////////////////////////////////////// 121 | // 3.3. updating trackers 122 | 123 | // 3.3.1，update matched trackers with assigned detections. 124 | // each prediction is corresponding to a tracker 125 | int detIdx, trkIdx; 126 | for (unsigned int i = 0; i < matchedPairs.size(); i++) 127 | { 128 | trkIdx = matchedPairs[i].x; 129 | detIdx = matchedPairs[i].y; 130 | trackers[trkIdx].update(detFrameData[detIdx]); 131 | } 132 | 133 | // 3.3.2，create and initialise new trackers for unmatched detections 134 | for (auto umd : unmatchedDetections) 135 | { 136 | // 创建新tracker时不会调用KalmanTracker的update函数 137 | KalmanTracker tracker = KalmanTracker(detFrameData[umd]); 138 | trackers.push_back(tracker); 139 | } 140 | 141 | // 3.3.3，更新未匹配上的跟踪序列 142 | // 由于之前已经单独调用过predict函数，此处直接用预测的结果进行tracker的跟踪 143 | // 在predict和update函数中都对latestRect的值进行了更新，此处不再单独操作 144 | 145 | // 3.3.4，remove dead tracklet 146 | for (auto it = trackers.begin(); it != trackers.end();) 147 | { 148 | // 移除情况1：稳定的tracker，连续丢失次数超过阈值max_lost_time 149 | // 移除情况2：才刚创建的tracker，就连续丢失超过阈值lower_max_lost_time 150 | if ((it->m_time_since_update > max_lost_time) || 151 | (it->m_age == max_lost_time && it->m_time_since_update==lower_max_lost_time)) 152 | it = trackers.erase(it); 153 | else{ 154 | ++it; 155 | } 156 | } 157 | 158 | cycle_time = (double)(getTickCount() - start_time); 159 | total_time += cycle_time / getTickFrequency(); 160 | } 161 | 162 | 163 | vector TRACKER::getReport(){ 164 | // get trackers' output 165 | frameTrackingResult.clear(); 166 | for (auto it = trackers.begin(); it != trackers.end(); ++it) 167 | { 168 | // min_hits不设置为0是因为第一次检测到的目标不用跟踪，不能设大，一般就是1，表示如果连续两帧都检测到目标 169 | // int time_window = 1; // 表示连续预测的次数 170 | // if ((it->m_time_since_update < time_window) && it->m_hit_streak >= min_hits) 171 | if (it->m_observed_num >= min_hits) 172 | { 173 | TrackingBox res; 174 | res.box = it->latestRect; // 如果有观测值则使用观测值;如果没有观测值就使用预测值 175 | res.track_id = it->m_id + 1; // +1 as MOT benchmark requires positive 176 | res.frame_id = frame_count; 177 | res.obj_conf = it->obj_conf; 178 | res.class_id = it->class_id; 179 | frameTrackingResult.push_back(res); 180 | } 181 | else{ 182 | // 183 | } 184 | 185 | } 186 | return frameTrackingResult; 187 | } -------------------------------------------------------------------------------- /src/utils.cpp: -------------------------------------------------------------------------------- 1 | #include "utils.h" 2 | 3 | void getFilePaths(string &folder, vector &filepaths, string postfix, bool sort_){ 4 | // 获取目录中某种格式的文件，返回其路径 5 | 6 | // 1，根据后缀进行过滤 7 | vector res; 8 | cv::glob(folder, res); // 返回值是路径 9 | for (auto filepath:res){ 10 | // 获取字符串文件名的后缀 11 | if (filepath.substr(filepath.find_last_of('.') + 1) == postfix){ 12 | filepaths.push_back(filepath); 13 | } 14 | } 15 | // 2，排序 16 | if (sort_){ 17 | sort(filepaths.begin(), filepaths.end()); 18 | } 19 | } 20 | 21 | void getDetectResults(string &detfile, std::map> &det_results){ 22 | ifstream fin; 23 | fin.open(detfile, std::ios::in); 24 | string str_line; 25 | // 逐行读取 26 | while (getline(fin, str_line)) 27 | { 28 | vector res; // 当前行按逗号分割后的结果 29 | splitString(str_line, res, ','); 30 | // 解析bbox 31 | int frame_id = res[0]; 32 | Bbox bbox; 33 | bbox.bbox_id = res[1]; 34 | bbox.rect = cv::Rect_(res[2], res[3], res[4], res[5]); 35 | // 判断map中某个key是否存在，如果不存在需要先初始化 36 | if (det_results.find(frame_id) == det_results.end()){ 37 | det_results[frame_id] = vector(); // 初始化为空值 38 | } 39 | det_results[frame_id].push_back(bbox); 40 | } 41 | } 42 | 43 | void splitString(string &str, std::vector &out, char sep) 44 | { 45 | string::size_type start = str.find_first_not_of(sep, 0); // 找到第一个不为逗号的下标 46 | string::size_type pose = str.find_first_of(sep, start); // 找到第一个逗号的下标 47 | while (string::npos != start || string::npos != pose) 48 | { // 当即没有逗号也没有字符的时候结束 49 | out.push_back(atoi(str.substr(start, pose - start).c_str())); // 字符串转int 50 | start = str.find_first_not_of(sep, pose); // 更新start 从pose开始 51 | pose = str.find_first_of(sep, start); // 更新pos,从start开始 52 | } 53 | } 54 | 55 | void drawPic(cv::Mat &img, string savepath, const std::vector &results){ 56 | vector colors = {{0,0,255}, {0,255,0}, {255,0,0}, {255,255,0}, {0,255,255}, {255,0,255}, {255,153,18}, {255,97,0}}; 57 | for (auto obj:results){ 58 | // cv::Scalar color = {rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)}; 59 | cv::Scalar color = colors[obj.class_id]; 60 | // bbox 61 | cv::rectangle(img, obj.rect, color, 2); 62 | // label 63 | int score_ = int(obj.score * 100); 64 | string label = to_string(obj.class_id) + "-" + to_string(score_); 65 | cv::putText(img, label, {obj.rect.x, obj.rect.y-3}, 0, 0.4, color, 1, 16); 66 | } 67 | cv::imwrite(savepath, img); 68 | cout << "save vis img in: " << savepath << endl; 69 | } 70 | 71 | void drawPic(cv::Mat &img, string savepath, const std::vector &results, TRACKER &tracker){ 72 | for (TrackingBox it : results) 73 | { 74 | cv::rectangle(img, it.box, tracker.randColor[it.track_id % 255], 2); 75 | cv::putText(img, 76 | to_string(it.track_id), 77 | cv::Point2f(it.box.x, it.box.y-5), 78 | cv::FONT_HERSHEY_DUPLEX, 79 | 1, 80 | tracker.randColor[it.track_id % 255]); 81 | } 82 | cv::imwrite(savepath, img); 83 | } 84 | --------------------------------------------------------------------------------