# 目标检测

Source: [https://docs.qualcomm.com/doc/80-70017-50SC/topic/gst-ai-object-detection.html](https://docs.qualcomm.com/doc/80-70017-50SC/topic/gst-ai-object-detection.html)

此 **gst-ai-object-detection** 应用程序使您能够检测图像和视频中的对象。这些用例展示了使用 Qualcomm Neural Processing SDK runtime 执行 [YOLOv5](https://github.com/ultralytics/yolov5)、[YOLOv8](https://github.com/ultralytics/ultralytics) 和 [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md)，使用 Qualcomm AI Engine Direct 执行 YOLOv8，以及使用 LiteRT 执行 YOLOv5 和 YOLOv8。

该图显示了一个 pipeline，该 pipeline 从摄像头流、文件、或实时流协议 (RTSP) 流获取输入，执行预处理，在 AI 硬件上运行推理，并将结果显示在屏幕上。有关
            pipeline 流中使用的插件的信息，请参阅 [Pipeline 流](https://docs.qualcomm.com/doc/80-70017-50SC/topic/gst-ai-object-detection.html#gst-ai-object-detection__section_p2w_33y_kbc)。

Figure : gst-ai-object-detection pipeline
            
            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JxML682xo27CzggLrQe5LgfrptAUsA7bTFcGh+QIRJpeWhs35Ej6BykbvcSlnCxeR/DTCNck09s7RTjsJR5uoNUJJKCDjvbBkLNM6T8ivZupPRuKfe0JRf/HOG/pVzHQwV4+t2kArnTozXSB6V3g6oTTAQZTpveduoVvBOuNufTbbDm9MnEacehFD27cdCr4aR81BR8r3ZThPOMKNv/zg2wsytFLbm1O+vFXPYerk4dbd8ulPah4rbqot+LOJQTIz14B0vAvHYf2D3TYkXXbt79ysXxTB2wiXbrsclph5htXgPN7HE6Amo476Wg6z31baKtMuBohZZyO92W0tPOD01b8iu3mm1aJBaG+fmgymCnTq7HwjZgp8i4qzdI+My9UA8S0q54/N61hRVSs/UWZEJ0LhHqXTXcLjFh3hm/EehbMnWcPPlnwBN3EK+ZYN/232VT5Lc794p05N8lEGVKpGOS3/Togpx2uMlBXDiNocwD3c6ct4AM/7dSLbUdx3OZff2W+5utJtHJG7n3GMpHPCRIy1PVUiu0NXtWGuVKBHHCxQ1eedGyoRJ+8nW7xaOwBeb++Wt3+VYsFa8D6Fvw3tU/8R0xwF3kCOS5x1bOPpiiOsCpNurxDxTjxZGxTxRCWJk5qSF8SrL/bqIWbhQ6KW0ymurbf2auMBSePZWGKMkmECyIxHCbfaniz9IDW2CAfvcvCvjmDxVgLyxc8BfdlTNqtSqTrXTuthXFv7NIdwEwXxnXn8R6zwhtFYxp4HrjBRqUbS94uNTodW2sbSGG6a4lp5BblWh2dAZM4mU1GktkGUHGB+2E37ssdyuWyDxHweLJf7EiAiF4oeO3+sQ4pmdzUxVRFLg0jOe8eeFIAAABDoAAk6JbSaG9lMnt9yFbc9XUDzAVWRLgZRbmskQ208bOWm9SNJCJmKq4+15tBSYRzC6N3R1MZrmzRXIeSlEOdu10fCNklj4wY3InbTqNQf6mxjnGHPyhqtfmCHalqQi+NCYNs58M5RKKdlwGd7O3hPLFq0pXJgMOkCcZ82bjqXSEOYhZtIMPlm1J0dKVWoa3747ncHcVbvWsZXhxnW57cuHiMd3qctZKqwJXPHwZonbrTpIqihDdyarEXARyPQfsJf4i/2exEa8FtgTElRjYN62S6m1/ZlHemqegNE9uGv1yeEWtU5V92rOYWreNp9ie/t6aw85J+dp+0U14CdmBuvpIxFHLhNWjzKAasR6PSGbnyoeUPym8ZSc8KcFwAr1ROJEFFG8CwFRT++pka5Yn7g8ewN3BC8KyyKBgEHZnPaieFOrm2j9F0SOAv/ombsb9Zb4h2HdjlA2R6oCOOXOS8aLpCXN/Qf6jVYxHr07vpyzrDTG+y5gtAtEVslniJc5bU/eJYGFIXWyHiVLdtpt2juFGVTcY4+v9RmMR6SWHrVcAn58Tiyoe786JwBCIwRcI3lhgc7LK+AAv2uzGpqxB93KnQcUtpU61Yic8Hc3V7VRgtSfixItXRJ+z8wnUBZ4JZcGTY9iP4TZ9d8rKN1239W6mL8k+Eu02etTzD/ix4TPuKKQK+/Ai7mSEHw274IukB414YMAVZAbyop1j2TlCHVS/h19t8Xqg/Hq1hLBAlor1WRNjmU5f+ZsBZv7Vtvu17Iottz6+f7kvL9HPPwf0QZPhDn0BY8TyXivkIeGCh2lb7cGwf2OnSwPAg77Uy1UrwvC7F9a/mHvKIFyZuvhkslf1lgYAPYrNQGPCLxOl/IY/GW33km0nBtEIiZyzIABnsxtaZRPnokhi2X6q+kNeLECP5VOYnKL1KaHsSzd9BKb1bdBZAjdsh2pAQMqByiJHy8InfIfDuyo5p9dJ4ex5SLBFxWX6mbBKjqWhz1GPM3ilOpZVpxKBZ58HfvTeFIBTogXlePMwACa/mtgb6G9lBbDFz1Lqs43O/8JZy2bTEZ/LKi5kvK7XrJvL2F5V4xGnC+Z1Ka8HQAoVko731aGsKtCK1XBuH+Nl6fwIuWMoV5FLXANEbEbegSI+yOajCsJ+hbv751VdpE7pAWQXkctdjoBokj0dGNRbBAWkvKFDkgtmwALVzvyVLbeHE6QGwnvCEv836EzvJq7K0I/QbHsQ7MWXgHN2g8q5lI5YNeRBz+NfCKXqHncPA5t85tpjmPV4En8cZpWtBTENQssm7y0rlLD4vN7zBiQvCPIgBbDtaLIofd7TocRr8xsb5HlFZd+DBiG/jcEhQaU+4bgCqC5CKONSAhAeC1liIW1C5c5gItkd5cZCpNjLIJ4b9DMSpaifrKlnRIeBXdSTkJpsrvakAAABh2fKCMtzcnfSn89qie+wiQUUWw0ldVVtAV8XIzRkJahx9uv+bTzdpgL9ln6+c7VsmzB79nmeqhlja1pNGPKPrfeo4Q+ZL6NnJbFOFJ0jN1j96cwn21yAJFDWUxFQBsoekpTECYpP5HIDTgqD+5cWF1Z0Kp9j7IbC1HD30hy7cAFNqTdlgTHklR2n7zLIE/Iujla7a6VsoLpNuX6HjLp5o8Agirdx9TNhrdX7Q0O3TQgob6Gvm4uH6OsmaF2RoCsuE04X89r3yFkJ+pTtw8UCubUphcEZb1p2cRyui1Jk9vjEijUXZcSdl9y8ugnWaIyK+AO6xaGLHVU/3bLUEMxYLxoFfdLb5NeFCjNzgjRZyyUAaefvae0Mh8+hSoeUZMflOv97B3n31kB66MUVGoUAlTlYuvjqvlp8bUnWkfP5IhaNMqNQfcQdCBcyqmLzLoKQw4Bm5VzDy5qlxOluomeov0w8tNkrRwdWFH0ccwoG9el/uejy+lqu2OXXlBqpybVaFgm2sFjDunykU2edWqijjqGokRVpSQqcZ6bGLc5l/6vYoVenjJxRnnpUSn9t0BY4dOROxW9Pq1cSkPeEig/42Vxp5GwBR736DjPtnQaivio7+7EB/YXQ62rPb35RtfzSAfGzqOaXrYL+YLch3MUEBZSaPrGaoMuyZRlYyaidpzL+Q5dSg/0ElQf+upEjdzUAAAchEoqS2ByaekAAABhTDkOhGcEHq6ORxNIiSKb9y5v+TLAU6YK5JxeBnfYnlUhDyPBkiZ2AMlcyc/ul+xj24mAMtgHFpgsiEdS7KHt/x2MkA39Xn5VWbHNT0eKg31aYBBZKooMcuVHvgzajSmOvKD3JS73/rpzhcgcxdrSnTQLN/NjJxkifVf4tedcFKkGQA1oKdKUYAj344TxRdNn0T2uGACwt/YrDfhP2LrcrUgvjKyMbp+DY+MtktIw0WdI7WEpz+DKPOx4Pk1yYNnb1vT21Nl4oEn1g0svdT4CSNQB/o7frsEv2P/xUkNuLsh1uxcDY88XYYWMihiMyxnB4yIL91w04JhsO+rvNPbPLDLe6ZVRtU4yYY5hjletYr5kAwzfPs7jqrkiSdb+yK9L39323IiC8W5g1hlluHnga4Mcg1JB8S8v4BSChSztgQBX/9p5HwNVDdhCf0jNDbQBXkGkYK8UQor2Ot+ZoEL1qkkjVt1b100W2cabKK8aXvCQ9LiKHs4JcowTs5Fn+cENlrRELg1puhtK3RZAIDSijaDLS6DIrUZraNNrpfU8HBgAsSyY5zunAP17fp0D9Krs3KWOfRsmfRmbZ1Loas9O3zEGvw4mxt1/oOXro+Ix9K08xUZnhiBZyyhOoLHbIgho/JOcjGmvcPXr36kEUmzAiQfrHqIwAwNGrRlOl4FQfonKXpS0CLcmrOn7hA7moRXojTYQNPg46Fj9intqkjhBxvNW3XkgmSM6D/E/GSOFvSBjSdcymGlFxogD7VcmUcRdNMXJJNqFwABc2BLZ3JEBGfrUmLNiT1KKU47mreW4GRAfmN1e55mrYHwGSzpXoM9SP1uuhNfpQ+w8re9sY4qsPQo1dQVoJ0+6It0wLSeuOvGSvpeKwHhSzQvcuDSKQOPeBQr/Htl8YDIbYmTZXlwvFdL+vfKVDmhUhbaXffwoRIdeRGFNuqiKhUkGOzz1rslGlhe0oPc7GzxQEyHSUqyyaquybTIeT50t26NhdXJACzmKWnbPvGdVBWGFyAMRnRgPDSVQKbXLA4Lwd1sht+XQUFNb7NmfYXzsCyrPLhh9lUPHlIFmQq0bNCQs8BXy3rVhtn5ir5VR5cojzdps8vSLeQ9dzUYrOoPzUwLacxqeiQ98ikKaa8AapdUAJNMlm7cmvwbhoCUGx++Ze+SKpBA+rqXGnCCLBMhaBk/uh1qydxWOYuQDgi8n1tV1P/yac/ByanAWu3H9Wzcip38wdaeK6yayKdOfQWZgarNN08Jqh3wY24ydCq8NtwuHOt3F1i8b+Dzhf1oTBfw7IGot0xc5CfXKgkl4ro5ge1H1HG4Lg5/TlMbMpphuUpwgZI7E1G1o2njSSsV2M8kzI5rFH5I06/NudZZw7/4LobqxluKskfjOLb7Tv1Xcr5e49HinITl1+IMN8wCe6sYq1krdNv1JDwdwKAuu05QuLeZHmdaHW86n9CcZm1At2wLOTQQIUOLUYcAfKhNb5j1QZd3GZ1z78zqZeq+egye3P/HGGkUHnjALbzfjTVxhonJUiFawSgmw73e3+N4dl5zZpbwWGnquix613ETNvGywYrzbbxvahCvMKabns79DfWj9mfwRyzjD4YhupYOGBd/FxZVB2zRuMP9sW08RDX2exbZSB8Honb/2ogV8yp3b/yjB5sWJVwPr+uT5A4FJklDq4SBl6zdE6x4kRo5mDAZ2dwW/FuZ3ed4vwvLzCNjuHbj2brYlgf+nvwjL6narHamXXWDF24IY2DNUSiuUCpiY0C9C6CxgrvuEAY9h6/xE354/BfkDGuqV/ermRTlmlfmzVj4cQeeqr9gN6gaW3iNQWjAnw7HGoxrsK+UC1EdseFkOwkm1fAzdqFPhIeKMl0aFA/Xjn3snipFeL0iyLPtsRb2glur90RUdICPF2l1w0Wql5WBCldVJCB6IChZhyMimZ5+1EF2XLQ/DG+wGoFW8OZ4tWRjRtR9AiGgAIrbDbNrd25EZOvWGWUKG2/U9uy3uHDchQSrFglIHbXgrj+9V3EpVr0MTKIRuJhyW5cqg+6RHdqtF3D7+ebBFy7XiHwuH0hrjwgMsEhcSH6gy809ciKMYjsbHDRjtKeOCwfg8FD2Y1zMkV01VKZGkN8aDSQ02/Dq1N1YMfd93vKp4IY2fA3+FB1ltR+Nfzss6NTcXxPOnx4Q1BsW5BfP+zFyuoCI2paVewq1+tZBgYSS7Vys1GwH+9UEKhDkA05GCL+UrnlHjmlcvCPS6QEmA41kO71TUoj2qDqu+n5HR+C36meJqRtXcA0vzEQSHvp4IhSYRCNA2IzudGJTazJhr58ORTFL5/zCOLJz5OM9ZQQm5/MU+eWOrunJ/7MEHKYHGKegoyASiWzLUDftI1B7v1LTSVhP1VqAtoScBB0mS7+ToL4xiiEa06+hbIjSJ67Nui8J68nZqXYOOKtBEap1Fk8a1ZiaC8Jzb7MDi3RQlBWbz7sjqmWVDngz+ZBP+XHG4NK9dAWNtkLLHaUynDwbfEf7OpzWveFrByLLd7fFvl1vw32pXG32U4gSLNCgtxm4dfoLNhB/H45H/OLhZURFwvEhPa6Rf7KOF688zNuELNnJF6jctd7ry+SHfKzWDKwkI7ECshsEHWQDxuGFtzHVkLdws9nbbnz2eU1gzwz6fn62mUqRf7Aw3SI42ByjsWy82wLEeL8jecK5k01Guhyf7cjStm/otP0wa3C4pUM8/h1lSZ0bVTRZkxGBTvt2iEicHAewZsXxKXcSROPyThO3IymDht32AMIyl/KS8zuX/f88z254tY3TY0cJ9C714lmR+1y4tcUiW8YUTDOIK6RaucSHQEVm/ONd3+eTKtEFOcDr0/ypzx3e4dSARPx0zfpSBy6H315KncjZppOkp4l61ZNU3QxPI784vYxgI2rdnfWtnZn43VOsOoiTe0VWcZvH8THAwSC9OGS4oHArOeCJMWIpWRO2gHDiL8v5YLV5aSRKpOio0L2UaztZsMG5m5aY6qNUeIUQcoF/AAiWEW90YfPra3FLxkbqb/vwz/OahEWgAsyVRIskTHBUI0w/T7aalLEnI4s1QlJjBaWuXlYok3tP/gzyrq8b0OeVp96Vztx3zY5XyjjJK9K61Gx16KpuuoXsLNkJGCTUn+EMplOj+pcas6L1e6KTRd0eeMbcUgPqfvFN39ahr3MGQyUQto0vvK1QMFmiDv95gXTlygHImQdBBEBAl4WDJDdu+I4CIasGfAa4RS92QMcoZW5LZ1KzGpT7ZPIc4ImNUr97tQS5bSDjFeA3v8Z4cbsOesMJ/qOblHnSLXe4idffxmTmFw1B0K96W7lxTPgNMuae7ON3pg4K5JevTKHmLQEB8//+T0R2zTzk5bDTGNXo5M4zvQvso7Mk5FX/Nays83R7c8wN/tWDFdnQKUJhYkfA9b1Hv62JXz+5NlGbqCdR8JnFyLdunrhIaSNpETuY8jIO6pJhwokhg1wPj3O5wZxmjq7yRLaZwxX7WJulCjY7cu0nwhXCVes9sENzWWsFKQDHMlLLkpG39UNwXzA/sZ4W0lP8NwF2UQiPl3Ils+pjk4yNZY643AMvzq/MXYYyXIBB1D6kkNj3ywXHQ4QB8BvaYe8oPk7gohRvBmRK+PKLXWNgSMX4ZppYEEJ6vwJCSSt94ung8bM3vrqCee3dp4fkp26vzrSWdvZzOGluKA4k737CU2VSGEn3ZxLjoLlG5yIssRE4nyoyT5oJtAwY4FsuQSATU2XcjhECny+9KRE+UT8Ism7YVOEJaPTdCSmdvsWjvFfjEWRbfsTz8MzbUYZrvn84mgOeo9SlYiy1XAOzWRR2+lo0vTltHI92iRGG+IqEvDbgjKa1rUvCs4il3G0xm6wLWmt3AghrzxlW54/GvBRsxpSE9IQE1DMpl+vJvLFTv2nnaMCLWYVUWHbx/5dC1xQ20dq54xAyYaQ9sDLO4OsW485J8XT0srf66cOtMTMmQr9EdD0DVLgKpGPKgL9Jjv2283hg0Q/7fpdBnX1kF3tqwQIIBCxAMJ1rNn0deRuFude4+4U9R96zuSwhQkN69YLCsQvD2/lx+oSvyHgJbjjl1zy4CvbH13ZjgF82Pc2wuqeqWw+hesY+BIoPlsa2TNkBsBPnp0dkUIfS+ivRCFuWKc33rpmhh8tkOEBoxs3gIkSw4W9zkwHQ8KPsWd57IyXvv3H/QCeOiFxXtBSFUDlLY3+ibPOAe0CwIKkC/Lvm/4AJd0wq2r7GFKuNDVKb07LLOXfEzaxKhjqaSGx6tKKoA0IR7ieNHQBD3hwP0THsbpMyxF864MAm61y9L5+/ifrulGCe4T7tRAg5oQxZP2OK6fZ4qNKOs+4eMhEjWLHmz6VpwOVSNLr2G5dfyrQCJClUNsviz3d2uSzv5iBfBv4fnDTHszXxtxKdJqAK1QjiyTTfc7UFGif0cxfc4tf6sw11PnlHV62K7aGCS5bVhYVWSjlbi0DCmzKbLWRI2dyo865vbujh2jxecTwFhtQtHzSE6dc+Cjr3P9l8EN+XalceaiikUfRzBVKR4bQ2ly+BCTaXz5G2+IosXH09V8Tqx5tSLSr2riOm/y6SX/eM7oS9JrpbKcdIpjKTjrGYtXAHoiom+zFPrgyIFxsTnIqhi/+RyFjc10uoxEcsvjveH1Mm0QalVaEncj+AJkrxiIPl7nxauI53rTMNb9faMe8BdffbwtM3b8PKS8kGzgSpRwYdMSwMzbZGqrTN2/DykqWu2Fls0+QIZ7nmBFp5SAKZp7GyRcOfnB7EXSElvyONET7/vjye1fGzAu1WH+v5g961jjZHlsjy2R5bI8rtbI2nbI4qs7R8WE38xf3WLA8FyCRy69gMmwEWa5bH94iK/lbFFN37Ruwh89f4UpfkkLJPyAV6lx0DH1JR0Yrn1KiK5nNlWrB/Cezfmi1lbvDJcTaZP0FtEiQmA88AgBb9YJkkYQ3bYwkIAFGrY8tIyxmdw4zMWTmScWmEpHQWO6HNexWZA5jTyrOWveHogLOAUXgt086/7RBjhPSsvHmkQCCPa4ai873cJ7Ot8ZfaHyr8kege+j49LqAQstggUkSdaMiVTmL4r+tnRgOw1P8Fm+wbY0WSCquY+ThAJLwfTGnh0N113/9fRLIWEJLCthCD80Xv6XmSuAWqCkdLW1xYvnf7uyLVxr26nkxOmnrcDC+etc1eyWyzErnJnZG701hLTH2ssYLjBzpv5yahJJBUsR3NfB81NB++Q6SYTJ0Yvv5s4ANC3joNco4IYUgytBresqZ85cgKnBa5AIr04iKLDlHwyMBal1exX6RjoBVN8G2KvClup7VuTp6pEH/dhETC0K6XM44AOGSrY+tMntJKKfJN+ZQbXlPTXRxOh0DKUVlQEmVsIvPsy2CAv1Jtshf1h4Kpmont4KyFn6jUz/y5dAX9nBV7IuOoSejJrS6p9T/6m/WMSR1u/sVYD8sQr4sOfWQ6v78iriev3HUC6F9Vup/NLP+viZpa/P33sgJDNQYBDdFsOGAnQ5rN2D4qAudA1B1w/BTHNjxVJDTkvxSV3AYNAAAAAAAANbuY+HT7U1Vt0wNEGYPpDPpTilGKhNQaNVd+ovt2LDAe5SzEBB1yk5IXQnDNVveLsZ40465W0D2KzeddBgnqgjC7ARA0uKQAAAAAAAlbt7mo1Go1Go1Go1JPy1PBQW4pgbA0zT8hMb+vBASGgwlVjt6W0irp3bPLGJH8p2e4YWxFYbiKrtT/5kPB7nYU/rocPhUOtIck+GExDcRvnl3S4F3NVEpstZhMZnzRSiu11K7d3sQFRxt+C/tHzTfmldG0AT5+QaXnGfhH5Q4S180WlkLHPnpJpYNd/O3CuUEshoWtiGJWuUKBzvUfjfIrrO+lUVMQQO7oxdUSS19OXIPk6suqotSyU500XPERjTOkBIx/S7ixJHNgk3Y0ttc6nmksd029nJb47pNkW7zGexwLk8+aOJSQGg5jREm1UGj0VE2qR1NrBstUfjjig8nRFpYd1M6gEP7s/k9O3Lm6I6edMdLf1GDv1uxtZzWQY8XxGNLDPUqbaLPaTvA2dcBcrN9wfPKBqICwBS71uRXsTKzn9vnbmGZR/VkMzoRRm+rj8DmJXHEf/u+z9zPt/6uRyBNwYqVqUj4CywReJ1+winYrd/d8sM0eHsErz2iZT60iXtpgh1GXurI2sP8eF0yuloBQ3fHYVy+8Br6mjgcDgcDgcDgcDgcDgcLehhbK/UNAAAAAAAHoiKcmV0Bu/De4nIptiZKoj8JSFSxTnZ25RDw2LNvEe/kA6GLAo8HN+Oy3s5noRz5aACn9iP9GdR1WsSsozaNy3gDJlDdgHkvPLuLJavQZVyLmS5X7xIj73sq5rySjk1oyDWkTKINaRRKE9TgGBS+v0OQb/xI1qs9EjPuSHAJ8rcyJjGp6dRFLP8hrxcKJ8NlsPKtLRoT8TKTBwzJC/YSf3d/A3yLacQxZgWzNLFo4VzXmXOrtI8uA1XNE0q3J3iDGK+mIrbkPFmTzq3oJYr3mOVxBzqUU8Gl+aVxfap5E69pCS98lsaDehn1I0dYNNv+CufgGFSAAAAAAAAAYEviDNjTVmpEfVMkLwgt8lDm1d4zM6JbSuMM3X0YCRyPqI+Jp7gAsqQAAAAAAAAAEJLHiwcYsbxYIxvFIAGF6hoAAAAAAAAAAAA6aQAAAAAAAAAAAHTSAAAAAAAAAAABUJ5SAAAAAAAAAAACdGgAAAAAAAAAAATI0AAAAAAAAAAADd9Q0AAAAAAAAAAAGjSAAAAAAAAAAAA6aQAAAAAAP93npPD6VGD/F8iYdUfMK9HrIqFYZASDJ5Jta66wG6RPaXv//ebE/ts0yWjFKX9yhC5tb4iAAAASvLPgfhKgHfUQTRXkIrGW9wM/FvpDLfgLb+XxDeOhGbl8bIGL6hoAAAAAAcxAiq6VxYgegLi2YNUkqbIsxUCwjd/YF3wym08BQ7Q7z+tEVSgAACSDczhWUyEKkA5L4+DWXkyPDo9pS71qZlVuiyAlRIWMG05mSHHoIuSyCVh3IV5uNGpqmVAsvGXwOncGTpKE3S6DGKOyoGfcygiNiT1w2gnY+j7s9rZFHaJyFHzJdMa9alkDD/lWlIn4qeBypMgzjMjSNCR8/e0cvwJDqwP3x/pPlUvyHcG2U1u7Zt9CCwdZA4cCX+UtNp4HGcCPZlLAmFqO3G5Cr97piaxNAEpu9OTAx6IoVdw1uviPB4vWevZFB/QZFXXSOrVZ0Y/aXE6DiYf0z4pnwQ8jgnLjOL3QdMnIZPEnJ+Qa6PqJ/cXURmE3C24wm3BSAAAAAABVAAAAAAGmpRztWGpePUNz0mWlJr/GB0L7Cq/1wrBAqhTPTuAe4qEJRnFhgOHkRV5K6KFAPsIGlJ4CNQ06TMJ1q8CuMJgYeETfVFHEHTPM7+kadApQchkXQUFoii+lrbbfzOZcBiJ7qIF2lzcwGj0IC+dQgVTO+uoIOPpFMig3lZJ3rJX/nAE3FE3MCbW+MH3Xd1P9BEImPAwdUQ2sjLpvkfxrDMtbggy0Y0QvDsKaL5XxHSmuBp4codRkB6Lxvic4AIXrWIgeA/bTA62y6AO9hYWNF+UacZ13SkAAAAAAAAAAAAUbsar0abY3DfAIlaHFaCBYhRef100enqHLZomfwweZ4lFnBJ+h3DOEvZNEyHMmU1KMG5skADWADSOde2JvT2oHlecIoAN0qET0/EdywBLO6ad3WPivn23UJiFnGvQWm0V3lIi8WmVdEwzNq1trFYJKNTBWu12u12u12u12u12u12uzRBBGS9yQHWmF4bvc8W5wzoUKvlWsHzlvzy1UtlQz0ZUzVYOBuJL6lUTw1QOrw+rw+rw+rw+rw+rvLvKfzWO5lBVUhrdjNd4Xp0oP5P7BTsnLNoDy1HVC08JXl12u12u12u12u12u12u12u12uzRBKuWeoNdllltXZ9sKYcE5R7RsqNrvyYo0bFlX2RzF/cWlwMSoGVUAD1bpyH7d5Vh61Xm2QgErMriRawL673kCi7s0miWFhL1KWCCTG4wA1ArWbNETJ/XfrSg8/i5eD8plPpX5E/4shM3e2zqOaXwZLfQ+9NHgsyQPFIK87YtFBkO7/lNCsjXYCoiDB1b700fe4ozN/hxjWJ4Ktg/7NMSwP92MA/CU8YmqDfw/kFG6OA0fanf37slF51s/F2FKFWPb0aAMrMdUxw4hewez8HTy+JM2LT9cyGstJOZwZEf27UV26VJ5vOL2xuWaehtH02j6bR9No+m0fTaPptOwnta9g1B7J6kSLly3KJdcrpBr3n70zfLz2Yb66vkn3GdhpiJWDnvyhynHGkeXL/kehN+0fg4Bh3LDDPl38hH5RL2fI9c1dXfmo7TarVarVarVarVarVarVarVarVSuXb0F3X/BLE8J+S0WMeaTDrFv7lRC5WD3koMAanJ9uBqMvarbrHNV1rhpRUkWnBK4jzs4YcoDyZ3AeYi2h0U4ynm/eDT24wLltkI9x1Y2kDoi7rKF+BFCgZ8vuMhIT9T33BWQumwSNaDQinjhoO26iiPcngpd8+BdSDdIpJGCteEAMFa8IAYK14QAwVrwgBgrXUqV453+wZg2s5Jm5wWsGq0m/37Q9ib1ysutpiY/Ylj1bgd/+DAJOh3qByxrXO+Rcusw52y3lMx1UsuaoH50mC8JorQhcBkPbf8qT/DxTBGzahNSACdrpzOMUw8aXlB3kINvT3urrYpJz73vWu/5vKkED6upcacXHiq1eQCVHVGAOU8M/cXp8RS6PHto4dWpJBlGL5QqDG4xnZn3B7Cy29iy29iy29iy29iy29iy29iy29iy29iy29i1Hs7xB8rgmhmNySW/jM2p7u9JT4trVgDM8gPbg4Wg8CzVulWnn1dhC/lMYr0Lcwoikorjvl1D85r4PYf67h9ns4C2bAAupu0YD4CVIkWsLDraoiIiXXK9zP+WVFNh7Lau6CQNDLFKWpUvLgdTJEPqjlzxoyfMc8HxW8zhbVH0HxgPSIct7VVZ03CwO0P5AGNOcP5vqAUZjiQFCVHANvLI/ROtjSdi/TuFZ2sWKNgCD/j/34ngYqLs94NHPz6KWcjoU5i5HQMtva6S9qlHELbNoLmvpV5h6Rq2sIY4nN3tJy6HfIxwEWNh+ZuwcVH+EDb96yLPyjQC8lsfG+OAGAxq4iZ46BMYcxJKMl5QJAVrgAf/CyMRjHeTfCVnNwx8J34wfD6QZg+tlIipMh4xuwwjM70nU5G4CdGtwstuznxxCf1Id0EhkYx+um2eYD44CjAw/hKZH0q7Ihk7MHKEpm3UWPJA4itjPXDItM9BPwhv3kcgBQkNSPGja1jcEgIP5c8efZMPSj3/UZJvhcNcfDMdvFkQ1YGY33o5U46bH9WWTOdkfSysVbsL3Hgfu6fwTqbQiWxTDV/UlQXxXqQ66AF5JhmRR72lhAME4YAPSckEiPKl2NNGVTU+lQmR8N069L43BZ4UwSXwP6rrxJCxHJouzPNBm7oPly/HglMTSvovsHKcbodbSMQeEL0jodbSNJHIG1lIP3pfuZEfaLMgY3GG4A8lBRfK8ONpdrNJ5m+G6dgUtZIq/u8N/ovzsVVjZses0wBIHUyU7z3v74UFCxxS2bNAN+oleVsc/ePCNdkWDsBpKrWX5kik+GzmXccqbvcGO08re7qGClr22beCzdaP+7U7m/yudVeGX2+3UWychPsymv8bboRwgu4ZDHi4qZPss951i6kR76Q1B9xefv60iozm2zWmSaHgqHFPl5rIiiGgptOv2avp8x9AhTBlzNm0bZQCQiOt7aJApF++EXqSLUjcBbuEv6rcSJ/WkIxps3vFpm248K+Umiuki+s8t4FVD48rMJLdOAvvYwAT6d4EJYhz9rkXKVWx0JHiDCFRarW5sijrWZEvhr1owMBeW6q1dd9JORievDNmyo9tfJ3H4yUx1tMyBZxwAeJQfB1ZeOSjOXOkHpp69Mp2HAR5uhhEmemrPWqq1yfWWKFJtq8dKXQW9wcOT7eu4oci8r7G7m4qUaH8CItLtR1SJnaS+pT2hAK3v/OGCdIeAlpdGhUlc9XXiJjqm5xy9uPDKcVwOdkbl7sTKwOJepLSRHVRAyEKS91kQ3u66NzIZ+VHUmbSwBeB1qVCUmvfGZzeqYKr4NjE7m/7nDVXgXlU4PEYLMhO3anC8hoq0yku4sVFoLGV9Xk5kd1qvbSJAHSssQnSLZyYCgmDZ51Lzq9PDatWVyJtlC45bkKP3GswOlLyMF7UlNIHOGJRSzIzD815bf0Rr+4Bq9sfDrDEadb+vZvH3ZgEW4WR5ufCBeA96o+IZc3sB6z4d/zmi78XMTDzetaXlIwudlc34YtsNQWnIhSwELio4em1RQdSo0mQuPq4kTQcl2lLQxXFJRMUiP42lAS8XpGfvHqTd7cJT8wlNmeHK1BLebxsvAXKav9OKfYzDG7SkOp9Iu4JWQVCODdSWrLVzIkbLWICHQTP1zWkBqB5Fi+m5RAB2Qhi9iBu8a/pEZ16Xzzd+OTkqDfAvZX7rBbk/vHNVFmEGB+H62pL81rlMKykyLFcKc9+sJccRcUlq3Uxr4HTUM35P3U664RYCx/c/851+RGrM797w5+sKt5EmAWiMFbW4egHcLgcPVoNAEggtw/h7RHj0oRr80KW+7x/n3I2bOOLSBTZTZhPag6b9+faH9nHhbCYTBCk1q0byRDA43mG+hBf2gNS2jxJCG6MtV075pNJEZOZSbksVEJjIK8l1o7IRIR9h6OkBmX+Ko3Keu5bvFh6yhI6RMkpR3YQX4FdnISn/tfPQFhlY9eW1CE8TStTTSOI3oo3EN8pRJMM9mkgGrAq/3vO468OOw1mIEZ60vkPqPnSne892YgrliI4fjtYXIYZjwZ/bfiU/Xy7nYZ0RCD5pTYEhFvZ84VRbdOsjiz/gb4yokQE6s9kiJ40O0awRCdBe81QWTRGZ6UBnzEHuC0rUB55fOAXc/0ycthW8ajZuOGYiudUCQE9gf4l1YUp59Y28FBOZkrlmAl1x+G+8OlK/YNyuMcO1xDYhOqTGBw6RpvgHfd7Rn7yMTbKlfE7IAOc1FzbrLOJKUmASAiFfHoYr+n+755qxIkM3XiZvPJiA8jRXnLLkVKiG6gpQRZwbHqVaRuXFdY45SM4KznuNS6zUSqEhlCcA1GpJX87hMoOzOoEqdTmMp0P+7l4rWPWpfUvmlvA2roq91y7cG+Wx8qYOuQblj5cuhuAdYfxniCX/9ERaJrS9f07JFkznZS4lLAS/KXyMOZE/bvjLt97p0/HOcPe81Uaxbuw3nr9TfA75bCX7jAzZFWtqPKdKrFmL1ACUZCcT6TX6pvI/ZwP57yTxCopmZUDMEBO6mE6nWn3FkDka0fKC1wugq97yUKZlio1XZH1xM8biNa2vCnP5jjq5x179ICFgnQuhArN5jfXzZG+aaG0YF97ZgaoOSEx7+gORMsIUVP8gLQ4lbvUVf9g0AA71Ux/HL0yOED1ya3kHKhKNpK8Ry6+l3H50ESkLBsnhyjkPl5EB1v60SEVyvR690+F/NPb2U2H4PZjwrRR7dyRAqrSHgkTNAig5d/uYkXr3B5HO7T1R69xa42vuD/Yt5mRvjZSCSJVXU0l+G6DJ0y5EDy8nWeZU6hoE0NfM/6+LVCmzyBiB0dr15nW53sCkSaRXEA74E1iveBvLTOeO1rNtl5x28ok2D8QDpYKAXGQBbwskr2h09piYp2VL84HI/N91jzLYkYCZcVMNm4MOUpOiqY1iVl+VJ6LcSVd4zR7ak33H3aus+MBV6PFWVoeYOOAAuyYqmSX47hFrwwB5ep3uwPnQusoyQIIeVtj+FgdF01sq6x1W3QdZl0tGHLiMoSyjNvf9zA7yz3po/kcPQG2uNt3zkjeBDiwMIeB4+LibrcfUXik9sCG5q8LP6FA1xuxLyYOK/2XBML97RdldZio1JuTnU2HcFeJ+gYQGep5MEfgS+PJ8TqSurBuGY4Lyvj2gOC6BRS9O6AOyRuo1Ke0bleahrIIhCwG61otE6aRCg8l+ApdnLap3ELhhheHNeVBY5wnvCamgHzzHPZqhoKkSTSCDmZ553VfsTVk349jk6gHKrejiWiZDb/xiXaNM+3g9H/zdjaXpodmcI/iyE1KIBhcIAVdvmCS90AIbUXOrWBqYKxB19yVc2A2ytwxKNjPy+u4DX3waI3aQUdW5uncLeVOfcAuLYHjo3lGk58oBU6GObsaqA5SUocBuRPVcYc+msM8F+4DZdC4CseALz9LSSvi8i49cQGvxVdSXf3/rzaKv867pKd1jVUs0vXbr2V2xNKoty1Bjh92CPYgDBStyYS+XT+UoL4C26OkrX0GOVYjgNAWCWJWqrxwqbtfsihouRMprh0fSL8aM10r9fjVVg9iEzvqAxa4tt8Xy73K9dv0QaJX+920amj0swNn8MROn2UheEIf9uTP4CtBmL3D23UAkXP6BBwDenGkOakBwuXruh16Bt+7Bmin/cebVtni40QZIy48/14zy9u9RWqReo63AgbZxiKi/AaZdk83B8jCAWXwGl/nQ6vF1Kao0qEaykER4QmlAsrS0CMfRo5zULx30XHnOBQOUWadnh/0Gs32iMy9QjO5HK+UG6TsjaR7Li+ABzacYWx11+oSlYg65VZwPIfvZqKsZitRht8ZjXXtS74ABB4z0AXYJy3DMAz0Ejz6qgLxgvKrPRiuJS4TjRo4ENkh5ArV5RJwLhDwcY59UDWUDCCxYO0a/GkL9RQCH3NhSCscsH5v064aLYv+6nvNsB8m4erwDC2U045FhuK9Pa8XU4gjZh9lsZsVlKDYPO26T5gX8mesRqfPJnsTBohjHg1NIT8IqVPP1vJR4/IG50g4ui9ri1V2NEQjAuCLdKH28S9lJGsCeYMMpYlzea0BiW9zvBDPNZ6lfga3yYL5Uf8HQuM/c2pSVeZ4eWNebQfeWbPQiJy1k4KHQo1L3MY8TTB4fB6TqvURA4BksN4MKhxjsCxcmqENrjrxog2dVZjGhHFz3BB/cjM4M435MDSx5To6Rd+tq2W/Gib+Spkq5m3m0/aJ+BOxTeY7M+rc4U93yR5b/vKixg2/H8vBPFlKfFIP+Tuci4mi94cz9rnLHnx7hkQj6WqJqm16oW/IgcAyWM5kKC8AdX1fDtvHFiOaXhCMKxYXWSIGUm37LIibPZxvf8VN3br/b7GXIHsw1h1TEX7fYWaCLHO9/ttodDsJamtRA7CUFSnv2Zm5uCcM5vpxWZGGafEMIJAQwe3XVBMCN7nHnPx03ptlGMMHkbDM7wvs6362twYcsIqssAjgaaVw0xPCD0Dyr0roDZphpkCA2oCBhfmw5262floaOD8U/qfLCNf3QVG/6PyFPe5ieAKfKWoeF1TrPBQz4H/sQM4ZjVF5mTvrclDOMPQINgLJuBWAUOq+zqGjs/occKI4Yp86Br5x+ucqJ+NBHCf/O/PqOveAQCnNKkbXcEdsu3gARlbzm5aY1dxFjNRZ26w7ZJKF+24hCR2ppvhopozKcTL3CITNARNFez2Zft1RubLnWBO2gjF4t23++r3GtetI77FuF8CxeINOVlobj89FHek3Qksc6Rc9DJajJjNqxjrWbiBxv+WVhri8tz/411u/M5mfZ4EHQQ5PhEpPDhe/1rSppLi5qIVc/3law22DPgYhjps49bKp6/Ehqvat9EM3HJ4LcyajY2ffGfrGAboJ189qhOk0V/2brN790LI0GQlJm1dooCGaUxnNoIyBOQqDarVa2wLUCfqXI0H4OFaXqqWSEpUmD+K5oHVags/i1OTkBGbR7x56SYwN+fdUgBP8OLGq7yicH8aRApIgFN4ovvGEU00O46UgUsgSvc/JdgkAYKUbZcD5Sjfqlmvv156PhW7PAU7LUFHNHNfSGfKJOiDUl0RpEApHIJAVQYOhzYTkqyanUaWq22vnIKfaAnMuBHHjZb6xymdhsv3KNWm3gaJAj1bOG3kCpqPNvz7Jg+aMY7DsCuYEuXq49XgEvKr9alywRp8ol3goFzB8HwWGHzjc/T2HDbh69/Xf6j36k/Tl41n0GHvc9OjQ6Ziouhbt7Pvu8A998ovs+rpwktfPlUeFYlx5AAmeZi3xEQzBONchGnBnYmOnzVMr6CdK0+7EDhSvkP/WSpnhaRMeVaabXWRVRKFSD/yQxi2BuUUocaDGY8k/5vMC2SQEtY0XFR/dYYuwsLEgl0fMQKqdUTRwHqbaJ74p0A4W9YPyN8lkw6HPjxiU7ct8Sc5WhYMJTPGiSdoP3VaAJsAkdZRVtxkOPOPtm97a7idjd5UM6PPIKG2PvFzbxCHkRLJudM+p7/PK7x5qW5qG5Q319eLIw/1RgwQ768M7YgFsuEGoZuB5a7Dg8jaT/VhujIzneF1TUYwxZLQ1Y65RTK1Z5/ivKtrjJ2kpeW3sWFKBh6M27nzy8hR1WH9b4HGyCoNXRf9w+K4rHLTNXRpU+1v9ImfDcH0b/MPn052NkfDy5XwUTDYsJKMns4gPHuxcNMMlw/OTzXKTTPMz/o4HOpk125wTjwNVfpDQdscrA1UcLCEL0jqHiMYORldrh8QFjiIy0zkO1oT7F5uCjd1GPMSV43AHFP5hQqDh+Wpg9LZkuYgNNAZ3Sof/AXLfDAmXD7iYMICShPKykJLf3XubUbds7fbOQWjTBzy3NyqLTQjX88ZCVDbaSpKSu9EHC6VnU4r2MAHCOX/H5wCSlx4tFAWXo1bnO8lhtUn5SK7IAk433mnesVPxbmyRgbYgGWlz2PmQSVPfgxRubvOxnMFPkpNQRb+DoXframe6DQaYO8f/eszBBf1x/6vFvBRYa/v+kHgbKoFOjgZFWSoj6/FEF5AJfEgrf6U1A9b05DPemfoAIBrFs582ThNaiJ0VEPHMNmLg6kVfcdaCyUH/xgU6Q+EXjnBQUhRYDJaM36Br8ciSPHoaO8RSogmBIaSRBhbiGMuADwNOMOtjyF4pXfAnGZI6YJhHp0iNPm8d/VvhRyADFT7TSNGqBRngsanapp1ACr6lSLGFcnzqtLm+8GzuV6zrC3ftaTwRtQRNg4zpi9tJJS0TsqGq8bdgAAStU5d/VbsV5Y425YNLRyTJxYAGQ9xj+W3GZUxNYt7hZLIaqKFuiMuG9I8us2o+JxToRVXPkH99C56/aOM8HYJa344AlNbaohAHzGxNZGUA1TyPnVJBgKiKF7FAYtMZwOc5gJBPTvgwwBuSJya17czC8QBawg2DBEjFnAFUG+s5A8etrrZRB2Z/swXzOoXOOVz1+Wk5kA0PwVTcmRJGRQBu0e4Pi4bgpWGXOfiB41tfrHIxoNL4FqJyCP7xEgVcMu8gM/125UbYnlVFugQ9laS40xyDY4UJXGCElFeK7o0OAlImPn1vcd3QoL5nLAeWLmBR0DS+dUDxaDosBgjb8Npa+s9m7eoijGtkAzrwFpQRPRCy9e1gEmdi4FKhTqxyR8+Cr6ru9KzDmwIyS7Alktv71Japsm7EDGi0Hb/ndnhmExMkk7zfZ5XIufPjlyFBsXYubtGNJGys1teWbjcHIBtGH6zLH5zhK+8V5p+4B+FPEfuB0zCTeScCUHBICUGYDpdmqUchgyttXt7KN0KX1mLs/aIllv8KYQoyyw9vxiS1lJbgDjgIIfNecTHRR+RKnAhiZFXflYaa/QfBIFKGa3tZE03fuA05hnio5JPvrSSq/6onoHoHKFErDt6xMns1s6msrz/VnWmUcPr6MPyPBoneugxGaHnrvB7PslTj4I3kBd56pZ9MqSMQxX9ApmKV4dFsgdlRvFJi4VqmMOp/6QlyGwJZDi1mBAjJsgs1eUTNVWHUC8UJK1MDND/3gixKQ9ot6yVP7ZYKv7DJtJ421Oum0zZ7V9seUH4r5JYqjsi6vWSPcwnf5qzd8z9i+fWig/l+gh9juNCvcsa7iPKvDdDK7wVJ022QcQ8uY/EXIkBuUqdd9tNCqyWvF8p7VmYB1a1ys6BeyDscJydRxEo4Sp0JyjouFhM1iMaGyYLs+RHfyUIJim9YL9bmCXkTjl5vRYRrWhqicaH5mEPyNKWOfve/CDjbKAUVqf9snitnb+58Nl3vJK6c0Q/tMtPvYsXQL8Kn3F9NUdIhjSHvaBnV69HpQ+mQ5ogogMnqfPHDFllmumidhJXE8txYq8ckqR6IyIrDPklglE2ICkfDAnoGAV35hHeREVJf26P16UhRsOmMYBSAzuwxXNYNE03RpUlTviskmQdvyk1svgLHUs2sNA9jxm0RKu66sEog0dJoHra5yjCFrbWIq41u+aWBkvLXdIqqtsQkLuCcN9bYi+v59X+IvPJjlx/6vQb3tj3RAGSiTXbGAmEjRuh5hc6yQmbC3S+v40DdiO33vGkWGyzETLiQr/36ZEfYfyNh14znnInCdSetngGiOYV7KJ/5AAXq7zygilu3pZoAcHDASx3J4h7vErPlBoji2IuVcj6WlwatLsOUW5IOR3NjspMH6f+GgFPihMxsDsNZYTbyubOvSRWgee/o0nwMosIts6a2eq43YJ81vEWlL3Cs5dp3YYzI3/sagLtRFyIT1ew19lnaEzlYEVAHN+cNk836yn3GjHZsiuXgRWPKjRqqOcCqfKySUMSk8bXqI2sE4RH/4y+2a11e+SEtwIjiu6X9xMjyG6L4nCX6IRwQiaX09gzD4OET4WPcrV1w27SaPtOGoJuVlPazhkjVRsG4ZJyP70Xkmb6x95KxeqVeiT2gRP5wr749rPoGjw5MU/4WxJTRdiAE85vQhN+f7Xvlu8arJQ8g2rYBGYXS0vTZ86BRFZOVeY2lT7pFqEY6udoK/a02+tlru1LiEpu0uvgaiy1rfZIGBlIArc/n+3ZiW0RSfeMVIudJjbCHYiwdMLcirGocZWpOUohdM6HO7wv78u56i+nVinUoiTxEYU4TjK5yeKTXkAO5cLN/PqtEf349NNf+yV6BYzQJ3awZyByUrab2tEF5xTmgnQJoOAbdBCrYvtNwR4ZyZslMjS4B2ekYpW8mJVe/USsrexnx0yZ0DtDbx91A5mUe51pZ0clpX5GfuyIkifvW+666dCy6ZD/AYsNv6W7dnK+AMCmUqR0sIUBCLR6hhIrC4B54BspnuYq6DFBJZtB9qy8WhpR3fkrCNHMxpjGGYaHrRolTzIJHhzRxELgIN/rHXl09lOwVEbJS+Rd58yvjvCyUkghpKWRgnpZa8/BxOD3dP1czV7F/j50UjTsTrjupcEPRO9OE56vx0IX0SdCmix/XuSa9ihEDDjtW1AyilOb4AFBFeAL6RQOFMPoH5MkAkjQpvtUvXvnhRI8fk4y4LbYyhcWMWw8JOFSzfRoMJEAomwgwrupEv4/OjF+VFLTA8EMQIpSxaqwRlDM3No3vxHSLvHQ7EP6vI5Z7Yqoa1a0A2YUpKHvkiqQQPrcvOPG8yUU+f2Gp8vgboAsdbULA5Q0ZX31eLuY8m9G/d/JbMJlzThYwBy0sfrt4a8CVfiAIVPB42Fu2N4E9Eq2RslndPZdP4ku2MGZ/35f1v3UVRWnIpdgY5NIlKB/6dvIyfPAerGPrvGWtDHOkKtgjJPNeQkv2l33QdxIFD8rtQA6L/c/9VKI6hAqFjBfLhMwepfxGI1Cz8u/RvX3yF433ObDNHR2CdVe//fqT+Yuc4pCjv+QgjgsB9RcP+5/Rk2nMGDCCnCZZrdS/i3vE6jJgOHKRiuAiSyCKWlfJtAaBVCCorXpcz36UdPJnCa5FA6uQ921x3C6hlJNWhYKh7u/B1ernF0bH0zjQIDzUBQ8CKW9XyMX5OH8mgBesHpAhwA6OY3Dkm9r876DXcYEiu8tdps6nSkAb/e0ENVR6pF5MiR69C+nPSYFtiI2BxZbvru0dsbnBZgKWzXBQnlnD3YVlgJUxSJlOjBkBQ8OnBRS7H6ifxEVWvq0g/nwzVYfsqWhWiyH4GDLCbEXQNdZzyrsyYszYl5EpVpxwLQN9hWi3x6pCVd9e+JfZrt4gY6unoIjOjsYZlIIdfJhXXiKIHycjzPsKm9S+hnSWa4vr5VwQ5DWjO8f0zjMZFgrGUjlULJvEfCN2GlF6p2xjlQJjfTUN4e+twRVvR/ozJ+28Yb1p4L01++ZYDAlHxCUR37hkQUaQNiC6Xa0fal7rpSdgykLFHpGzioVvJ0Qskd5swtIn4OzPQn5BRJ89ggjnf/QICOM6aPmJs0FveH5I+hCAjQbkAW7FNpwF+DdYC9QEOpqx4Hi72CSlv2+TF6KeYsHzyhgGU2YSPI4Hb1X/bQ/Cv6iXGpDCGjQi99DI0K9uuFCuX7IqQ00GfLKVHZL5V+pPOBgkZC0L3LkSmjD+3YYrmoyUVEZ5FCirp34Ho+aGGpG86wX0+5iWh4BPuPwQDBXsj2t4ObBZKrONXhQGq6MnuzipIfZge03p/AqGpaTifLD051BuJjA/jfrjG5iI6iqZyXfenm9eJLdgHgjyHRXqEchPNeWn4psrtCdOg38GnOg3UOpioK1ODx7Ps1ofLrp2fuez634klpcfOEEg31EXHeH2b4RxC36gVIPpWGuZlUvWWBD1JKuKOfsmiZDmTGveqCsYiOJXdG8IBB5mqgAatM1nXiAn1+8683R2JBkb8jZEYp5JKIxr95uEXP+xmY/gSibuPYOxkXmQMUl9G6TDXyZCRFQSuhQOvdKx/4etV7bE6r6Yv4ut1kRStWWBaNgEib+qosib/I5VOfbtPpHS07cqDuCxuEmmTcgh3d4wf/M3joHiAiEGp+KUoeK5dUSZwnLvGhei2p9vv7fPyqW8YC9ve05uluhOFCj+jlHZcbRhCmtpBIhAQxwJRzjNZcNe2d/Fz1Lqpy8ChGSR6eAIEU6LzVOmHMjLbd3/s00S6NiMclw9UNLVjui9Bjdmz5211jctGr4r93KU4g7r8+z/s1TfKnpBA21a3qUaIaSKU6P3Kz5+zH2RW1WmuoRmhucI4uD1Da0N7wdLjK3vBuFtfcz8M0Yuuib35XGgiDc8gBDEFHpRpgDCffTkqKZsSK4eutVI5pfnlGukS+/nASdRvmtZ1Ey80+48z+9he381napjPEZfHlw039E2OzhAGXEWPYf8MYpeZfAJHTVszFLBDnZsyEiY4ScMCYcftUv6uvcFnqIKhoqS1WGj58JU1uLyufPe9yRmtcjCoNhHn2w4X/0bqjDoRCwfzejPrQJucmrIkivWJp37ogsiSKStxBbjlDmOrBaDZ0E0EjaAVHMMUzKIwFfYAxAD3fJpPMQCeIHX5WgWERh3c54MYU5XH+VzYaLKoVvALD7Xt761J+ggyPQkLsrNBIRbFWbknZG9Bhk9COJfxYxFA9o7UACWhDpxlyg6MnB1DsXO0E4BJxgqhCAHh4f0hI8Didnu7loYXoeKrGzBVPN4i+rUniSwvlX/GBBISJwerD50zZVDkZIc4MV8jVdAhJSKqf545sOP7WgYQkjilEIDGZCRaesslZ7R7+KC1xeMNZtCaCPl/eLiTLOkzpblNchF9ruQKsOcmw5eipbxXxBxzRVLh5m82pzmsFMazRwu8ya9M/+b24kzbmBfzyu8ealWhQZkDSmcl2mm3qWc7K7mkH0idyi6xztGqU+cJwXqi1aS/t39HCiEL+fTDVF0x3FJy96tY8R6z9ELVmBr9RBr9hUzXWZWi1r1bg6Gkan6FD5qq9e4TUdSZAwIc9ZpB56nl+XtfYapYwm9TUdRttw2zU66K2a7BtWnpUrS5L5QBs7UsZJQ/c1C7HwR/a9cEa8VkNSSrnSCV6yLjkHxRoZH+Rp3q+TvNZnDpJGsMg9XaUC3J9DPBM4kNiZCowFPB7qAjMeCcqiWkTDecbYwqZxk0seVb7tknt0zhFex0+OoQnpgXUs1J3YC1LexNec6oG0Rcb+yYpdeG2UVQB7CA6PQJGGBZPrdvdanB8Bm+LNl6X54+cNpeScq93wzChlmSAuigfS43zMZECFR4jeOdXU3K/jSYgE2qoORG3JavYiN4GO1YjRuBi6WNR2GP7WHIKgrSMZk+75bGXUEvMa3i1dbS//VleYnJEZYZMrfOdtOIsVqd5jDCUU+tnSwp3vh9VVdkqzdZ2LI/6fRdd5D3GI8wC0pdgvAc+YrxPIpziR/l++V8LhpZwAkSqeRQaxz2gGerhXXr+Fcsv0hjNFWyQQuVpYeovl9YgT8nH1EpcQ866DxR3Td/xuVhO6C7vP0aLBOG+4Nsk+4LJQ594ZPngsanXvGGA2M9FPzNoB7X+N/hHpx5AYL0uYzrgSL2vypKPQ0P3sB83Ew/1/DoPE3XFKfLpwkUI2QlGG63o3QKPOhUvNEwvm1gaNFQtfXaYeEpm4FBehcbaZ3q3H5La2sziVr9VuQ0hSqjrs7k6G6HkERHJq5BulFiuRDcyOd6CfV0/8bHH4xqNoql3RV5g/bb+3hZG/gh2kf5fC3556QsK2w9Ma5Ugul9AHGxj0uBQwQmLQF63J/u4CZkCO465C2UvRIxe7PAkfvb/e+veXf4sKcjTxNalFn27pm7fs6lHYCSIUVf0kr1ba+tv2E92w4OrzF3n0iCnCkK1o0HipJ3pHbS80VvaCJFok9kAXIvhUYm3YD0HZS80YIVdjIFYdyYNCtqow4BuKZ/N3vk3J4T3ucoLyD+CcRBI2or6BdxokLwj69V8HdB4k/8yGf4aAR1rSlDS4fEGShUg2grCCRdoOHQv5IBkTUQJhyYMLMjCnL8IZgziVX0AUZLcW4vZ77e+Mpmrj/1L+IsdmAea7lcmsE7WU3dcZQaM+0UFgmG0hFYPpv99keiMAYbyN+8vUIK256Xl/njyoi/kbjbJ23zB/ODeyLeJja/Nj3eXz2cZUt5u2yDoIJesEVzFEKNAlSSd1OjfkiHHB/nXryYZ6+bTukr6NJEaEOtl4DwPHjiBrYDDrvnQeE0+ZkkdkIk2X8I8Q38/M3cxya7M2rlG6dmx8WgdVLw1InBT/sE2i4sDtM3Zwga299SZJc6sV6QLdjzfzbPqXmbD0oXusMC6YSdoM/S0cm1Aiw8PhHocSi1QpXtKk2lnbv/Al0cD1pUb7u4lzAyKJc05qtv8jt6JtzEuF/A7+RPWAimAjuw3LPrUFdeYd09UwkiJgb+t91sbuFUo/iuaO6e6RPBUBTzMvuT5wQLIYDPc9imncVvqK3zNl3btP/gtWpWvgDalSocAfFM20ibUJ8zFRWrMQdvrGmGL/2058KJq6dEPQzZnaQG566zXgAQJFHR6ty4AGPaezP3zOWevcXqo8CHsr3sh9fXQJMxEZc6pOPuDc7w4nEfX3eTlgkdQR1uEymB4ab5Ua2YNl4WFcgKl9svWFsiSodUdHnqYX9kY45Vc58ScYMU42gXlpF4XfKY31aNEdHsOzQAyYQtRSqFVD8XbQlstQWo0Vp1i/3OaOGZhM1AjMIB0HiIXaZXZFUMQEdXFXayyz6CwzVIPsTBLF6sbFzk7aHpgYuy8hVbzqMGLzOMKx6q1BECvwYKPJDopJDwrpo84QZjVm5C91KcS6j0S+lmWUPBDwdn9Ga1A6rO+lVbPWMgyZ45WbNfbYHN94pzIN2NUwzP9dh0kpZ8XNq3Orp7Iqqp5GEpeqHw29a4x/sEBFdDXZZ+Cftn1H7daetEW+V0T1zYQ0992V2YU5bW1iJvy7l2lNAKDNTEFUb2PnVS0BI59phIxiWguOmxJrS5DcA+4uXFADnVgLx9A99i58bRwYTCKPZgY8CAX7SbaqglOprjMjdDgDRvTmYpa7ZB1ZK38sWzlSEVCbb5TyPOsFCC7DXfAJoUlh+yaADrE3jy6L0pdre3mNTRvP6L+Z2XUeZatW2lvt3uu9UbvUfzGFijKb4XlP6gkVT9BEHD7OGRkc1vWaMN/1N9FA0mAn66rrCTUb/qxmekpN2+PEOVKAbh8t1FXQnr3tyTlrPQOuUedItd7Q7OCSEzedgwzYAnvmPG18NGUYYLFq4dg/NlJku9Nvj3c5FgzrqvjWC0nQgvxdfliWJY5aJJ8GmWgYMECOXNmZ56AW+8UTnVDNlWrxE99HBe6alk3htsGZZ6VNbX2rZZxsebKeH+jh0ewBfWBXbJp+2fGQa2iIF+m226bTi4b2UNh0E6HOlRaUVjRbbzltHyrLnZoV1DkJN5t6TohwaRklHyWS636YlmhXVBhjpYu4SAVelAUFnjXiLgnqIjQLGchiu0g+HIXh/GBxRHMoD8TRmKPGyQFwaW5k0tiyhHZtlRH9S/fjyjG+qREbebh8RoBSDEuPGRnxNyrLziDZ+BmWd/GnFpCg5n09j3gC3Cjvz75XYgxa9JlB8NzDc0YovYyiIAgjMSypwR1ZmOhUHMZAapKMlXlzwMdyim+TKHu+qUZvPwFwysXSC/WmvY/F+idqpiGB2wTwXfdg/eI6dvlRujQJx8nYRXFAPdWvEw+HjAFdQAsXS1j9YKjtn8+eFjszta1KklM016OC2XMtCneq6uR/bjYa7/KMc4asB2n5GVeaGWcvsQiEWyRgE1iYpc58dS+MM+bR35Pvuw9jbwz0ez39xMkakWb/fXYA+lDHPJWyQZKPvP0rfyvULen7aFb04g6ptiBE0bApvJoPQS9+okVaVDyfkLzeVhm6eWCj+5vXZ1n5ytOQ/R/RL7AF7WV6EhhU1+iS4Mdxdph7nJBfOr/9fH2FR6xDVAfwYSqsxVg3dda3GPYWZjeuk0gg60I65WOqqv4AcC2YwBjQqJxqkz/+RcPTV8FqqUK2yDI069dHCfD45Mfuts4b0SRny0EaXNls45EES3+yG4vqQH8fPtYdKD/8X4TxX1QZR4MkOIoeaswRr9ouLOj7QkUS3HV0u9uRUrxJgrLBNYZQVCy/OYfbaEhgWpo17/ZE4qlmUsghkgl/1WcZFvRd0z1Bey+BiuAKS+dh5cAOokj0RYxl/YXGc9XySxsWt+N8GNPRISCn6z9v+ZvSy12Bxiw8aIHOZc0utR9Q56ni+qVKpch3cE8fVgP+iM5okW3udo7o1zMr7YdEN+Mkb2XwM/5NlA7Bsx9ZebWB7SYTcSfkDqmYJJE04CAPgdYUpwVysMrGfgIHt/9ZRrX/f6nr1gZ5TnyeQ3CUSBwrMFY+n47j/bl0izj9SK/e5TLPGML2A4rAmfh3m/bLdMOcp+GrU/blhw5BcmqHkEV5CDq/kkWiF/FRMXcOJ9HvEf9Yv9tMbXPi0HpZ+im3KTUnzsV/4XUXSgl+f9ijng5rSCwrAhprb4F4EjY0gPrh7fVNrnguVyjdgOBEjTA7QKdGv6EDKkvWOadkzT6dlFGhV4GcMAOzrUe/cFE9LSv1x4Vz742cy6wMWgEZt9P8u5lYSGAYKQE0cnN49/kN77XevDCM0zuIVWRknB3zWSy2k9cSrCFW0hb2MBv0WmsMOFQk1EV2/uuFk4sAgEAstgrNmYzp/m2XkTi0QVBh1dUH30YjfqG6kLXnAo136zHhYg5hSQU/ZcPHFDaUGeq95bBvyMzz9AQ1VxtQ5NAAfXONVZKhGkgDNY3n7wlxq8zBCzqQCKdMWjKl89yYQaUPtgZvpHT/CdtHFkFDtxjHUAeVmDEnfzqR3EJy2n6HS+GRaSVDfj0v61540XRTlGUVb6NJt/cAiWArwRqc1kVvPU1MBt+E8+gZNVsCVYxGjJMiEvwqXI3VUJoe8L+GGVl3L4LwTNAIKEd7eV9242cHNpnA0SybzMdfP1DdA/yAkUzmegD1qAo+kYDiyAjm3Zs3kCgAuwEj/YJ0T+qU7SgzOrm0cw5zF6mHbf7KWHDIW7TNLhKIp6WHzi+kdhKwICpyPIuveIHriRD5YE22yutnDAD/3x/c3y/djpwYUVS+r2/SjuXySaiD6tTJLOXXVBytbG3LpbLwLGIL9Nqg6OiDYukIAAAfMJ2SKQsa6u1DAF6/EJdIFcw/jQAWvqIKNwOSWiWHrrQNFL2dqEVgcuUkb8z8rQdylv+Gq2fEFXEFXEFXEFXEFXEFXEFXEFXEFXEFXEFXEFXEFXEFXEFWcDK/Mr5vazgICtbNG0hRZRHwSOEnpabzHHZNrWEJcWsOVkXzmkwWIi/cZYL16N5qXBegb8VtSH3ktqQvUInuiXR74nGJ/IS+JJ+cYowLWy5pCCwcEd9nSUUxZEY0E66J8D+WsaE0h6sB337RoW1gzf4Kk04Er78jgicnvu92hX239wxiujf0KwmEHHFRrwJaB0PwCNZS/4JVt6IUc668W5Uu0/QjIGxZkj0aLoOXahHAi4G0CvEFnmbsvZ2ll7SBhVffTebFntmqd5UgvQrxCb46DGPH/B2RpokFdnT7FkZZ0/dDLOvmpWQXmWjsDtxZJyie9AmOFhkv5aYv/VOuEARpgVG/znAqCtnnd0mVmESsJc9qIlwXkmLhhTrx+TXHrONd5nl7+gJPNrIl5XdRCIULKR73L7q+1s/lKGQ0fntDGDVrOujVV6EgRDpTdSfa+bpfMbmUoWy6Cnsty2oM8xwtcjszCqtN5BftI93uZ0iHbpCgibWRp3e+oK4Y3Dniy0bh1MN1Lg+tB7MKGJP8fnlHZmH1nPUR7YBR/KpritkEsVZdDZiImWunvvQqYIw4BIjp3xwNWg1j3Yrr8HBEgdWMy+lcAifZO383mHJVRWU0d7HtrCu20qIQmNVG99P4gsVwJenrERxoaAABVj25tIADcYp4dW5pI46x8Ty1BJzxjfbRtqOwTXTTuF6CtIJmlWX0bNpazPFi1CuGyzXZK5CUi5UrPD0MHgKy+ldpmnW5bp+/C+Ch07waggY6p4gTVTmMvmORElo/OemEPVG4VtTWnkmTfgipYnHtfC6MFXc+G8DFIs2VtTz8NuuBnaIKUZj82ljBpa+/N/8lSqPvLTe8QJDwzZXOPHVfR2Z5iWeMgvLqOtWXJAf2Wsw5uzscSbUxOu0DD9Rx94QHFd4yVAESUPVA9rPUzKmDeG3MBY3hXzeDRF/saZWlKeYxrpY/If29JuBupMErzB/KVLKr8xDEiqoIE6X09QXjYIrpylIvMD3gVwDQNRmK6AAAAABLoJ5PcDlxeczlh5bgARw2GX/SnfzPyR+EpEGcnU7SqV1lNIYctle4hPiamjgY/kcapEHX6zFcgI3maYCZqMb8ZRs6TpmdgnFRb/+NX8JdAaAAAAAAPVgAAOTKBpTDQCB4AAAAAAaDAAAAAONAAAAAAEKgAAAAH7gAAAAACXQAAAAD9wAAAAABLp397DlbBc1cDELLBnYg9SqW3XqzXPb6InTbc0hGY12yJsuEIHUuefb5uvZNz+f3t+8R0y+aB18v8n9cL8KXvSGvx02PCad9+ZJC3BuxW0uyxjHhLDwCipPjDgiIpr8japzD/BIQzRu5Z0JDM+4pk+0Pf3+QgAAAEQAAAAAABToW4+KnG0WRBRE0A4vM7oPH7w6A8Y2Yn2Pxm1FYKRd1r7bUlwpXHl4eK6jxYH3qUf/QT2aSk7tSAN8W4kuiAK/FaG9P/EUkStIrXcLdAi9cUqywvW9MtCenpM9wsRdaRtD7nX3f8g5N88Itjd/8Aw0HowklkUvltG3+mQlMovo6gb5nZ5jnf4ZB00NVbmt4AZALAAAAE8gAAAAABgMtwoRoAAACl4AAAAAAxGAAAAA/cAAAAAAS6AAAAAfuAAAAAAJdAAAAAP3AAAAAAEugAAAAH7gAAAAACXQAAAAIFxZc+cr3LGd6OM7kOWk3ZlgS2ZdYVyNWM+fqQSZhlHYGKTvofTw23Q30ULfbRW4t94o732D+jkuu3YS1gST2LttSgijVjPn6kEmYZR3Yeb/CoeQWHpKMdXLLHVuFXy37xPXv7ieHibWQHXYpIsSGe14Xx/zBCweAsK2npsCObAQbmm5ZbtcpdDO/B8M3xXpItBWEm+lkihD+I2USpimD8PH94c8gPtF/cigfRCBWwAAAAAAZTAAAAE57GtuMTG802Z1kOFuJsnfRrtYScuEZDtDWl6z4dkCAexandIt1Yr1CX+qW1/rhL/ZpJy7KPY6faabg2N9QxOj9RFwIVPub8s8mdTdiQNpHhzm8R0qVOl5qwHsmJS+Se3MmK2IraIh0j8GqAgywPtLWjrG0RxEDReISAtFGmfZFMOruANyj+KMjZ6tDRgqJ32UHZoRbvSYDZPcstdI4LemToEp/Fwm5KaCMd2mjRkD+OCyVf8OKyns6O/MjNP0v/UigyekrKCkGHkyMoeMDJXBzztqPVqyBnPP0bBPLheXlAjUrGQKqn/oLdwDJWba4frFaAfaVEIicCisNhPcxjSFn/77Gr4ze/I9c1dSCjLIpFIpFIpFIpFIpFIpFIpFIpE5zBU1C9yQHWfy7tsfa6KmWv5vDGQ7Vv55aqWyoZ6MuHUNOUKxAfnwBiM0In4VFWZKtqPHCNNeXlzQNT7BTsnLODbWCYcwmEL0bSgwxpTYFfsaFvyH3qVuLzR7jI6pVHA2olARAB9UsE5nkSAx/zgapk5j0nl2lm1cyMNhQAI7USeijToAAAAFr6jmcW+4RWRNWblRuws1CezRf795nqb70ndJ0V+27ZPhXCiNkDQg/2k4FUkyVq/D4UfUrxK5d0k0s3Q+yjnJ+i1J2qTUlhlq8wPKdZ3KvF0pRq7osZ7wBTD33lDwkG1bCj0A70Bs2GU1SpAHT3PNeh8tqn0/AyPURNcw2T4ak+SZr4O2RWOz3wdsisdnvg7ZFY7PfB2yKx2fCCq/BZGNB7yu0gY8ZSXPSSp1ohIiyWt64YR4nZeuW6tuenWSXgskvBZJeCyS+vCGlhvYNQeRLrljI3aKXYVc7sKud2FXO7CrndhVzuwq53YVjh/XkzVGTxl4uvU2lGHPqbSjDn1NpRhz6m0ow533O2N252ynbqrbwtBt3y27UDODB1/YPmrOcPGfxXMAAAACPOJNOusAaaWWvYQb7LQpTgybf4wNBKNs15cIDRWUGCTp6JX7mm9Ya76RCllTbVreMI/nARwU4PVnJ3IdP0ODkkGRKqwnVbnIoP0dO2gccwV2i1hjDOARIhhr/73WCndns4unaGEyoiDUr8tdwxqwn98wMAAnJeehKjeVh0NomEPqyF1ugkLTIXCVyUlUNsKBU3jm5xUiSvV7VA1c76mLVpnohBhirsfRKdMt2TUQOn3T+WStee/9xAxPAGAn+AKpWbDtpyOsd9nv00vTmgBMnh1l007BaTTLy21gChKO6qWolokxi4b1w0Dgp1PVSADgK2ynrYhejJVVICbR9JRQ9LUpemG3p6cMv1R7RaRVfDAsHvJU97vgn58Dvm2jho7+bLwL22Nrv6dJmPOoXxubt9MzO2MZgbH+93s0s5cX37DFkJ3KOKSA6hQCVnRdeeN+aoEeAUYvWDOz1mRl8T4BTA9YpbcNceYw4Wqmi/fdbxaqEyfdZbEI69tex1ffYHQBt8hEikaF6RoAAT7aGgp0Cqp0cL013yoGiab4fmNiPH6T2hyOO6Hq/x+SHA4HA4HA4HA4HA4HA4HA4HA4G+wsF4KJ6gIFeYS5ZrjuIMjVMKaZTuTPgNxkwxDiMLwKHL0n8VQKyWk5IK7kFsq5IAC3mTyTRXzt2yt2eL2hOe7bslXZKuyVdkq7JV2SrslXZKuyOOrZ2ZJ/cZeg0yPfrbvGEn1z8p4idwSHETY3eK+A/CTHIROj8z6toEo+l4Ou3HTwOnqSubBqfRwOBwOBwOBwOBwOBwOBwOBwOBwOBwOBwN9hYKmoXuSA60wvDlFl2DGRp166OE7Z60hdO3fZ7hahYKjiTOJGYOAZnTM+pRapPXaQcHWX+fHOb+GyJ2VYpv+XelC+O31vgjaq1KQiZ337eMala0H5/VmpLLdA0M1tFwDGXb2taj31Mexjqfp7AlXH/2O8yndpLab9XFYzaJKX6RDpga7R8D8qoWobg6JwQVH9SilNHHEmnNofEHWfN26pSqfCW58gWRjqk9dQ8xJmCYmeD3cwVJEgSuhmwySyz1R4SPCMOVLQtwdx93L8DaD014lRR6psEgCpVDq9HD+AUiDuy85gY6ICijY2jea2eYJYa+m1qJgBAD2e6wJwn1tEZff9vDA+15AMYgt21NyRI8gJE/GQA0P8KnxiLGXyRVC971fMh24RgJmzU6ocr21gSSd/2DaZfDCRn4tOWrArw1DlnW9n7nEy9vyYRVV9uRbBZLNdPuqbNr1+HbqvbkBESItnK7d5mMdTvMwj7A1WF6Dvv9yFQMUcmLjdlCp9wrad35lwGfFuUTXNqDW+etN+FoeYz0NlCyRQ3G2nFXUlJ304m/aYzggleXugJ9vBtJmH94oTXLStgbqQU+GYwALzTjtTnHCzqun5TFJhUBsqFHLEQNaFFG1LtCr0kZD/m8qRKOLv5g7EokBQBUJ8mjXl51omGFI301z//9E9EKRXPRW9bt9mEc5Aw4+0Nwddv7L4Bk9qfQLSA4u3QOTtjuQE71ZC/kFbJA9xnVKgrSFbe01jbXbzuwY9t7zSk8/VCQLahSq9IsD9BViM6eo+PGWCJoHihF7rAMR0dD9j8h6R9xEVclBlK3sU6qQVOlhz2j3kHf3DO/upPJE6zBfKTQwGmvZrzrX1Co7n6xCKyqHk5pQ56d+T9za9B1dXoiqicJwgZGUQ8uPsPz6Hqv2CiLENpeXpJiys6pKXXj4C/C26CMUAuIjwwzR9Qy1mIyj5V+hO9cyvT1gdLYqJs0xKfzHmp8AM4IJq8o+ruZW5IA8yiA2fu6g4le79hDEXIvcSckXNkKWRwq/Y4c7p3Tpolx7RIMDNvH2iuPcXsts6xnlxi6fMdcjTKjuimGMPdwsuy9lkrvSFwSfcWpkLSIcdHG7aPRTFiq6r31coUxSuvMT74i/3bGnG0aAE0aAyZCkKb+sDDYFAyWrwLD2AxFeL9htRQumv3QAlRPX1UaeA9gwXWRjqBAYhMK0ub3if+VIU7HBOZ3/fbF0s/E3XSxfyaZufJCwHBSnJVI1emecxRIWsRPO6A4MAJD3s8z5B0/ehBRaE6g3IHAb/IDBunLwSYuHWoWyIHrq/J7K5ApFQi6nIKc1dxkAKrVVz1xr+51u7B/iuPT2GZzThqJUmx1vsZhvv0CtOL3gBLKEUzUG6PVTIDVQ7Hdlg/uhBWJ/YzBvQnMS0eH3M4dGC4Hat2r751wMKjkrcC/GeHF2fhBmrYIfobb/3TLxav1aHnvHL8YvgzrgG+OVqhDY76QQlTbY/xUGOlwkpbdv1o+vKNZnJ4MtobqcwKYBZacM4zGm/bAR9mRZ+9eaX8R/oRVvdFYKyYZNgdmoN76cCR4BeLEZKKm1szm1jRslecA93egqMeA9AFAV+6nr1Po2NNYowpkNERs4xtD9frEcYCj3RtIvq3xGu+uNx3oEhCoVkwQclLUbYCwibNi7U0WoBxhvVkoOsH7rQtsCIBFE1gAlkl+jcx4kkFzA9/Kb5UqZ/pB5GzK5OufhBmrYIfobbcC/GeHF2fhBmrYIfobbcGAzQ1mrO5gJH3/RCR066RIGmJ+GZ67K5j0TTzjZCo+TZue6KzbEooiZrgSgJWJrP0PxtV3H6vvGJ9RftMIDgf4cuPyPMgOaQO/MTB+/sR+l7xMjg9PIeJYhJCGqO58hcwx7bWOKdP9pNMGpuHiS64VZKBBHLb0W/fW1dF/esIzgYXS07XrEoq2H8GXaup852kXw7v9pXwtnC6ld8PdcK0eMWtUDKqSBsdHDOTKndI5JPc7XyDzuD8p/1H4datu2yziR6MU8ja0ofNiMjG4z86OWCBW+fZ6T2C52gy74YQFXpx7TPsjujntdjlcxvITmxDiBm5Ol0DdX9HGJUbmB0X+S+o6yWhWFggiaECBWORT3WpBWmsdNxREwV3B4JHu3SFkb0MRWwwuVlk4ovaeD7As8SuRf2gO+W8mrrTRXGq3d5l4gPxtEZqmv3HH2iUUVD3sIQ9ZEHYNU4Jlph0b8BAR1wl7E1VglYMjts78DA0MgiRSDYgvL8CsDvt0YC2M/6BhJLhwlIpVML3Bu5ILFgC3VpJSeki/yGgW1Am2COgXyOWVP1l+w0ZgbvdraPlDgG73lI37nOhFtKEgjrxtubW3aHtb+xprQYhm+lYj3aGdNlTHI5sQhZplqDIhF/BVD39nZ0vLMlk5f3PsNKM9ypLsKKVpiDI+yRp2zhGp7ugov6DUe2FEpe65U/fcFLyi1jnzOtBkhs2SP72lNxye5UCme8c+vzQY4sa+LB1vVa5VZvdBH+tXuD0OeQxfZPIj9GCcH8XfAgpaMdhyPxl7dCzi1aGEkY3v7/vBZdPYeRNmcfSrpZyT5aqZcWLN4zUrqcGsuZpXcDSlgI4fie1ACykG5lNfF1Ae2g0L1al7ow2DGIxVee7XjqUxI6qp7hHjc5FTYfP2alqRRonICbOqELVAtrmANSnZorAaLHdRxIskkpWsTNoJfJ8s7UUbJOhTgiLvC5oyjz1JHW5Ou42x8opRLIKTj58pH1Zoh2ZnwQmIEdQPy78YED2w4e6tRUAV3QMlcQ1AhUjFKEh4IR5Y+L8lliyQPcWSeoYeh1Cu9QyZSV+WhRcPYVTGU1bzU6yJmmPYh5fkjy3/eVVSQLWVXJsjXnwQ/Pgy2CENAkXgAjbAIHbGhUofn5p18MK7xdbwn6JyqWhPkChSfdeCtiApEyACAXRpwHCRzJb0YtjF9G5gz7ugZezvXxacLGr5M9w7/lHTY10h5KC+gRTPuu8vCbYJ9kG6XBk7FiPjf93AFJkRsUrWv2+Invrn3ukVyXn7hrgScGktJGg2O5LBsI9f3miRhlvGad8vFkq5hilyjkDw03LCGBNi6egYyQ21VRIJEHM6iwAbQjJR+aznxKZX0R8D2YyuGxtgLmc6xQIMiVPUNubMkN5VxGd4zJow2he3s6Nf3PUALyESkHlW5+D6AZhw5vbirIh6E211E9mi7U2xRX/qL+77dWWDMgmNPWNKs1LY59bhdjJ7IWM6AiVhgX6jcGjzKzQVpb4gWggeR6FWHsBjV/ipMyhM07JxcvDyx/SHFLElT2lzvPjTnB0o+fG5Cq3nUYM1NX3/L7CCnMXOZyh4ADvkikfj8MIyXYD8oH4FJ0EbQMMf0K42W8dP/xlwvCmQX440U+dFq4S0vM5DSlKYnCAwpiLuDhieet9FshUEUB7Sf55Dxe75dUVQ1lV+PfBAtXWB/DJi8pyXsAHY8MLOKR8E+5pQTnHdWrnsDMvGaThpFZzSdG1TNBSPlr3SlN8UEh1jUT6+wK6N/du0ffzOvwmlX4NQi2qZe348I9acvXNs/j7Ktt65jgcoc5MoGseq0LorRoe/k4ywX5jXxb4Ef8V2CUL07abfnONyb/r3DQDcFw2tyIOFszcW3mVVgnZHfAWoABr5volRWgwHEuv39LfqEBMC5lOI1104ULI86AzwKHnaBjTVV5E2HwnA5Sib5QqJBbBzZJLt5sfJGDJEZhmuVk+p6E8ZLncyBeYOsAAtftM6VLo0J2MzXL2mOQlJjXywDEqW42FqEJfxuPBdIzD5vPlfLaxB0PK8COxy+PX6mAja/xKm3J6BKLKmK7e51FhnCSikB+O07Ts/Gwd0m7nHxJyz3rN8DugLfLDycwYOooxZPT1WJb8jF0rw2rIXGxtVY+nqW1JiJoRjzhybU3ulUq4pBSor/LOF3kTl4mwy/K+7gfOTr7lPoQqbFon7p6rr9x03laT47obIEf/hUFrniYg9NDmWCrucWL/pdQ3YMnMAjrOgwFmS/LmAhWkhqZ4btHQAoX8OSDteB/FlJSTqCOWstuvTa1qMRxixGml8hWd0bDe5W5QrSU4qi21/vFJEracdg2DdTuR0ui/7zAgOaaePTxiHwDCJccmLsm+SHmALYXLE9mAZ1GmwbfhKnEEtGGJV1YqPeGysUeYhxHLphttvr2duMyAU1VH+5P4tBtiu6AhqMA6JoueMxnhR5LvYjlEVsnFy7XvJLvwAW9UBgerCCX7xui1blOvxsbpeN4zakpons1NuWmnn8qVio0h7oxdH8vpo3rmN9jCzinvRa/weAbHUTa3I9GUCXBV/8vhfRoMCiarFLle0WcVtnBhgd5iBNV0RtoaV/8EttiTTIB8CKSVJZCKxAfLyYMDcTjfsNqi7u9StUk7j5rlKNeRDs53Bwv0vWylc/hjapfxMzh6stU6ZYvpYOtppnenb+T0E5EGAzJQmV4mtT4lKPKP3hI+E0LNO2m+lhvChoIdFvTEouFY7itzg/UgraSe72yJQmMxRBwWJ2TGncpiE3V26Xekxq4t8OvqDmMIWcj11i/lw51D0o+PQQApcT/Vg/eXiQipu3PxfFddzwk/HdpbdcLSSdGWs7lXr0Mimad8SwBVviDIAuxm8EcJeWI9/oNRRzuyuI0VCD2l9J1ftwWB/iXBRZj5HkOifgykIVYNskTKQBNhXYDhElEb169H3/LLGKzhYkbXmWK89SSuXWL40ursfWlL79ZiI5LUxvPSy5kdOK6NJJjA+1F1synIzL3rD0DFFJ99uwvL37fBnWI2XE6Lamg4lEv8UJ+Nu3Ww+QamxWMhNplb6TJFq3KdfjY3TFl+MD2mirpZwLWSf94TdvQ+gKdU0Lh6N+RVpEudP3mF9qp8/jwEzxFn13T1JTKUqCjPLyPU/2Ef77mMn4rQvHwGtD/pEMM5e9yklj3zFBD1l/TCHjEjv1r4gllZh/RzHzhrgaQv0diXqI6uowPnIFsFkv2rBkXoKd4PPl9QTu2pSzOT+70n4fGsV7ggIWO2+7WYUP9F1b1FdfoGmtW7gGyr4GQhuZAVQYWkUAg5VOzHYLAEuWPE6suofXEr6LvLFhwAGiLS6Xgbr+TuaFibeVnanGKfHjFFDQtNtgH+zWJ84BVQnIFCrgS6k1AT2VR6g/8Ufaq9T3IsrbB1vqgtke/Ja0gjulMxokkPoNEdYTd3BP2RsGPibJerxngiiI5hcnzm/mApdPELOEBSDdPgNkZRq/Z+fa81SWB0oQPGTFOlnBz17RXE9tV0h0tUzEUXz/uQJthAK9Hlp764HbKYyzeG778Ixs59CCFQPgaZb+G13OxMxcmg3y7BHVCdcBP5SKEO5YFMx1xAvSqiuJgjqPcIhWp5pNaO+s7gTh5FN/dt5IReDd9cxO7RIPklt174P6o83AoJZjwGlz/MfPWj/RcQBILiczrb3gGqYWX/UhD07ob8bXqT3P69N45vEDcLA0NOddJ2CR8hvOSGBd3T4d3X2/WCGj9seIaIe2u5Yt5RHPL/J+DYbgw0DqBXZyWOHUd6eBgf9ivQt5sialsM/Ir7cZ4EiiTfDifoBAaGF3037YVMuEpGbCjdPkat8y1oqzaNsVdV9UOxVLtNFPzTujmWAbCUcAs+dl1CXCXsyXtpbEzYnuY6F+ULSjdMUX29DZnAMUWeaGeZx/qcdwgqBS7Um7CmDQ7zhtnP5OUhoHWgX2qnF8Xh2iaK0/fNOwVBNRZZA6Y4rzIBMPz/YtwiaPEnMrd6kJquBv671yjpd66xmIApVM9nOFikyvZ/UcUFd4cXstihfSYC52WjyTtIzBAWVM4mVXwADm6xZeYZTogFobpl6R4+PzE7ZEWy9zB/2XY0vqdMTtrjcDc4RgjTsEY25v5ejUv6rRyEU1R4/8et5xvUwoqdrOj+bvYpNH0f0AoeiQYNJVQqMEy/EF9tGRmASb7XzQmsP4hHJpcGAH6px7KyeF9Ff7ZnxJwy87Zh06T76z1RDNYP2ILuDv8IWUInOR3L2B3Kw3wQXkyhT558w9WKl5UZzVVVxvOy92+4q7yBopJFCQSvZ8Mo8SBXQGRVmC/yWPUV7CCPhRSXf9rzEOuZNsEL1e/DrYI9N0k9vHzUwwgAgrhHXx42ny+X489U6RvIl14Wzzl6QS+07aMWM6xAl5u8NOymhaFjnvJJMztaX40/a5Osg7CpHtvBXOhnsxOynCl9QW/C5vbsWbtQ1YNL8fFKprIVUeHncyUCklj8X3gjf/stD1LCDgQ0ETfzpuOohhcPrCEQxB/R7QC3JBkI5iBczIg3tNwPmik+Spnl1Q0EA+C+tZE85E5sEiy1apna8no3dUrZE33VVXG87zbKd+ahQDU4YoV+d931ahLwlfG2cG7fGhdJlG6jpzwYo5jYD0bBx4iRfx2Ht9PTLi3mW/oR6eTkAP9dgsZGNe9ocpV8ofjhk+8rKuhu6ivGuSVP+Mu8eQ7XZt7uLVU1v0+xtsWkbJ91yj1aq7EOwL2OyGI6jSTi9ZKRKlehGUsIrg5ws8p/MsNNbwyq3n6n5BWWe4HWWSN5hoZzCHT58GQMyEopfNAmji28mk3XxJbL+fDsz14kT3LKJeOafVJ32ihhKWEAwLDk0LxTrVpVGIPcao/5nW6JXNl06ggvFzGyMO7rNO14UukI60pWimM3zEcO/kExhxF0+skTwkeYezNoZ+NdB/tmLIrByGtUoqvzaMLt4gm/O3Z/VO9AMRH9mBDsvltIUnfOxSOzB7qb55UwKPX48MQTTliQ1UGXYntqFfkBrTD0lBo7MOvrjOeaCNmUmu5NESimhYRJHlYyL2e8OvAbzKPAybIZK4rXWPkI5GXx1XEWVwykzhSVMTY4ft4043WDzrMPtsl9Fnody1G6ZxQ+2wBmyoTHApXMD3m7ro3LnLCjOhfD5+iAHHSblzJvMRp+FS9E9WpXxiA3fM3CyiMsgZZO40sUIEtcexUDSG3s8L486DmkPsotOeKvpFCPgraTQxA6jhnuOzCSUTqh6UfesxkdPFc3tUbIOQpRxwzrZBRz6uYqTIJaLbX5XrrwgD/EKRIEwzYBsJqQ6LiX3/6lu0agqbcQ/dBY9/QFiqYFby+BgRYVz4PwPhAfsfcqoXe7lH0j8Oy+5MIq05+M2sNtYM/BR3KBHLBWEMeWOfrALsVyTICcJGAHqg4HxzfwqP0rSGxFjs3woA8n8YMUyi6eImcmk816mbC6cKFYWaHtwOXcXCm/dt1sVXvIE4/3LkJVApYAHtW7HsBHf8++wB2NCgNVzUfoWqeL6HMWtWaR8ped7kH3nnA0YvE91qpPAkM1d63Xqfy6t2rTC7tXKKZ2WiYkS4FElvnQjPTIWzmJKodKIgLvdEhkvwS8RGXPR5h92hnkEHb8BMGn46asi9qEnLy0gnArJXmPDji7mTepDl2tRs9g6W9i9N5o014f8c1AJPoxK1++8hzc70x9qAV5uYkiAKA8CyHx18gcv/BhOg7IPqe5vjZnwgRv9w8EtOIjheqy4WQROBim7VXKnFW+H0ZeJtKt739J0/9F1Z09O2SLpcMENLEIJVvQzxkjhb25f/HN9IEm7QcMBZOBE8eGy0LIJ+tqTQh4vKVkiDzPwJ+dakNAZgdghFZ2IoKGdfsorvkwFtd9tkSPGOaTnDiXxC9ci1gA5tdy4WleLTlQNENDd2PS3q2YqJPeoZBGlLx9PzzIJYu+/bhcq9gw0lXryD8z6c/vcjcfK7o1vSKkbaWIKkwkySnlbSrk3SW2AElTDX9N0Pns8qblfRcoMuyJvO5mtUL0UrvrmD7jSIEpsPjUY0QB5l0GFRL+UY1a5i0T2Yk87ncMUBo54vS6dSAUal8BZgc4wY3ciN+5PJUh/WW7jC9rF0iBW2lY8Gti+1lkcxz8l2gAD5SPu/h5VtrQGoK650dPYFcs19gSTn23wcfzep4SbWhEwPLDHRhzHJ+z95C9vItfxfms5kBrReL5sGxYaCds7HE4r0rmI9KsTqzP/08zAx1yapO06renKV61gh6YNeKZMnoHcUZY4wMH5fCv5rjS/d3odeQ98EDWn8A3rtso7KWRcha1l163M20Kx411s5TUMpIS8p526ZU2xRvDQ0FkfS+fjd60uXS4UKApfz5XtNPaGXDjYshHFXUmrkO7ELKIa9vOurd5qxc33CQfA8o7gglTUulEd1i5CgDy6/WUpu2uFzopY6eBnateIPbheiaUPTXQrpyYrQpZ1l6hPa/pSD559VL5SIBvxCfO5rO6f0O2jXzFoC4SSV5QyNv73U+IZLeljHs/kDOb0qQaXoLOy2QwpJSVjsSkGhg5IFzfZ8ntCVGPHZi4xVhpOabohiXkHb9uPtsnmkdrbfcy30GFXwhb5NuuMT3ot6HwPRrac2G3ifSZgSCRTCsEKmdFJfXCV0/Kpfyo5y+i63Dgnkbu2bs0h0/cVYI7D4uZ4IKGe4qwRjKSTTm0PiDq0qR7UVeauWmikMNTC/VG3XuUqg622zsoyNje2NfmKCnEPzXYfifDaoIxR+yY8u2PLgrALCMDOlghkpmWo9ZEizSAkKpeOPhc/kbFoAlOG6EpbCnLGebltxclbYgVMiUPWHH67NKQRgGrfph0RL3lpk4Vh2sHym+jrHWhAjRSqzUshtefygSlqU1U5mUWhGT/eSm//sdSWN+mqhg6bdziLF0g9iUPqqeNcrAsbvVEYB4UmIrbldOe/iJjJ6YdxR6VOtbg5dC9d4GBWZHlKTfeFKskXMxXqNTAzSkrOgJVHcVv5c8WXIyRd217fN1O8DIjsxRimwVLp4LIJG99VxGALCMPRdhHf/zFOOK4DXgxhsXbqIp8lAWEKd9iHEYLq2hFRbWv//PPDy7/jvvg9ONge45VMqKlgqbW9F21isY1P/nDHiIGXTMnXCr0FhQTMTE9u28RZwcXQKM+B2D4+ACbVmMFU9JPUkf+V6/E8i3iEoG6svy1OEAUCtt7UEDw74FogOB/JQ9n6Vu6VS1+yZta7xEDgw3iznWH3S+vFtrIuodwCK3D4A+JMOy7DozmheEAFJyg/cuWEDX+EAsPwCCJe4CcyUYR8aRzTMCXOISPJsYYs5AbM6qMvqWhBpoPUg1PUK4sn7VhR3ONSBr0c3hLOirtqM7+elkkTmGu6FQ+MO4ddDlSUdapFvPsXfxvcz9rkp1kpOSUjYLjveqg/eIJzE1IinD665G7+c/81oYIZ4Astj2WQQKz+Dk4PO+wuC211wzU2aI4sVTPZ4a24x3x9qc5rhpRRtBlpnAx4ZQ0i35jmUayhnnyIZWhCbEgqXApNpMn5syhEIjr/hQDVgIIcir16WcJWlXvzAO96GppuMMzdVagDLXj9ry6GUcwPZPfATbpdVr326a/XlSpq94hzKKliDl19tAkJc5TQ2JoRSL7Jjt0xXXHebxABLCnHnsmtsBv4lehVkp7R8h3c0sXJXwcj03vHk50yXkhLIs7i+UQ6sipMmmHXleUvlwv5eySbMSxvs2g0EnaMe5Fjyx5EAkTu2i4Bpl50UOo9oFzRf5ZZisrXs3o6lG7Tps328C5/+eyHILj5hRdkonqGK1rPajFgs8Wv2VKH/riLRccdkqRjnWQ7vSBSwTK5BKKTVI7AK4AYRCwAy4PFxgiMFZDFKj99UPw3HAxonxTYk0AfNoYtMR9HOI1N8jdKlp//O4bHbB+yd6b4dYFGVkQRAqPPAWDwLXtx/jUD1KrHb9KBGfOR+X4Sc/PlYFiKJkiUCf55bSVqwjckhPmaJDRHDytAxb4wS/lrIDKkVE4S4K9DlTILMmvGDHFSdB8/AK8N71sGfjqz/HtO6icev7obqbNZzVrF8r2mY6XqMULVOgBmAZApfLlgpzigFeUOhYIfjD65nuPF5q4ooy3na4lbn5SVin3qJkxpachJCePyLs1mYGN0SkM6rJIdmMsPfeRdU87UPKBB+Q6KLTpBuQFdPqI0GcQLeH+kj+87fXVkpFMZjzQhHKnctnPJV8c1RAWYhQmmPGaXLJWMtPLUcWUmpKmqsPv+0qsTzIPWoKszv+PEhQogFJCaUaHE1s7L60mwKyyu/rJspkSk5NbkBpzC9vTe87NONH31xJWAi8QAVfWimm17oXJ+HHxATsQSphhw90FNk4pyLE2ggCRmzMACdBDkPDviz2y/MyLcDGu62cxf6mZ2XQwcgTgsGMTOCWAKsF0he7bKx1rNIbQMyWE7+jM+n2hGKEY8zjs1uAbKjVMFzEGUdGREdQVnv8yd+ss2el6CpziqnDxLU++bdcIz/jLoTKUzKRB/+SqdFv3EXb2dix9x5MHA05Lj4rcRh0pdsXDPedn3fa9rG3zoN6LqSsBB5ydo+WxQiNdHBFYJva7EuKeIqzhwZFUt6z0p0fceYF0y1N5bYywORjqRuEZfSFLpaIxR/+R8uB+QyVYKKrbHTEJUYpPvflH/cUqAohwbpORlSMauCJ5CTszv/WFttIryVnY77TinxDqUyIJvx4BRgo3P9cz9+7gxXo8Xnp46t2L3ftGeKs97/6fqt3MHd1wIybHxoTaxB2MYAf7UR2rWQHKvmDq2XqfSO5bH4FY2WmaggLld054Qw5Az2Isjv01HNCTuwNOMz00JHAl1IO2NJh3xUBls2RkwRFlHfdlEy2dj4mv4FkCr0as7qJrNUgVKIbQgRqRUj2dYrPXfSIVE7volFGV2oWUU8jbCJSGpNngg86wBdjUvCSvb10VP/6uYs3r3Yq/uJ9Fg2LrR0IQMvkz7wF+XROmBG9CBIG/a1Yk01X2VG2L8h9DgzI4W4VmYyCH/rlXFEdcr2Cbp6qFx/OBkm5evorb/7Q2umFcgBePFJxqlyBYIVc3WgUJUFvuGpXgdJ5GQkfHGay89F7gBkYGrH9HOIMNU16NP6SD/HOr5lo0oCAaxmM4WUb0HcxP3sTQMLF7ShX8ZC+hG7k10B3qnunLM8SqZ9MB7nfzl2o1JSGsRHNUl4aKKnYNfqNdl0pAihprCrx+cVUeLsqv29GvvDlfM6er2UAKAzXz51XY2PkKOdArxYT2P66v4RsTTiNmJMyltIB82S07Jau51AEKD9QDEW+lX7X/hvri8o1laDCKcpcxlMqiwi3Znij3LxVkXjNyeefXtwxBRbCehwL5SUeSUHhsu5+1QO5LPFIk+ICgmYdhkjR9z7yq8jdb9CiA0pWwpveSTyZ0P4vjKKoc8lh2kGryL4FknPfhvhBKDdYABAapkok516NQu6rotizz01THsqYJQjYa6aUzSRpu4S7AK34LU3XG18HVcKRxNYcLyUnwfxpGfYx/d26y72QkVU/qrjyqSTELLXZONWT6ew0F2C1jmQxRwSv3CYFhmu1OgoAp5x1Gdw9l5rLWt2pwTlACNgpuI/EtiRL2F+qkxGi2NUWTamyiPIj9GCcIrqbESl44MWkL2apdXLB3hMvijEl2oYd4NDyyCp3HHznO39s2eA7616mr2XPibn/sLZeZkce6mrc7QuZjVXwcb/HSfh5PfOfTxh7EiS/nYW6enxSAhWbgfWY7sEesCPLyTthM7Mi3bydWe9IAPM6M2H4dP9lF3oXH3lseQIBAFwofpoPH0unxyhRFQfV3sdVTpBbSE4M3x1uDVF/pUSNL/jyoe+2aJe0W7ct52N+kP8evLIWtdxCA4qr6byjzQl3XTSftcv9MMMycf5ZsOwVn2Z0lBHvOuba6eqRHmYB+2QIYAzKuGb8arbiaYiDIhKwDNb3Af4JqOdLOhjlHyFe4IunM8K6IoePRzMUIZTdTFdeLx6TJil+DzDfZ++g5og3GQi9mxjYNq+oS2grTILqI7l92KoAA1ZqZ5Uy/C72GHmYaqKNgEUF9IZft7vEOGWjc3tdjNEAz8EHdYM7TI5h9YxbtgxzDKpO6hKNH9Hs9u/q9IpEVAYP6dkbilFbCsP/Btb8zyJx1eGsJarXYuYJJ6heEFArJl05jSUabk8BawneK3W/XCgRJSsGmUZkgdrL3v3xztAbEA8oC5Qt3XOfBdzCbLzyH8FRDCu8hmHapluCKr/37yIkfA05gjXBHZccOZef8Ucqx3WxBu/cOcbrVwcNvC7JBA/jgOi7ZsueEsmE6lbUXMrNvhQMBLwpmn9eO4UbeOvVEKdvROrgnbKRAqE6j0PL6nApEc+kxiwvufBo9nYV4GL7weZVH4tbfWlw6h8mhON8Fu/Q9+X4fyhT76m2x0xybahjVLDevTyhzyOu6pcPTgmbU17i4NcqGRoSasquYmtt4aE9GPf3EG975+Ge4AY4ElNUeOpRG+WRzPWWOb2n/wYTZrhKBhT+SkzZdm81NEy7G0f+wiiO8n1T4KIGjuxQFzN4OIr02vNXaoFDogwYGffgoF/BayPa8V7Fwn68Es8vm2V4mkBoVBf4hqM1UvOLu5IxFUATkv79jmjqr2v9Vq3Rq092KPk7QzOWe4mYfeeFEgAAc8nHIEDnbcvubmKj7v3rItZD0vyl0oZysIaCEFoDyLYzun1vEj3yGIE4PFpDiZ9WofD+E4o33jmCP6FY6LbYOBwOFxVdY4HA302i19mZ4rzJAVJRuMye/u7l3gu8sMr6qSci5hhCv2SfopYt/CZa5Qg6SxSSCKC1suaQhEpRbycghGXo+uBmWuyiQg3WJJJtSG/3Zog6cUwqI8n/2E7+3xtWdsXsOtVLd80UOavNpEcx9DZEZXZK5LSOqOiPlxjjerBC5mz41xDpQdFOQF1nNgqsQRmKppl0ldGIMQGnG4RXAcGdEdC1L96dpSbHtQC7ayTZQ74p1U8pstW7Q7GYadPoadSu9Ea/a71HQL4sT8PnELGcNXXp/Z08qbB3mWCpHK+x6GCZEzNwfrFTIdBZLG/pyTgqdhqM9scsqG/DVfg7KGPInhqXEsMXJsL458LKGCe2lh5wXrFKvc8WJO0Jseh0e03joEO/awtp6MTUbwIlENIF5tDnESevRYcoPvgf5gpknG+rA7JEkbwJalIGCf9STt2bK/SYIpMQ+qMjOkYTT9bzMeiNYszH+P4fTRMYWoF1TUa0HNeb6aaIs5GDMy18RySu0kgzUjGNCMot0Sql4olK//ndIAAApBrH9g0Ablfj7r6uuupEKCfeVGA3KkRG7thbfaQbgcfzNKECkB1joKY1xUJ8cU4PyvZfRYsuxx8eizVOQr1o7HR4xZBu/fzvouEUbNi1PSK7y0DCfXAkYCaiKIoInjj59xr/3SVO7kbh3LDBQ38Vb6js2RH0kJ4jRtxm2nU6P0f4wNTz2257RrQv8pHqJ5/4yuKfmqxTwk07K0KVryJOTACCusqmF3jF6q8luGqGG1BwawczFoP6lL8h06f5sHIi6EnrApnIPIYHqra/LhvdrD9T01rqMQojIy8MseUPGDfx13dDjKYL/YF9In2Y1SQGoVW61+t13hBzUpJvSwGwn/EeaioKf9MeZvgPnUdcpAnEm0OjAAAAk0AABGoOezQ0XMrPc2+3KQWOb0ufUiYTJnr+4e8Vwsp5uFjxNp6zmGxZr3jfnVU0T2WVzD89+2hI6sSUaXyysKzzKpNNCOgXmhqhFAXMEsvp9NC8aIvIlYyROkezJUfq4ci8iJWYkuqog3bnWPf/Oup4GcfgBO8ZLJep6lvFqT78rOo5t5PN+phhV4PG39QLYqGNkCKDb5Zg19wq82VJotHIHZlLEl9QukA/b5/rKYCpfo74vXbaThe55fIJcwy68ZXpryneTzGa6mgkMIfqepTdJ3RlEKatcyV4W/rFIkE4OYwyRlIiBMc0+u9tf7lp5e7WLc0JITgwcKFD8q1ZF7H5J52p056Gr4dRDbv3jpaujfXRduZL6yw+XnUd/lIZYI+ssLqmynPfoIwSfzV+tph54XZ63L69EnpBsNx212mMuSylfQhYQ/n0NvUObE3o7FXNrMgEkaRuTG1WGwAAATQ0AHTeVWshe2IIHkQ5DKjWi8B4EObldJ706muk96dTXSe9OhmVYIT/yYJaZxLSKtHPduc0sHD7ZKbUfOwTx78ggdbD8TC1tudkEzrPG32hjffhSBHbKPaomXyf8nhbwTf7kIMBd2M0qaJIuUxJOdYXvQm96zT3uOdIeShWQv4Ojz45jJo+NIv6d/Aml72815rXdcTTMOrzIZAI4BFGQgAAAAAAAACoxJKDQ6p99GnJ+xFo6jTNPNOLz3eWYqYtyiQwAme9Iu721bstuJi2xa0tywtmExBKq3dQAsCAAAAAAAAAAAAA/cAAAAAAAAAAAEYAAAAAAAAAAAATWAAAAAAAAAAAB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本视频将指导用户设置实时摄像头流、实施预处理步骤、在专用硬件上执行人工智能推理，以及实时显示检测结果。

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    <div class="topic-detail"><div class="topic-updated-date"><span> Last Published: </span>Nov 11, 2025</div><div class="prev-and-next-links"><span class="previous-topic-link"><span aria-hidden="true" class="disabled" data-tip="" data-effect="solid"></span></span></div></div>
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## 前提条件

- 将模型和标签文件推送到设备以运行应用程序。相关说明，参见以下内容：
    - [下载 Qualcomm Neural Processing SDK 的模型、标签和 config JSON 文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html#ai-ml-sample-applications__section_chl_dgz_scc)
    - [下载 LiteRT 和 Qualcomm AI Engine Direct 的模型、标签和 config JSON 文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html#ai-ml-sample-applications__section_fsl_lgz_scc)

    该应用程序支持 Qualcomm Neural Processing SDK、Qualcomm AI Engine Direct 和 LiteRT 模型。
- 要访问您的主机设备，请启用 SSH。相关说明，可参见[使用 SSH 登录](https://docs.qualcomm.com/bundle/publicresource/topics/80-70017-254/how_to.html#use-ssh)。 
注释： 如果 SSH 已启用，则可以跳过此步骤。
- 从 Linux 主机推送文件：

        scp <filename> root@<IP address of target device>:/opt/Copy to clipboard
- 将 video.mp4 文件推送到 opt 文件夹中。
- 进入 SSH shell 并运行用例：

        ssh root@<ip-addr of the target device>Copy to clipboard
- 启用显示屏：

        export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1Copy to clipboard

注释： 对于 QCS8275，不支持摄像头用例。请使用文件或 RTSP 作为输入源。

## 运行应用程序

示例应用程序使用 /opt/config\_detection.json 文件读取输入参数。

要创建自己的 config JSON 文件，请使用 [config_detection.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-object-detection/config_detection.json?ref_type=heads) 作为参考。

进入 SSH shell 并将 YOLO-NAS 标签文件复制至 YOLOv8：

cp /opt/yolonas.labels /opt/yolov8.labelsCopy to clipboard

要运行该应用程序，请使用以下格式的 /opt/config\_detection.json 文件：

    {
      "file-path": "<input video path>",
      "ml-framework": "<snpe, or tflite or qnn framework>",
      "yolo-model-type": "<yolov8 or yolonas or yolov5>",
      "model": "<Model Path>",
      "labels": "<Label Path>",
      "constants": "<Model Constants for LiteRT Model>",
      "threshold": <Post-processing threshold, integer value from 1-100>,
      "runtime": "<dsp, cpu or gpu runtime>"
    }Copy to clipboard

    gst-ai-object-detection --config-file=/opt/config_detection.jsonCopy to clipboard

| 字段 | 值/描述 |
| --- | --- |
| **ml-framework** | **ml-framework** |
| `snpe` | 使用 Qualcomm Neural Processing SDK 模型。 |
| `tflite` | 使用 LiteRT 模型。 |
| `qnn` | 使用 Qualcomm AI Engine Direct 模型。 |
| **yolo-model-type** | **yolo-model-type** |
| <ul class="ul" id="gst-ai-object-detection__ul_ak1_stk_pdc"><br>                                    <li class="li"><code class="ph codeph">yolov5</code></li><br><br>                                    <li class="li"><code class="ph codeph">yolov8</code><br>                                    </li><br><br>                                    <li class="li"><code class="ph codeph">yolonas</code></li><br><br>                                </ul> | 分别运行 YOLOv5、YOLOv8 和 YOLO-NAS 模型。参见[示例模型和标签文件](https://docs.qualcomm.com/doc/80-70017-50SC/topic/gst-ai-object-detection.html#gst-ai-object-detection__section_hds_vxp_mdc)。 |
| **runtime** | **runtime** |
| `cpu` | 在CPU上运行 |
| `gpu` | 在 GPU 上运行 |
| `dsp` | 在数字信号处理器 (DSP) 上运行 |
| **输入源** | **输入源** |
| `camera` | <ul class="ul" id="gst-ai-object-detection__ul_o4w_v4k_pdc"><br>                                    <li class="li">0 – 主摄像头</li><br><br>                                    <li class="li">1 – 辅助摄像头</li><br><br>                                </ul> |
| `file-path` | 视频文件的路径。 |
| `rtsp-ip-port` | RTSP 流的地址，格式为 <u class="ph u"><var class="keyword varname">rtsp://&lt;ip&gt;:&lt;port&gt;/&lt;stream&gt;</var></u>。 |

使用来自视频文件、LiteRT、YOLOv8 模型、DSP runtime、自定义常量和自定义阈值的输入运行应用程序：

    {
      "file-path": "/opt/video.mp4",
      "ml-framework": "tflite",
      "yolo-model-type": "yolov8",
      "model": "/opt/YOLOv8-Detection-Quantized.tflite",
      "labels": "/opt/yolov8.labels",
      "constants": "YOLOv8,q-offsets=<21.0, 0.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>;",
      "threshold": 40,
      "runtime": "dsp"
    }Copy to clipboard

    gst-ai-object-detection --config-file=/opt/config_detection.jsonCopy to clipboard

注释： Yolo-NAS-Quantized.tflite 模型可以使用 `yolo-model-type` 字段中的 YOLOv8 值来执行。

请在 SSH shell 中运行以下命令，以便显示可用的帮助选项：

    gst-ai-object-detection -hCopy to clipboard

如需停止用例，可按下 CTRL + C。

## 预期输出

将显示检测到的对象。

Figure : gst-ai-object-detection 应用程序的预期输出
                
                ![](data:image/png;base64,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)

## Pipeline 流

该表列出了目标检测 pipeline 中使用的插件：

| 插件 | 说明 |
| --- | --- |
| 摄像头源：[qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtiqmmfsrc.html) | <ul class="ul" id="gst-ai-object-detection__ul_zyl_gj1_mcc"><br>                                    <li class="li">从摄像头采集实时流。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| 文件源：filesrc | <ul class="ul" id="gst-ai-object-detection__ul_z1z_x4f_w1c"><br>                                    <li class="li">使用 filesrc 采集视频流，然后使用 qtdemux 对视频流进行解复用。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| RTSP 源：rtspsrc | <ul class="ul" id="gst-ai-object-detection__ul_vsj_2r4_tbc"><br>                                    <li class="li">使用 rtspsrc 采集 RTSP 流，然后使用 rtph264depay 进行视频提取。</li><br><br>                                    <li class="li">使用 tee 拆分流进行推理。</li><br><br>                                </ul> |
| h264parse | 渲染 H.264 视频。 |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70017-50SC/topic/v4l2h264dec.html) | 解码视频 |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvconverter.html) | <ol class="ol" id="gst-ai-object-detection__ol_j34_ddg_q1c"><br>                                    <li class="li">在其接收端上接收视频流。</li><br><br>                                    <li class="li">对流数据执行以下预处理。当模型需要浮点值作为输入时，会完成此预处理。<ol class="ol" type="a" id="gst-ai-object-detection__ol_m5z_cpr_lbc"><br>                                            <li class="li">颜色转换</li><br><br>                                            <li class="li">缩放（向上或向下）</li><br><br>                                            <li class="li">归一化</li><br><br>                                        </ol><br></li><br><br>                                    <li class="li">将预处理的视频流转换为其发送端上的张量数据流。 </li><br><br>                                </ol><br><br>                                <br>张量数据流用于 pipeline 后期的推理。 |
| 推理插件：<ul class="ul" id="gst-ai-object-detection__ul_k3l_35k_pdc"><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlsnpe.html">qtimlsnpe</a></li><br><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimltflite.html">qtimltflite</a></li><br><br>                                    <li class="li"><a href="https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlqnn.html">qtimlqnn</a></li><br><br>                                </ul> | <ol class="ol" id="gst-ai-object-detection__ol_l2x_zjq_nbc"><br>                                    <li class="li">推理 runtime 在其接收端上接收到张量数据后，会运行推理。</li><br><br>                                    <li class="li">生成一个张量数据流，并在其发送端上显示推理结果。</li><br><br>                                </ol> |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtimlvdetection.html) | 处理来自任何目标检测模型的推理结果。<ol class="ol" id="gst-ai-object-detection__ol_ol3_dky_kbc"><br>                                    <li class="li">将阈值应用于所选结果数。</li><br><br>                                    <li class="li">加载 YOLO（YOLOv5、YOLOv8 或 YOLO-NAS）模块。</li><br><br>                                    <li class="li">生成包含可叠加在目标对象上的边框的视频帧。 </li><br><br>                                    <li class="li">将这些处理后的帧发送到 qtivcomposer 的接收端。</li><br><br>                                </ol> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70017-50SC/topic/qtivcomposer.html) | <ol class="ol" id="gst-ai-object-detection__ol_dmb_2vr_lbc"><br>                                    <li class="li">从其接收端获取内容然后组成帧。</li><br><br>                                    <li class="li">将包含这些组合帧的 GStreamer 缓存推送到其发送端。</li><br><br>                                </ol> |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70017-50SC/topic/waylandsink.html) | <ol class="ol" id="gst-ai-object-detection__ol_kjr_fvr_lbc"><br>                                    <li class="li">Waylandsink 将其接收端上接收的视频流提交给 Weston。</li><br><br>                                    <li class="li">Weston 在本地显示器上渲染视频流。</li><br><br>                                </ol> |

## 示例模型和标签文件

| Runtime | 模型文件 | 标签文件 |
| --- | --- | --- |
| Qualcomm Neural Processing SDK | *yolonas.dlc* | <ul class="ul" id="gst-ai-object-detection__ul_pfn_bsc_ndc"><br>                                    <li class="li"><em class="ph i">yolonas.labels</em></li><br><br>                                    <li class="li"><em class="ph i">yolov8.labels</em></li><br><br>                                </ul> |
| LiteRT | <ul class="ul" id="gst-ai-object-detection__ul_ccw_1sc_ndc"><br>                                    <li class="li"><em class="ph i">YOLOv8-Detection-Quantized.tflite</em></li><br><br>                                    <li class="li"><em class="ph i">Yolo-NAS-Quantized.tflite</em></li><br><br>                                </ul> | <ul class="ul" id="gst-ai-object-detection__ul_pfn_bsc_ndc"><br>                                    <li class="li"><em class="ph i">yolonas.labels</em></li><br><br>                                    <li class="li"><em class="ph i">yolov8.labels</em></li><br><br>                                </ul> |
| Qualcomm AI Engine Direct | *yolov8\_det\_quantized.bin* | <ul class="ul" id="gst-ai-object-detection__ul_pfn_bsc_ndc"><br>                                    <li class="li"><em class="ph i">yolonas.labels</em></li><br><br>                                    <li class="li"><em class="ph i">yolov8.labels</em></li><br><br>                                </ul> |
|  |  |  |
|  |  |  |

## 已知问题

- 由于使用的模型，远处物体观察到准确率下降。
- GPU runtime 不支持 Qualcomm Neural Processing SDK 模型。

**上一级主题：** [AI/ML 示例应用程序](https://docs.qualcomm.com/doc/80-70017-50SC/topic/ai-ml-sample-applications.html)

Last Published: Nov 11, 2025

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