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批量生成测试非重复命名的图片数据

时间:2017-09-26 20:59:33      阅读:209      评论:0      收藏:0      [点我收藏+]

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今天要测试100万的图片数据的上传工作,测试指标:100万的上传总耗时,调用接口的耗时,图片处理耗时等.

但是一个问题是没有100万张图片,咋整啊,感觉有人在坑我,绝壁是故意的.让我想办法搞一百张万张,哪怕图片都一样,命名不一样也行.

然后就想了一个办法,用一张图片批量生成100万张不同命名的图片.

1.获取一张图片的base64编码字符串

2.然后进行base64解码之后保存到本地

3保存之前进行图片重命名

下面就是具体的代码,分分钟搞定.

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import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;

import sun.misc.BASE64Decoder;
public class Base64De {
    
    private static final String suffix = ".jpg";

    public static void main(String[] args) {
        String str = "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUDAwUKBwYHCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgr/wAARCAEAAQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD5Ojki358356iuZSP3Rl6037THHJkxd/Smy3UUsnmfZjx7V+uH5WWLMSRxFlPzdqeZ3kZBP1xzVFbiff8AK+B6VYik2qWbkmgCZ5ArlRFkA8GoZHSSUb4lXaOppktxyXz0qnqOpJ5DfMvK55NBcNzP8X6zDY2cky3AG1Su4dPWvkj9o34o3ur6kfDVjOxjjIMr9iccV6t+0F8Vo9G0aSC3nRZSCoCnmvli/vJ9SmeeeUuXcsWPvXzedZgqXuR3Pp8owLt7SWxXLu53yIFY8tj1p8Q3bh7UAKBhadFG0sqxIOS2F/Gvk7uvV1Pfdr6HR+BtLt5bkXV3HiOI7mlP8qu+K/FVzrkv9m6dxbA4jAbGT6UzVJk0rQbfQoD8zHfOR1zWNBbu0oS3fCMep6V6UP3D5RF6z0hrq4Ok6XIrE7WubhpOh/u/hzXoGg+CLqDSzfJZEEx7Mn5s/So/hR4BTXvE0GmTW0AtVQNJKg+Zif59K+jrX4ex2sMGk6PojTtsA8tE4UetY4nEU8MnJnZhsLKu7PY+Wbz4feI47ry7bRWkBU7gyYGPasVvhn4nkhN6mkOgQn5NuQK+69C+AXiHU2T7PoJ8wjlyvA9q63Qf2Qdca2kTU4Y1ZznG2vDq5/FuyPajkVC3NzWPzVl8F65bkyT6W4z6Jis640e+gl2+S6n0r9O9X/Yhinh2PAuSvVYq5PWP+CfQkxcCzjIz1Kc1jHOaDVpbmc8np82kz88IbOdnETo+T61raX4Zvwv2yGxLbf4hX2B4o/YH1CK+MlgiKEOThKu6R+yXc6ZCtldWTMW5LLHT/tOj0Lp5PBO97nzN4V1/UfC2nS26eekUiEEL0rm73Qp/Et95dmGZiSzbutfWviD9lDVrebGn6LJJARySteceOPgH4q8KIdZ0/Q5FSJsSnb0Het6WY0J6dTWrl8VTuj518SaclhdLEIf3iDDH0rObofpXonxR0lTGNXgtkjRl2yMvUsPWvOZz5P7vu3NdSfNqfPVqfspWHRw+ZVm3mmtZcHhP+mdV7OT95g1ZSCaQ/uhWtL4rmEjrvBNla3Woxyzjbbt8rSejV2PjjwpHq3h9rSOPMkK/KfUV5p4U1qe3k+xyS/uzyytx81eh6J47XWZo7U7FdE2MCe1epGUasbMSPK0N3pepfONjQyDGfY19K/Avx2dc0u3gmbLJ8ufwrwr4m6RDDqgvbaQkFvmwOM1f+Dvi9/DviNLWa5YRSyDGexNb4PESoVuV7HFj8PzUbn2LZq9zGGk6AVI1tCV4dl9xWX4c1EXXleUxIaMZzW6YY+75Hpmvqo+8uY+OceWbRqr+9lfBpwGzgimTRzQ8xA89cUsUysNhO8/7uK6jkFlCvGdqgH1ApIkVYss5PHc04wgsAzbR9ahuwEBVTwBQBDeGZG3oBsxyc1wfxR8a6b4c0qa7nlCkIcYbvXW65MYLF5DKAAmetfKv7R3xFn1W8bQLWTCxkkhTjNcGMxCwtJs9PLMN7Wsjzz4geNbrxjrMt5NkbX+RM8EdK55RgfWnO7O/mN1I6ikr4LF4h4utd7H2kY8i5RVAweKvaLGrX0DMBgHrjvVAE5Iz2ra8NQJPPGGXO3mopWVRWGM8Q3BOoyb5WGF4HrUugW8zmH938rNhVAyWpdet4p2ll8vgHA57123wB8BTeLfFOn2UsLALIGZR0xXZXqKFW/ZHVhaHtZn0n+x18Elv0g13U7VmCkBNw6A5/wAK+zfBPwp0Kwth/oIV26NtGa5b4EfD+DRvDcCW9uAFxn5RXt2gWQVULICAOmBXweZ5hOtUaR9FTorC01JbkOg+BNPtgFgXB64x1rq9N8GWuQ8qAnHYUmkwxNcK6xtgHn0rrNLtopgDGuCOvFcNKnFv3tzGtWquFzJ/4Q6y2ZaIHjrtqu3gSylBBhHT+7XZrbDAUqPxFPjtthJCKeP7tdyw0ZO6PLeKqR0PLrr4Pabc3RmaBTnttHNN/wCFP6PH8zWicDrtFen/ANnq7lmUfTFRyaSoBzGMnpwK3jhNA+v1o6I8vufhZogiaMwJyP7grzX4rfA3SdT02WBLUNI6ncFUbcY9K+i7nShvyVwPpXPa9o5dCscanJ54qamHcY2RrQzKsqq5tj8lP2pP2fpPCcl/HBZgQNIXAUYwa+VtY05rSd4JodjocKGFfsR+1X8D7fxP4cumS3BkALnag9DX5Z/HzwXP4c8XSq0RVI5Cpylejl9Wz9melj6FLE4b2kDzBIzFPgHj0rV0q5WG5QzD5PeqM6JvZs/c96gS7mkfaucDoK9+MuWVj5drlSRv67pSWU/263jGzOSVOearw6nNbst1ZqVfPX1p+m3ziNYJlzFKnBPY1Tu5PstwYyTjtmuuT9nDmEdCNQfXg0d62VWPPHriufWWTTNV+0GRlEbqyDHvRo2ovFLJGZG+fkc9Ku6hDHqUAnBAKgZx3p0Ze1XN2D4lyn1T8FfEg1jw1ZXqOWaSLaxPrivSrRC6Bj/dHWvn79kzXpdQ0mTSJpQGtn+QZ5Ir6CsnRlBjY8DpX1OEnz0Uz5LH0PZVmbnlTZ3eZ8v1qTDBguBnscVUjd3fyfMUD1zU0gkjb7xY/wB6vVPEGTK0pKrwc9qhmUouJOfrVyK0lVxLkkHk1U1SeOBXeUYxyKCox59jhfirrqaJptw63IAaE5z/AAnHSvizxvq0ms+Ibi883JEhBIPavoL9qnxzJYaPJY274aWQgHPavmmbIdiWB3ck+tfKZ/iEpKET63KcPyQuM6cUUinK80tfLao9ywAgHJrqPAQjmnuSyALHaMyn3rljzkH0rU0u4lsdPa4gfBkOxsHtgV0YdqM7sh7mx4sgsba0sIbHc80il5g/PPFe/wD7DGhtqPidr6SNPmmCpx90AdBXzzDJLq88I27ykRHFfVn/AAT9t0NhbTMgEn2oh3PWpzOV6Tkj0st53Udj9B/h3oDWujRKgBDAZwOld1p+jOFXaay/AdgF0qHYAQVWu2sNODEBl4x0r8+qLmkz3Ks7RsyxoOnRqojMY6cNjrXRaXpy24Y7efpVXTIhBtHlYArcRPMUFeh7V0Yam09TzKtTQhSEluF59atwWiH7y9qmtrDOGxgVZa2VDsB69q92lT0PLqTMwWhEvCfjS3MDY5rTWzwOF4qC5tmPrXTyM5/amNPaAg7lzx3rF1OwyAQuBu5rqLi2IRiay7+HKFSuRjpUTp+6XCV5XPNfiB4fS8sZE2jDghuPavzL/wCCgHwkbSbq+1WxswV8wt8q9K/VXxJp/nWrluAM18g/tk/Dy117QtRhig3OUO0ke1cNOXscSmfRYCr7Wm6Xc/JG5SMtLGkeGzyCKr2qHzF3HHrmuk+I+gyeFvFVxY3GFIkI6dqwQySMFVfmHSvpKMo1HzM8fE0vZVnE2Ps8SaPCAcsnQe1ZWpHc+5uvvVt73au0LwO1ULqYyvxXbWnD2Vkc9kQIzRSB4259u1alhdMI9k33SKzMBWyKel48a7Rnrz71zYeqopoX2kew/swaslh4yntY/uyL8vuK+qtFML2Yc8MRzXx9+zo8q+PY5FU7TEOlfW/h9JmgVWHbrX1+Xu9E+bzZpVbnQhJlfbKm0ewqyjhQAU4qwGglmyyDb70rxRuMgV79kfMEsFxAIWQDnbxzXKfELU0s9HlupZcbYyR+FdCIDGxbd2rzT4+ahLb+GLgxZyIH/lXNVvycyO7CJOUD5N+NvjU+K/FMhSdmjjcqgzxwa4oMWAJGKdfGaW+aWTOfMJGfrTT1r8+x1edfGSbWiPu8NCMKewAAcCiiiuFO+pe4YFWkwLRolyARyaq7WZSV7VbVttuGX0+atae4WR1Xwh0f+19dW0hfeBGQS3QZr6o/YNgj0/xBeeE9SlTzIrktEFPT0r5z/Z8NnZa293eMAhwOTjHvX1D+zj4c0bSfiY+uWl6GNyyyLIrjAPTGK4MyqVFBxR9DlNJcnNY/Qz4W3Ev9kwRSnoAORXotmiNglgeOgrifhPPY32mRuArBcA8d8V6do+n2ki7yQAccYr5enQl1Lxc9WTafAZIuB0HPFathbLgBl/WpLS2tIkCgZBPNWoYEWfZGOMV6FKg9zxalQlWLaoVRgU9LdN4Zl/WrCwDAGP1oKBHAI4r0KUZRepwVXfYbIgRAqDHFVLhXJP8AhV5ypHAOaguVUcgV6SlFx2MDLulk2kD0rNeBnBLc81rXWQDt64qlKRHGfXFclV32Nae5zmuWe6Igjqa8S+OPg6LULKchATIpDfL14r3u/iM4IPSvP/iLpSSWEnmKDjOK8uvC8lJHr5fW9jUPxh/bT8At4f8AHkxFp9+UngV4JB8kjTMOR0Ffe/8AwUD8A2UGoSa86D5CccdTXwZqdq9heTIwxlv3f0r2cDNTjY68zoLSoupdu7dYoUlBzlMkVlrggueuauyXKyxoQ3O3FU5PlJC11Tb5rHhrXUQEkZNISdwHH5UL0H0p2EV9zc/L0qE+UGel/s7X4tfGCpIgy8YCE+tfXfh8vLbxnPVRyK+IPhjrbaZ4tsrhCMZAOTX2r4Dvpb3SrdnGAY+1fY5VWUsNY+azanK9zvAn2nhRimuRbjAOaddzuU3Rw7Pqarbbh2G9lx619HdnzI0ykSMVBZSvXpiuG+LmlrqGgzRSOuZIm/Ku8uZ41hMTj5cdPWvL/jV4rttJ0OaWT5QiNisaijGndvQ7sJFyqpI+M/Gml/2R4ju7EEbY5cD86yhnv61b8U6q+s+IZ7/dxLKTx9aqE7jnFfneOdsRK3c+8o35EmFICdxFLSbX3ZUVybFE8J2pgd+tS26+bm2z94cfWokACcEDGOtdZ8JPBGp+LvFUYgtRJbqwyxp83JHmNcNSliK3KdR8Nvh94jvoI2ttIkdCOXGc17n8PfBOv+G7201mKG5DxsPkViFr034E/CEQ2IzH0UYBUYBwPavVLv8AZ+1LVYgLK628ZQheleRi8bCs0kfXUsF9WgvesbHwY/aDu9DRF1+/CL2jLY5H8q+kvhx8d/CviO3i8vUUaQ4DKHziviPx/wDBrxz4a0vYNIuLtGf55FXG0evFed6b4v8Aip8Kdc+02UdzLZeYp2xscrzXO/ZVNU7GVXBqvsz9dtN1qyu7dBGQQf4ga2tKO84HT1NfGf7N37T2q61ZwQ66silwAqvnIr6i8G+OE1BF2cA85Nb0qtHm5bni4vLKtG7O6iX5iSenSmzqCcGoLK6DfvN3Bqw8kLcs1drcE9DwZ88ZWIRgNyelMnYMKbPMVbEakj1qF5xjpz2qozVi4wurtEE+CeRVG7KKcirFzOwz8oqjeSKF3EH6ZrOo09ilBoqXhVgfp2rifF+ZYJIW5HPWuzvArJIMNwOOa43xlIgtSoVt3qK4Kzaid2ET5tT4i/b88Epqvh9nKgEelfmp8RtM+w6ssSNnYHUke3Sv1o/bC0wXnhWeR4w2xT1r8rfjIog1ueFrfyx50jKT1rXL6jjLc97MI/8ACemjhlYooUHpQx3de9NViTg06vYk7s+alHl0AcDFIFw27NLRgbC3cVNruxKtfUn0l3i1O3kjONsoIP419xfBe6kuvDdnJI+792Aa+IdBhe51S3gGAXkXB/Gvtn4M2s2m6FbQyMGAiU819JkV3TZ4WcO8VbuerXPlRxAyRbstjmlSa2lj2rCV9zTp0kkiALg4bNPmcbNoQdO1fYHyBm6oEW3eReoHOTXy7+1x43WyhbR0Y7p/u4r6a8QLbw2c25zloicZ6V8U/tUaut74zis+vkxjmvIzSo6OFdme1lFLnrptaHlq+WzlnJzTghC8UFQWBP8A+up2AC4A6V8JObm7s+ySS2K67iCTjirllaCVSQxJ7gCo4YvMJjXqRwK9d+C/wbXxPahrkbjOwAI7fSol7seZnRgsPPE1LM8+8H/DnWvG+vw6RpNpMxc/NleMd6+wPgb8AofAWkw3NxpvmsyZMhHI/wA4r1b9nD9lLQtCt7XUIdNzOy8ysoNfROpfBbS7fQQttZjKxfwqOteNi8dz+7F2R9JhsHRw1RX3PBdF+MPhD4brGmqsI84ARiBk13Gn/tifC3T4wbueFVABLvcABfyrw34y/ArU/FHjWK2voZBaJNyqnHesD9pP4GR+Ffgtc6r4a0qCWSCHLMMmTHfiubD06FWdjvx0ZOnzRPtf4fftB/CP4lQDTbLX9Pm85ceV54PX61peMv2d/BniaxN7pdjAodchFGRkd6/G74UeOZPBOuXWoTtefaWsQLJrWdspKc9VHfOK/R39iv8AaH+K1l4X0Ox+K9jceVfR7LeaYHL/AFz35FdGJwVOlG8WeXhMRUUrLQ9C0X4WN4P1lIBCI8fMGA4x6V7h8KPEYyLeR8hDj8azvE2k2ms2a3cGNzLkEfw57VB4I0q606YEAg56DvXz7qypVtD08T+9panuen6vEYsoSfrVifXYY4Sz1y+jXF0bXLIQRxgVFqup3EELiRSOK9mONskfL1MIuctaj8RrWwuvLe4AHdc1zviH9oLw3o0nlz3YOe4PSuZ8RPDcb96MzN/F6V5p4y+Fmr+IYTNpjsoByTnk1ccZdndDL6Uops9kg+P/AIbvseTfhi3QBqv/APCztIvIPMW+ABbAya+crT4U+JxpzpbLLFMAQDg8mk0LQviLo6fYdQindFb74U81q8UgeAoLQ+jb3x3pyrIReg8DPNYmq6vBqVsZYJw2ex7V5hbS3yWrCcS72wDkcCo7TxRPol19kupyd/3VzWU6iqIlYJU5po5j9pqzkvvCd1GhyzcAV+Wn7RmjT2/iRxJj5Z5BzX6sfEuCTX9HmKtwFyc96/NH9r/Q303xjLZhfmJaRc+9aYP+IejiOWWCafQ+fwu19opaJBtuGT0or2up8i229QpT/qyaSkLY+T1p6vYltLc6T4VaQ2q+LbSLy94WQMR7Zr7U8C2bQWkEAAAEY4r5b/Zh0Vb3xO96TkRqFII6V9c6NbQIsSIRkoOa+wyWi6WHv3PnMzqpS1O0iiiK4ViTUZkIfawpbaGWWXzInwtOvbZosSM6819IfM8rOf8AF00aWM7MvIjODnrXw1+0RKsnj+QHrsGTmvufxTaC4tWj67kJJr4p/ai0KfTvG5vvIOyXOD6c15GdU19UZ9BkulWx5qiguFY96syQSEZjXI7mqmCCOelaFjI8kWwOPpivg4q7sfVPcfodpHJqsZkbcmOR0xzX2J+ybo+nZtLSSFWIJfn618h+G7ZG1cqwOCufxr6j/ZN1l9P8URrdynyxGNp/pWWMbVFpHv5LFylqfot8HtEtG0+3iiwpA6CvZrLwpBc6eqTRoARjNeL/AAAvoNRt4rgScY4r6B0VQ9vGuAQW6HtXyjtJ2OrHzdGrzLc8V+J/wBmm1Aa9pdqJPLzuUL97n61zM/wf8NeI9Pk0zVLUxNOpWSFkyp/Ovq1dHt7sGPaDkdB0rP1L4W6JeJ/pNioJ7qcE0qdCcZ3iTTzdtcsz488L/wDBND4EaV4iTxVZeF7YTK27DkMrMe5Ga9T+I37Pn/CQfDxdE0TTYY7qxZJdONv8h3KR8o9Af6V69/wqy109x5EkmzPCFq2LDwsLRklcn5PuZNdMpVkrNmM8TGM+aB51YeE9UtfCdrb6u6JdmBBKV7nHNbHhPw6qtHlAzDgn1rptX0pPP2xwhgOeat+HtM2TIREq89hXGqaq1rMdXGupDmLdn4fXyfkTB+lUda0m2jheOWAv8vJrr0tWWBjjOB0ArJnR2Dq4HHQEV3zw1oabnl/WG5as8p1tLTTkaSSIbMEjjn8q858c/G/w34OikFxLGixn528xVx3/AIsV6T8WtJub6NrOyRo3uPladednvivjP9sf9ma41z4Zapc6fqmoXuoJIxSeWQ449FFZUaKnVSke1T5Z0uY978GftZ/CvU7oWl14ntQQAdouIznPYYPJr07SvEfgzxLYrPYXiTLL0KkYwa/B7w7ffEHwLr93pVrp0j3N0hg2SwOXBzwY/Rq+5f2bNR/aV+Enww0nxL4ya61Cwum3NYzAh7dPUHnPHbFevXwVGlS5os5aUKlebS3Pvm98F6NeRMY4UVWB+bNefeJ/hk/243FsoYKflYUfBr41jxZDblLlLu0mX5HHBRh1Vh7V6TeQWmpQF0I+boEGMV5bl2OiSq4VWm7nkGq+GNulyw3K8lcZFfnD/wAFEfD50nxjBqyxbVFvsYepr9VPEem21rZSB4iQB3FfnX/wU20PTp/D4nhXMjXTEHuAK6sFd1rEyr3w8vM+DpeZPOXkNRSFJI5fIKcL+tKevFe7KPLI+bbvJhSf8tBS0hVtysg3c80RvzKxE03Y9z/ZJVPNuCFGWfBO2vqSwjGyN4wgAUDmvmv9kDR5Ghnun+6ZcE+lfTlgFhgCFAcDg4r7zL4tYNX3Pks1d63Kb1qmDshkwPrU07AIMoDt9ahjSNJtkUJ+pqaXldteueWZt/bGZWlKjBUjFfN/7WHgCXUNAbU4oNzwucsB2zX0pLCxbazkZrhPjB4egv8AQrq1ESurxMfxrjxUPaUmjuy3FKjW1PgbJ3NuXaVbBBqzp5HzEHBHernjPSf7K124gChVEjYU9uazbRmVQc9a/PsTB0cRyo+zpydSPN0Nzwwc3/mMeg79+a+gfgZdtp10k0Z+c469hXz74Z2tcksOMgD619J/ArRbLX7OOBn2TQuMsP4uK5Mb/CZ9VkaVz73/AGXNWF3plrcJLgOmSPQ19V+DGhliUSoG44NfFP7MusPZW0Ns+VEMhTb7etfZPgG8S4tItjZ6c+vAr5NaVTtzSk2mzurNAvCLjB7Vb8pnxuY9e9MsHiZACOQOc96uxGJo93Ix616dNO58pVi36laeFBhigP1rN1XUFdxHGQuKs65q0dlbsyMMgd64258QxXE3EwLEnODWeLnFLQ6sJQqcl0a11MZSMNW54Zsg22Qnketc3pSy3pBByD0zXYeHbaRAEI7c1jhqb5lIVdpKxpPH5Y2g9apzWUTsSoGTV2+DIwTv61VAbzNu78q9e9zzbJO5xXjTwbd3DvLaPtbHy15hregzor6X4m0dmhJPzlMg5r6CvbNZxhzkd6zL7w9aXgKTxhwezLXDWoWnzRO/D49QjZnzGf2ZfhPr14uox6BYNMj7ldrVQyn64rR8RfAyK70caRHqD7P4AMYX6CveG+H+kyS+ZDaImPTjNNPg6wjfD2pGPukmrnSqezR0U8zjSleJ4T8Lf2ZdP8D3JutMkKJK+6SEfdLZ6ivT18MDToffHT0rqpdHSzAMUY9sdqo6pC7xb8kEdayVDlSuZ18d9Z0Z5v4ysdtnIXfdnsK/Ov8A4KRaLcX9utpbrjCyMO1fo98Q5Y7WydhxjtX5+/tvC61q8vWt9Pe68q1ZYkTrk10YSdKlVuz08JhZVaNj8/I/APivU5Zk07THl2LlnVcjFYt7p99pszW9/btG6nBDV9qfBLSNCvPB0MOj+FmF/JbkXfmdjXzh+0/4Rm8JePpbeaIJvG4AdBXs+0jVm2jzMbg5UNzzTBClvSrWhWa6jqcNm5IEj4FVd25D6mun+FGhza740sbO3gLBXVmOOK6MPRdSvFI8idRU6bbPpn9njwfD4e0UwMpA3Atx1r2KKECAKj8Y4NYngXw7PbWm2W3QKFGfyregjRpTAOAOBX31KEYU1FHxeKqOrWcmbMLu8fmjAPv1pZZVXls9KiYsjecMY9AaATcffrtOMjuPMkTgdehFc34zsppNOaOZy4ZDwo6CunaUxL5UYzn1rH8SNssnQrklGNTKK5bG8Ir2iPhf4/6UNK8VyhECK0hIz6ZrhY9ysEH4Yr1L9paDzfF6NL91gdorzL5BIGIwMY+lfAZnFfXGj7fCP/ZUa3hXc9zgHq4r6v8A2WlsRGDcIN5cEn1FfKHhV1W8x6HPFfT37Meq2sGqwQTEFZB8oNeHipTdJn1mSe7KyPr34UxNpviiK3t0HlTRhjjtX1l8M9VlhtYI24A4r5U+ET/aNSacAExkBT7V9O+AbofubfZzgHIr5SpPlqHt5knyHr+l3bTYdXBGMHBrS+2JBAxc89awdEmWODPv2qTXtVistPe5dsYUnBr1KNVShqfJKm51+U5r4reMbfTNOZY5hvPAA7ZrhvBl/d6hebroEFm4rzofGW3+IvxY1PTYpQ9npsohZexc5/wr1bw59ltY4phtyemK4qjdSpZ7H0McK8NQtY9H8J27LIseB06muy00smQo5Fcn4PkFxbrKCM9smuxtWjjt9xPJHNerhaTcND5XHOUZsZeyyMwV8ZzVbzNs+Se1ST3MRHzNyD6VQluGWXzUYYHrWqUkccafM7mnuEgyBx71DMgIIAP4VXg1JXXk1aWWPZvJq09NSp0+XYoCKcSZRjj3qSaGS4TcXwUGfrUhnXJBFQXdwseNvQ/eq3VTjZkRhKRQuNyFjIW9qx9UuxFEQ/FaV9dW2/vXLeKtRVEZ4pBxXLWrLkOyhRblY4j4jXYvIpY0kB2jJFfOHjn4Lan47uLiaO0KRs3MntXuutaidQu9k0TOGfDbB2rfs7LRYNEGYChYfMhXkfWuKMPaarc9ylilg9j4t0r4SzfDhriC0sg5UHMmOMDrXwv+2dqEGofFWdY2+eIYcbs1+lf7W3xD8N/D7wxe6vLMlssMD4z1dj0Ffkj8Q/Etz4x8XXviK5lJM8pKgn+HNe5gqU6cG5HFjMesVojBXJ+Uj2z617n+yp4Bu7udfEFzExQtiPjBFeXfDjwmfGPiyDSYciMMGLdcfWvtT4SeBrfwtocFlFGuAgywHXivq8pwz5vaHyGZV1GHKdPo1q9nbKHJJxyN1WkAUmWMYPvUi2+0Bc8CmPE4f5MEd+a+o3dz5NybdxbLUcR/Z3ck+tWHuDG21TWVZsNgcnk9KszPLGm5FyfSukOVFrzSzgiqevJ5lq7np5ZFPgd2AJHOOabqoc2xUjgrzRM2g1e58iftXaSba5g1FEIZZCGrxe4keTkqAB1r6Y/ah8OxappU8pDAwrkY7mvmfy2BwQdv8RNfB51TlTxLl3Pssump4ez6F/w7P9mu8FuWXA4r3P4Ia5LZalbMJVUw8jPfNeAafKy3YIzwea9a+FupwQ6nAt4+AenNeJXjF0T6zJKidXU/Q34DamLu1SYuN02D6V9T+A5YkhhdGBOBzXxN+z7riTRQZnwIlBHPUV9c+AtdgNjbzF/vDue9fF4mm1WPqMfDnpqx7dpE6rEGDAr3x61kfEu5vb3w7d2+n/6xrR1jI9TWTp3igBPKikGO/NX47qO+IV5CcjnFdEW4x90+djR9liFN7I/N7wz428UfBr4q65p/iq2uIDLqLSi4ljbYy84+bpW5p/8AwVB8I2fjuPwpNygkC/aRN8nX/wCtX3H4z+CnhXx3ZyQ6votpKsjYJZACQQe+K+Gf2o/+CT0Wt6pLrXw+kjsSWJFtjOGzxgjpXRhIwbftD3MRjaGKpqK3Ptr4EftF+HPGOk289neowZQd2TzmvZ08d6YY1CXcRLL0Eg+tfkp8Dv2bv28vgtq8dvoUtrfWqSBVSWVwAufZhX2F4V8FftY634YdPEPi7T9HmkhJxBG0j9MY5Zh/+qut4iFN2ieTPAUK7tOVmfVNj4t02+nEBnAz0IFW7+VJYf8ARnZhjOSMV8FaKv7VnwP+KCS+L/HWp+INEnK+X5VnkA+nHIr7a+HcuoeItHtdUvLV4vNt1OyTIYfhWUKs6krI8/HYCGFV4u5btZ5hN5Rz1rVSV1jG7v71Tvrf7LcEKvHrThmOIMJM5raLqRdpHnygmiWafZ1qhqN58hIP0ovblgM56Vj6pqLCEhG5HapqTXQVGkyvqepsrEbuQO4rhvGHiSCOGTz3LHkfJWzr2oyW6ec5xx614n8U/iHa6GZpri4VFQlnLNjaPWsYp1VZHp0KcY6yOz0PUJbSxbUv7PEmGyN5A4/GvJ/2gP2mm8J6Fd3WpXkGl2lsuZJGkG5vpjqaxtU/a+8DQeGBHp3iazkLP5aZnBJb0r83f2wv2gvF3xh+JN7pl1K8FhY3BCW/mfJMw6tj0r1MHhPZtNmGIq007En7Uf7TniH44eIbm1tdTuk0uKT9ykjcTf7WO3414iRvkbe/QVozX0aQNC2N38AH8NaHw/8Ah7q/je+H2dd0e7DuF6V7iiqs1CK0PHqThTbkz039knwzJdajLqzRjbLtUkr0Ar6z06ELFGsceAFGcCvO/gd8NYvBmgRWXkgONp3Y68V6a0k8DBoQCCPmHpX2uDoqhh1FHxmPre1ruxYFqmN0bEnvu6Ux9iH951PotPt7hirHGS3X2pGOIiGXJHc10pWVjz1FJHP2d5FtRfMGfTNa0UokXha5Gx1MbUdhz6V0Om3EUiYZ+frXZyoo0I0UP5rcCodeulisy2O3FF5emG3wqHg8muT8Y+P7bSbOUXC5AQ4J6VhUnZFUqcpTsjy79oPWbWHTrkSgnfGQB718syOfOdRnbuOCa9P+NfxJn167khjfKMxwM9K8uO7Pzda+PzysqlRI+yy6hOFDUSFzFMWXrnNdn4U1gRBJlYl4yCMVxgGG3VseGbny5DukAHoa+faTVj3MrqexrH2T+zN8R4rqPeZPmKhCCfukV9seA9Znk8N2bLJk45wfYV+XHwd8eXXh3W44Ux5TsN2Wr9DvgJ4vPiPwzaKvynZnr1OBXzOZU1CWh+iUXGth7nuOl67EgyJcY+9k10Oj+NdPSdVluVVe7E1w0WmSX2nbY5jFIf4tteC/GUftJ/DTVpNf8K3Caxp8jc2qfeX6CuOjNdTkhhY1vdPubTPE2nTpHHIwCj5t27jFXS2l60oT92UaXqcEsK/PHQ/2rvipaRN/wlOjajZukZzGYWwB+Vei/DH9qm48QxRajZazIksWP3UrY5+ldvtoKNiZcP1+W8D7Wl8J6ZGvkQRKuTxgYNa2maNb2kKJhWwOjJ/OvFPBf7TF7qOmJLfQLLcbcGu88KfGTSvEH+jTXaxXPTk4ANXTnS7HgYvK8fhneSbZ2TeHtKliAeyQHPzYAIrVsoxaR4gU4IAAz0rFj1e0WHP2wH++c9T7U6PWYpDtjmz64aulVKad0jyq0a81Z3saV+izAlmANUGlHkFfNGQelLNfRtGQCSazbq6ETbkyfWsKtaKZMFKW6GX97JMrAcYHBrBvNSFtuaUgjHrUuva2tvEAOrHpmuJ8ReI0QujOdoBJ5rlU4zdkd9GOtjO+IPixIdPklV8HcQBmvhP/AIKG/FjUdM8L3elaBeGO4vcKWDdB3r6a+LPj6y0/R5p5LlQiFmfc3OACTX5v/tT/ABG1Hxj4vm1OfT5JdIdyiB5dnI7jNerluGk53NsS406Vm9TyH4bPd23jO1uLu4nuLGxuRPdIZjtJrN8Z6wPFPia+8RRRLFHczu0KHsD0q+b2fWoxpWhaTJDBIdrLEck/iKu+FPhNrPjTXRoenxuqxti4cqcZr6unhJSWiPm6+JpRjqzF8A+Ada8e6sLHT4so3+tlA4xX1r8HPg5Y+E9IhsRZBcAF3A5Jq38JPgnpXgaxgtrW0VWCfvBtzn8a9PtdPt7KHEac45Ar38HhKcYptHzWLxjl7qIbLToYrdVjixj1FWWhQrnI468U0SFHC44NSC4iL+UvPrzXqR3PB+0yOGzeZt8Z4FNnm+Ywxru2j5uKuJExz5IO0jqOlVjAIR85wWOGY+lWM8y0e4S4CBmGfc10el3SFtqw5x6GuD8OX5kdAx4966W1vprZ2YHtXVzFyg0dFqE3nWziPUBFheeOleKfHFvENvp8ht79WRgdr+oq78XvipN4QjG2N/Lk4ZhXjnir44HUrZ7WFXmjcYG89DXBisRTpo9jL8K5S5jzjxC96b52u2dmLnJUHGapD6/nVm8uJrqV5pXPzsTtqtgDpXwmPqe1rXPpI3irJhVvSkkebCNtx1qmPvGr+mISWZTXGzbD39segeCmj+2W0Q5kaQA4619/fsxXH2XRbTzJjlVGBnjGBX53+EbmSK4ictho23AjrX3d+yj4gt7/AEO1iDeY7RAsd3Q142ZU3JXP0fK5xeHs2fW+k3fn2CvFGGOODmuf+IevxQaeFlj3BDl8AcVp+DjNLZxrCuOMHNW/EXgGLX7ZoZgMN99R3r52MZe0N4SVGoeceHYPB/ibUVmvZVkikGHKRqwH1xXbL+yN8JvGOnGSysGt7lhuFzZNsP1xXknxA/Zg+Jvh3VW8XfBrxHJazLky2F1IfJlHpj8/zrK8MfG/9qj4SaoLLx98GLiW1JH+laReM/HqBXdT5GtToxOLmklCZ7npv7HWqaEwfQ/iPq0Uaj5BIFdQfxrntf8AhH8YdFuGk0rxMt4wfcGZBG3H0NbXw1/ao1DxE6wX3g7XrZ2+UfarcLx9e9eqaAk2vkXTWjJvOcv1pTjCMrpnNPH1YxtPU888CfEX9pTRvD0trrnw4W9MI2o0N8oZ1+nUmvQPAnxIv9fCNquj3Gn3AGJIJuoNdtp2j2tvEqhdrf3lHeqGq6XZQlTGihyxLOQAanmb2Z4WJq0q0tjX0rUY5stv6jjms3XtZkt1kUDtwM4rMu9ft9LiYBxuC8YNcN4p8cTL++e4PzZwuafNZHJSwjlLTY19d8VmCMmecKAfrivJ/id8WLWy83ZebUHWTtVPxt45lsbaWe7uyIsEnLdq+Hv2wv2q5rBrjwv4Yvc3DhlcK/QHivRwWFc5J2NZ8mFi3Lcd+1t+1np+tak/gDRtd275c6hLB/Dz0BHTNeJTeINU+LFzDolgoWzhGxG2ZyfVvSvMLu8nnuJby9uy80rF7iRjkk9hXq/7OmqJehNOliSIF/8AWgcmvucBh6duV6HyWNxVScnI63wR8Eo107+z/MY3SyZ22yYCj617j4C+GGh+G4DDDp7CYjc8vGWNWPBGkWtlkQoh8yP7+Oa6u1aOONLcJ/wPHNfS0qEKcLHx+Jr1JTZAlnJbzeYqDb6VMHW4LbMdOoqecjyyM1Sd5oJkEEXy554rpilayPNblJ6sctjJK3mM2AvXPemyRRRSl4TyR39anllY/PjA7g1Tn2O+5WPXt2qkrCbsS208ykrJORk8qB0q9M0awgsoYY6nvWfBKc7I0BPrViRpNq+dx7UwTufP+g3Jwma7G0I8obW3cdK890C/BgWbyn4rtNLv/MhTyq0lNrVI9KdJ7dTL+Jnge28Xaa9rNEikRHYcd6+VvGfhK88L6s+n3E4wjHt6GvsTWNXtobOT7Q6xbB1evnr41TeFNbu2vLPU43uskOB9a8XMo05Qvex62WrEQormja55I8vDYYHHcVEDkZqe8027tnLRIGjzwRUC9OmPavjqkFz3uezsLV7SwSBxVEdRWzpcQMQkFRa5vhU3VuaOnXv2SQHpzjP1r6I/ZR+Kk3hbxFFpc9yxViMEngA44r5xUsDleorW8N+KL3StQS8SRlaM/IRWFeiq1N+R9Zl2LjGqot2P2A+FHi2y1e2iZblc7QcD6V6/pX2Se0D5TJxn1Nfm9+zV+1WbbTrTTdRvTvUjMh7e1fbHwv8AjNo+u2EE/wBvUsyjk96+YqUZwm21oe3iaMqi54PQ9n07RLe6h+UKo/i8xc5+laa+AdDvIQk1msmepKVzGneJ1nh3w3CsCB92tzSfGGWEMr52ciqpqL6Hz9eWITuWz4E0aKUbbGAKvTMXNWrTTrawD+TGox0+XAFWk1a2mj+0Ocbxmqd7qtuqkxTc96qUFJ7GKq4io9SO4vms28iScZc5wKyNW8TRRSFHtQdvAc1X8Q65bxSLL5wyvJ3V5r4w+IUs5MNgwc7iML1rmVkd+Fws6zu46Fjxh4zMUzltv3yABXIa5cj7IdRvpWOBlUXrzU9npt5qU32i4iaRycrE3Q1fu/BstnaTavq4kzJGSkPZMc1UYylJJHX/AAW1Y+Tf2yfijq3hXwbqGotPtCDbCkZwTxxmvzs1fW9U1q7l1PUpzLPNIxd36jPQV9Wf8FKfHL3/AIgtfClq2Iixd19q+TtOUvat5/3ygK/QGvrsDSnCnsfKZxXlUlaI6SMT2yhep4PNdV8HvE82h6/BBIT5G7DY+tc5p4NxC6ehq3on2yw1SCe2t/MIbJxXuYZSp1U2eHP3qdj7o+Hmofb9LilsZPNTyuFz81dijzHmvFPgJ46sNS0qIXBmtnUbSDXrMmuWY2G1u9/4V9VTnGcUfLYqlOMm7GlJJMPyoZ5PLwp+btVOK6mlj82Sl81Tzh/yrflseaWxM80XlvCxYDBOarzRSqdkIxnqKkthvkikTfgZ3VK4G3zhnGO9BMitBDLA+8HvViV2uNoY9OtM+1QHgROWPUijyt3ybXHtQJOx8yeG9SPl+TJL+tdPb6iy2yyM6IB3zVO3ttF02ISGxG1OprK1bUI7oq0A2LnpXyeI4lw3sLQep+3YDw4xX15SrLQyPixLrXiC3Zobtoo4kOQn8QFfP2rGdbghXJIY5J6nmvorVma/02S1jGJXiKr7145f/DDWLa5kn1UMis5KOPSvm4ZhWxU7yloerxNw1UwkY/V4aLqcmtxdbfKkumAPb0pHQxjlt3v610b+HbbT+Z0830YVQ1GK0D7I48E1u4uUbnxM8JVg/eMuH94wrc0232QjJqjHaxRSrvQk55xWtiMQhoxjjpUqVtzpw1FRd1qRyyRqcN1pIJFIbbzxUUo3ZBPzVEnnRDPrRzI6pxSnzI29H8Q6hos8c9i+3DZYg17p8EP2t9X8MMlvPqDNErDBc9K+cxdO58s5yew71ZisrxkCwwOM85Fc1elCaPTwWYYiD5Wm0fpP8NP26LG5hjS51dMccE16p4d/bE8I3LKTrsSscZyetfk5o134s00R+U0m0OMZBrsfDniDxvNdbbYyMxbhVU8150sNGGtz6bD+xxMPehY/WvTv2pfD9zCBDqsU2eihqXX/ANprQrCzNzdarDCqrkq8mMCvz5+FPwz/AGhfH13E2hpdwIcbiUIAHrX0d8Ov+CffirxSEn+K/jC9u8sCLVCQMelck6tOMmlqayw+CoxuzpPFf7Yej63djTtB1I3ZdtoFrHvfPtXY/CjwX8QfHzJfXOmHT7VjuMs4xIw9xXefCb9kX4b/AA2hjXw94Tt4nUYLzJub65Neuad4d+wqkW2NFAx8orjk3fY83FZjh6MOWkcroPgDSfCdihc+a7Nw/vXBftAeKZtG8L3EauDlSFY/w161rzJb27Rrnhjg9hXyj+1741Ol+G7xrq82hFbgdaqi5uskjzcPKpiZSb7H5jftb+MpfEXxm1K4mufMW2GxNvQGvO9HdJ50En8akVp+Pbl9d8W6jqDks01wxyR2zVGwtYjGRIdrKPkx6195Qi4046nyGJv7eTZASdKuiH4ZXwB7Guv8P2tpHIlxByGXdGB3PeuUndNRiYTjEpbj3Ars/hxYytaFZJAMfczXdTcpyRypKMuZnb+D/FcWlxMtlMkX/PTzTsxXe+BvEFybtZJ9XV90u9P3n3683s/A+ua47faIoyFHO5P9YPQY716T8HfhF9ikOpal+9df9QoyAv517uHhN2PPx1Si6b0PZLWaV7FLk/eYUC6lBXjvzRDHMYhbrENo75oWEmTDjFeuoXij5GVkyyt7LHJnFSSam8gx5f41AVVfu8mmu8sA8xxkHoKPZshu4NcXEsh8teVNWrO7kjIMp+bvVQXPmMGRcZ61Og2EOV3L3wKzegJXPBdRnjMD2iKCawZ1kc+SE5+la14jCYyocetVrtF8wTqACOwr8G9pNyu9j+80lN3kUGRhKqSHB7H0qZ47SaM2+pAOjDaDjJFPuoSxE4GeKpvM8MpymQRnntVUqs4vQxxVGFSnySWhyHjn4c3Nsz3eggSRv8zITyK881DStQgm2SWUgIP3ducV7W14zu2V3ZPWoJdOt5yS9mrH1Ir1aGLqKFmz86zThKjiqnNS0PI9B0m41fWE077M6lgPmZOK9H8NfCHTpGCandAgnqO1aOn6TbW1ybkW6q/Y4GRWzpyMG3O2OeeetYVcfPodOU8JUaCtUVx1l+z14P1BdyalECeMkipW/ZO0Wfi31ZJCT2ercDxhsLAi/TitG0v5rTi3vGj5ziM1zfXK3c9uXCuBl9goWf7IujWsi3M0nmY6hDmvVPhn+zX8P5gkGp6QrhsfeAzXK6Z4o1WzG6LUpCevzMa7bwj8XrnTpI2vtPinA6sF5FS8VWfUifD9OhD93FHqXh/9iL4Xaqqm30EqCM8MOa9H+HX7E3wy0eWOdPDO6ZT8ruoODXOfCL9oTwRcXCWt9dG1kONu5uK+jPBfjnw/q9lHNp91DIn96OQEmo9rWm7N6HgY+OOwqajA1PAnwk0jwtaRw21kiEdgg4rvNO0GG3KzQwAevy1nabrtrcRq9u4542kgGt+0vJfKBcgD61oqUGr21PjMRUxk2/a6EssUpj/doB+FVpLb5GeaQHAqeTUEYZEgx6A1QvLldjMjdumamUbM82dPm+HU5rxdItrZyPFJkbTnNfn/APt1+Jby5026S2RlMe8Nkde1foDrVpJeRGMr8rdQa+N/29vAkh0CZtK00OsgYSyGPJH+TRhXGOKUnsexlbUYyi92j8tNbjjhuS3nhmJJLE89ayGmnEwKScZ55r0jxB8EfEaWcl3Haqzs7YQxnPXtXKyfDbxZYlXutEZQATkRnBr7elXoNL3j5jE4DFPENKJnaRowu9VW1E5AcZ8w/wAOa96+FHwxZtNVxbxSg4/etg5/CsD4VfDHTdWkWfV7eGHyQNhX5Sx/rXvHw/8ABWm6MitBtaMnj5ulevhp4eW0kceLy3H0qd3BpGp4V8B2umRQzSWwBA4O3gfSujtLO3hheIKAc7uB2qyLlZIRZM67F6GoZfLQ5Q5yMZ9RX0WHUZ6RPicVOom4y0Im/dPwcUy4uYUlEsLHjru6U0zxyPlpDt9cVBcLA33QT9RXTTbaueY4ssi+XB2xEg9SKDKJ1CrKB9TUUIlEP7sgIByDTIoYpHzGSremetappBylqC3XkF+fXNWbaQxgxgZHc1SEV0JAu7AzV9IFtk3u/Uc1MkmCVj5+uemKrpEsx/eN+dWLr5DsbrUCqrfd/Sv58P75jFRIJonjc4DbR27VQmlSXKlcVp3MUkkRCEjjB5rDecR3psn+8OlVTWphiFoTRWUY5FPMGG7ZpIlcIPm/GpV8zd97pRUnKL0MKUHNXIxAoIO0VcghTAOQDUWxj1z+VOVXGG3fgaTlzI6aeHhe6ZejBOMHPpUiW07uCCaitQcBsYFXLa5IBUDJz6UjsUXFWLdsV27QcHofercX7hSwk/WqMaEncFwc1JI8irtJJ4oFLRFmPV5ICfLH1+bFdP4P+M3ivwg8baRrM9uqOD5auSG/wri2hVgCRn8KVY2A2qD9BxTTsctSlTrq00fTfgH9vbxTopEWt2CXEe8Yl3fMBXu/gL9u3wDr3lxX2ptbu64JlXgdK/OjF8JSYycDtVyz1TUoZU4dtjZUBsDNaKtJOyPFxfD2CxKemp+rnh741+FfEEXmaXrEFwn95XArfTxbYXCApKgz3Br8qNE8d+IbKRpINVurNuwjuSBn6ZruvDH7U/xU8Nukdv4kSdBwFuFJ/WqbcldnzVfghtt09j9IJdVtJVYrKp4wBmuO+IHw+0XxvpL2l/bpIGHIK18reF/2+ddhgWPXvDvm/Ph5bdv6V6f4Y/bh+HOpKiajqzWLnG6OaEkGudp3PFlw1jMFU5oora5+yf4UhX7UukQsoOD8gqHUP2NPCeo2Ajl0lGEiYCiIZGfSvQtM+NXgfxYqjS9Xt5o2kGcHH866+HxzoZjQJdo21eArjNaU5VFLc5KtPGU3fkPz/wDjx+xl4k+H13LrnhSHz7JQxMIGGSvD4fG+s6JeGC3vmhdH2y2zZyuK/U3x7q/hPVNPZtTMcKgElnIwR7ivhT9rr4S/CfxXeXGu6BrEdlqJf5GsgQH+o6V6dKtWp6xZ72DnLFUfZ1af3nMeFfi2JhHZ63bjDc+YvUiu8RvOhj1C1kDwyR5jwev1rxrSvDEmlWEDxzmcxpseVurV6T8LrlrjSJ9Omcs8J/dZP8PtX1eRZvXjiOWb0PneL+DsE8tdeEUmjZ+2AQGKRADSLOkUe5YwTTlhhuEZpPvI2GFRuqRP5aivvaU09O5/PM6MqcnB9COW5EnzqcEdgamsmS7w5kClRzVd7Ub+RyTxUsEDQozsO9bPR2MnoX1jJ+ctu9KfO7PEEZTj2qtZXJbAzVxZH3fcc49BTTS3A//Z";
        
        BASE64Decoder decoder = new BASE64Decoder();
        
        FileOutputStream out= null;
        String fileName = null;
        String filePath = null;
        try{
            byte[] binary = decoder.decodeBuffer(str);

            for(int i=0;i<100;i++){
                filePath = "F:/photo-100w/"+i+"/";
                File file = new File(filePath);
                if(!file.exists()){
                    file.mkdirs();
                }
                for(int j=0;j<10000;j++){                    
                    fileName= filePath + j +"_"+randomFileName() + suffix;                    
                    out= new FileOutputStream(fileName);
                    out.write(binary, 0, binary.length);
                    out.flush();
                }
                System.out.println("finish count:"+(i+1)*10000);
            }
            System.out.println("End");
            out.close();
        }catch(Exception e){
            e.printStackTrace();
        }finally{
            if(out != null){
                try {
                    out.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
                out = null;
            }
        }
    }
    
      private static String randomFileName() {
            int prefixRandom = (int) ((Math.random() * 9 + 1) * 100000);
            int suffixRandom = (int) ((Math.random() * 9 + 1) * 1000);
            String prefix = String.valueOf(prefixRandom);
            String suffix = String.valueOf(suffixRandom);
            return new StringBuilder().append(prefix).append("19911218").append(suffix).toString();
            
        }
}
View Code

这段代码可以直接扔到指定测试服务器上就可以执行,省的还要拷贝花时间.

前提是安装了jdk哦

执行命令如下:

先编译:

javac Base64De.java

再执行:

java Base64De

然后不到30分钟就产生100万张图片数据了.

你知道图片中的人是谁吗,反正是我比较喜欢的一个歌手,超喜欢他的歌.

 

批量生成测试非重复命名的图片数据

标签:pat   rcc   cpm   base64编码   tcp   zmq   ucs   jdk   执行命令   

原文地址:http://www.cnblogs.com/fxust/p/7598480.html

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