{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, \nimport tensorflow as tf\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\nimport matplotlib.pyplot as plt\nimport os\nprint(os.listdir(\"../input\"))\nimport glob\nfrom tqdm import tqdm\nimport cv2\nfrom PIL import ImageDraw,Image\ntraindf=pd.read_csv('../input/training/training.csv')\ntestdf=pd.read_csv('../input/test/test.csv')\nfrom keras.layers.advanced_activations import LeakyReLU","execution_count":1,"outputs":[{"output_type":"stream","text":"['test', 'IdLookupTable.csv', 'SampleSubmission.csv', 'training']\n","name":"stdout"},{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"columns=[i for i in traindf.columns if 'eye_center' in i or 'nose' in i or'bottom_lip' in i]","execution_count":194,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"traindf.info()","execution_count":169,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7049 entries, 0 to 7048\nData columns (total 31 columns):\nleft_eye_center_x            7039 non-null float64\nleft_eye_center_y            7039 non-null float64\nright_eye_center_x           7036 non-null float64\nright_eye_center_y           7036 non-null float64\nleft_eye_inner_corner_x      2271 non-null float64\nleft_eye_inner_corner_y      2271 non-null float64\nleft_eye_outer_corner_x      2267 non-null float64\nleft_eye_outer_corner_y      2267 non-null float64\nright_eye_inner_corner_x     2268 non-null float64\nright_eye_inner_corner_y     2268 non-null float64\nright_eye_outer_corner_x     2268 non-null float64\nright_eye_outer_corner_y     2268 non-null float64\nleft_eyebrow_inner_end_x     2270 non-null float64\nleft_eyebrow_inner_end_y     2270 non-null float64\nleft_eyebrow_outer_end_x     2225 non-null float64\nleft_eyebrow_outer_end_y     2225 non-null float64\nright_eyebrow_inner_end_x    2270 non-null float64\nright_eyebrow_inner_end_y    2270 non-null float64\nright_eyebrow_outer_end_x    2236 non-null float64\nright_eyebrow_outer_end_y    2236 non-null float64\nnose_tip_x                   7049 non-null float64\nnose_tip_y                   7049 non-null float64\nmouth_left_corner_x          2269 non-null float64\nmouth_left_corner_y          2269 non-null float64\nmouth_right_corner_x         2270 non-null float64\nmouth_right_corner_y         2270 non-null float64\nmouth_center_top_lip_x       2275 non-null float64\nmouth_center_top_lip_y       2275 non-null float64\nmouth_center_bottom_lip_x    7016 non-null float64\nmouth_center_bottom_lip_y    7016 non-null float64\nImage                        7049 non-null object\ndtypes: float64(30), object(1)\nmemory usage: 1.7+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"traindf.shape","execution_count":170,"outputs":[{"output_type":"execute_result","execution_count":170,"data":{"text/plain":"(7049, 31)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train=np.stack([np.array(list(map(int,i.split(\" \")))).reshape((96,96)) for i in traindf.Image])\nX_test=np.stack([np.array(list(map(int,i.split(\" \")))).reshape((96,96)) for i in testdf.Image])\n\n","execution_count":171,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img=X_train[16].astype(np.float32)\nm=traindf.loc[2,columns]","execution_count":195,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"m","execution_count":196,"outputs":[{"output_type":"execute_result","execution_count":196,"data":{"text/plain":"left_eye_center_x            65.0571\nleft_eye_center_y            34.9096\nright_eye_center_x           30.9038\nright_eye_center_y           34.9096\nnose_tip_x                   47.5573\nnose_tip_y                   53.5389\nmouth_center_bottom_lip_x    47.2749\nmouth_center_bottom_lip_y    78.6594\nName: 2, dtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"a=Image.fromarray(img)\ndraw=ImageDraw.Draw(a)\nplt.imshow(a)","execution_count":197,"outputs":[{"output_type":"execute_result","execution_count":197,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f92ac0ac7b8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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23R6EQiabCFzDss4szESK49xgKxWB4K/zvtBK83GmbJ3755ygOzePyXFXq9OCZPual3O358JTwZvTbY92EHpvIPEd2oy60HrivyRPs5iOBUGmGzVtZ9EjR8lyjGU+MOPEuUYypHX4/eSog+iAMaELIqR0pnXyjdDqzXKVOfxs1AOxlStoiSbJTH7quYcJ0tSW05sPlm9sik8IJh9B70Rq5kg39jUBRQyhqNF7jH1kqXlsgEvVpwNkDanrRlMIk1+2pTOXDlt7uYhvWga07Tn33lEliHIZvoUJumGnKMMP4D9f7Y60B5nbyDzH7Jyz1S17VcjYN01uhZl3wgzXa0HBHcqS2BpA5FEZ5KDwSII31SXGv1XZqXybmNLcnXDpnFZduVYrBNhfX8eBdfIf07Wd+Q1YaXQ1aN7VMzU58v59q/5mDjeoUdGiRLl20aOnGyz6kap4B61XqGmKa5p1WwmSsEs02BGu9XOyljDwO3/3x7K/ukuounmEINkMq2Xp/2zwrdkxY/V+RiQyY4Gn8a+qs4ssWp/aqHdsaTqLMiEmo5BLGL188gVGZybL4dAZrbyjiSdeU03WOa4mUy2xyLwx2tuo03amfbYE8dFsLjuDKZTY2wAjJq+wio709aKzDK+gYZeviUN5TVyjqyOO1naCWMyQcq8VBur6fz9Nc/OUkkYnR5oSnr46ZpQafeqJpCpn7577yH0aR5WP5uKzSMKZXCU9Ro2IDv+OlpoRY0fJcoxlLvi41vILLnQzp2UmvnNqgJWjLsS6u9Z6Cyaod4Ib8R7ZrTxYXFSe3i6Yg/xJC11mAcBPLUMgCevLj9rHdjUELX56mhGz1U241jjTgagtD8PBE3/Su+kjknX19tra0BbVw2D9snCTW2sg8L0X6lAcI982t711DqMI6Rq5V16vLcmzKmy1gA1vbVcyIxUz/GFCfyhpeFjBBmIqhWufTPVDJVgK5nPQfyIvOeWj++ekjyXH5h+FgDwtRNn5fNWwOWWtWKPFaF5sGCunhJMpXx8T6udARXZNC+vlVaFvx92l8IwpvOiRImyvxy5j19240z7II3eaj09MSBWYfAlnyhYoVCW19yJqcCUcrYufh2jwLZgJNuogGASgwr2cNxsXDvP9+8nfOtOXuNChoGWfjxg6/czjT3MejCinOXFe6067KxQM1CbW5CPtwoIiCpMnoMalnX8NnpM64PjfWMPY2XRUqA25zysBeKZcwv7N4kgZx/nPDaBnXaGlzCPc6+p68hteXyLlBDDmcxu3KO+/k+c/jIA4Mq5Rb9tpC2zb22IlbessYaa2/Zjqge43tlofsiShOvIa5MG5Lc2P4xEjR8lyjGU+MOPEuUYypEH9wYZc46m18mapKRuMrVi4jbDknyZlIhb1+8NcIbm+z21DQDA5eryxPlDB9p05VeY3dcneUZWMdO0wwJ6Wmr2Z1NKNhhV9PRP0qt9dTijY8Nx2Hl2LZMqA4AtDYqx1n5Hz9k0tFil3JkDm6Y6j4E7pgNtcHE3Q+DJ+2B71/N+DMdKhFkaTJyDbgRTumNzD2znXsBcvwv3fKEkz0wWZGRBU1lCUxKtFnP6HR7G/H+kelM+T4Xn66sVuUcDrTCle1dAMPUpTX14t8wt8NWNmSpHe8/p+u25dHVe9lZm6wX2k6jxo0Q5hnLEGt9hBJcKZvENzJQUYxhJTnDPjdNvM6ajgJAams60SgICK07NB3Ymg2o0MLJhMvtm5NkI4Bl4BpsgXouz53kOdTbTd3n03FlyTloDqZpzZjVzKK9PK/fAOAPkyNasp+aj2uNMZVJDZbWRPbYPqqk2X3SGljoTdGJw0JJ+svMrz28Dsjw2r5+WSyUnVbab6bq7ixBQ5RqNR+mmG1am/Trur/n7ev2LOp+3zdzw275cuwwAcEV2DSaVd9j/C11JAzKQd3/5zsQ5JmrsUw010m3O8oJ62Xt+kESNHyXKMZTX1PjOuXsA/CaA05BX4seSJPlV59wCgN8GcAnAVQAfTpJk88BjIZlgHtnSNznrx0mdzcIHwKTz1MdnkwNqGiD4l3yjWi3KlBiBGXMErhwA0z3I3+O7lm/0tvGrgr9IzR/mSApxvtk510KeNtZDEs5r02kEdtAPtoUseS2zgHStfE/NKR7TF+0Y4IrXPppqs+2xeG1ZAI/lDKDlxbRiXvPNrIZLxTpoTWQae9j7ynQez5EdCwQN2Up0rN57W9fPe8biMZtuHvtPOS81P+MLADCupJ9pHu8XX/0x/91XbpwDAHzo/hcAAOcWwk+FIDbGBgLfYljP/Qp4rNU3SgqvK+feEMB/lSTJIwDeB+A/c849AuCXAXw6SZLLAD6tf0eJEuXvgLymxk+S5DaA2/r/XefcswDOAfgxAB/QYb8B4LMAfunAY6mPb4Ei1AwLFdFsZNK17is1PWXQk2nf2JqbOMfJipTgnitPGh+MuBM22bRFPvvMOS/+3fcttCffr7uqRQkHtrDgfgaYcZC/1tRINzn8RiaazcYgxfHkfjxmVvNbDcXoN7dlm2cA+Zo+K9kYg9VQ2Ug7i12s5mpm4h/W/8627mbBioWxshgn23rqoDbZYX4hVkCrsw+Zz7SZB6P52bbWS+UQD0lU45dvybl+/m9/GgAw3AnzeuDybQDAo03JCnyle8FchzyzJ4tyPRu+FVdY+3HmWclrs10uDD3b9GvJ1+XjO+cuAXg7gC8AOK0vBQBYhrgCUaJE+Tsgh/7hO+emAPw/AP6LJEl27LYkSRLskwh3zn3UOfe4c+7xrY1vjBE0SpQor68cKp3nnCtDfvS/lSTJ7+rXK865M0mS3HbOnQEwmZ8AkCTJxwB8DADe9Fg1GcGlKqSytcsMlAxNifioqSakNjVIBkoWadJ5ZxryLnqoJkbI5XKg1655MI0Ig3J9U3lX9NuyabDXNp22jKnpWXYwSWs9zgA0aOpa89eTYyLdJGI31UeN5vxkqpDnY226T2/aQJGa9NzG81t3wPcALExevwfcZJh3quYcvh9eMX0OG3hjAC9ss1TkIjXPDiTzsISabQ3YcR09SWVOIIxdeheKezq/8OhnO/Fum2DrdKYmniY2zXIAqMzLNdafljXvdeRc9XcEd/OHl54CAHxy5S0AgJf/6qLfdvrdAgb6x/d+Wq8jp1FKxvWp6rxsmrU7Lr9+AB7nnAPw6wCeTZLkfzabfh/AR/T/HwHwe4c6Y5QoUe66HEbjvx/APwTwlHPuK/rdPwHwPwD4HefczwG4BuDDr3UggewWcWVwyn/HdNV3zUia448WHwEAuJ1aekcA6Ot7qqzBlGLQHqFufbIj7n6VUQdRHed1PGU13sB3Lp1sqEHQxTigjsIcyayCySoyP6dMoC/0d59MUfEcqW2ZhA61n6W1zloBc66t+wY9UEvSzSqKOYE7G2gD0hBTH4AkI5Bq1WIq2JnmHrTrOEFPnlNVlwU7hZp701AD2j1YNT6vcbEYrJts9aYVtkvjupKdx6ZppxqynsOCaHzyRlycSXnEAIDnb0gobPpOuE/rezI3WiXBggn3Ndu2jCnZqktXOx42nXeYqP5fYn8+nw8e8jxRokT5FpIjb6hRRIJnFb4IAJ9fuw9AaFU07ssbttQyDTUq+tatKURWNf1eO1gFBFgQnms1AzV1+RCvQ+oj+v+7OYVAlG6m6EfORR+dKTPT8olaTzVLK6eOPtvyiu/w6UKAs3a1tt5bEDnpK2oNahHL65dtJMl57RhQS9nDabWoxWhzas0ZM6es+PjFON0gdJDjt9JysbZOll2H8+gmk3XpBOOEIhfLCKzt1zTNxead1lrhdTA9yGcICKk+z+WgazVr4itNZXjeZvGYgtDONULKb1ODVmwcY/BU6HbS1+RTocZ/9wC1cTp+US2aJiiFgb/O15II2Y0S5RjKkWr8mkvwULmHk3Nf8N9tDkQjffLZNwMAqjfk7WdfXMMGSzL1i5zIZekAJhJuOYzmz4JzrJb3xT2+9DbdPNOK50E3RHRe6/nS0HSk1v5/kHkn57WwCp/hNlIztzLsNH0zhttqvm132koAgF1t1pnV/PY7amp/PAPoYTlxw1seovFskQ1jHVwrax2xKIcgI3Lw2XPc7s/qPGRd13riY/dNPGOpJuAYwprJALQ2CJx5/K5blTV+uHIbWSGQitH8nrHWTjXkHLfOi/9eXhKL6nw9RPVPlWW/+qJYF6NXTQlzRwFp/QUAQLM2ma3Ja14KpGMmI7jIuRclSpT9Jf7wo0Q5hnKkpv7mqIp/u/cAPtB40X/3s4t/CQC4dUnMtq9deVA2GJuFWGi22cJI68GLhqabbC4FsrkEyb7dQlpuUrif72Wf03+crkLecWjO5xFH7ldhVUwBX0q5Y607QBN9eShrZiveaPZmATM1F8xH7wYUxqkxNuDF/fuK1bduAE3ybDCNzT/sHCkV3wE2XBdBSjye3batrsb1tnSpZaOUtU5Adm13JKjX09qN0UDM78S4gpWazGOmKSm3SjFN7goA81UxzQfTafJQIARCGSTlHO+rBLzaKWWPGk3Jes5qes/i6X1jDw0EduwvT59rUolzPe39CN2T0620rIyTwqGJpKLGjxLlGMqRavw763P41d/8cXzqB1/23/3Kxf8XAPAzZ0Tz/8p7laL4awHkA6XTnlqQt2+7pU0e+mH6THfkadr9wn55bz1q7Ianp95fqNNscI+pJQbA2FgDCKCUbKrOzjkbxGlnAnFA0ATcb9Pgm7MtswjwWEuCNmazi/lyS+eqDToOsBysUOtsqxbctR1OVTYzqU9qP8sTQK6+212xXKjVAWCrJxp/dVeurb2tabX1MJ9SWy0/VX4FvVnDqbCGvaZc6+qmzlGtRdTCmEpTrvtKXSizZ0xw7f6ZNQDAmZqk5hZKsmasNgSA3ij9MzrRkOc0BU9mE5KqnMsmQl0pbXnRqrDtwkKj0sxP9htU3VHjR4lyDOVoOfcKwLgKbHQD0yrTZfeVhB33F+6TQoU/mnvMj7nRkrr7zkDehPTphkbjU1NmiyqAybfbOPMJ7K/pbdEOx++OWdevzRrH4XpYHMN0luUeYEpqZyhvcqaRtgzzLDUj2YLJsrM6DhqbmmB7KFpwdxA0AwEdvk2WrsvQtq4qpltvk0nHavz1gWjaUiYOAATtZWHAQBo4c7OT5kroqlbsG4211ZX5b3lfPWjzfkvmVlqT76ZWFYhjbm9JiRVGislmhm5sMdotmZMnpVVNX6gYOKy2Wy+RM89CwfV5oKY/XZ7kJfSivvp0ZbIwius3VZG1XjNo4+Z0uokKrSwb8+jlwLsBYMNYe7PFDpLYNDNKlCj7SfzhR4lyDOVoTf0i0J8d49LM+sS27UzA6pGpW37buZp0LH1yWwgLl9clGFQsBVNqUQNVhUzgzH6XDfLZMdnKrGxdPhBM/GUNat0cSqrp1mDej7nTFxOfZvSdbjDRVzryf5q2uy35TLksai7W6kqAWUybofJ/VmrJ2Ho5BP48yWdZTMpacZKOOhuMujI8Icc15vyiprjYiGNodEQgg1TuBDX5d0zX3o2erFF7kG66sdM1KUPFqA86YtK63TCv6pZWHgooLiA5zW3pT2twVGOCxMiPDVWbN/FntU6iIutYKpu+8krbzvQm03sAsFCR56rhaw80zWnp48qaKpzRph0apHypHQLUFysSJFzQY79iYqalTO/CvEYpTDHyOffVeZi8v4eRqPGjRDmGcuTVeUkpwcmKZXqRt9zVwQn9WxsvpJoryBv8UlMshatN0bAWqMHgCRt0WMLEbibFl9d4YKTnIItMVzH7K6OQYuIcr/Xl80pHetjf6oSUHQNte33Zb3UjbBvtKquNBpyqa/ppXtoMUPUrcpy+Ki9L4TyuakuxptbqN0LEi4EqAkXIoFMzgJV6KQ2uoQUwVw1rvtqVidwci3U1VZ7Ej1O2NfXWGoS16muv+KE2suj2NWDVNpVoOwoy0v7y1c1wX6qbCqBqySfjZNTyANA5qW26mgzyyWepZdKjujQDvR/DGbVqjFKt1tIB4b1BsEru9GQgn68TaoLYLsiXaqLNT8zKc/3Sijwf7YVwre+cVgYgtSB6J4J1dXFajsnn6eU9+cx7TrmufPaLhoVqMC5id/jFiX3yJGr8KFGOoRytxi8kSGpjzJQC1TF8bcvLAAAgAElEQVRr2a8PpDKJ2p0+EQBcKIumZ4XV89NSBbXWDmmwQQYck2bFkY+2ppLI82eBM3yjr6jGpv/Oiikg+Gwvbssb+c6Ocqx1g8OWqL+YtLQCz2if+qZSRW/oHHs5FMlqDI0qchxiOJKi5SfQMXrsYTP4m4OGgkCqmg5TC2BYz6v4kvPPVLsT21rqpw5Uw9jUHb/b7VX0OBO7e03f7sqY/q5WXbbCI1fRAnb/uWUgrtpVstzS9NtAP4dhf6dxg8Ee10q1oLlUuuIu4TlKes5wnM4p0d4rl1TTG+xYoyTWwEZR0mazJfHR8yjRb9+RFGZhVa29ctDqtweybbkrFqCljD9REyvg6e0zAIA7e/JcNSqG5UfTgLwPPV0HW39fLo5iOi9KlCj7y9Fq/LGD6xRSYBBGoenTE36aBj+If0qetM5QNCyj4kCIojNS3zfaPOsrUdOvjwP4YVnfyK/2Bbb5Sls+U/5eW/y926taB97SaPTQZBB6CjQZqPbpmlZgutrdxbRmshqKS0NroELaNnMJ1GKDKfmyc9Jca1cLZ6aV7bcqn7bRAv1+ftceTMJyfbsvavxOWOv+UL/TbITLYeIlOfFAgTgFjdjTnwdCxL6xrNq9E44zaKjlVFRevtvyDMx8NRTHJCtiFY53d1PnLp4OKnt8QazD/qy2sL6gFshsWNCFZ5Wz4Lrc65XHTvht66fkGbnvlFidF2vy2TBFT/551phFZUOtnbkQB/DMxpolWTi35bcRfLbbl/OfbIoF8NPn/taPuUet3sDeJNfxV7sP+jG3e7N4qnC4KH/U+FGiHEOJP/woUY6hHK2pX0yA2YHvywYECiPWX//1hpBvPlU+58d8aOEZAMHcuTAtlEY3lgNwZnc4WSFGaamNTROfKUPSQQHAmtaSdzNVfkMD1GAN95mTgtfuzMlxO72QtukpGGV8R8y28o6pDVeYd31dSTLXBvq9qYorqYk+J8ck7rzQD2Zwqa0BvMV0OgwAhtoQsLcgc+vNa1WXWY+CmubkMxixd7wJJiUZerNGNcxxWklPR6zgG8i56BYAQFvdMLenlNd3Cjr3cMzynl4T+Q3q4ZxDDdTxts5+Vcz50ZVX/ZjilNw/985HZVtD1iwZBjfx+g/ImP68fHfu38naLXwtpC63HpZgWussA4CG5LIu97FxVq5/tjgZ3LtUkeYttSUx0d0tTeEOwnFaCm5ild/6THj27iiw69Y1cS9JH/+7pXf4Me+bfwUA8J1N4bI4WZT1mDJ+Yl4/vf0kavwoUY6hHKnGL5eHOL+06buDAqGy7YtblwAAT14TTV++HoJqTzws3/3iI1K595MnHweAVLugSiaoUTFv5GmtnN9WUklW0FmGE9aU03K4UJfquMcWr/sxhOb+5cb9Mtfr5wEAjS+HIM6pZ6jFRaOUtoJmGb98DQCQ9NKpNfue5hUVv/+dAIDugmj1ZMZG9zQltSfXWN8M15pozKi2IeNbSxpoOhcCeJxRuSZr1mzIfBhUAoJ1s1iV705Vwz07rcSRrAx7blcCaFc2F/2YkWr6xoqcn9aOZRRnsHOo+UCmMAFg9ztlloleWn1jUeca7llvQe7V7e9QRiFd1nOfCRV03bNyHb/2A78OAPjU97wVAPB7n3mvH1PX5hbdMzK2PB/Smyem5P9n65PNMSiLBVmjM3My5rYTje96Qa++qkxCbz8lzwBmw/6fXb4s/yFXgD7XVzeDRfv+BeGwYDMUn742z3m1+AZ1y40SJcq3hxypxq8WR7gwvZlik7k+kDf51W0FymyLZqqZFkOFW/J6/NXi9wEAfulNfwoA+IUzfz5xjrw2SBSmXZgWtBxxbO5IoTXxv7zyff679c8vAQBOPima4aGvSLPD/j1B0137IdFCpcuisQYvh22X/kCsm/KmbBvOyNjVtwd/j2m83rym6t6uraiuhhhGfUX9+HktljH+f3Vb04BqDRSv6bZCULV7dfn/4pKYB9+zJNrEUlcXMpyDhKoCgYGHWqei/G9D03yE2o5Z1S4zbAb3wpQnjbX2ubDx0fNCcf30dQG1NK8psump5/2Ysqb6LnxOIcwDbbB58Z4w12nx+5/rSRMXsv088p5X/JjvXhS/eVM5CHaGwYIjL8K81uOz+UaWUxAwPHj66NqmMOQe4Bqfr2xM7O/NvapCsQ0vwF+sStqOIDZazZYfsPR1tNCKGj9KlGMoR+vjF0Y4V9/yjKWAaaOskWFfYGFeSUVlWtl9UUA2T18Un//y3LIf01SQDyHAee++Bps5qJ9kW0k90xaN8MKOqKYrWmhR/9ugjc8+qc0Mt+Vz/f2yz5t+/mk/5s8u/HsAwFfUj//7d37ebxtXRUP1FRQybEwy+jXvaMmtMsZ+8GFpr3zrYnAKn/rThwAEcI8Fo3ROyf9r6+pb76rm3g1WAQFHswrVfawhcQzbSPKqFiKRVaed0wp8lMNvSCmd1EaSgm7GaKitzYtmHuqPDgiE6ob1uLEt1/voPaL5X32/ZHvOXjVFT+s5WhPA7R867///tguizf/V1z4AAOivyLP3trde8WPeUb8KAKg1RJumW3mnIeBV3WZBYSw0e8e8rOOV5gXZpzP5DBKMZs/Bwhse0ikQaOdOsBa3i2IR/6/n5bvvviTznyuHZ3im1M2FEudJ1PhRohxDiT/8KFGOoRxtcM8NcH/tTsqkZKOGaklMH1pHFo8z9MAOMbf2tNVoBRbPr9Vb+rdtN1DMpDhqutVSJN9XFxAG+6/RENx52IBavld7os3Kfs2R2NqXGwE//tkOZ6B00K1gvlb+RlyCcaul85LrOpUY87ckt2TjF98DIBByEuMNANPv1ble0V5r18M5mC7bvU+ZZvaUySZk6nyAjew8c0XZeLEUer0VdNAtrVK0gVCa+jQrSxrUKpna8IqSDBD4s6OAnt5aCJxVNhWHr9V1FtzTXheT9ql79LyPyn0o/f2H/JjFJ8NzBABrb5d7t/UdYa2+qinXYU/Odc+Dcq9+9NRX/Zhs85Sm6e+XBcUUXdr0BwJA7MG6uJ6D0zLXQT/o1ccWAqMUEGjYgbA2rq/Pua99MAxRWgNCEtL1nriLTfNc1HNbxORL1PhRohxDOVKNP0YBe6MaHqsGUAx7shMwMlI46Ni0tOV3598qgZ6fmP+S7JPTlx6+S214I5OBx4N6PBV3SOG9ry4prbfUZG4f1hptm7Z5a2Vdv5PjfWxLwCC/+cJ7/Jg/nnoEAPAfX5COwB/+0F/5bX/0yncBAJb+z6/INbZFxbmyYa753rfItveKVfFXKxLUuvXSST+meke016xSFljmZRIGkaWnr7yEIwOHTaZlrS80JThGAIq1jGiJsctuI4e2nBpyoSLXYevHq8ppV9WUVLskE+uZRhbDhlYO6m0cGwAPvyvfkUDXYE6+WH9v0MbrCtwBqyPVOnLbAaw0VPjrxftE079z8VW9nqApCejynYGNJdnIAMOo6a3GpOVJC/KBiyty3FYIYl+sy80i7frtfqAfJ+dfonN1A0b5wjn8o64w4F2tGn21Ffgi+uNiqinJQRI1fpQox1COVOMPkiJWBjOpNlFMqZEplkqnZHoMaf0Ovu/0CwCAiyXxrbfHk3XkTOdlefaA8Jaj3zaXo8VOK6B1jMnGCbsK6hnpG/5n5kRz374UUm1/8Kxo7P9x7e/J8RbDcYY/IICZqyffBgCYf16BMI3wal9/q4JRlI+v/5JoiPqWgSdrGo+X3z0ZNPWorhpJeQDciEy0Ycx990iM4LtmZD3ZhMT6rfw/Nb9lIqKmJ9/bKQX3LDUDrPXlDUkHDhVkM13XtG3HpAVLWtevbrzlJcjemoLGTsZViwDSNdEUYXlKdlo8H3z/R+ZF+zLttVSV+7ExnPJjCIZh8VbTWHksRKI1VM1ByJQ9T6NaJ5qem2+Eh5h8EwSGMZYEBJ7EgbaKS/RJHRoGn6SgT295/6aZlcIoQnajRImyvxz6h++cKzrnnnDO/aH+fa9z7gvOuZecc7/tnDuccxElSpS7Ll+Pqf+PATwLgNCpfw7gXyRJ8nHn3P8G4OcA/OuDDjAcF3CnN+07wALAaU3tPTongbuXaoLNdgb3TQg5O4W2NWdl0XlZxF4vmUTFVXxQT012k0bLGk5tmng55u9Av+rp/t89/YIf8+oFSX999ZqkkW5dDTROTNe4OZnH2lv17xR+XT/bemtIrx2WDO0zDIByW5hjoU/qL91dl6F8LuTzfnjpawCAC9qvkMizQY57VHaTVE6syhuV5FwLJbmHZ+vBrdmdkqDtpmLUe4rMLJiU34iBPjXjE9PEhFMhlRmbZLhmmM/UrJjPC2pSkx6cFYVAQNit9ALiDwiuBxDSkgz2ls0N8Qg9Uon51F84Fsd/tS2IvWs35J6fODVZ0Ue3omoanXjuAx6Tn6ZhTFIqpLaR7ny2nK4xKb2eyD3n3HkAPwzg1/RvB+D7AXxCh/wGgB8/1BmjRIly1+WwGv9fAvhvENoQLALYSpKEr60bAM7l7WhlmBSw3mviziiAQea02unH574MALjzFtn2pfWHwyRJn6zvKWom24k2pFn4Rg5vPmo0D+5RTV00GoZH6uu28gFVfgPQYpBPMgMBwD9YkjTehaaAYZ7aPOu33dlVHoAd0YaDji6/DcjoJMda3cYgnU3ZUUGM65Nvd94RpkPHZ0QjfO/FgE1/pHYztU9eXQO/s9ZZVhjkW9IOsoVmuI6O5hUZAFxra82DuVa2nKINORqG+9lXwA16aU2H/iTLD7vstqeUhNUQpFKznmvIHAkyYiAOmGRdyitxY3CPmn5gbhmfg5U+L0QBSaa6rpIhjL3VDgFh32SEaUlmnbuTVivXb7tfT30CwHa35oloX0teU+M7534EwJ0kSb50qCNO7v9R59zjzrnH+4aUIkqUKHdPDqPx3w/gR51zPwSgBnk//yqAOedcSbX+eQA383ZOkuRjAD4GAFMPLiUbnQZ2R+EttVyQt+S0av7/7tynAAD/5gdD/fcfPicpMtaEsyEGKYaBfK1FYVUe9SMbYh7kDfHNbt+5WiToG3N0VQ0vj8Lbm2CQWc1HMnYBAPNVGXe7LtfcYVupvumDTs4+fXN7ZKeJRyR10SROG0AmvTDLkTbeqCyIpv+ui1J3/v7ZF/0YrrWtEANCnMReG/3eVtFo0UIaGnqyuJP6BIDbNQGoMO1En3SnHqCq62oFDHM4/0Ya4xkR3KJatW7GlFV7n6iLFp3WcywYfDKbTJLnMfjx4VqZUvZWgHkwOK7BFKZ+b/qb+HgQpTor57g8t+q/I7iH59joBnBPotBeR44+3mrbE4a2tZ6LlOirWyEtOGhXMBzmWAk58poaP0mS/zZJkvNJklwC8FMAPpMkyT8A8BcAflKHfQTA7x3qjFGiRLnr8s0AeH4JwMedc/8MwBMAfv21dhiOiljbaaYKFKghWRRyUSs13tYMbKr/buoBAMBTuxJGeEvtBgCk6vr51iZriS3gYYSfUX2v+Y2VMPJ11yJdH8WdFMYPWgmbdxi/U+vW1/ta7LMXaqpXtDVSX9sftVuqRc2bna2/i3Oivcaq+WxbZw/40Jr9kYn+npgXq+jtJ8QAe6AuUFUbhyhnMiDNnMg9YyYelGKukb6whb0CwJz5+6GaWDqMijP2csJE3Nkymi21LbMvG3v2x2kNVjHAlYr670s1sTSo3S2TEK81a6U0zVy5DrQg7f08V0pH5hkXao3DTdvSOMjJiqz9d1y4KtdnLA/yOy4Pxepb2w08D/TlmcEg6MoaZMzWjKZYICXnPzUXwEq9qRLWSpPAnjz5un74SZJ8FsBn9f9XALznoPFRokT51pSI3IsS5RjKkWL1k6FDb6OOF9pL/rvL88upMbvjSTOyo+maf/9VSfEtvFtMxO+cDgErmqR0IxYLprhbhThrpvNsl9eimntjZlSS/dN5hQwd04yp8mury0Gzr9cIS8zOq0M1X9fq2uzBNKJgH/vBON1XfmTMYAZw6lUxXy+eDnwAb50TN+jtDaFxntN1sOuZpZPKa8QQmo6IGc6ehkBIf52pbKXG1lxY88uVldTxnocAs6zLwD50N3sSCNzoN5EV1nV0R0y5TQZxiXvfGigHgq0yLCp+vyImMTHzqf6Neo65nGeGqeMx3aOELlDQmbtjOS872W735Rm060qQEKvy+j2TdmP6Tk18f4nGy7G1FgDQVXexUTYuTCnSa0eJEuUAOdoWWgCQAE+sBazPj84/AQBYVBaYaU3RWJaeh1WjPXnnEgDgk8+/Wb5/W0iV3VNJdxPtGsQLq8+Ydpk+4K3INyEL5naNhhn4FlwajMmBR57UYBDr+GcNrQy1JgN/DE5RmwHATk9BKQpxLWuwxo3CO3phRrTW+SnRuJebQeO/qS5BvaWipI947Wntk2GV0W02JcoAV0+1+2o/gK5Kqv22h2INeOvAEHKy8jGr+a0w4DWlhKt71RD0JSyYDU7KOWtNFpptTX0SLMTUIQDUi+mgHuHF1gKipqfmrpnSwK1xHVaYZG4ZABCtIgZ2l+oy6nQlBAZJTEp675EF5ygcmbeokJdnZrexYdrisRTcQC72KFeixo8S5RjKEWt8BzdyWL4VWgN9bkk41H5u4fOpkZYd5/2LLwEAVu4XTbndkrfwyiAAZ8icM0gmL4mcaIva05zvyDxwY9bXLxpfv61v7VDzL29t6xtuaUuwWqbxAQC0lSuwoxqemt76+PRhLZsNACzUwznuaYqmf0C5/myqitp7VjVaJcNFaMVDj/XTFunwOtYGUxP7EY5bVo3GczZMWpDxlLPqY09rCnZjFLQ6gU+0LiwH4qAi313pCd359iiteQHgfF1g0bSgNvoyZzICAYEPkAxCZBSqJWHNeK0Vva9lU7LFud3RtPMALGgKzxmh5G9pyDW+rN1D/vjWm/yYLzWkgOd8Q+5dfTY8351tWRNPJNRVuHY/6G9vBejjWFFNz7QnoM/OIVV+1PhRohxDOVqNn8i/wk447edWBJzzw7PCZnNWo+I2+ktNef+s+PHVedEsD9SC/8g3MP1N669Ou27qu4Fq8Urq7chINwEnSH0CaY4/GSv7bBno8I4BJwHpxpy8JoJaehqZ7Y3CepB7sKkZgHkFucyWQ50DWyMzxmB5AcmYG+aMCdEuXX7+/Uzswm4j5LZkrr2ic2zoPAgBzsYOrDR02645Dkt+15UNxzaDoA9+X1WsGmrVogFm8bvVksQfNiuTWQG2vsrCk218hs+OL7t2k+twECScYKAn29K66wurl+TctXDPLk/JdTDmYOHJ/Zo+s4QnK6NQYtJOLMFONB7AlbZty0puDHfIVtlR40eJcgwl/vCjRDmGcrSmfiFBUhuHGmsAt1clwPOF82Ly/4dTwg5jTbO3NgS3z+o8ppGsO0CcNc22Qk7t3cBjzOXYfRO4q/jafLLsKMuN2T9rypZ1665J+RDHzhoEO0emxFpKgdzUtFPR5G9O1CTdNKONEtit1a4Hg3k8tu30S7O/cggmlixb0SCHtehMWYJRm4PGxDam+rx7lYR59HyvOeix5TOvm3Ezg/m3Us4E3Kw7sKzBXQbuGhWShoYxPDZdLtsvkVLUNcuSZQKTKVsGANcNWee2pvNe3hPmnUZZ5vHR85/zY17snQYA/OaL7wVg6jSAkKpra8/7Xo5bQZKeflpX29qFbg64aT+JGj9KlGMoRw/gcQkSQ5Gc7Mlb9nPrlwEA721I6m7BpPMulSSox7c2tZqtOON3TM0MDN6RmoyNErb19I1UQ45JLjUgHdQZZd6oeem8O4lodWoW23pqRmv0s1rEavMp1fSEluYFlfY0JUborNVi2SDW4HCxnn3PRRpqe9wt1f6XatIkglp8nLM/z891nTapx5PYTY1t5bD9EILNa7QdegnGIUuQb4FlLAgek5YCj2fTxfyOxx7l6EMekxblmrmv7F1PyO45Tbc+3z3jx/zpiqT22nsyH9s1eNjVn6FSZ0PBPYntLDxKr227J1bj2KR5Cy6JAJ4oUaLsL0ev8YE0x5xq0SevCYz3LxcE0PPhmSf8kOtaw/xbt8Q/ujQlfu87T1zzY77Sk1TKV1oClLAFPLQGgn+phTCplI6mtDL139YnzcJGCVgZmHQaocYhRRT2oZahRiFYx/LaUetm/Xjrf3uOOM/9t//7u8bCJPNdL2MF8JzdHBhquTBZqz+tzK5cV6bl8op98piMJueoxzH+6obGSLitnxN/oNAa4Bg7lpqaa8a1t9dK8fEDcxm0BgjH5T281g08Cy8vS3szFsg01cenJQAAG9pOq97UQq1huGeuoY1VIOcYKRNPKqSkfyTKukS2YpvO+3okavwoUY6hxB9+lCjHUO5CdZ4z/b8BMNC3JabXJ669HQDw9oev+iE01/6DU88ACGmk5/qhrv9fvfgBAMDGsrgFU+8OAZ7vmXoOADByaWz6bk7vvW4G62/7qGUbTpQ9cs7QYnkc//5L681NPX8jRQMl23qZuVmXg2nNQG+9dcC5uH8QptjYEMSj08z1eZRhjhvBXnmFTHpznKruU7w5aMaK2DWnGU23yAYXeW3ZeRwUePPdbi2OPjP/WiZ1Z8fkmf/ZeTCod6cb0nlOzW7Sg7+8Jm7AyFRUFvSZL2m1ZcGi8jiulzbxLSOaG6dpubg7OR4A4XmI9fhRokTZV45W448cijtFjGwjCDIKK8PI+lWp3Pu1ue/xQ/6JUm4/UpVa8692JYD3b65/tx+z+YrsV9A34pPboeb/zfUbqWkUC0zdhWBSNsAV0oOTVWHULMVk8jjU9NQ6NkXFij1qmFpxUvuw4pDWQB5Ih99xjvYcS0pxzTf6QUE1H0jMpACBkCqjVrZ17esDwcTT8mAK1Vol5AHgnaYFYOvYCXIi3boF5zCItqPrP1PYvycDrz+r+WWbzJVrxuPZoGuwPGQMa0Nk/ukA6o7yA6x1gsavVJQzoSrnZdOOUdG04tIgHAlFC8bq9SSj+p0bpNugAcEKGOnz3VW+hqEJRu8OqgcGeq1EjR8lyjGUo9X4DhiXgIKBHSZ887FJojZQfHb1tB/zpcWLAIC/3BaQz8vbAo28eWcuHFp9oPGUvH3JZAMEv7CSoZW2Hh19SGotapGxeTf6/VQzBDjopFVAsdVyWcpqSh5E1Gt1WgnG/x2oNUBGH4J9gNAIk7pmY8z2YUHD1PQc5UzlmdW4XgsTnGOpr1X78/xsD1Uu2cq7Ueq827qe9tpXh9JY5NWe+MQLJUNHXUyDtbJxESuEPHc15WYZdNjkg1ZJXyshtw01e9a6srGK7aGsQ39MKLhCuk0ajchvam5WWxaNxu+z9ZeOcYUcX5zWLxuEmnp8Mu8U0z0y0TWVnaNxAYcszosaP0qU4yhHH9UvJIFqBEFTU/OPNcq/txx8qP9+54dkDHnKfPdLcxzylql/dHsztEW+tiQWwrmSMLZk66+thIj/pBanJqFmzIOY0qpgpH7aZAXaet6ydrbMOweFte6co2WgIePMS7tyXQ/OBM49Qp6vqzZlAwcL672o61DLAJLy1mPgufcMZ4CCeqgpCeu1MFhyKPKan+ufSY0FgnXDYz/XClmaM9Xt1LnIXdgw2pxCa8BnEExUf7DPI24tqJ6O3xsqQ5LhDiQMt6lQ6paOYVMUIETvCxktbhuE+BqwnKi7y5LsEXRVM5DdYWbbaFJn10qDGNWPEiXK/hJ/+FGiHEM54uBeIhVHtoSIgZFiOshnq5EKdzTQpm5AUlbgyZ4hqWSKUPcfmj7qz7UkUPiIUk8z4JPbF16DfKSTYlAICAGvrksHkSz1FVNJ7N3Xy6Oz8oEvBQAVLHVWuqpvZSAmO5tGAMAzm3I9y8sS3BzcE86xPBMCnnKNcj22gjDb652hQHsdDFzeGC8ACH3prND8LvrrmDTDSUxK2nFbrQim+NR8vtEKc2dTippSkJ+o7umcQwCRc9ou1PUckl4cm+sIXAHp+gjrMqwNZE47GsjrGLpzuhocv6YpSNJkAUBJexfSzM4zwxnoyzPFB339GRKPry6DxYCRDYzbanq8iqHX7gzz6GPzJWr8KFGOodyF4J5J4VnhV/oitbXI45pWJCkzCd/aozmDadTxrpgJlAC43ZYA19W+BMOmlQSx6cJb3zP4KOTFa3eTfsqywTC410UIBi2V2FaK/eXDOXw7piSdXmyb5WBV3HN7Egy7qXNf2Q2acmtVG3Isy9xWZsM2al1eG4OMtnswiS+zvd5nDXSYVsjpsgTZbFCOwU1q+qVyoMX2++u1zWrF3SUNKBZNwCpLTLpqYLA3dkX7k1acgbfF6qTlca4qa861szBjVjkWM4xMZHECgobf7GtDjaKhCdf/MwDJCsJ6hv4cCNo80QW1lXOFjIodm20VBf7QSk3KCt0dmOCgKnY32F9X51VH7j82SpQox07uTj2+FV+RoH/Td7KQRvXfR2RP5ss7L1bAt6bZtNGWN3mWI876tPb/ALw6bA1Cyi7LAET/OY/xhY00uzl15LQcaGVYUAr9ze2BaMMbW6L5WqtBQ1VX5LZVtmUBFqaCFjynrbMIw/X+vG2h5a+ZhS+kGw9akTGBblHm1s5JXXIdec0ni2EdQmNREbId3VOaLCh6S0Vaob3aW/DfXdsSCDYtnelaL3VcK2QtOlXW9mWFSXgzhcCdPQPLZZsuavpqDgcBnx3GHqwQAj7M+PYlk6YjSKvXk3vHuAAw6ff7+JZ5vmnEOHJBjkkDb5h8kgIOy8ETNX6UKMdQjrihhoPrO1+Qw+9S4nK+94HLfawDAOAblJaCYTjpKD+ZL79UgMZqqrFmJzXGt5I2WrCnvmBbfXpGeq3G3nViXfiCEXNp9Ht3x2lG4GsaewCA53YlYn9tW7Rfa0uOV94Mc50S0mGwu9Xl2VW/bVZ92ulCel3zuPdGXitPao9Ghvn2bHnT/5/AG99ySjMZ1vJhNqOdpH1rew5aGCwTnjIWQ19LXBnxLuLq4wIAACAASURBVGnsplMKKzpVljG3umIVEbo8VzZMunp6an7OvZDTSntcTENugQDgoRWwrXGAnU7Q/NS+eQU4FE/orGN8JN/sR2u11FYf39ROeXIgNWAGg4PKr15bosaPEuUYSvzhR4lyDOXoG2rUx8jpdRHM9jxMcylj4nOTNalY8afgHnsOmmLZjqsNt38jhzxwT9b8ZZDOAnCyOH6bGsoG+pjOutmzwBWZ4/aebGOThdKeofnW7CEh5UOTvmqzqi+hGZ4DUuKn2p+1nHgQU2LTaj7fHITA2ykF4zDISTfJYuSXtQ5hWU9GsNKMCbzx+plCXemF+gpOm2bwTkvGsrcgABQLEqibq8j52dk3e5+AcI94z2zNPXH4DPJlmYXsGBJp9lOmul5jxsQfGnN8nCHFHFuAGlPXTN/xcZ+kSUCxq67CKCdoXBjF3nlRokTZXw6l8Z1zcwB+DcCbIe+jnwXwPIDfBnAJwFUAH06SZHOfQ4gkkMq8AzIOrDtOKUe+CT2sN+etVs58Z15p/a4EhO70JDXUbWiAyFgFeXBTIK09smk8anWbMmJw0LY2ojBguKtpsFe09/utzqwfs7YnOUvytxHEYYlV+jp8OCXHe3I1NG74s+k3peZ6oy+a2gYp2Rbr4aqk0eY8PDlYRDeHkk57tnMWQLoef7ss4xhMY1Bvy9S4X+kI5fSzO1Jxt9nVlOogPHLzDdn/TEMsCFtbfm5WgUMVOSYZZzpm/6YCXQjuYYsym44jAIma3kOyTWE7WXXYEXjPVOdNKacdLQbCYk33NYxV+w4UQl7c1oBky/DqNRXOq3wR3ooFMOKj59Kf9jdA7T/SwHhBobp9w8DTHlRS9+kgOazG/1UAf5wkycMA3grgWQC/DODTSZJcBvBp/TtKlCh/B+Q1Nb5zbhbA9wD4RwCQJEkfQN8592MAPqDDfgPAZwH80mscDW7kUpBdghXIOkIt7IxZ4OG7HthAtI5N+WXedDZjqLXTt9viQw7m0px1wGQtOn3SSopPT96TWVCILRyhZrW8bxTyzVGb0re/3Qq+bautBUnaWqzcy6wPgN6sao+mtoXaC5qarcgaJbm2v70m7EXjW2EMoc4fePR5AMDfm/+aXl9IlV3rSYrxSks+17uh93xdj31pSlqYzWprMPIEAMAT68J5uPycWDX12woWMviXrftE6xbOy/VcaAaDkbERnmulLcdu9YI2bg3k/wTA9FT7bRo4LjV+XmMSCoE2OxpfGY4nx9CaWNnVZqh7YR6MLxWVPap2R62+nXDPuguaSq7qd7VJyG8yzOhhN/l/Gm79jqaWBxV8I3IYjX8vgFUA/4dz7gnn3K8555oATidJclvHLAM4nbezc+6jzrnHnXOPj/b2vqFJRokS5fWVw/zwSwDeAeBfJ0nydgAtZMz6JEkS7MP2lSTJx5IkeVeSJO8qTk3lDYkSJcoRy2GCezcA3EiS5Av69ycgP/wV59yZJEluO+fOALiz7xEOEE8smDX5rZlTyrxTaBIVD0hd5MQ47uzJi4dIu0vlNb8tS6+9M57sB79fUM8i1pgizKOx4rFZKZbtbAuk6ZqAQL80mA3BufKcdtSdEhP7RCMg1c7WJVD2obmnAQD/6dJnAAB/vvtmP+b5PTHOSJpJmmt7HTe64o68tCWm/tp6qACkaX1rQVyUB+ZkHa070OnLtZ56SFCFZ94h87o8HVCGDJgRzWfpvfxal7UCUgN/I5MW66kLd6ohdfhFX3UY1oprXtb0Zl6qjsK0aNWkYBnws3wIAFCohXtXaMj4ZFZ7IZ6Qz1bfRueS1H42y8r1HA5k3uOKUnkZsk0+Tvwuj6zzsIE94BAaP0mSZQDXnXMP6VcfBPAMgN8H8BH97iMAfu/QZ40SJcpdlcMCeH4ewG855yoArgD4GchL43eccz8H4BqAD7/mURJN1+W8bpJsOs786bq6wziT1htMBgC95WCOR5ri7R3R4rcHElS7XF32Ywg+2dISQDa2sOk8poCoGfskzzSBPAb3WIdv226RjHLdpbVHzYBSCopJLzRFG46rsv/ZMyHwdVo13LwCVx5srvht91bF8CLLEFuA/cjMV/yYdzVFU7/Yk1TbHWX5ebl10o95fkOCcmu3ZR1KGwawope0qpYXq9KYngOAt58StqNHp24BCA06bF0/15banZWJQLAG2LRjsSL7P7V91o9ZaWs7K/1kQLOUJa9EACLx/togH+vmR6oxqyYVy1p/sgRRSuUwhi2wCMopap/7ctO0t1IwT+Lptc0c99HUY/sMexMh/TspGwaeYmGMHLxWrhzqh58kyVcAvCtn0wcPd5ooUaJ8K8mR1+MnxSSVmprwxfPIeajhs75+3vH5ljT1zomqKFY/3eiI/7paC2m0Be1rzxTfrHLf2SaN2dZV1ODWNw5NMyZTQqxpp9bhsUsGXNOoiZbYasuY6XmZx/eefsmPOVHeS83xnKmco/Xh2YGc+LiWbYj/v1RZS13XKzsBlru5rf56NvYCYKwgEvqZ1JiLtcALQI48MuDQOrIWFK0ArocF1XAcfXLy6c0vhnM8VTkPILAUFdz+sZPAejRMjQVCrIOafqESzsFjMf5A6G3ZaPxCVYFdqvmpdVPaOMMMZSG/E5wU/Hqc/38ASPRcnUFIwRZckgIWHSQRshslyjGUo2fZLSfpIh1GJ+nn8OVnau2TrKbPY+DhcbJgHwAos5hZPm53RNN358LbMvjmndTf68Yn7e/T+toW9GSZfKwvSeDO1a5GyjVSnCqy6QogY25BtM6PX3oSAPBQ7bYfw8aPbPbIqDyQ0zhSr7lleAFplfjiGtVmqSYRHYUMa3zERpgZRxk2lMG3Lpp6thw09mllw+H1M9JutXqev00hU45nJNb7YRuD3N+QDAE586jVrcanVUYfn1p5YDII5FWYUSCSZeDd0DZhKx2JI+Qx6Q6H+c+FrZmnxh/5eIBtwaVrm+HTs0zT1Pi+Hkzva8+cu1QcRQaeKFGi7C/xhx8lyjGUoyfbHCNlojvFOTMo582bJLOPlbzXVdYdMH87NfVLNyXg9fKy4Nef+sGQzjs7q331kA7g2cAdTWwCgFqYJKD0gT8nJiYJNe22B+tyXgaMbuyFevyFGTHxf+IeSb+9o34VAFJ9zxm486SdhgiTFXI0idkvsG/M6eWR1izod+x3b8kiSwpKIVZqUJmMGtVnuzo3uWfrPdN8pJbuOU9zfKYUTP22726bBvLY70LP+nrqbyDg8HtTso6k4LLdbgMVuAZd9d6lOwPLXJe0X58FX5EAdL0l19ZpT95zSqGYU0CvMh6nTfCCCfaxEtNTxA8mzfVCFtqvVawlc87YLTdKlCgHyl1pqGHFA2/YQXecF5zYB9yTQ6+d10k36ckb9dTj8pZtLIsW/+K7Lvox3z/7DIBAxEmx7Dr8PwN+h2lgUExpMYJ6ZD5MIy2YNNjD0wLGua8igSsGwGwAj0CXstad2y61DKJRiz03klp9m5bcDzLcqIagFskuqZn6hmacgdhuW0lLNYi11QoVgKQlf9+JVwAEbWqDarR4NvpyjScru34b53ajJwHRZQ2uLdXDmEFV9j9X2Z8GghqebD+8h1VzX7MBRGtVPL56AQCwuSbnL1RyeBZKuj9bX5E80zzLIzWduJ427VZQMI8/sh7HmYAql80bKmwVZ5h4KqVh7JYbJUqU/eXI6bUL3UKqPZbX2pl6+lSBAtMcNArU30zBcjvsKqgfdQOKuaL+5edelC9Oihax4AdCdpmaa+bwtnFMMwM1tTLap2Y/dW362qbfS+gtADzWuJ6ax7MdqWt/YuseP4YFK+caokVPV3f8NlJU8/ysTbc15tSsrDF/fkfguZbHrUFQSlmsin47rJVryfmTtj4+s8puY2IE1DzUuPwkrx0A7A21oYiuQ2sYrC3y6FXUqjnfEOhsiq2oJ5bCO2df1WsXy8daFSzSIXiK8RDLuUdhE1CyBwFA9xNS0HRSDYS1D2q8yAB4aAGNs3wRRvt6WK/eB2sNuMz4cXVSa3sourd2D+nM7yNR40eJcgzl6CG7eXx5QChb7Gfemkb4IvcWg61zqKTbZPvCHgCnv6gaelX85lJN3va77Xk/hhH3ECFnQ4zwZqcv2FCtuqtRfhshHmdLdxG0KC0Gap8AFQ0amxpqSzX11c4iAODV7RD5p2aeqdC3D9DjVV038s4xwj0wGv+pHSl0IRy2qbxyMOw4/I6cbg3TJJIsOGX1benTshQXANYVcvyUk3M1yF1nG1LqHKe0EaadI7U5rYH1jqzHbjdo6tPTAvl9qS0WC6G2LOwBAhjHa3qN4dh7RpAOv/ujFx712x78sxuwsvWQwIQrD4d7RqCOb5qZZLQzQhyAYotpOG6cYeBJGY15oDUAY0s0PSxNlHXvJ1HjR4lyDCX+8KNEOYZy9Fj9UhJSd8AENp/mjc00eZNnPzcB8MHB8oxWdd0y5JDPS204jcykI4Gj3nqo7c4CdpjWKxdC4I2mIFNsTM9Zd4Cm/Ug/bVUck3asPCtmczMIJuln1h4GAFxZF1O/YOq3aVIuK0nniYWA43+wsZy6HoplnmE6kB1jLfMNZWvQSO1XK4bjrUD2J48Ae8bvloIZzjtME/9iYwNAGsDzamdBr14r8Ew6j27Aq+qOsVedrbVva3CW1XkMllqsPf9PE5+pT8sLcKYigUMG9RY/GXye4TUJtrqSBlQ/JxwGN11wrwYa+avPabWmdvbtDYOtzqo+Anls7zx+x2rHhOw+nbB/oZcx4fWRsaZ9tTyI6bwoUaLsL0ffLXfkUlVHRKLyxeV7IVijgK+nLHDHQn/VinAvi6Y68/nw1h/dVmiuRlRG66J9Gq8+5McQ0krmmuWhBNPaiQWuyAc1PcE5NoBHS8Fz+JmqOH7HKrKqVtCVTcBrW4N6pI5u78n5CyYFevaEaKiH5oRt57Hmdb9tSWGs5P6jBWPbWy0q9wBBQXujNLwWCJqVtfbbg6AFyW03qyk3ptqGdn8PipGxp1Sbz5rqOqbPKBZQRMruHWW+qVfEymgbeu3NvXSzDfLxnatvTRxzodhKXaMN7tE6+oMn3gYAeNOfX/HbRo5gHLmOyh9/EQBw758YWPD9l2RuD0rVZeu0zHW0FMa0L+i9Vr5EC9llis/z6JUJ8jG/Ew16l3eV9Uih7iVznIP4BLMSNX6UKMdQ7kKRjsO4avxVan/1c8a1DIQXpn+GKpTytvxn+pVw2JNfFi1WeFHAHKOtbb8tYe7EkbtPm11uhDfklrLqLqpmGPk0mGU4UfiqToQNIC0sN4B7dH+jaWsZdhzCR692F/0YAlQ22+kGn7bNPIFHm9oA4os79/ptZIilv7vdF03dNuCY1ZamyjT9Rqhpmr9NvltsyHpMlQOgaaqSBjdZUA2FjK+0Ap4bCRCmYs7BuAFTdkPDoLvTk3nfXpNjs8GIZVYqKy9hUlGuPI05FHO6snKtea4TpRBP+JN1Sd/d93GFzq4YwugCgWHj/L8BjF6SB7HKT/0+8BkBpfMCxGo/KhDq5e8I92N8j1iQhAOTXcempIsdtWjVOHQ9ZeAxKdQkcTGdFyVKlP3l6DW+S+As04iqc7pc1OoW1ltdky8XnpE34tzfiE87vB7AFRzt9ckh6EZrG+Gtfa0v/tk7q5IBoOZeHgTgTNPzwMl+BNvkt9smG0zYxljAXEF820FZlv+lzik/ZlNLW8nE4yHIO0FDbN0Sbbg9XkyNAYCyttOur2g0fkv9RbPkpbrGGDTxMZjSeISpT6KVtT0t5xjMmTSLLm1pR/1lBuqNtmENDN32ihpg1W3DhViQ8b05jb1YI0fPr13C0F3UiPelELup1eX/HkCkDS33DBw3yzVPf35tGBh9n/6kxHrO/4W2jigY5EySsR6yf5vxrpA+VzIKaza8Ic9VRT8vfsZo/HdLo9M775IbsnO/3rOG2Z/NZAlQIwNPL/yEp2q9AxvSpqZ8uGFRokT5dpL4w48S5RjK0Zv6QAqHX1C2ETYPYFDvxJeDzXLi01cBAMOb0pzBJ79SfYiyhf45JllGahshjfaKgjeqisu4pMw1BOvI/9PsNjQ6R0mwUbNkl4N9CDqBEIQqmTQWcfiViqZ/tAqsXwtzHej56re0Zt5Y4YS7l9uacntCm20MzP4XNO10VjvzloiamjxOsatppFcNqaNm4Uhd4PEy5haMdNv8CzK5mccVRGXcs+KihL/a77sfALD5YAhUdU/I/PunxTQ/e15SsLb5yJaCeiqKg2dn3by0FvkJeO/+5Uvf77dd/LcCgBpp0Ddl6h9G9FlL9ifgmThmMggui/v8VwEApz8vf5+9fB8AoHN/CPpu36tu0bwGTWfknDXTdbfoErhDUvBEjR8lyjGUI9f4buTSdNn635mX5I14+m80zfL4M37IcL83sdXqh9DwWSlvBPjolV19u2qz77IG8JZKAQzSGp/WT9GUpGyuGYhoIZNKsn9PqzXA/fMaQDClRsrqRllTPUaLlZa0nv8R0ZDX7oTE0WBTtbgGDpOCQEynr4VrLe3osZe1yePJdOoQALRnB3Yuy3xKbasjNFU5rTTd1/Ra+5iQ6WfW5T9jbQ322MN+W+vijJ5fefWWDL/CBdHMb1qSiso3zQgIa2MQLLDeUK00tQKYHtw1YKMbRVkbwnI/v/sAAGDqdwLkdvSSPms5qbrXTbLHzLNWdczoRQEQVV4MQKJTZTGhCvcKL8P6+yQgvN4NqdTePe0UI89BEjV+lCjHUI4Ysitpu+pyeCud+RvRgpW/fhYAMG6rA2nfiG/Qm7i4GUAc17YkbTfIuEhzpkjnQlm0F6GuY8+2E5aR2nuQ01DT14QrDHhX81fVQhgzrUCZlpM3PIEz0wZAc6GukGNVsS80T/ttX25IvfhWVebYn9dmGTNBq9c2RCMyNVRuaV3+imEtUl7CsvLoVXYNxFVjI/0ZOXZ9WSyIpGg44iqqxe+TIpvBlFhU/emga5h12xNaO6/lAWBpQaDTZ+rpxhyz5XA/Ls+JNUDmnh0FK9mWZEztsUjnU38hLSAf/OTTYR6H7TT5Rkn2uc6JMTAmMHrhZQDAHD/N3IuPPIg7tw73G4kaP0qUYyjxhx8lyjGUIzX1yy3g1N8Cc5950X9HOixvoOSZ9a+XiZ85TtIKZmOnJaZoN9n/Xchec6RqZqrPBucGjr3i2DU3h9BTz8EKMRu4Y/169u9L9XX/Hava6GqwhxwAVM+KGX5jTlyXmzsSxNqYDsEsT5Y5JWNLa0pGesX0g9OGGKWOzK3YD3Mc1dTFaWpw8LHQSMOPqTJNy7/1uI1wnP6irNvUkkQSp2rBnWHAbqDXSMy/JQRlIw9i/E8p9fZCJbgMF6riFv1fV94NALjv/9MeejuBOusNDep9I5I3j2y9Sc7Y0dPPI0nyUKSTEjV+lCjHUI5U4xc2W5j+7b9BCueQfdse5VvXgCjGShVNckqywtjKO2p2fsc69rZpwuE7rxaY6gsAC2p6WgGknLYdZEkrTSLO83UBErFdlBUGCXsmuLhQlmq6TlUr3qbknCcaQQuyOq/Vkf37Whm54wxIp6UVYqwGM5VzetkYaSUl6yrGts0WCSdJgqqfbGcGAFUFnxBrv2eINGe0ApC04FtaiWito7Zi82kNsL/9KUO2ua0B1NaTktZbekqCegdhbb6l5aDfR6F46AuLGj9KlGMod6E6z6X9lLvhV5GJZ2fPf1W7qfxtQ/GFL5UF8FE1r1D+n6AepvW+1jrnx7ChBRtj1mA0fqZ0ig05bENMamyobzyrICHLEUehpt8cTPrYzaJYHrQgqDEBYLGZZr4pKeR1MG2YhFpqxZDZyFRUJsqn4LR+vFQVsyAxHHOkkybk2HLMUfpducgBab6nu5NjVONXVJv3DQU3tf+i0mozLWotMFbnFVjdRi7rFL/1t4hv/83K13EdUeNHiXIM5VAa3zn3XwL4TyAA26cA/AyAMwA+DmARwJcA/MMkSXJAmzlyt9+wGSYeAJh/Xub0163LAIBLc1/cd/em1nTfX5ECGMv4cqMvvuSq1nuzsAcATirrC8eT+87u39b9qalu9iQ6P1Uydf1jMvkqc6uxGBijmNPIf0urZfb6VbO/+u+qMVnkwrZZANBRbd7elf2K02GbY5NH1fBD1eaW4TXRc/R76WaRthUXmZEL9UnHdE85B5sKXCKDEGvu7TZG93mttiHmuapYZ+QDSLo9XkQ42d1+Hu+CvKbGd86dA/ALAN6VJMmbARQB/BSAfw7gXyRJ8gCATQA/90ZONEqUKK+fHNbULwGoO+dKABoAbgP4fgCf0O2/AeDHX//pRYkS5Y2Q1zT1kyS56Zz7nwC8CqAD4E8hpv1WkiQEmd8AcG6fQ/ydkNmnxST8w+tvBgD8yMxXAAA1C85Rk7qfeV8ulUMFn6fsGs7qPpZ6W8xUpvi4zQKA2PftTj8Abuy+QOgnP9T51E2zC5JsdkZy/mltYHH/7Jof89ymVHbt7KaDgra/W78jx0m6as63zKNC+iflUvCdXE3VZdZ4H2rKzzVCXUKprvXzSuzZ7YZrJL33HW3ewUDeXC2ArtiHb0bdIJr4jzRu+TFPtaV2obF8F9LF38JyGFN/HsCPAbgXwFkATQA/eNgTOOc+6px73Dn3+ACHQxVFiRLljZXDBPc+BOCVJElWAcA597sA3g9gzjlXUq1/HsDNvJ2TJPkYgI8BwIxb+Oaaer+B4l4VLbHzVaFafuoB0RRvq4ZmFazRZzCJxI22Om+umE6VXekFIs21RLQXqZ2p8S0ohftvF0Qbs7pstRvSeQzmMbVlq9EoKx05142xVh0aqGuVNf8zci5SNLPrK2D6t7O9k+nnnu3cOtbgXIpENdMr3ulgUkcDwFjnVNA0nGWTmapKnJhU3kznWUgzO/qWdBvbc9l78MKOrD8Nr8Kc0nXvhlRu0j9cTPrbSQ7j478K4H3OuYZzzgH4IIBnAPwFgJ/UMR8B8HtvzBSjRInyesthfPwvOOc+AeDLELq7JyAa/JMAPu6c+2f63a+/kRN9XYV+ngFxjPYEBHLqS7Lt/37fewAAly4F39jW5gMBumtZdgijpd9u20SxWSWpnemTNtykxiFEd1NbahWNVcD9yOAzNOm83kihx2qVbHdN03sVtpwaDqlx5dhDo/HdnowpdtLQXQAotbINTpVe2thz7MNJlp7RtHr9OT3jm3XR6iebLb9toSrrxoIbpikZwwDSPAZAYNn5Wue8/+7ahvABVJVCHKeFb9C1w738ljVD30A5VB4/SZJ/CuCfZr6+AuA9r/uMokSJ8obL3WHZ/RaWxg3RMC89KVrjzxcf9ds+NC0FHmxzTbENGMvKATxKBExyshTKPxkT2NBy3p6J1HshK45aDG9pCCstgUFAgKT67ICBsW6phcBWWixVXTMxglc3RQv2NHI/Vk3vdk27r1VtCKJK2OUVf7DZh855YFDF/QVlnl0Qa6Y5JWvWqAbrhu20TtTlJGy+CYSW2Q2FQA/UhLBwXLbyZoNNFuRc7QR22vamfFcuawnvrFhAxeLXyaT7bSYRshslyjGU+MOPEuUYSjT1KRrwK25Immf2BbFbf//eN/sh5x8UNpeHq9KAIa9pBgN+rMSz9fw0zctaI9Cm+Wp6vVGm2YlX3QgGroAQuPP8AMbVIKEnU4WbQwX7GHegpySZ7br2mtM6+HY5zKPTkDGdTFouJVpjX2nIdU01QnXduYbyCmiTC9Ya2K69ax2Zm8Xf++tXjgIGMnu6jtbUZ3CTrg77H764dTIcSNeKbkj3lFKjL86HIZrOO05pvajxo0Q5hhI1PoV12tui8Wsb8vfKK6FhwR/Pi/avaVunpdIkK47l2APSkF0KWXr4SSYfIKT8ekU5DjXeSdPP3VJ2Z4WWAs+7rcE+C+s9rQG/UlMshjsK9rH1+L2h0nJrqq5YCFZFX9OAC6rVp8tyzilDAU6Q0YmqrCdZccomBXetIxr6Tk/U8WIlgGp43R7OrCrKdsIl8w6tAB5nu2NSmKM0999AOwWPZ0NjjsKWjB9FjR8lSpRvZ4kaPyvKw1dbF41TXw4a5qnrZwEAi1VJP713VloczRUD8ISajr72KOfdSh+9rWMWSkHTMTZwsyc+6LmqcO7Zmn0WAuU15GTKkOdn0Y+1PBgTqKrlcH9DQEqr/dAzntqzq4CZgolVEExDdtuG+vGWF4DgGs6b53+octuPOavFTdd6J1LzstfB9eN63OmGOZJdaKMvVs2tPbHO+pbth9MmBYOm9UZT4b6Wp+WYrqONQY6B5o8aP0qUYyjxhx8lyjGUaOpTFLef9MXELHWULDLE1NC7JUGgv65dAhBM3rdMhcJEmu0DH3gKJiXTTzSJadbTrAWAy1Wh82KK75k9cS+2a6F2/s11QfOxW2/TYP0Z+OMxizkdeSs6ht+RJmzddKKtmWAgEPrSyTbZn6Y2qwS7Bkd/UoN6NNV7mrq8PggIRB/kVHRew7TbZepzZSDm+0pP+AlsXQJx+7fbMqbDGgRbc9DXWgO9HNYQ9GfDXIuz4ta4LQnWRlM/SpQo35YSNX5GkoEGpXYl0FPZDbTUpbZWiK2K9n2yegZAOvB1uirYfNJs2/QTtR4DX/xcLwSQ+yJEU76rKYFDasPn9s74MdSM72pocDFTNQgAI9XqtCqmK2EMW38R8LIyEG06Xw6VhOQIeLUlQTVqeSCk7WY0jbczmKwAJFvRmgYJy1ozbwOIDzTuAEiTY1JohVztCu5+dyjrODSNPcg50B5ozYFuS0aGO6BIOm35oMEwqpoGIdNy7JK2DcNeCLZ+u0rU+FGiHEOJGp+iAB5X1iXRzJJtFlnsKAx3T+Gwm6LNXqwEiGhvWvYncMX6rVl/N68BRM1pik+pt99dfwVA2kcnH9+t4bxONby/yQ1A356Q4VZizuEBRKK5fcpvPPk4LGpdvK2Dp/amps9jAmI7K7LvbetYGzu43hV/DfXZEgAACC5JREFU/6xSYBNsBAAreo1rPbFO9gYKKx6E66AwPdrqTG7jfeTyMauZc6nHSqLGjxLlGMoxf+8ZYVRfffyCNl6o7AZNW2prcwh1d8c7ogWXK8FvpVAL2ZbNc+pDU3vTf7eyyxbcY9F+bLrxSDVkDs6WBdTzivL5tUzm4P6K+M0V1bQ8jh1D4Tyo6e8MwnWseQCPbKsUwjqQc5A+PrWybW/VNy27gBAjsMVC5MpjHOSlVrCctnuyP4t8Ruq/D8aTuqqn0fxSScFC2was1Nf7SmZgtQBK3WDJldbE4hmtSxGWh28D6VZb30YSNX6UKMdQ4g8/SpRjKNHU30fcUDvBtoOJW26RgFLx3nWt9e6EwNeKE3OZpj770wMB8FPX4Br/tulABtoqrLXXbc1CqHXntkFF5vNCd8lvI50XgUTsxEsXApisGPQ95IfBHVjtiKnf0mDaTNWcX030LcXIb3TkkxV98n9tFqL0WtNKuWWPU1W36oWOuCxrbQMgKsk29uPjWlnDmy4HqcN9R97EjGIaTzexsLG+bMg2l1f1P5MkrN+uEjV+lCjHUKLGzwrf+iP5LAxCiqrUSfRTtbkGjsYdE9TSz+2KaNhm2ZBLqqYsaSdaauMpQ8HNajZuYxqun1Ndt1SSNNhuOWjzL+zcl7qcBWXLLJl6egb1NhSie6MtTTd2euE4BMUkqj17pfCobOu4zT3R9MOh1ribph2Mj1W1SQa1cqsfUm4vrQqAaKzNOpbmAj66UWazDO2yy7J8wwREiC7351xRNg1GNJjIbrnzz8taF158NQzpTAKgvt0lavwoUY6hRI2fkWQoTmCyLdDbQj9ws5U7agVo4UeijShseynCRTtt8ZdvITS/ZIGJ9/9Vi1ufe68g2pRMPmTesYU8zQy991I5MAG9dVpafn1u/TIA4Iu3Lsi8TIZqcaqdmsdWW3z8fj/MY9iTR4NNMzeawf/mNSbDjN4w6+B6sq2jTELQhpq7Bk5bnpXrWJyTeETVwIL9GmXSdyPjvxOiS5e8VFaYcttwIOpSLTynTUi++KyMGZhCnGPg02clavwoUY6hRI2fFX37s6VWcTMUbBROiTYu74n2qmxq88r54FOOSqrVh/Jdz7R+3tBGmPR310uiRU/WwzkI9WVbbLLkXqis+zG0BmgpjEypqi/gmRMfliXAzy6f9mOuvyJAmYK2xyp22RgzLENZLYTCgCy74TrGpSQ13nkmXkzIqCpjB6eUbfdEYCuaV84+Rv6d8d/ZbINZBfr2ZROroDXAGMP/397ZhNZRRXH89zdpk9hak1QpsQm2YqkEoVa6aNGF+IG1iG5cKC66cClYRZAGVy4FUbsQQRQXIirWoiULRWOXEk1RpDaNbWhpU/uRqLWk2MbocXHv5E3SxJDGztzHnB8M8+6debn/dzJn5twz985kWf1rLtQil9ahsG4ZCBOapq70FbzK5/ErvuNUEHd8x6kgHurPRbytZ7+cmapqbguDWiauiy+pOB9v5zXlEk5LYuIvmtZyt5b+vBTC5WxseUsc1JIPcbPx7vmx8QC/TtTm7Hc1hzHl2Tz2fHIwe3JPNuNvQ2t4Ws/6FbXfMTwebqMN/xbW50bjnPnRWjjfOB4HF8V8W74bMNEa17GLY9mc96W1nRpa4sCd5SGcb2z4J65rv+vi5PQ3++aPRrPpofh4vA14caKm8VJMQE7GEN/iAKsVx2rXs5XfhId7/j0Wu0oVD/Ez/IrvOBVEZrO8GulqNSaNAheAsfn2TYwbqD/NUJ+6XfPiuNnMbpxvp0IdH0DSgJltKrTRRVKPmqE+dbvmYvBQ33EqiDu+41SQMhz/rRLaXCz1qBnqU7drLoDC+/iO45SPh/qOU0EKc3xJWyUNSToiaWdR7S4USV2S9kk6KOknSTtifbukLyUdjuu2+f5W0UhqkPS9pN5YXiupP9r8I0mzPH+6PCS1Stot6ZCkQUlb6sTOz8Vj44CkDyQ1p27rmRTi+JIagDeAh4Bu4AlJ3UW0fQVMAs+bWTewGXg6at0J9JnZOqAvllNjBzCYK78MvGZmtwK/A0+VompudgGfm9ltwAaC9qTtLGk18AywycxuBxqAx0nf1tMxs6u+AFuAL3LlHqCniLb/B+2fAQ8AQ0BHrOsAhsrWNkNnJ8FR7gV6CXPlxoDG2f4HZS/A9cBRYp4pV5+6nVcDJ4B2wiDjXuDBlG0921JUqJ8ZK2Mk1iWNpDXARqAfWGVmp+Km08CqOb5WFq8DLzD17hhWAufMLHu6RWo2XwuMAu/G7snbkpaRuJ3N7CTwCnAcOAX8AewnbVtfhif35kDScuAT4FkzO5/fZuG0nsztEEkPA2fNbH/ZWhZAI3An8KaZbSQM5Z4W1qdmZ4CYc3iUcOK6CVgGbC1V1BVQlOOfBLpy5c5YlySSlhCc/n0z2xOrz0jqiNs7gLNl6ZuFu4BHJB0DPiSE+7uAVknZnLfUbD4CjJhZfyzvJpwIUrYzwP3AUTMbNbO/gD0E+6ds68soyvG/A9bFzOdSQjJkb0FtLwhJAt4BBs3s1dymvcD2+Hk7oe+fBGbWY2adZraGYNuvzexJYB/wWNwtNc2ngROS1seq+4CDJGznyHFgs6Rr47GS6U7W1rNSYFJkG/AzMAy8WHZy4z903k0IL38EfojLNkKfuQ84DHwFtJetdQ799wC98fMtwLfAEeBjoKlsfTO03gEMRFt/CrTVg52Bl4BDwAHgPaApdVvPXHzknuNUEE/uOU4Fccd3nAriju84FcQd33EqiDu+41QQd3zHqSDu+I5TQdzxHaeC/AvW4RCxJ4UcbwAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(m)//2):\n    if i==len(m)-1:\n        continue\n    draw.ellipse((m[2*i]-1,m[2*i+1]-1,m[2*i]+1,m[2*i+1]+1),fill=255)","execution_count":198,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(a)","execution_count":199,"outputs":[{"output_type":"execute_result","execution_count":199,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929c8ae5f8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train1=X_train.astype(np.float32)/255","execution_count":200,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Ytrain=traindf.loc[:,columns]\n    \n","execution_count":201,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train1.shape","execution_count":202,"outputs":[{"output_type":"execute_result","execution_count":202,"data":{"text/plain":"(7049, 96, 96)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"indexes=Ytrain[pd.isna(Ytrain).any(axis=1)].index\n","execution_count":206,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=np.delete(X_train1,indexes,axis=0)","execution_count":208,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.shape","execution_count":209,"outputs":[{"output_type":"execute_result","execution_count":209,"data":{"text/plain":"(7000, 96, 96)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y=Ytrain.dropna(axis=0)\n","execution_count":227,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y.shape","execution_count":228,"outputs":[{"output_type":"execute_result","execution_count":228,"data":{"text/plain":"(7000, 8)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y=Y.values","execution_count":230,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y=Y.astype(np.float32)","execution_count":231,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y.shape","execution_count":232,"outputs":[{"output_type":"execute_result","execution_count":232,"data":{"text/plain":"(7000, 8)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.shape","execution_count":233,"outputs":[{"output_type":"execute_result","execution_count":233,"data":{"text/plain":"(7000, 96, 96, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=X.reshape((-1,96,96,1))","execution_count":223,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.shape","execution_count":225,"outputs":[{"output_type":"execute_result","execution_count":225,"data":{"text/plain":"(7000, 96, 96, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Making Model\nfrom keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Activation,BatchNormalization,InputLayer\nfrom keras.models import Model,Sequential","execution_count":224,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=Sequential()\nmodel.add(InputLayer((96,96,1)))\nmodel.add(Conv2D(16,kernel_size=(3,3),padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation(LeakyReLU(0.2)))\nmodel.add(MaxPooling2D())\nmodel.add(Conv2D(32,kernel_size=(3,3),padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation(LeakyReLU(0.2)))\nmodel.add(MaxPooling2D())\nmodel.add(Conv2D(32,kernel_size=(3,3),padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation(LeakyReLU(0.2)))\nmodel.add(MaxPooling2D())\nmodel.add(Conv2D(64,kernel_size=(3,3),padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation(LeakyReLU(0.2)))\nmodel.add(MaxPooling2D())\nmodel.add(Conv2D(128,kernel_size=(3,3),padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation(LeakyReLU(0.2)))\nmodel.add(MaxPooling2D())\nmodel.add(Flatten())\nmodel.add(Dense(8))                     # Mistake fixed removed softmax since we are not normalizing the probability its a regression \nmodel.compile(loss='mse',optimizer='adam')","execution_count":234,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/keras/activations.py:211: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n  identifier=identifier.__class__.__name__))\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":235,"outputs":[{"output_type":"stream","text":"_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_21 (Conv2D)           (None, 96, 96, 16)        160       \n_________________________________________________________________\nbatch_normalization_21 (Batc (None, 96, 96, 16)        64        \n_________________________________________________________________\nactivation_21 (Activation)   (None, 96, 96, 16)        0         \n_________________________________________________________________\nmax_pooling2d_21 (MaxPooling (None, 48, 48, 16)        0         \n_________________________________________________________________\nconv2d_22 (Conv2D)           (None, 48, 48, 32)        4640      \n_________________________________________________________________\nbatch_normalization_22 (Batc (None, 48, 48, 32)        128       \n_________________________________________________________________\nactivation_22 (Activation)   (None, 48, 48, 32)        0         \n_________________________________________________________________\nmax_pooling2d_22 (MaxPooling (None, 24, 24, 32)        0         \n_________________________________________________________________\nconv2d_23 (Conv2D)           (None, 24, 24, 32)        9248      \n_________________________________________________________________\nbatch_normalization_23 (Batc (None, 24, 24, 32)        128       \n_________________________________________________________________\nactivation_23 (Activation)   (None, 24, 24, 32)        0         \n_________________________________________________________________\nmax_pooling2d_23 (MaxPooling (None, 12, 12, 32)        0         \n_________________________________________________________________\nconv2d_24 (Conv2D)           (None, 12, 12, 64)        18496     \n_________________________________________________________________\nbatch_normalization_24 (Batc (None, 12, 12, 64)        256       \n_________________________________________________________________\nactivation_24 (Activation)   (None, 12, 12, 64)        0         \n_________________________________________________________________\nmax_pooling2d_24 (MaxPooling (None, 6, 6, 64)          0         \n_________________________________________________________________\nconv2d_25 (Conv2D)           (None, 6, 6, 128)         73856     \n_________________________________________________________________\nbatch_normalization_25 (Batc (None, 6, 6, 128)         512       \n_________________________________________________________________\nactivation_25 (Activation)   (None, 6, 6, 128)         0         \n_________________________________________________________________\nmax_pooling2d_25 (MaxPooling (None, 3, 3, 128)         0         \n_________________________________________________________________\nflatten_5 (Flatten)          (None, 1152)              0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 8)                 9224      \n=================================================================\nTotal params: 116,712\nTrainable params: 116,168\nNon-trainable params: 544\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(X,Y,epochs=100,batch_size=32)","execution_count":236,"outputs":[{"output_type":"stream","text":"Epoch 1/100\n7000/7000 [==============================] - 4s 545us/step - loss: 167.8203\nEpoch 2/100\n7000/7000 [==============================] - 2s 337us/step - loss: 10.9389\nEpoch 3/100\n7000/7000 [==============================] - 2s 341us/step - loss: 8.4894\nEpoch 4/100\n7000/7000 [==============================] - 2s 337us/step - loss: 7.2843\nEpoch 5/100\n7000/7000 [==============================] - 2s 336us/step - loss: 6.3998\nEpoch 6/100\n7000/7000 [==============================] - 2s 339us/step - loss: 5.8690\nEpoch 7/100\n7000/7000 [==============================] - 2s 336us/step - loss: 5.2874\nEpoch 8/100\n7000/7000 [==============================] - 2s 337us/step - loss: 4.9121\nEpoch 9/100\n7000/7000 [==============================] - 2s 336us/step - loss: 4.4281\nEpoch 10/100\n7000/7000 [==============================] - 2s 340us/step - loss: 4.1654\nEpoch 11/100\n7000/7000 [==============================] - 2s 338us/step - loss: 4.0158\nEpoch 12/100\n7000/7000 [==============================] - 2s 337us/step - loss: 3.6504\nEpoch 13/100\n7000/7000 [==============================] - 2s 338us/step - loss: 3.4144\nEpoch 14/100\n7000/7000 [==============================] - 2s 337us/step - loss: 3.2583\nEpoch 15/100\n7000/7000 [==============================] - 2s 338us/step - loss: 3.1399\nEpoch 16/100\n7000/7000 [==============================] - 2s 338us/step - loss: 2.8862\nEpoch 17/100\n7000/7000 [==============================] - 2s 337us/step - loss: 2.7575\nEpoch 18/100\n7000/7000 [==============================] - 2s 336us/step - loss: 2.7035\nEpoch 19/100\n7000/7000 [==============================] - 2s 340us/step - loss: 2.5437\nEpoch 20/100\n7000/7000 [==============================] - 2s 340us/step - loss: 2.2986\nEpoch 21/100\n7000/7000 [==============================] - 2s 348us/step - loss: 2.3179\nEpoch 22/100\n7000/7000 [==============================] - 3s 366us/step - loss: 2.2934\nEpoch 23/100\n7000/7000 [==============================] - 3s 364us/step - loss: 2.1258\nEpoch 24/100\n7000/7000 [==============================] - 3s 362us/step - loss: 2.0797\nEpoch 25/100\n7000/7000 [==============================] - 2s 351us/step - loss: 1.9942\nEpoch 26/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.9898\nEpoch 27/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.7474\nEpoch 28/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.8480\nEpoch 29/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.7770\nEpoch 30/100\n7000/7000 [==============================] - 2s 336us/step - loss: 1.6498\nEpoch 31/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.5702\nEpoch 32/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.7128\nEpoch 33/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.5578\nEpoch 34/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.6113\nEpoch 35/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.5287\nEpoch 36/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.4435\nEpoch 37/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.4228\nEpoch 38/100\n7000/7000 [==============================] - 2s 335us/step - loss: 1.4667\nEpoch 39/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.3142\nEpoch 40/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.3021\nEpoch 41/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.3150\nEpoch 42/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.3734\nEpoch 43/100\n7000/7000 [==============================] - 2s 336us/step - loss: 1.1959\nEpoch 44/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.2453\nEpoch 45/100\n7000/7000 [==============================] - 2s 336us/step - loss: 1.2605\nEpoch 46/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.1248\nEpoch 47/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.1342\nEpoch 48/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.2171\nEpoch 49/100\n7000/7000 [==============================] - 2s 339us/step - loss: 1.0996\nEpoch 50/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.1361\nEpoch 51/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.0801\nEpoch 52/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.0471\nEpoch 53/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.0855\nEpoch 54/100\n7000/7000 [==============================] - 2s 341us/step - loss: 0.9946\nEpoch 55/100\n7000/7000 [==============================] - 2s 357us/step - loss: 1.0295\nEpoch 56/100\n7000/7000 [==============================] - 3s 364us/step - loss: 1.0722\nEpoch 57/100\n7000/7000 [==============================] - 3s 367us/step - loss: 1.0654\nEpoch 58/100\n7000/7000 [==============================] - 3s 364us/step - loss: 1.0129\nEpoch 59/100\n7000/7000 [==============================] - 2s 344us/step - loss: 1.1150\nEpoch 60/100\n7000/7000 [==============================] - 2s 337us/step - loss: 1.0776\nEpoch 61/100\n7000/7000 [==============================] - 2s 339us/step - loss: 0.9283\nEpoch 62/100\n7000/7000 [==============================] - 2s 338us/step - loss: 1.0169\nEpoch 63/100\n7000/7000 [==============================] - 2s 337us/step - loss: 0.9514\nEpoch 64/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.9455\nEpoch 65/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.9440\nEpoch 66/100\n7000/7000 [==============================] - 2s 336us/step - loss: 0.9357\nEpoch 67/100\n7000/7000 [==============================] - 2s 339us/step - loss: 0.9835\nEpoch 68/100\n7000/7000 [==============================] - 2s 336us/step - loss: 1.0022\nEpoch 69/100\n7000/7000 [==============================] - 2s 340us/step - loss: 0.9427\nEpoch 70/100\n7000/7000 [==============================] - 2s 354us/step - loss: 0.8809\nEpoch 71/100\n7000/7000 [==============================] - 3s 369us/step - loss: 0.8869\nEpoch 72/100\n7000/7000 [==============================] - 3s 371us/step - loss: 0.8997\nEpoch 73/100\n7000/7000 [==============================] - 3s 369us/step - loss: 0.8884\nEpoch 74/100\n7000/7000 [==============================] - 3s 365us/step - loss: 0.9117\nEpoch 75/100\n7000/7000 [==============================] - 2s 339us/step - loss: 0.9277\nEpoch 76/100\n7000/7000 [==============================] - 2s 341us/step - loss: 0.8558\nEpoch 77/100\n7000/7000 [==============================] - 2s 340us/step - loss: 0.8549\nEpoch 78/100\n7000/7000 [==============================] - 2s 345us/step - loss: 0.8557\nEpoch 79/100\n7000/7000 [==============================] - 2s 336us/step - loss: 0.9203\nEpoch 80/100\n7000/7000 [==============================] - 2s 336us/step - loss: 0.8833\nEpoch 81/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.7998\nEpoch 82/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.8287\nEpoch 83/100\n7000/7000 [==============================] - 2s 337us/step - loss: 0.7769\nEpoch 84/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.9039\nEpoch 85/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.8078\nEpoch 86/100\n7000/7000 [==============================] - 2s 342us/step - loss: 0.8435\nEpoch 87/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.8692\nEpoch 88/100\n7000/7000 [==============================] - 2s 351us/step - loss: 0.7895\nEpoch 89/100\n7000/7000 [==============================] - 3s 363us/step - loss: 0.8126\nEpoch 90/100\n7000/7000 [==============================] - 3s 366us/step - loss: 0.8514\nEpoch 91/100\n7000/7000 [==============================] - 3s 363us/step - loss: 0.7673\nEpoch 92/100\n7000/7000 [==============================] - 2s 349us/step - loss: 0.7630\nEpoch 93/100\n7000/7000 [==============================] - 2s 338us/step - loss: 0.7500\nEpoch 94/100\n7000/7000 [==============================] - 2s 339us/step - loss: 0.7896\nEpoch 95/100\n","name":"stdout"},{"output_type":"stream","text":"7000/7000 [==============================] - 2s 337us/step - loss: 0.7167\nEpoch 96/100\n7000/7000 [==============================] - 2s 336us/step - loss: 0.7615\nEpoch 97/100\n7000/7000 [==============================] - 2s 336us/step - loss: 0.8500\nEpoch 98/100\n7000/7000 [==============================] - 2s 339us/step - loss: 0.8449\nEpoch 99/100\n7000/7000 [==============================] - 2s 337us/step - loss: 0.7392\nEpoch 100/100\n7000/7000 [==============================] - 2s 341us/step - loss: 0.7660\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtest1=X_test[0:10].astype(np.float32)/255\nXtest1=Xtest1.reshape((-1,96,96,1))","execution_count":241,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ypred=model.predict(Xtest1)","execution_count":242,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ypred","execution_count":244,"outputs":[{"output_type":"execute_result","execution_count":244,"data":{"text/plain":"array([[68.42293 , 40.648445, 31.32189 , 38.82413 , 50.606194, 55.107697,\n        49.206154, 88.355446],\n       [71.43724 , 38.558178, 28.268354, 39.335754, 52.105152, 62.139687,\n        52.447483, 91.65094 ],\n       [68.617195, 38.772655, 32.56009 , 40.464546, 50.404873, 59.25434 ,\n        50.41061 , 85.40568 ],\n       [68.77582 , 39.971745, 32.35911 , 41.228123, 50.532772, 55.12233 ,\n        51.26473 , 85.21943 ],\n       [68.823044, 38.735256, 29.800077, 40.465687, 51.874535, 56.974617,\n        53.11865 , 88.04351 ],\n       [71.07716 , 39.099857, 29.145071, 38.546062, 49.93799 , 54.977085,\n        50.559647, 90.757225],\n       [69.67876 , 34.859585, 26.867296, 36.20166 , 52.929394, 57.917606,\n        51.19121 , 85.88781 ],\n       [70.32129 , 37.610233, 31.119106, 37.5959  , 52.890495, 55.61387 ,\n        52.852123, 86.62949 ],\n       [68.21773 , 41.12201 , 33.20292 , 39.67828 , 49.906445, 60.766594,\n        48.537426, 89.014015],\n       [68.67934 , 37.846973, 31.277067, 36.47356 , 51.373528, 55.298355,\n        51.407673, 85.66772 ]], dtype=float32)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"m=ypred[0]\nxtest1=Xtest1[0].astype(np.float32)*255.0       # have to multiply image with 255 otherwise image is shown to be black\nxtest1=xtest1.reshape((96,96))","execution_count":245,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a=Image.fromarray(xtest1)\ndraw=ImageDraw.Draw(a)","execution_count":246,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(xtest1)","execution_count":247,"outputs":[{"output_type":"execute_result","execution_count":247,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b78eb00>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(m)//2):\n    if i==len(m)-1:\n        continue\n    draw.ellipse((m[2*i]-1,m[2*i+1]-1,m[2*i]+1,m[2*i+1]+1),fill=255)","execution_count":248,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(a)  # with MSE  worked good","execution_count":249,"outputs":[{"output_type":"execute_result","execution_count":249,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b764898>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"m=ypred[1]\nxtest1=Xtest1[1].astype(np.float32)*255.0       # have to multiply image with 255 otherwise image is shown to be black\nxtest1=xtest1.reshape((96,96))\na=Image.fromarray(xtest1)\ndraw=ImageDraw.Draw(a)\nplt.imshow(xtest1)","execution_count":250,"outputs":[{"output_type":"execute_result","execution_count":250,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b6b7e48>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(m)//2):\n    if i==len(m)-1:\n        continue\n    draw.ellipse((m[2*i]-1,m[2*i+1]-1,m[2*i]+1,m[2*i+1]+1),fill=255)\n    \nplt.imshow(a) ","execution_count":251,"outputs":[{"output_type":"execute_result","execution_count":251,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b691198>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztvXmsbllWH/bb55vu/IZ6r169elXdVdCdbpoOY4shoITQRiHEAStCCMuyOg5WI+zEYFkykEixkKwoSJYxfzhYJdoOiUgap0EGYWSTNJDEQml3Nd0CeoIeqmse3njfHb7pnJ0/9vqtvfY++953u6v6e6/87SVdnfuds8+ezjl7rb3Wb63lvPeoVKnSelFzvztQqVKl1VP98CtVWkOqH36lSmtI9cOvVGkNqX74lSqtIdUPv1KlNaT64VeqtIb0uj5859z3O+c+65z7nHPuZ96oTlWqVOmrS+4rBfA45wYA/gzA9wF4HsBHAfxl7/2n3rjuVapU6atBw9dx77cB+Jz3/gsA4Jz7IIAfAnDihz/c2PaT7Yvwg3jOdekRshDpbwC+4dGFa7JWNbNYyHWsKJTxA2fuj/+HstJGaxa9kxZAZ+qROr3L6rP3sn3pcze098sxvR3N0vw/92mdbKvUP47DDmPYpNcWbe82LTNokr7C1tNwHqUb5nk0i65X/l6kz8COnUMsjE3nmIfS8Adpma7QVz5rtt+N+vU0bdKdpC3X8sj3Ui6aPus59msUOuJHcbB6/5KDtuXDA2jHUnXpq2SzTdbHbF4Wt2+iPTzM3rA+vZ4P/xqA58zv5wF8e17IOfd+AO8HgPH2BXz9D/wUFjuxX4OZdOQ4jGAoH3MziyPqxqH8fKeRMuHazpeOtEyzfwwA8Jth9pa7E7223E6HObq7CG0fzGI/+YFkH5xv4m5o+dAmAKCVB6UL0DK+aXxhl9vh4R9fjG3Pz8m1DTYaDluvxLFuvzgPYzxuZezSlnm5+PI0RwvpR7y2uBAqb2bh/tGLt5BTe2E39ON8mKPFzjCpFwDazdAu53x0GMe4+cqsN+7QWfMWNuki2W6GNvzQ9coPuJCYhbibhPnjRzGY8guMt88uhK+4lQ9svheOw6NYz/gg1L3YDvUcXenvbsd3uDhI181CPNkP949vh5OD43BsprFQM1sk9c0eCfN7/HBcZcb7of+Tm2Hu/CD24+Cx8MwOroVz04f5lcc6uQC1m/Ls52GsjWnaN8Bz//gXeuMr0ev58M9E3vunADwFADsXH/fDqcdyMz69bpCW5wCH08ip2k4++FG6kLUbsfvNkayywgVca0WGMFn8iBa78kDMi8oH2RxOpSPSlvnw9YNrOPldUi8QX/TBtJNxxH60Ui4uDuH87Hwc1/ggjIkfg1ukkky4HymZD5DlOTfukfOhvv2pKdOm/ZePsd3ufxSDeZ9DLXfC/A0PZAGdZYsmgOXWKL2PgouRvgbSPngYx5ehnVDMk9vJMc1zHcxC+ZYcVm5px31pjx+zk+N8zwyyC2XGd+U9MV/FbE8W3lYWInmGzVYsNNqXc4eyIM5b6V8sw/7rQjjJXnzEBWt4yIXMfPlSVTeRPo7CcXI91jNYpNLOafR6lHsvAHjc/H5MzlWqVOkBp9fz4X8UwNudc08658YAfhTAb70x3apUqdJXk75iUd97v3TO/dcA/jWAAYB/6r3/5Gn3uM5jMPe6rweiQoPilS8sRc0iiDUj2a+pcs6I6qqw0r15X7/Bupeb3DfHPdhIxFTvw77XHQSdgTP1NLOl1BPuo0iXiLg7qfZIRWUAo+NUGedExOzGsfz0HPe0YUJGd0U2NfNCcRUcs2mffWpFFJ1dDOMZbsZHPbwb9AjcMnB+24mZM6lydJTOuS3n5KHFvb6dq9APn+sojPS63BQRfZgqRAGjVKNyjorVgRGRM4WXnjZFBqIzcjLY4ZTbrNjX5RbLSt+NPpSvyGKLjXArFRvxgzDHA9leUadk+7XcGsi40vGU+j88DMfWvBftltw3pNI2VXR/ufS69vje+98B8Duvp45KlSqtnr7qyj1L3oXVeLAwnDoz0ZFpLM2KSiVQo0dRTs2M+pVcj5phs6JSCuhEOUgz0GBulIyqIBJlkHDTxBwmdQ+MRhcw5i3TR3KExmjKLfcHgOE0/J7vWu4jHEk4zPC4b0JsqA2by9DtWKX/qnBD6MfCWDbme+F/KvWiQtRYDiiVSP9HR3HMS5Ee2MduNEnKhrGlZkTes9iJ7I3a+JIC0WUadkoHg5lV7pGb0wIg9Zl+sE41k1LJZ5RgA9GMq+RhJSi51psrIwEtdjjH0g9Kr+a5qFTF9zox57mkfUo7g2ksRG0+BsL52fa2fb/KEnOJKmS3UqU1pJVyfDiHduSS1XbjtnDvZQq0SIAeQk3LFV5OtCdvcDpjJ1XQg6ykDddLa/rQLVxoeLEXuJju4+0w8v2q5VSy33VLcojYD44xH6szTSzExEduSLOP5TDLETmLmJHmRuIQKagRezPHaMsoqKhhG2Jm7FuY4t7a6jpoBpR+cP9rJRo1cVIQ26DkEuum5Mf3wZrRiN3guUbm00oePgNLaf+MQJabBUvvlZrRBBbCPX+4H8nYIiDL6kPCNdrqKTktduOAaGIsAW8W0l7PtJ0AkeT+kZwU3cPo0JoMV2POq1Sp0puUVsvxfViN/cBoug/CKjk8DJspcpF2My5/RG9FbX5Y1vzIrFvK1bl6R0gTtfDKtagotxJDJj1w367WAkAXeUXuteTg/WVWV3Z7ScEo4TgQ/cHA7AVdR/hmqpcYGCQj6+a8LM1cuVYAQLNMG2/VAMsUqKLSgAUJcdgE3rg+q+SefnjMvvf31uS4rg+8M2WoezF9bNMjAVEW+EIdA6Ujcm6rV9GmMii4lbK4j6cFozPPnNr/5ab0lbgk08T4gBp3WmmI7DT7b+oKpPvRShD1OuxbkwIBk2t+IXUL57fjGB4hfd9OocrxK1VaQ6offqVKa0grFfVd5zGYduoAwnMA0BwF29RgGuSc4WZEL8wfCtoPYt0pmnqzbvlRBgZxVnwORzq+qMhtRVMRtzu5jwAUZxRFxIm7MdsSc9awsH4W2mjmrIj1FURTepoNQxsKNjJlcm8wS0vB28/3+uZEvT87pdsCU9Y6J4V7TPvqP5DWYxV30dsyHDpVVhpzHsFbA+LpjWg8SzvJ59oaJx+Ky9yyUJxu+vpYJZrVmkV/W8N5IdAKiApPbkcGajaOdapZciZ9HKeiOxDNiYvzobHjy/2t03g/BVIlJkcx6y7pk7Ibyh49Fl/QzReHRQVtiSrHr1RpDWnlHH90tETTGmUUuQc5jKyozY19LTMR5dniorGzAAnHme8FrQvNQAPjFUcwicJI6TJqTFRc7T2VagK0GN2KXm2UKhTo4sg9+p6Aqnh0RipxmfmK3oaGuVFSIfdT7rF18hqd+I+TGROevNGHdnYKFPFShuAYI53Q9KjAF8uNU7OVSjAJqEVcZoXDsx9JvymJqVnPwILH6TyU4ht0g5QzUnE2LChUFYAjz3x4eLLirTEAs5GY+JoUs5VIN+TQCw0cwfHFembnQuXTi/3noX2h7llMuoP46ukYF+JV2M7678Nix1cAT6VKlU6m1ZrzAKBLuQdJ4Y0ThiGx3hxi9jpOTX4WEjmXFZWcYsOs+p2s4INF2GTzrm5swQ+dlBEnl21KEFE6YeALSJCPLtM5AGZsBbNKDKohv8mFzFMgx9cgH1RLJCa/FAC0NH5Beh/3sm2f05JbEVTDwCa2De2+nFqYV0WhpYwZUIpoJERuODvXl7IYfEWBPKavBMyM70pAkkG6fw5jPQWVk/XfSgpAqgeIZmK5tixIPgvqKErzmYKN2FfbZruZSid2Lz4/n4KbKF0kgKhReYxuEXl3N/anToWlyvErVVpDWjmAx7UdvNHsqjMIuYVoyP1WDJ3lpvSiSDmT5TDc25CzNEsDlZ1n7FdhrMveOWr3ybmXO9G6MGq79D7hXhrRx/RDtdjj/hK83Eghsha4kmvcS2Umov7IJYfwv1zb4DwU2pD74t5WJIdzpkybwomthnmyn3KvyPFjGeo96EpNCPXsQuyrAlfEDXVkHHAIruLeOI8BmJyTOafT02lWjzg+29e0zVJcQJWGdP9u6kKqj9F3wLrVjtNz1rmG0sTGzfB7ME0BVoCRAkS731Hzf++hFqly/EqV1pDqh1+p0hrS6pV7yKOPpKJwuxnE5iSo4nFQylEZtzgXyoxvzrUMRa9WMdWmDSpbNiRyDv34LSiFATkb+piLOG4VNFvjpG9UxjiD1W936Dkn2wATwFLx3iISsq+94JmIyif1Hzci3RGVcsepUgowYiYDT8qOKQWT5GUouhvxkziVcdofAJg5xgmXE6qUMiayearIVNOdiYBLE59GHTa2qJZiNw+ZWA9EsT/H9bsCgEeDdRYALhTx1VvSRjTOtjp+3O8H3x0q49T3wIZWV8VfOI4O4rWNm6LI3E9FfGsC5XPU6Lqz/tZnMHdnFv0rx69UaQ3pvnB8C5BQrkvgzpK+3gb4IvDdZpFCbjsTpYcrqcZGN4o/BYEo7FOUcaYMo+oQpKPmEwP9VS/BLJGD5XTk0PTqaqOOEovt1HynMdXM8suwybkSif79lpifYBAFHzRZ3LgISoll2h1pgopDepwZcxqVSRqnMFGqpX1jX+mlF66lykE9b5VqymnTekODPJeayixH49yODtlncl6T5yCLOUBpKzGVZaa+0UHBLDlK+zg0sN4mN2NKdcMjc0rmY0hYsAEQUSkZvSZTxaztrypdGXPPBqFyyPNrnEiV41eqtIZ0nzh+Ad2SmeqWJmGBRrWhk4yssFYq0IQJ5CgWxpqBalTHYPQALZjphb7Uct6a43x/JQYyc1pmxrPACzW/uez3MHY2moJSbmj1ERorkH3c0EsaqcZl85GChNih9NiZOAnDo1SqsZJLxDfLfCxYxgBwZul4XCbBANFspftnK/kUIu/mv6mriMk2WKb/PHS/nI8dwELMioQsN8v+84iOVazX9Ckz9Wn/LEjoOHuehpg9imMugXUoPYzkWTM9l2+s5HJm/E7l+JUqrSPVD79SpTWkFSP3PJrZUhVogMl/12ThrJsIe+omkrhBrqmkab3rxAQzVEXPyd2g+WVhkkx4CT+tYqO2YbqviSAJiKdJJfZjviveaFTuje397AD7LEejuGMgUB2bbAMSb7ANeszJCbvVGKfKwVxJZ/9nCDRV6ln3iK0U669KR1gFm5ivqFBdFARNVp2N2bZLZVqTxD5In4OOz4y1yYJ1qkk2QTKmfaJCc2m2JfS/H0uCTMYyAOJcUYHK7ZDd1mgQ1zx8m9kyxJgD0uaRQSku0vsmd7reeY5t43ao5+hhScpq/PqbBjX0VqVKlU6m1frje8DN2iTkNMln2WlTBY2Ebz4muFxWXRvJRxbHjdv0susbNigVtOJx1270+zGQVZuea63JhKsZSm8JgEfqs9lyafbJQyWHsUkbovhSE5VhSh0BN9RHnqKtiQqm/ljJWdphvwgVfZQYmIHVmtq8sGpNLmHTQY0zbqzaStsPgozS/liiFKASiB0rw3pnIKFSzACauorhwbP545iHs/6cHT5CU248pxh58Y2PyTLMjTyXmVxt2woyotek9VBVcFMWCSmR8uQbkDgTu8/xBYmf8PRSBfBUqlTpFFr5Ht95H/3aYUx0srQvGT7ZpjEifDeLTpOaXVJgRjPvL32UHLh6Wu8nNd/xGs0lZtXlvmwgySrolTfftlKB9CcDx4RGOOb0tzW1RY6SmbqsOa4hIIll+mJBh4wLm4Hk6cpYX7dh9qRiMhy41LxZqtK5fj965tUMupv8T3Oi0Yc4DYGetpV453nqH3giHCzUNTfjsT82bVnuVWfNcFR/tNlztaRmRKa3KghAjZjjFMptIipNRHfVTAkFl/fcJkphFKqt9JPduh47O5w2Ma7jPahy/EqV1pDuD4DnMGI7/UKSXYjmvhTNhXseavwbcQBpzN4akmTD7vtzilr5FGRjr5GY2mt8O0ontCrMzwXWNDsf+pxwj0nKhRJwDYEuwtlyjgeYvf0gvWaBGtSe+5ZWBXNtnrbvqeU3m3x3zMi9wtXl2HX9fadChy18lNr7U7T5cfy0QPTBT+SUS2GRNsYcnXko+A2zFONA5NB0yIo6HPNcM+cYhTIbkEyXPY/WfBV8ZjlM2ZeeWQ6fSVKLS1ll0LGsRgsekdOL7mIWuflgPyiG3DJUtNhhivLY3GS/68OHT6DK8StVWkOqH36lSmtIqxf1vVexHADcjMgIAS0MJNSzMfmpAlC982gzM2INc94XkkPQN0A9tDb6mOjooRaOFL+c0QB2o1SZx9BRSSBMaZaAjyR7aQZmUe++mSkjFxUTPkR2U6RB5sFmSfO6L9MwX7ZPLBPzyRkRORONE4uhS88tN+k7YbVZcszANRaspDkE6VtuQUayHRuJyN9lfbXl1QNvnG5zbF9Z33Kc3nsvOnFbVuoHvS7pO2/GShNuKdBsnkmXor6NBaGh3eUbGE7lHhtWzpXrL1Hl+JUqrSGt3JyHxRJou/QcIldvmFhjaE1kovxgOGw17VgNSzgozNFIAxqBR5SB8x0qU+LteVBM+s7Pzve5qXLjTAGXkAb0NKfYnqZlKtwnxAgrKASOLJoKtXNp+/Em048xoa35vVaCktvIWYw00G5RC8ZJT2HC9n71iCQ3syY/9a6TMgUgU0vIcC6BmHOqXCOHLICV9B4q+8boUckMp21kHoSJkpH91kCrffOmmgrnlBLj/QoSYrBRPnrzDbgJvUdTBXVjw9CfUbFnulqpUqV1ontyfOfc4wD+FwBXENbSp7z3v+icuwjg1wA8AeAZAD/ivb91amUecMsWaM2yvRGWdi9JIsG9vdEDNFwJ1bmE+9Y+x1eTiIk4w1V+scNElFJPU7qfN7FMLKKmnYz7WPPPSb7uQFzZu2y5bSe+V4bSgDrdFMifdk0d4OXEwLKxdJPOfbc1C5LDc+4aaw7MzXelpB0sT4456z+XAaWJwjB8/jwIfCmArlwmXSVcPgMrleI1aBts0z4fCjOZdNU1JZYvPymRDfvzGSXTWJ4mOEqifOdGxwbkw+Syc1WaSFtGMt5o1HHsXnQWjr8E8He89+8C8B0A/qZz7l0AfgbAh733bwfwYfldqVKlNwHdk+N7718C8JL8f9c592kA1wD8EIDvkWK/AuAPAPz0qZW5sEK5icVmZisU0xHZ9Fbcx3C/S71AwQVRo/WYBZmcnpFWSCXIru7zVLow9yxSSCaTGliHHOU2rK+wtHZZGTuOHPhCTm214Qpfzbk6zL4yb9fsN8l98vuTuH4c/ziNDgMAzZFYPPIItDbyLPeySMsk6ampR1CdjeGQ0l9GItZ7TBepKVcX5CwuXmiPY0vHVZQyMndl21/1RxqW9CPyjLI+JnEOGVFJk8HEa0tJkkHHMk0XZqQMnvOTFDPMWJGAvPu+MLACfVl7fOfcEwC+GcBHAFyRRQEAXkbYClSqVOlNQGf+8J1zOwB+HcBPee/37TUffDeLS41z7v3Ouaedc0/P2+NSkUqVKq2YzmTOc86NED76X/Xe/4acfsU5d9V7/5Jz7iqAV0v3eu+fAvAUAJzbetT77Q3A5qxrU3k9Zos1OGeJ0sNz7ShVgliizsWKpkv1uEvbsDjn3LSW+4oD0cTH5ZKJIBJwTJ5rzpqoMi8yxbobEXs5pOchT8h8mDIKlHEq88f2M2i+evAl4BwFl6f9sUt31n8r/naTcHF4WBb5LbGLBMLYenqsIkmWIZ5qGUae5rBQJhwJAFIlawFco9bFYXoMlWd9NnNNET2a2KReA1bKQ3/HbMS20tzEZ0x1PMeQ5NzlmZgSGtqdSj6NPpQB1d6oCDzOOQfgAwA+7b3/h+bSbwF4n/z/PgC/ebYmK1WqdL/pLBz/uwD8VQB/4pz7hJz7bwH8jwD+uXPuxwB8CcCPnKlF7zXKDgDDEtKlKvHOY+IFzSdPNtCvXmG5JiNvbqI7NZ16vmoXgB65z305np3UZxV3jJ+XzXri+UYEMyPgsIypZ0iFE70NTX3tpnBKclaafewSn0F0lcMV5kXjAhhOO6CZiYKDzIcF12h/yOmHmZSBglnQtstH3JbPJ21MUqnGFYSKviLR9COTeJJ0Y/RcJIfPni8A+Ek256oJtOIe+x8u2pgBBGvl8zfbM3MuHqnjfXqoSvxJk77NLdpEUj6NzqLV/zc4+VN575laqVSp0gNFK4Xsegf4wQBuaewcXKEKcfhIhPMut0ZaT/inX5YAni4B1aQcLSbkMHtjTW6R/kaBU+b3JLHqMhivBXH0Is7yHgsOyeHA7IZpQ51apILlTgEoonqE/ppN7p1zulJe+Kbj3rJ/TWGoml++wOEYlcZn/bLVDdKyvfYQzWolPYICqhb9az1To0vP22tq3bSJMPg8KLmo7sfCm1PpSjl9UgZJmdbEcFhKmOLRXTHrSVyCUuyC3AmnMxGrG+fiu34PqpDdSpXWkFbrpOMc/KhBh8jiHKG5mYNB4rAhUXF77qfmluUW9/YEP/ThuKV4aXmZPB5dkpTwLLOVa4gTR40UeFTar+ZwYHJ+KwE46YdyBuPWq1aRzCqRAFYy7XFvb4q47x/QvbcUOzCXFJKY+dJHScWlgCTXL8MEmaXUUb3koXYfzBRemeRiOX8O1CmlJI+FpUwB8st9uO71kyi7AhrLjSwWEKXvgUhrRrpZnKO4KZGl5v1O5u8u9/hWouvGgzOz8srxK1VaQ6offqVKa0irTajRhRRa1gSBeZDL3ELy028GJI0zGi8q4xhJZ0kvO5MQIw+RbEXj3G9d8dJWUaQJD1io3//cNV7bsph/hpUutH2SiaqoaJJzg4KvuprohlQGGdOQlhfRlBjx5H4py77K/UnQzEy5mSjOThpHIoanZfOIPLa93LsOMIq2LBFH0o9MyatJK2yI6Xw7UlBk9iLoGBMufQWick3KFhzy3TAVv32ShSQdh30gfiTvtQDEmKnYvlddFlNC58dg9f2wKcZvKFHl+JUqrSGtPqHGfAk3M8v20bFckpVqJJFGrFccOauAcsjp24IyqCt4aOVx10qrPlMz0eNL0zLZCN7MX54pWmw0F/ZbpQHL8cmF2UfCPy2eqc3O0Sw46q/k6gVmwT130zKUhKzJj0SOq8rBguLL5d5tsErBtK+J5JObR5VjxkaYXquUbIPPQZOnsB/W1JYNic/MSmsnhZu2745Ki4yMZJ7nUuZIId19xK2Sb0uaVF48RauoDzJtw5pwXc7J6YV6HMWb7twmet6uJ1Dl+JUqrSGtmOMjQHPnfaSFyyLxJHqAYZpwgCmOTzPPWdOQAmY0qqtwd7MXHB+I48mRQCGFUyy3bcpk7knpk92HuiqMNkv2EMqH43IzXZVLKbTIYdTJpBABN99jA2YPOsjKFqLbkosOp/368rh+SSJJPj4me5ymfbXj0P2zmLPs/pvmSE2WkYCU5BkRHKScvw/lJg2P5dnZeIsZeItJUIfGiWshz5j+76MjU+mtUBdjOUw1bbjRL+3IPxsCp6XPvoHPeva1BO6hVCfQX63b6kNUYpLYlCI1u/2D2MZD2zgrVY5fqdIaUv3wK1VaQ1pxQg0fgm1aBYSE4fIjOkqLSHcctwP0xy+J1lpzZsYr+VtTTJzclePtqLHSPGVUSolIOEjyqEvfJOAhzWk0M9preoe1kEnYJHoOEm3YGUUmtwH09W83CwrFFASW5JzLkzFocgd7uwyVyjXdAhUyDBMJad3oo/99isYbWASh9HdJv4IB24wVsd8jOcf88KENbsdEtJWjM16ciuTk+1QwGTpmAm6YTKXgvcl3JUfemX5zboaHqckNADrxtPMLqVv962M9yM65ZWxfPRez55pkFm5T1Cd9XPyxyUM5aIrfRokqx69UaQ1p5Sm0fOPghhZdQ7OEsAu55rdNlEWayLIVOfGDV2VUH/xA5dPG7ZSzWE7b7gXNTp5YI/XNTp31h9OCdk0jpIiiyUYbclT8CaffHCbngShpzM5Luq4dcqp+Uwr2seAQzo2neVLatBlgRcGWSjNp1CKtjuNP8tqnJjY9b6aD4CBKIApIKijntOzSSgOhssFRweWONEzDrGsWZJN7nvPJlGiMorTYMXNeSkzCa7m3ppAmPAEw3BcJTuqmf36R+HysCMX/qYDNnP0Aq9wUaZMpta49omVmD42Kz7BEleNXqrSGtHLvPAwGcT+PyOn9UpZ9Hm2ZZcYZmNjSxsnLpADryzw6IPcTX2jZky02o+SxEEsIE2GWPPqccqb0t90bcw87Pgwr82g/ssHR3cBqmfhwcGCzZUpdDT205P7DofQvdoT77qjXMKu8mq3S/lhJhv3PvR2TqDLZftOCYmju0iSP9K6zbxP1Bgvu1QsegJQCqDOxCNetQXIsh3KVprhvlzFqbETESDcqMVEdYM2KfAyq3zF9pFk2i8mYREQ6Tt+ZJSW7cYGr85SNXZAnJMnjJKD0rGR+9ixCDGemyvErVVpDWr0//sYo3dPuhKW4OZAl9frtUNTAejVN9hkcEBTaacET0txsL43Oa1dxrqiqIW/Se+192neNJ9cH17ACC+BZ7EoKcO6tC/HRdN+bOSYpVwHUx59cx8Zvi1BnKUPOO41t0XLA/udabTsO9YM3EhQtDENNeim6E6vO0AQalNb6IJ2YVjrl2JZUKpA0ahbAw2emEWep17BJOxgRmXEaMmeucCMLS59NH6mhVwmyAC/W96hLdTjLUQFfXJCgokdXxtVNHynlRQlIOL2R9pqFL6KFS1Q5fqVKa0j1w69UaQ1p5eY8IPUoopzpjoKM3R0FzZEbR6UFwTzNfGJvKXuT+b6piQqmPNttWgFF7PCrLZl4ThAJEzPULFVmWdG0X1/f54CKqhIOn8Q6KepaUAwzAXOsJQ/GXHFJUbUUF8CrktBcUw9EAceM+v3Q8S9T5WJn/BQWm6nYPTqK948I5sm6n5jVXOoz0cwIXuqPY1N8MQjUslh7Kk5n58TMar0lC6I9cIKzXaZYdnMD0iE2n6cGhfei4xaMSmhz6Yjvioj8O+EFtT4tw6O2GIy0RJXjV6q0hrT68NqjQZoQ8oKGAAAgAElEQVQEYCaRdw4Dp/fiueePIotxx4GNqXJP/cGtNipvzABF0pTiamoaWgALnafIPQrcfCgKsuExPaSowep7enUaHcesyFMmQRBONSZ81IRI9ukY2Wcqd+RsuL8AVsqJHohWkcl5zEMxlzzwTksFRtPn6ND3yqhSbYN9dMlvWzc5/fggijkcG73paPIbHkRRbng7SImuJdxa+mFDtbuUU5LanTghVKCO9wlzNtKARHua7fKa1FOIPUCzoCqKjZTUSp8UnnsKtjbGObBKW5/0Tb+FjfjujG/nYX5OpsrxK1VaQ1rtHr9x6nCjpw65XAoEkXv7oSnHFTzjUMk+64Q4cEDc50b4ZZ/j8Rx1BEPhYpvXDYc5YHxAhsnp79MmdKaYEaxj43NLuzJGvyVc52KEJ8/PDZO+5eawcDEcRodMNGI4FB1/hjT5ETJruEemW1CzpPERdzK5ysUKHF9jBUiZUhKTCNJJdSjhnOy7RYKa70XuRY5PnclQpIHhfvRI4hxrSjbCv49iGT8NnWsY72F3K9RzPfqxd7thM73cC2XGt6MOanxb+vpwuDY9L8AZgyjn3BKHQ2lpYDk2JZhCKrFYkVzKYjIARkfCeJGeUpKRnr0vvpMlqhy/UqU1pNXu8RuHdmuYpvaVBIyOq/ZOwM4yIg8Q0wT1nHMsUjXbk/sCfLSfAstooWWVpqvu+LWwQWsODYehIxE5tzgSuWmE3lJX4TaFJYz6U+yWoUOqchgbpxLuReUw3+yvzbo3Fo5vwTntiBp//k4dP4A+OCdaQPr7Ts65jVakMekUHux6ZdRKQn0G4xUaPcJCwCjdQ+E4nPafx50nBfQ0D8err8XwOMtLuwCAW18XuPjmjTCv21+K3Ly5fieUvXYRAHDj3w/v13g/tnX+j2/IOYn/uB3V6XSGmdygSSgcZq2Fe8tYm7IkBUAjIPF9t6RpxjQxJ3VA8XmQ+xN+rpKUmTM3784M260cv1KlNaT64VeqtIa0Yqx+MEe4NjbbzIPs11BsFoy+n0Q5qdsK/1P0yXPYS9UnkuZvl98+w7EDMcjm5NUgSja3g7jo78R41V4Ujne/+2vC8fEg0z38sSh+jj4f5N2Db3wUQPQEBICdZw4BAIObIoqKAuzg0Sj/zs+Fc1uvMsFeOFj/8RisM4xo87oxGQqIRsXNLOgmYLZDeQbaxP9bTjXpMdwgRwW3UFlqimQgp6mYww6v9s15m9dFgWfE1qOHQ4MHbwsd2XhJojBN4rszEGDXhc+E+R/eFvuZCebqRQFI09jxpXDc/9rY1+XGJQDAxU+F59Icmq1bZgbUubMWw/x95DAKuQTVj9/i8PX/dB5t1CUNjDpLyyTRnxZtVe5VqlTpZFo9gGcQvdTCyaC1GDHG2mI/nDaQXcaqo4JIlUslLsSfBSwDV1AqoUbHcbWkCccdiILnMBzdVrTbHH7jNQDAja8XhROz1c6tjSpcGx2EDtjYbAQuaTwC+W3DbR9foRdbxs0N96ApKSariBOx2aYaTHr5dWaCBlk4bN5uFW+uwLXy+2OsPqnPcigpwzmnxGIyo2F8MxwppTDaEBDnpJlKRKKHQ2O3vm5Hy1z6aFDKjfZF4qLS1YK/toKi7nc/9Cv9gQh968/9BADgztuC4q8bxTYohehc0axmFHe9WJCe98YyavLb8MkxDDJV6qny1RbJAFU0dxJgBADNcpSAyU6jyvErVVpDWm3STB8AKTaaTCMrX7MXllAnoXC8Afq0o3R9yqORhJO8Jr8LiSxJmtjCwGAXEslkMJVQPOfDcbkbl/aZgDeGwmBoYrn7xJaWOXcr7BPHf/Q5AMDvfOb/6fdV6D/68fcDAI6uGR/zLC3V9AKTJMYyGze89Cf0f/pQrJOcZXInBSkljkBjlqXJra/zyGHOxnql3E7TfQlnG/QDCsUUXjJFFsZKkJSClUyCjO2XCY8W3cBj4Xj7HfH+ZhlMdHtfCJUS3OPPxcQSt9611+9URnRAuvuWMNfTy9bxJbQ7vkMIdv/+Xtq2kl9Ul5btDMfvRdktOfCwLPf/bNMmOj0hXViJKsevVGkN6cwfvnNu4Jz7uHPut+X3k865jzjnPuec+zXn3PhedVSqVOnBoC9H1P9JAJ8GQNnp5wH8gvf+g865fwLgxwD80lkqSnKsMRSSiHReFHmKh4fBqadWrFTxlEk5NgAlkyrkJqrO4NejksSAsJHm4MuRbvNz4Xj9G+KA5jsh3PGFz3CaThb1X/sGiUVgtiI7L4Xj5FZojN5g811rzpNxZJlobX+7bMtgg4+2GnIr2wYUwkkxP3w5j5uUyXD1gPV9EDF+nvYdAA4fT7cY49txjMOjNIbC5sv0jov3H1+msjfsI0ZHm9JGrMfO20nELdNir+/XwG3N7Lwon6fhmlXcceuo+QIphltvxQHReESWmhc2C8RJlJ/dOuVmxBJa8qxht4Azcnzn3GMA/jMAvyy/HYDvBfAhKfIrAP7S2ZutVKnS/aSzcvx/BODvAtiV3w8BuO29Jx95HsC1M9Xk02gm5BYaOHF6sk9xD3deWMxjlJ2+P34ettiukIy4s7wkXFiDXpquS3vqfw6aVGJHDgNuB9MzZC6diOeXzfpKzkxORYlosRvvo2InZpuN1zQKDi1MxAGVACf0AsuDbpr7O+LHDfty07RM7lUW7ku9yYiNt4CYBdudiDnvnMWmp2CtOD77v0/LuPQ8EL3Xvusnf1zGSmWjkaAustNyNG3q+DOvuIWVKPmORIRY+GkdM8V1z2VhukOn0hfZLfocnzTfZQwHaeO2ec/bNxCr75z7iwBe9d5/7GxV9u5/v3Puaefc04v54VdSRaVKld5gOgvH/y4AP+ic+wEAGwh7/F8EcN45NxSu/xiAF0o3e++fAvAUAOxcfNwvt5oIwAEwmAtn4H6TftxtZLUx1VK6z0m5WMrp7N48j49GLpgmSZD2ZUbUp9rs5Qjm2LgdKpgEx68kqozl/gDwnv/+J9Ajwi1F92DDSuemIcZds9wjeiL2r0WvvLTPiXSjk5T2x4JSeEMjzydpP9OVxMbjv8tJNulClmNreXq1mVt0HjJPQmuWZMepW1hs9dvkfp06DyYhTXzd+T8FykJqNpX8FCtr7h9nbJZCguHkefIRPzXx+NiupN7SSNxGEuTYKKmwDMOOa/k3iuN773/We/+Y9/4JAD8K4Pe8938FwO8D+GEp9j4Av3m2JitVqnS/6fUAeH4awAedc38fwMcBfOBMd/lM405fZJ7jXuowqisJdWUUF03HZBNzEDZJzp+kSpZLeSKNEtCC4dtUYjBAiyaTTgqADWqvVfNtfP7JBTWhxSht09alcNxU4Zu2x2kwY+XcDrK0ypbjs0zKPZGklSIXjgk5zP3ZW5Nz5dK1CC82PvfzdB6L0ZOYLlye2cKoTggKUseX/iNTyh1pfKKPyKCy9t2RPqrl5TQPMXVayt4P0wahuknSTHafe3uFgpsiWbRfTSNuYOftZHBmA/2X9eF77/8AwB/I/18A8G1fzv2VKlV6MKgi9ypVWkNaceitkL8uCWstYg4TSfixdMn4VOcKu5JIqWKimrFiGxRt1WwlR6uwynO9KxjD+AVQNM9BLYlXW24+S7YjafkSuIaSZJeJ4YkiM1fK2Yy+LCemICYTsdsrnmOsABXnky1D2kaeLdb2taRs5TNeZv7sSf523s/tgA3vLWKuKgOpHDPPSYNSaibc1Fc9lM/MgvkR0Vehy/PbIYr4VHJGhaQpk21RtI92qDmeP8uTZ+shgMeCfOjjwPd6dMi24kNbbg/LfiwFqhy/UqU1pJVy/G4EHD/ssPVKPKcZX4WjdENJoNBa5Ew4TM+lGWgTJcwJiSjCDzmeokRSDpfBgUuctqcosuEFsqW0FLkmV9ylQRmz+1m35VCZOa8YWDRTdCWKPDUFSdG2D645SYFn72O/FYZrpJvBq6JcJBfLvSdNnSolGWZFxZ2awTKlKRA5a0cvwYJZsCcdFhSAKnmJdNAZT8gmB9fQW9Fw4xNNjraNnBFb5R772KTSiZVuaJZlzIJm2c8g3W42fRPrCVQ5fqVKa0gr3+Mvt4Dpxbj8je+mDjRadhZtGYytlnNIuyIS+MP9ayEfZiTXP809mDriKIgi7X9oK/2dSB7c91NHkO3VbZkSp+uyfrCvSejqnHtalUnG4QgOKvWRZlFKOwl02GXc0/ZR9R9yZHwDk+SRQBON9lOoh/dpqwUznKbr2mQf47U8Kk2ewz6czNpnN04Buuh+HuYZ891j/MalkY58CpLqhql5EDD7dV4zyUvA+RcHoJGEZBzfNfv3zTQug72m/Rg45ElnTqLK8StVWkNaeZpsP/CYmYgxBHEoQIT7tGUUAZhWm8kVF9t9gEQe4SQhoiyZOpopo07Zm6sjTCl19Cl701zHYDl1zvVyzhk6yWspZDZpI7s/cc1s02OJ46t1glDhApPQvlEfYSUytZxIlzOLCGDgsxmUOhWz0rZKpO0KAMY+39i31DpRkm5yvUyS7uuE5xraIxCK0N++qMBrlDii9cboATgdLq0vjEOSsIqOYUOiDg9MivWDa+HaWCIrTW4ERUtnkmYmFpN7UOX4lSqtIdUPv1KlNaTVBttcAhuvOQ0hDURgiIqWinu2MjZNQ+FI00YpO6uCfKzJLgNtqBmoJOJqIEq5xSyNvI8iXVHU5/8FDzYFceRjtfer+CziY8H0qKJ2AauvHowZAKekZNRrFH8LIB01cyYJOdIyHONys2+/ojnMLftKXO23KO6s12YeQ4F4fGv6zHPOOfogGMWZVcIlZJWMTSq+lwBZ3TAT8RMA0AlNFLYczaxJ+g4AoLJazKSjo1D46FKcdCo3z38+vBDNPDQ6vxgnpJ24as6rVKnSybRSjt90wXxnfda7nAtKKG2bLZfZZRXEwBXWQlVVU1RQ/HFxzRRfCffIAS8FLy6eyxVGpeQdWl1BgRiVScKpDFfKFWXq/t0VzhHUYiK1qKSTmSOLZkX2u6DA8zmAx/7OJR7OtfVGy+DJDSWAZV855plJ1/aRUkgmgSXmPEJbM4BWImXl5jhybtuNPIajlQZ6iszMWw9xbqJClGKKaYJhsOX5LndMB1rOTTgyfRhjAYZr4TjelxRzg1DGxp3IYd6nUeX4lSqtIa08hdZy02G8H89xRVcQyaIPTOB+f3IzsLj5TkCKtCbKS+6ckCSHyAEWNGPZLakCblL9QWMSFqi5J4PMJrOYSwylPT5TJRW4+Umx8kq6hoHCWI1DUhZBSPUgyZ5WTsm8dATrWB/1LILvMKacj/tuGT8TjCRzpWZJZNQvoxBiY5ZcpsGOe2Y1IFoKdawK7Orv63XOC6xOTX3Z87Xd5QyzHza6TtTV+KR9b55LDnZKFEyE3VIo0HiLscjklox/LmnXdsLF+Xaadqzu8StVqnQi1Q+/UqU1pNWK+gNgvhexyOFkOETcNTHN/a4NJBzXZD/IzLPzsUwvfFMi2tLEl3qMJag6BmNkmG8N7BnFtSgShjLLLGRTqDNFeJUy0BKNx/ZPC72FVAoM/1O5xky4g75oS2cDVe71S8ScdTQ9mnEMs9DOqSdk2v5g1leo5qK+/rbIykyZVhTDdcvTN9WRmNdPEXylRCvq0VnA0WfjsqR5BTO/gMRnX59f7snnemXo8+9HJq/9XDzuZM4PnqQmL5bZfUZyOx7JdvdCEPXz4K5npcrxK1VaQ1q5cs8Pc8CJ/KN4ejroR3uJH6Z2inbcN2VEE5usqLDXqLBLOYLltOpjrjMiHMaETo5RaVKTUJqeKjPlWKWcpAlTk13m8WX7lnv+FRVOhWgrGog0A/f4Etgpy3prA2pq1l5KQgbcw/RR2n4BJJQrs7QNa3rMFZiFmAF6P+fKmnAzABUd0xLTY0FXbPtj+4/CXDlKZTTnkXMXvpyetGmlTs6ngq/ixY1X6GMffu88EkTig1d2tMxkP3Sg2wwvy3wvVHhWZV5OleNXqrSGtFrvPCfmELPaNcKhNB3STHzvN6Jzd7cV2I0fhVWuWdC0E6sm3JN7bBSkip5PtoWPyjXGMot+8QYgkSVOiOAOI11Q4lCoab8fPQ5pqu3546NfpudNVgIQ5Z5qBcBKTDfGssb8lCfUtNanTP/Qyw8P9BJHauy6QmIOhcUaLrzYYp2pGc5ZM9ogvdZx/2/njtJUvjcv6QEKHoQMh03IsCbYKHgrnkqsk1KW8fnn+GcPhUL/3oVboYvnb2uZFz79BABg4zWRdmm2NXqqw2unezpaqhy/UqU1pNX747t0D0WOqM4oDbNe9L1TGtFoDo/Csnn8cNzwcdXffj6rz1yLEWfD70SLy/1qySnGdh4GWOFYn3EKIXiDqbkLoJbTkn7mnL6XBARxj62RdBZWHJB+qD9+OCZ+7FlUmhhdpg9KKUX5YV/yPa2dcw6j3aR1QfQbhkNpcks5tAnHd8l9yt1trLs8vmE2nhJpBNtCUtVSRKX8GZUiPOdle+Cpwn2l92JxOdw4FnHi43/6pJZ565+Ha52kkSd8nfBeAJi+dZ7opE6jyvErVVpDqh9+pUprSCsX9V2q21PRb7wf5Dx3SDm2Lwe723cBAEffcAEA8O7/+M/12se/9Hio75mgFLSiVS6u5tju8EOKZEqTxOef4nImSyZmLPoFLF3v2kmeconYmd9HwIcB6bQU8Rke2yiaKPbHrMPSxkZpXyHX5GcyH3mZAjgnTwhiTW3c/rSbElZKQCrWBMnnMJL8b0sj/jLZx2KXFcrBmj6RXiuBt4rh1s15oP88klyI7BPngUfTDzVRZlu45B0sKElJs4uMJxCOf/qHbwMAPPaRWMHobpisdpKGmJ9eNgM5KfZAgSrHr1RpDWnlHB9ITSFMtTQ8Eo4/Ze4kA8cdMcdSWAEv/O6fAQA+8S3v0DJv/9ZnAQDPXnkrAGD3WaMEyvy1i2mUCPKRJVk5S1EBlyqISvnpFUzS2H70FUt5P1Q5k3EPqxTTCEAF3/YIKaWp6wxcgMpPC2GmUlGsqkVFVebVZjknpQe/EQoTszS+Y4NDpvWosg/AeJ+QZ5pnRTlolbb5HJfYWC+lmTyzUgjtUhIVSkyZCbb0zNU8msUASM5pLIh48cI7bgIAru3eAQB88eNfCwDYfjZi2ztJLTe7GjSrd98icSvMO7D57CgxE55GleNXqrSGtNqYey0wvuMwPIqrFOOLNXNZCkfMhBiXdprxMAjLbXsrrIxv+9+iY/8zx28JRWiaKowsX5ET/+/SHhjZPi3zuz6tvEbXKQBONGT2sO9jrmG1Zb9W2sfTnDcopJXi3i+PYIPSfjPj2MWQ05Q4TPs5py7FHlBJiWY4cv7dWIjzMBRwzOgwdpLcnw5dfk/mzIa+VrOiSGkFVyTObS9ppi2TceH0macw65J5U2MWjrJrhVeKkXes1Nf+yxBvfv/zewCAS2146LNLMSjB8aUw6UePNEl/Nm5YgNnppkxLleNXqrSGtNqYe0tgctNjZPZyhOyqI81ewGq6RR9J47fDCji8eiWU/dyzeu1rP3AeAHD3m64CAI4fSgLyhfJZWqtSlB6N0lty0dT0x6k6/DRHCVuPy8P6FjT/eUw3Pdp68oi1BQ31aZpqTfyYgXsSl1nCYCm5lLTg6qTUd5nV/+dNUnax05/Y0aFw/mMrHUndWbpsG5knJg1JJSfLTRX4w2GV9sCZFp7wXCBKUyr5ZL+BODc9cI+FlBOCfF4g6TfjZF/+WBBrBl98GQCweMc1AMDxpQhbp/utlZYB4ODx+P/ioSW6SQXwVKpU6QSqH36lSmtIKzfn5QkimAW0uxLEmsE0yEeDuYlQMpMkArMg2zYSWrgxIJ/uZvBk2no+KEhm5/b0WpuFaFavMgNYyaPQqGiXZORN78vNOPb+ko/6SQAeK0bneeDL4rwoswqhorNQAcaEiD5lfbRioorkbMOKsVSYybmheDSmkW+kjIjWTCRht1fcWqgYa0R9xk5oN9O2ki1YBs6J7fcVqqpk5aHgk6Ht2+eRgb1K/gAl811eDxWHww3B3BeSPLrdbelbuv0FgE7DaYffrWx5iO8HgOH2QiMv3Ysqx69UaQ3pTBzfOXcewC8DeDfCOvZfAfgsgF8D8ASAZwD8iPf+1r3q6gaAS5RZcqRf/pQaG6MAPBZOTzgvnd29Wd3E/NfcCbGeB7NdvZRH3lFYro2cI0o9wk59ZrICIpfIE1GUKDeHldrNkzVYsoAdIJVOWlVwkVMaZRbvX6Zn2gSHmpaht50vPBdHTl3wX49RdVJ4sC3k5X4q1ax0xP/Zfxsuvc2kqqJCVqG6qVLPJijhtV6sPMvVM+5fyiwcJ7Z/f+/5lZ6r/D8eh8ZmRtHbHMlEzBjup9APmY/5+aypSez8pQt38fLwNPfBSGfl+L8I4F95798J4BsBfBrAzwD4sPf+7QA+LL8rVar0JqB7cnzn3DkA/yGA/xIAvPdzAHPn3A8B+B4p9isA/gDAT9+rvmYJjA1QY3wnsJ3RLeHmS24c45rkmDNLILuOXioG5OMXIhUcB/DDcBrbmO+m8clOM+cxFlqXR+1FFnUFcdubp5sKN8qh5NvNc4UIPCoNZI4whS2hwmmL+eDzWIY26UbmnaPRfu8YTtmdsifOAColsNTgODWLahISw/EHsxQwY0FUKuFQxVCIoaASmL4qBceuLj2WTLC57sYCu3JzKqtJIMwnmHVtbxiXYHsYKmwPDce/G5Qk/q6Y9Y4CW29MEAaNUiRSzfxyGPzjV6KQ3XoXU8ndg87C8Z8E8BqAf+ac+7hz7pedc9sArnjvX5IyLwO4UrrZOfd+59zTzrmnl9PDM3WqUqVKX106y4c/BPAtAH7Je//NAA6RifU++KIWlxrv/VPe+/d4798z3Nh+vf2tVKnSG0BnUe49D+B57/1H5PeHED78V5xzV733LznnrgJ49V4VuS5441G8B4DhXTq+08ncpb8NeWbQpci/jPKnE3MHxaXRgbl2kXIiK5KjDX3N3cQo/Z0E7cwy2aqoa/tYyr9GYsFSiKe8KINVZuZFe5/mkzcXR4ep+DyY9pF3FFMZ0FLx7IXtRCmEOM994mf/pxP7/40//zeS+0YHlPljmaVsVRbbBZNjxkZKYcLzbcRpmYFP9NBEfxtl4woMs1DoPcy+bZ/bASIhre8DXVBkDzk0wm93PXjndUdBMT18JfweX9joVdCaUwDw3Bcu6//NtMFiWnqIfbonx/fevwzgOeccfWDfC+BTAH4LwPvk3PsA/OaZWqxUqdJ9p7MCeP4bAL/qnBsD+AKAv4awaPxz59yPAfgSgB+5VyWu8xgedQrIAQA3DSzBZcE1/Th2rdueJNecmOz81MSuHoWVzksijtGdeK1ZBtaouhKu9pbjkgtreqqsLIwCkJhwmsWsjziVMJor3dxfCnWNlPMrh88DYpZMfgXus5QglQMBcgwyk1kon45jwR2YkRx6Y0sUkPdWIE1uS/tipm0lQ7ENGkpwzmnJKfK4CEVJKgMilaQjUi/VGow0lCsCYZOopPcX21f/BjlvynaTUOnhUZiA87dOnsNuP0Samnzxup67hEsAgK1Xw7vMMPLT87GRm99wRtc8nPHD995/AsB7Cpfee+aWKlWq9MDQav3xO2B0uFToLRC5KKnbGMsxdo3mO8f0WtzjD8ySOgn3+YOweSKQBwAG85CKyAJEQtvxf3aDYYtzzgtEIBApN73ZOhXOeoAekcP1JBDzr9aTHW3d+R43/GDd4Z/jMYFJsQJGPcpTZ7m235FSksiTYhdYovmQ/aBXnZ3DGBWnf38vdVYpqo0mAknvTeaKprZccrJtZqbPYnqtjEoSWJf1x+oKIEky21k4uXEnVuDeGrzxhhJ9qnvtRji+HNVm4+PwkMbPh3d5eTkA1A6uRoW5PyN4B6iQ3UqV1pJW66TjPdzSwy3NyiSaeb8p+/DtsKlLVm1NmZUBeExcPifRebysjJAoPQAwOgr7IwJ5tDuFZa/ozMI2lCMKGyjAenMOkVgCMq2vy7T8oY28Q3Io7FuLCSBKHA2ZNphJMwt70byN05JEfuvP/UQoU9A1dLu87wTILKI+JY9nZ+vqSTx2rk6bh6+AilF68719AayTO+cUx8poS7fCw9+4EVFC7V54OLMnzwEAxlfCcfRyTKFFWlwSTv+WIEJZHdL2s4Me1Pskqhy/UqU1pPrhV6q0hrRaUb/zGBzM4aZR/vUbQfRZ7gRRnz7IzcLauDJvPPrhD41sSM8mlllGBeJgFurysh0omoTYVO5jb7cccmwzJY6tTxVOzIcWU5z3lD4lwEnP39ul54G4VWA4qgSrTyASYxAo1t1UIH73FMNzsJCtMw/1ZMstNk4R4zNzXGnOdfuQ+SUkZQio4naiZKrLFHclHH5Oud8FYLYepS3gCc+l1Iaa9WyeP/l389VwcfxiDBTLbNAM+3bz3QFZtfFo3J+d+6MQlmv0UsDmb22Eh3DnyVjm6C019FalSpVOoRWb8zya6VwVeQDQbo70mj2mN6ZLqp8wdIsxC86ysqae0b5k2T0O97XkQiXT0LL8GzgBvJF3tRcNxl4MB4UHl2afbeTcx3KYLBx2ktwhizZU4sYE05BOAxtpQgsb+oBMhpcKwJeS52H+W+efnnsFQJWeyjwJQ9/SayVwDjLpSuM/FJ5rnhIMMLBolj1pXDDjoZSVpGgLk7Xzgrznd+7GSw+lPizMFHx4zWaDfgQAsPfZoLQe3Qjm6sufiGXu3B3htem9Ta1A5fiVKq0lrZTj+8ah256gG5mVbMxcTekS6g2XVwAPl38F9MSl2dPUJwk5vLnWHJDjBxNIyzZtkxkoxpf2i9ls5ZF0knoy7g70nUFOMz/l4JzSXlX3vyWdxSlbvV4CjVJUGe7RSw4vGWflvNjo4RozMDMvFR1xCnHsCNjJx1aca22031ftYz5GqxfJ+pboTE6RWPIK9BKlFbvflkhEkztivp5Zp3+aV5fR938AAB8xSURBVAVuLQCr+fl4/4vvDf8fXb4IALj0yaDgYTJNALj4qQ6D47rHr1Sp0gm0Wq1+49BujzRiKAA05N45x7cReJTjZ+64Zh/vGim/KdjQic1mIAk5T1sMKXjIjCiMtQDr9Uy8mHMTxD1x7uYbCsq5DI6apA2n9nrYv6ZlMm6aOJyoi2g6WLuvz5Nu6D22q5RYfFoWMNrzLOlFAmrJNe4FcEzuAGNJ22A/Fv2yeT/yqEPFdvMjgNzltiuMtQf9LUiLpKW4O3fmHRxKstCNVwVKPo4vRjcKE6dQaI7ZJP8YPRo4/PL7woCeeWsA+Tz8sdiRzVfnJyGMe1Q5fqVKa0j1w69UaQ1p5Qk10Ho0xvdezXeKie4LK5pJV0R+L8CdRJzlfTQLjuLQvGwDBrnnnRXpREzME2u4QuSZ3ByXJMQQvwLFn1tTZF6e9RXMWD3ASgE/XvqtY5OT7E/iM8CYA/mWI9mypH0sKbU4D4NTRH2X++7bvk9c/1xGOQComMgiA1Il8Q2yMkWTrJpg5d0xz6x3PxWj9t3J5ohZkOmRBwDjO2GyhzeCu6YbG5P2JDTC7RgTptjw8e0yNPjQXvA+vfBNQfR//spFLbP16Q0sPl3NeZUqVTqBVm7OW24Pe4o8wAJfwj8aiw/Qla/bYcy9cGiOYpQdp+YR4bgWzjsP7G7zpbBazveCh9Nsz5gMuQSe4N0W+p/+bjIpIfQ/HCNIKI51yXRQmTeaK9x/UuSY8CNt10ou5JBUTmpiDNOPvEzMlhsH3ejzkOMpPupqVrQWqi6TNBgnwCgZFVRTiOhDrpcrDinBAICnb3+ugLTKUtadjcdKloMFIcyFNnwaban3nhRIzaTL+NAmEnHHHU175TkOxmfI/foBYLIZJuLCRuD0X7p1AUBMyQUAu99zG82vF7DIBaocv1KlNaTV7vEd0I2bZL9IZxz1uS+s/u02cagCcLhTWDW5p5f9koUFu4OwSg6uB8eIycPB3rI0UX5Oim3XFWCsp+17ScqN7VgzjtQV2uztZclxE5/9vOE+p9b+yzQkkW+y6EKlPf6AwlTBD57lTkpGGvqYcjE1lRnzZgTMpMAVAGCOyzwicQKIUukudRYqjiPru4WGnwWK3ZMYLBApc6TiHr85jhVvv5K+WH7LBEiglCg6Dzp22Xdvehwm+1Cc2R7ZC5Dfm0eaTw2Xtw4xKqK5+lQ5fqVKa0gr3+MvthoMZ2b1y6O3yHG5Gzl2IymzB3cDp9eIvNYCcJboK7LKK8zxqonkm8Ffcy1/OhApU+D8OeyzSzTd1PgLhyq45ZbSYof+xBPaRzkuDfPIY7s3mlCy0P8sInASLTh7HjaCT5eBi0YSV5ARdW3fcgCPN8KaRtkt6RjyePbL1CITzkkRRj/mb/tW55aYQtQhJkxlnEDrwhxTVktfT4sTSI2/pMSe3Iid3XpBnHKY/2ErhhvWPb4w7+VOX5TspP0bh0FanQzDhJ7fjBN6e7qJZXc2Xl45fqVKa0j1w69UaQ1ppaJ+NwDmew7YN1j9eaqgIUZ/eBDNFBqOO/PLt1h9Yp+7LQmzbYoO6LEnWUlHrwaxa3Qtyq9qZlIQhlywprbMm6zkQZYrmBJlVMF/3t4DWNEyK1Nog2RFbPZ/XIggFCtI21JsuAH5EDc+30vnBYiRf8Z3xPQ6penOaveocEs7YPvOjLGdKhvjQ9PtRJYt2Prj9xRuinFHj9QsyWd3yptvMwvnsQuKobO5HdrkdiQcN1+O9QxfuCE3srP9Z8b+L6+GAfiZ4cvz0OATF2J2XAA4XkZt6aDpep/ISVQ5fqVKa0gr9s4LJgtvkjssdsJKRtPQ+G5YEUuJKAi9hfjzu0Vkg0zAsdwJK6BN0+VZnideDamJtl49p2Vm59M0Xbl/fqhIjnmMt5LHmJwbGC7ItEe551wpZp9eo3RQCMHNui3Hp9nutDRXuYcZy1og0ViSXI6OGafQKO4ysBWVYakkkirjBprEw8wH9VK5B58ZB+eKij8rFSw2aTJMob828o0qFbPnaRWJap4c9OeO5em1SWBWYt6UMS63RALaDyfOfy6KHn4W7IpuN9jquokxJcu/Q5kPvh4XH40h4u/shyg9N4+Dcu8/v/YnAICpsY9+9NZb0ZzqghqpcvxKldaQVpxQI/xZqCz3lZu3wpI8uRlODGxizWXZVqex9xBj9+k91lc/9/UXCO/oVjSFjA4Cm2DSyVIEmgjRFW5WcCgih6IZzZqG8jTbvdTNMGZEcvUZOW+cg2bGPXU4R3/uUEF6JGebnjNRj3InJZkeu38eHYkJVUypvuC4sthJ/cjz/TAQn8Pec2Fgk5sRUTMXKWu5JXqdozhGlhvsC6eUiEqLR3a1TLMbnvnsXJOM1aoVOCbu25eUDkwZjTKsAJyTn5lKdOYcI+34jfDQNp8NFW5+9hUtwwjPfiML4of4PEd3RYK7Ge7feyzO1VJCO9/4/0LsvX/yeHDO+c53fj7W4zzcaagy2+aZSlWqVOnfKaoffqVKa0irRe65YEbpNqMotXldUE4i4ttMunofxeUsuGI3NnI4FTOyRUi2ByLau5HIdKJgcS/f0CJblwJs6u5bxBx4Sg49Ox4A5ZBeuVkQJ4vYFnVI09hkX7Y+N4K4N3o5KnpwFOxpXpKGtF/zqF46eCIogbi9oeJvaHDwFHfZj/k5iuqxCSd7HJrsrAlymSHumHRjfDe2MT6QrcI0HBeSt/D4YgwlrVmDxaq6GdPBY/M52YbJs1s+vAcAOHwkKmHV2zHzIBwa5Ry3Y0TncSuX+AxkO5RiXIKsTGKWpD/CkfjMfzL0ubtxU8s0DwXR3EvYuMFBFONHYypQw4S4ZWjs2Veir/2GeOfNLsl24gvhYX30tXfGfmx1OD7KlNQnUOX4lSqtIa2U4w8WwM5LXeo9JRyplZRANOMMj6Nyj8oP14VznaTdareyeNWAYqHdImqqGKyTihVHTnkjgiE2XgymvWMJXzw7n/qDW/KnoCSUw3DhtdFg1N+b/ZLfBRz+clMUVldsmttAI1GUMVKM9Ws4vlgK2RPNcwCw+3wYlDV5AibSEYxyVBEvJp87zahybXExSEvzc/F1YrSjVhR/N79OosxsxH5sPxeubd4MdW/cMOHSBci1vCCc/rEwD/NdA/IRiWUk8SubQpyHTvHz7LzUX5Lo6LlnFHq5556a9yb9kztfCoU3Py957ceF97Pw7gwORdpdhnlc7sn8zmMnjwSjj61wbXo1/Bzdih3ceH7QU0aeRJXjV6q0hrRyyO5sz6X7KwHlTG7L6s9tt7d5jMQUsyvmn23ZC5kVnlyzEW7kjvusWjk/ERLGHOefewkAsHU1mIsW4vdsl8aSFxvHpfW47FzBU0yhpQUrJduY7aVrcjuOftdb44dD1ZIoZHQn7hcf+mSo9ODxMFd3ngz1HD8cx3p4NVzjvn18O3Rs67U4sMktkQokwak3SVAW5wP35b79zltDp4+uGqnimXDcuEUATh8ktPOy7FdfCf23e+3ZlaALIMBrsdnnlAp4yQBVZSARzbTynpjQ1XlCkPT9TNtkW1ZyYV0XPyO6JIGG+4kRC5jEdRCeY7dtr4k+Zz/Mh/rx70T23c0l1dxR2qHOVDMb+DPppoDK8StVWks6E8d3zv1tAH8dYfn8EwB/DcBVAB8E8BCAjwH4q977wo44kh8Cx5ddoiFmhJQcBmoBOO12WO0Wu2mCTbun4/50cCdovDXpBhD3pwICYfKNZjfmsG6vB1Fj4wtBtXx4NWyiLOelPiLnMIlWOI+SayP4ZPBbcvdBYdY0Gk2BC80uio5D9CKDaRzrULj/xs2h9D+Fs1qaXhIdAR1yzB59uRX+37jRdxo6vBrKj2loKOyb+Tw3r4eJuPjJ0GcLYR7fEVDOzlDGY5Ko8JFtZU4ypyQP8SWOPSgMPKuH+3+1uhRuUeecDbYRx7H3OZFWPx/eId8yXJCpaCiOYgTy2DYm4Rwd1iY3wjwsHjHWia0wV4OXAovX76aNFQ2PUonqNLonx3fOXQPwtwC8x3v/boT8oz8K4OcB/IL3/m0AbgH4sbM1WalSpftNZxX1hwA2nXNDAFsAXgLwvQA+JNd/BcBfeuO7V6lSpa8G3VPU996/4Jz7BwCeBXAM4HcRRPvb3qsG7nkA187SYK4YUyw6seFiYloaUx2VSPT3Hh6JAq+N8qdiuo9FUWTDaw+JNBGgxEzk8MOjXv+66wF0sf3SpdAPE7RTfcILOfNImmudySoKyTLyMGFJIEwpkyepSLHyqamsNUFDl9si/gu2/9wzsr0xgCZi+yd3UrFzYExB0wvh5EjMgBakNN4P1ya302e3cSNOyPgwnKPJcOc5McUa0FW7Saw/7WixjRxynie2AGx8A2pL+33lv5rnT95Ya46Lc5yWSa7RP2FXvEcPY2cfflrCal0P5mG3J1vIZfayAxo2zrVxHjrZBtAvYvvF0Ov9rajQXV4OnVruhvs1QcdhrHt05NMw6KfQWUT9CwB+CMCTAB4FsA3g+89WPeCce79z7mnn3NPt0eG9b6hUqdJXnc6i3PsLAL7ovX8NAJxzvwHguwCcd84Nhes/BuCF0s3e+6cAPAUAm1cf90C6Sm2/Iko54VDTi4HD2ggpI+Ee49uBDZJ7WdijOxQYK8NsmxRaXHndVCKbHARn/+7IcHyJfe3noczk5VBmfPmCFiF4RKPr0IPPNNVlyiSrgFRlXpbqKYnAk0kDCu4x1ebpuax//5zSkXBT9bU3HIaBOzduUnTpA1+o1MtjEADAREAjOWBmcsuwSlFsLc6JIlLCnqt3HIz0V2hfAUyc2xI3Jjgn4/ylhC25lGWVa3mY8eQZ5IAd+b1t3vbB86+FJo5FsbwjsGT7DnKMCoiKl1SRLYfxoZhXX4x8eXhAhW7af9vXwewE+HiBzrLHfxbAdzjntlyAir0XwKcA/D6AH5Yy7wPwm2drslKlSvebzrLH/4hz7kMA/gghAt3HETj4vwTwQefc35dzH7hXXc0C2HrJY3I3bkSWwgmOL4hTiDh3bL0cbVzD/YDUIIxT96sLA/HckCWZ+/iDyM297OVpZqE/PgaRCzYSEYWRUvyzL4Z+XI5OJXNJ4aXwT/rOm73xaUkiGasuN62dmleeWCMLxXSUPDJEEKIeRH3Th6mUYtuIKaPC0Zraci5UGtNilPINcmkgct3cnGalI3UgYiqvQtIPcloF65gm8/RY1FFYbt7Q1EcJhOZBU6Ynedkxq4mQ+iXRb9wxprZzAfSlkhdDaA+tlJXCnGHjNBxLDAq5Nrob7t9IwDhSJ6VlaYoOUkDQteQS40l0Jju+9/7vAfh72ekvAPi2szVTqVKlB4lWnyYbwNFlm+VROjJNNcSWUzCFFjm97uFKThBMnjk/2VvByX0O5n5yfwH3eNmvjV/a1yKjq0HTTwceX9DSK/dgbDcL583TQbV9rkrOpNBQxoYz+2BNqFFoI4/gu8hcV4H+PpE0mPWRK6XEnCRaObSPBS7aaASccBwZDqVSgdxn9/9dpoUvWVDaJhub64ONdC+cxSlcFoA9JTfr3Lln47W+C/ONb78MANh5Pjh6TZ4NliG1HgEqBRRJOH0zDYMdHYhka0BsI4HxzrepXwnnbWTjduyK4KMSVchupUprSPXDr1RpDWnlEXjaLNZgroyg2ccqyZZbTEgXDhRvbEDOwdFSrolsOIxDUyz3INNU2aiMFBtpItsI8rCbRpPh1isSDUb8+v0ps1fKcZ6bAUvmuJMivSRKrVk6DiuaEnef3186pyGrNYuv9XYMx7YwDm2DPgcMoV1I7EGFm4bXTrLUpgq3Us47zXZbEGEVcJPlvLMu75qQg+9dIWx6nufPtkXlIrdBOy+GwhvXoxh/eDVUfnRF/BGOQwyB4Wtxm6hgM9mKOttJRo8Sz9JuOUzaBCK4p5mle67Ev8EXp6lIleNXqrSGtFKO7yCrkvFGo0mGSp88RhvQ900n9xgfxvOKwJTUsY1VptClSswrTLNlE3I0twLs0k/ThOr+Tly1Jy+EViYXHwIATC8UoKYFBZG2kUF9FVxjuQ+VNQrS6den3L9vzespwcgN21ImWqnAFUxlvTzwpv3hsU+uDQtmMCrR1INOOP3ScChtL1PA2fZVGuLBeLzlYc6L4JXcdJpl+k3uy8E6hih58N0bmhgIW8Kx+c4u9qg03ov3HwqkvBQqntxfxjoQ8167FT9Py9ltXwcm8/To0PfNySdQ5fiVKq0hrdac14V94OSO3fjKJUnGcPSwpMLaNEWy6CmM5modV2bnwyo7KcE1hdosqs7wjmUx0gZ1A6x7M8a86wSCyXhyOQw09JH/SBHro75Mr2m9xrRETqmjyLg7EBNXcE9tOR0lJ9030zxoUz7lXPAU55RBKgCl9xFctOxLFWxXI+lKn0vpqXyXlrXl+Iw5L53R0+jYMj2E1Zl4FZ0g9997PuwzGwoOjDBzJu9w3VbsK7mutEHOn0CH2QbnbB4nO49h2OyHRkfmvegub0m7oQImU7HfgOv8GwrZrVSp0r9jVD/8SpXWkFZrzhsEUWnr1SjmjG4HWXJ6Jcj2FF9tOGj1mhLRaXRQcDpWsbMvf1P0G12XFLw0+TVG0TSRbYAc/ag/NV7y81EMJdrQmtByTy8bf4BIt2ii6xtfiF7rofoK+HGK1rYNZnNV3Lp655l+ZBl1adYreQn6zGRnSZN0UNFkzHleTVSpsjJRUuo5EV/b/lYhZuRlx8xYx+lYdXyD/vPQ/HiFevKQ2XY+mdGXY+PcHT8U3w+dR2mX9SxMLIfRsWxh5V1ZmuCh4/1wP8OUje7Ie3Yn+ptsyHZg9vC21CNxF+Ym7Hnri56OJaocv1KlNaTVhtceAtOHgMP9iJHflRVr86Wwum2J0oMmNwBY7JQz4SZgEFFydAxcaK414rdPf/zo3Rbr7HF48QRMfAY2GDAx/NZkIAZjrhwlS+uUkJqm+mVyGYAKt8Tk16WSRmtdH9hfKgnVdGjqzKSJaBYzbcg1SiCUbixFL7+UO4cxZWbJgpKRZRTXn+HO7bU8BoHtY+xPKsEA8XnkYKXTlHtWoalBU7MAqUm8CPHYo6lvsSPmvW2jfBaRhX4Sy+j0ieNLFH1G8puSZSzEEOQkRi/qzLsHP0g8HE+jyvErVVpDWinHb1pgcitN7rAQH/etV8Mqx/27NYXQD5+rWZdxk1AoHIbTfjw+0A9/mHngWS5PM89YuDo5jEkkwVh16uPOfazN/UHhhEzV7o05pNTCFOIW899ZzsaysoDCi8mFE3COT88p57aZuDKJo8lANklXs/gA9lzOXax0pPveJh1AAj3OdAOWU1N/kIN8UpOf1MlrOQQ5a6/0O1QkbR6mfU/aYOw9xgkweh3u6XeflziHXwg3HT0cJVW+87OLIhVcMGIeX2dBF229HE4cXYmdvfnOYM6bSNY3Sm02TsN8z6H9w8rxK1WqdAKtPoXWRSRB/7nK6n6NiRgNZDdGniVUtL+nJKCBkEULx6WjjWruC9r02Ji0Qc5vykaO4nQ84US8PTrgSNs2JhpBKHlUHKvpzv3QC9pw9VUnp7CGjKzduMc1bWRxAZg6SqPVWlJYcF8N/pGf/6V+eaFv/+mfkDoz6ayg89D5SJpIxaOSNr49w9urUkkmSNm2qLnPocjh/uy+pi9VUJoihHv3syHTyORmnPTppRClR1OJbcWP4J2Pvxzaf1d4IH/6TEh77m5mHm2I0u/kllgHIo4Iswv+VMcxS5XjV6q0hlQ//EqV1pBWHnrLtcDIhNfOgygyYGPJU4wmnqL5aCvcMJQw3YMjG4+KPv5BzlSx1SqBKNorlrsg9ma5zfOglZZUZDdWGO23elZlIB1zX/Q1l98lz7mmPw/sP7cVreuLuqXkFEAWJoxKrMwvQO7EvUjBSmoyZMWFsoUsxPocMjOeNSvyPs4DlWzF55ElOLH5ClXELyVIyUJ0xf7Ec/p+imltLiCbbmxthtKWeJTODmInXzsMCTjefSlkbP7Bd/8xAOBbtp/RMv/27tcCAH7nU18PAJgeyf0bJibFpIWfnC3aZuX4lSqtIa3WH78FRndTxcjdt8pKfhhO7n0prGCjg7hyHV8K3Zw+lHljGQ5BjjK5zfqiU3WzL4kOJOS2k3As3qbHGhNgIccxQyQb5d4wlTjOEu7EKtVcpvjLk2+EcaQedzlwJJyU+tTUZj200vtyn3Xbb3LzeK8VC04u684AC1WJYZRKJWkCCIE+z/pmyV54cOKSNm0/0jb1fbB5LDJAFb0X7bvT81YsgHtys2KeCg4I/vBA9J2//bb48GeSl4XBRyfX40dwow0X/+8bgfM70dp+4spjWuYHHw1SwI9/y/8LAPgXz38DAOCVV89pmXY67A/mBKocv1KlNaTVcnwfVvmlgTLOz6XhtA+vSpQcA5xZSA7CbpiaW0rx5I4vS/jhReTmky8Sdyn3C3AnAecIh5/vpbDcUqSWHhcwlJv4bDQXrvZ5AodEV9FLqNk/n7efJJBgrLxxZiIrgJ30WEjM6eSiAnFssgup7Jv/h78RyrZ9qcKJ3zxNjpRuBmas6hykqcgsAIj3Zd0vxDCMyTdESrAIb7lxdJfjQY80Ko+m5OpLFcV5FKLP/oXPpLkhD6+ZZKaXwzs4FKDa+E6saGMRGp7uhTJbO0Ex9NwrMX3b743fAQD469cCx//Jr7kOAPifJ/+Blvmz56+cSQoFKsevVGktaeVafT9wiWZ3MBfAzk44Ob9IeK3hgvNMszrkPthwISZT2A5L+vVJZLW7Xzwf2vrMl0IZRtUxLrwxbhuhpv01scv2eaWEkrHT4ZDAeTOXW3LxodH851JE7nJqy0RYrnFuyVxslfuZPvKcRqdV1I8pM0zrKSWipMVC2zRl1Lko60euJQci7DRxLxaBLT6PkpWFfU2Pds4ZLanJ5tHOs96fOQbZfuTPnJYAANiTVOTDF0IijTvfFjLGtzvGZVYCE5Lzd+P46TGaLvf2F7aCTmpjFAdyKBLsq8sQx++/2PnzcOHaH2qZD/jvxs3RyYlkLFWOX6nSGlL98CtVWkNabQSeJgTRbE0gTSrsuk3aWzIHbACe4qqIQm4zyITDUd+mcvlCCJM9uhrFrFefD9jnR58TYHMnuP6uH/TT9jXrRu/aaZRH20muUXm0QZG/75VGhV/Jj73JfO2TvimoRU5QEWiDfvanuFdGTY5MKHFsTYZU+KX3FxVng5N/U6mn8QwMSIjKQPVCy5JmAFEMV9ASzYNT08YJIKvUdyFVhHYGIp/PJxW0DPgKAJsvhshO7ZWwpXz1PQImeygq+7zsE5tJmCS/HRFEnQRU2NwM544XoXPWuvry7SDi/5udtwEAnhi9BgB4cREVgDePNrHszsbLK8evVGkNyfkzxuh6Qxpz7jUAhwCur6zRN4Yu4c3XZ+DN2e/a59dHb/XeX75XoZV++ADgnHvae/+elTb6OunN2Gfgzdnv2ufVUBX1K1VaQ6offqVKa0j348N/6j60+Xrpzdhn4M3Z79rnFdDK9/iVKlW6/1RF/UqV1pBW9uE7577fOfdZ59znnHM/s6p2v1xyzj3unPt959ynnHOfdM79pJy/6Jz7P51zfy7HC/eqa9XknBs45z7unPtt+f2kc+4jMue/5pzrR2+8j+ScO++c+5Bz7jPOuU87577zTTLPf1vejT91zv3vzrmNB32uc1rJh++cGwD4xwD+UwDvAvCXnXPvWkXbXwEtAfwd7/27AHwHgL8pff0ZAB/23r8dwIfl94NGPwng0+b3zwP4Be/92wDcAvBj96VXJ9MvAvhX3vt3AvhGhL4/0PPsnLsG4G8BeI/3/t0IAdx+FA/+XKfkvf+q/wH4TgD/2vz+WQA/u4q234C+/yaA7wPwWQBX5dxVAJ+9333L+vkYwofyvQB+GwGsex3AsPQM7vcfgHMAvgjRM5nzD/o8XwPwHICLCJD33wbwnzzIc136W5Woz8kiPS/nHmhyzj0B4JsBfATAFe/9S3LpZQBX7lO3TqJ/BODvIoaqeAjAbe89EfQP2pw/CeA1AP9Mtie/7JzbxgM+z977FwD8AwDPAngJwB0AH8ODPdc9qsq9E8g5twPg1wH8lPd+317zYVl/YMwhzrm/COBV7/3H7ndfvgwaAvgWAL/kvf9mBCh3ItY/aPMMAKJz+CGEhetRANsAvv++duoroFV9+C8AeNz8fkzOPZDknBshfPS/6r3/DTn9inPuqly/CuDV+9W/An0XgB90zj0D4IMI4v4vAjjvnKNf2oM2588DeN57/xH5/SGEheBBnmcA+AsAvui9f817vwDwGwjz/yDPdY9W9eF/FMDbRfM5RlCG/NaK2v6yyDnnAHwAwKe99//QXPotAO+T/9+HsPd/IMh7/7Pe+8e8908gzO3vee//CoDfB/DDUuxB6/PLAJ5zzr1DTr0XwKfwAM+z0LMAvsM5tyXvCvv9wM51kVaoFPkBAH8G4PMA/rv7rdw4pZ/fjSBe/jGAT8jfDyDsmT8M4M8B/F8ALt7vvp7Q/+8B8Nvy/9cA+LcAPgfg/wAwud/9y/r6TQCelrn+FwAuvBnmGcDPAfgMgD8F8L8CmDzoc53/VeRepUprSFW5V6nSGlL98CtVWkOqH36lSmtI9cOvVGkNqX74lSqtIdUPv1KlNaT64VeqtIZUP/xKldaQ/n+n1nlqDXLSmgAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"traindf.info()","execution_count":252,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 7049 entries, 0 to 7048\nData columns (total 31 columns):\nleft_eye_center_x            7039 non-null float64\nleft_eye_center_y            7039 non-null float64\nright_eye_center_x           7036 non-null float64\nright_eye_center_y           7036 non-null float64\nleft_eye_inner_corner_x      2271 non-null float64\nleft_eye_inner_corner_y      2271 non-null float64\nleft_eye_outer_corner_x      2267 non-null float64\nleft_eye_outer_corner_y      2267 non-null float64\nright_eye_inner_corner_x     2268 non-null float64\nright_eye_inner_corner_y     2268 non-null float64\nright_eye_outer_corner_x     2268 non-null float64\nright_eye_outer_corner_y     2268 non-null float64\nleft_eyebrow_inner_end_x     2270 non-null float64\nleft_eyebrow_inner_end_y     2270 non-null float64\nleft_eyebrow_outer_end_x     2225 non-null float64\nleft_eyebrow_outer_end_y     2225 non-null float64\nright_eyebrow_inner_end_x    2270 non-null float64\nright_eyebrow_inner_end_y    2270 non-null float64\nright_eyebrow_outer_end_x    2236 non-null float64\nright_eyebrow_outer_end_y    2236 non-null float64\nnose_tip_x                   7049 non-null float64\nnose_tip_y                   7049 non-null float64\nmouth_left_corner_x          2269 non-null float64\nmouth_left_corner_y          2269 non-null float64\nmouth_right_corner_x         2270 non-null float64\nmouth_right_corner_y         2270 non-null float64\nmouth_center_top_lip_x       2275 non-null float64\nmouth_center_top_lip_y       2275 non-null float64\nmouth_center_bottom_lip_x    7016 non-null float64\nmouth_center_bottom_lip_y    7016 non-null float64\nImage                        7049 non-null object\ndtypes: float64(30), object(1)\nmemory usage: 1.7+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns=[i for i in traindf.columns if 'eyebrow' in i or 'corner' in i or 'top_lip' in i]","execution_count":253,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns","execution_count":254,"outputs":[{"output_type":"execute_result","execution_count":254,"data":{"text/plain":"['left_eye_inner_corner_x',\n 'left_eye_inner_corner_y',\n 'left_eye_outer_corner_x',\n 'left_eye_outer_corner_y',\n 'right_eye_inner_corner_x',\n 'right_eye_inner_corner_y',\n 'right_eye_outer_corner_x',\n 'right_eye_outer_corner_y',\n 'left_eyebrow_inner_end_x',\n 'left_eyebrow_inner_end_y',\n 'left_eyebrow_outer_end_x',\n 'left_eyebrow_outer_end_y',\n 'right_eyebrow_inner_end_x',\n 'right_eyebrow_inner_end_y',\n 'right_eyebrow_outer_end_x',\n 'right_eyebrow_outer_end_y',\n 'mouth_left_corner_x',\n 'mouth_left_corner_y',\n 'mouth_right_corner_x',\n 'mouth_right_corner_y',\n 'mouth_center_top_lip_x',\n 'mouth_center_top_lip_y']"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Eye /eyebrow /mouth classifier\nY=traindf.loc[:,columns]","execution_count":255,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"indexes=Y[pd.isna(Y).any(axis=1)].index","execution_count":257,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtrain3=np.delete(X_train1,indexes,axis=0)","execution_count":258,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtrain3.shape","execution_count":259,"outputs":[{"output_type":"execute_result","execution_count":259,"data":{"text/plain":"(2155, 96, 96)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y=Y.dropna(axis=0)","execution_count":260,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y.shape","execution_count":261,"outputs":[{"output_type":"execute_result","execution_count":261,"data":{"text/plain":"(2155, 22)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"x1=Xtrain3[1]*255\nm=Y.iloc[1,:]","execution_count":262,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(x1)","execution_count":263,"outputs":[{"output_type":"execute_result","execution_count":263,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b65f5f8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"x1=Image.fromarray(x1)\ndraw=ImageDraw.Draw(x1)\nfor i in range(len(m)//2):\n    if i==len(m)-1:\n        continue\n    draw.ellipse((m[2*i]-1,m[2*i+1]-1,m[2*i]+1,m[2*i+1]+1),fill=255)","execution_count":264,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(x1)","execution_count":265,"outputs":[{"output_type":"execute_result","execution_count":265,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929b62f6d8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtrain3=Xtrain3.astype(np.float32)","execution_count":266,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtrain3.shape","execution_count":276,"outputs":[{"output_type":"execute_result","execution_count":276,"data":{"text/plain":"(2155, 96, 96, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtrain3=Xtrain3.reshape((-1,96,96,1))","execution_count":267,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y=Y.values\nY=Y.astype(np.float32)","execution_count":273,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y.shape","execution_count":275,"outputs":[{"output_type":"execute_result","execution_count":275,"data":{"text/plain":"(2155, 22)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1=Sequential()\nmodel1.add(InputLayer((96,96,1)))\nmodel1.add(Conv2D(16,kernel_size=(3,3),padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation(LeakyReLU(0.2)))\nmodel1.add(MaxPooling2D())\nmodel1.add(Conv2D(32,kernel_size=(3,3),padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation(LeakyReLU(0.2)))\nmodel1.add(MaxPooling2D())\nmodel1.add(Conv2D(32,kernel_size=(3,3),padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation(LeakyReLU(0.2)))\nmodel1.add(MaxPooling2D())\nmodel1.add(Conv2D(64,kernel_size=(3,3),padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation(LeakyReLU(0.2)))\nmodel1.add(MaxPooling2D())\nmodel1.add(Conv2D(128,kernel_size=(3,3),padding='same'))\nmodel1.add(BatchNormalization())\nmodel1.add(Activation(LeakyReLU(0.2)))\nmodel1.add(MaxPooling2D())\nmodel1.add(Flatten())\nmodel1.add(Dense(22))                     # Mistake fixed removed softmax since we are not normalizing the probability its a regression \nmodel1.compile(loss='mse',optimizer='adam')","execution_count":277,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/keras/activations.py:211: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n  identifier=identifier.__class__.__name__))\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model1.summary()","execution_count":278,"outputs":[{"output_type":"stream","text":"_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_31 (Conv2D)           (None, 96, 96, 16)        160       \n_________________________________________________________________\nbatch_normalization_31 (Batc (None, 96, 96, 16)        64        \n_________________________________________________________________\nactivation_31 (Activation)   (None, 96, 96, 16)        0         \n_________________________________________________________________\nmax_pooling2d_31 (MaxPooling (None, 48, 48, 16)        0         \n_________________________________________________________________\nconv2d_32 (Conv2D)           (None, 48, 48, 32)        4640      \n_________________________________________________________________\nbatch_normalization_32 (Batc (None, 48, 48, 32)        128       \n_________________________________________________________________\nactivation_32 (Activation)   (None, 48, 48, 32)        0         \n_________________________________________________________________\nmax_pooling2d_32 (MaxPooling (None, 24, 24, 32)        0         \n_________________________________________________________________\nconv2d_33 (Conv2D)           (None, 24, 24, 32)        9248      \n_________________________________________________________________\nbatch_normalization_33 (Batc (None, 24, 24, 32)        128       \n_________________________________________________________________\nactivation_33 (Activation)   (None, 24, 24, 32)        0         \n_________________________________________________________________\nmax_pooling2d_33 (MaxPooling (None, 12, 12, 32)        0         \n_________________________________________________________________\nconv2d_34 (Conv2D)           (None, 12, 12, 64)        18496     \n_________________________________________________________________\nbatch_normalization_34 (Batc (None, 12, 12, 64)        256       \n_________________________________________________________________\nactivation_34 (Activation)   (None, 12, 12, 64)        0         \n_________________________________________________________________\nmax_pooling2d_34 (MaxPooling (None, 6, 6, 64)          0         \n_________________________________________________________________\nconv2d_35 (Conv2D)           (None, 6, 6, 128)         73856     \n_________________________________________________________________\nbatch_normalization_35 (Batc (None, 6, 6, 128)         512       \n_________________________________________________________________\nactivation_35 (Activation)   (None, 6, 6, 128)         0         \n_________________________________________________________________\nmax_pooling2d_35 (MaxPooling (None, 3, 3, 128)         0         \n_________________________________________________________________\nflatten_7 (Flatten)          (None, 1152)              0         \n_________________________________________________________________\ndense_7 (Dense)              (None, 22)                25366     \n=================================================================\nTotal params: 132,854\nTrainable params: 132,310\nNon-trainable params: 544\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model1.fit(Xtrain3,Y,epochs=100,batch_size=32)","execution_count":280,"outputs":[{"output_type":"stream","text":"Epoch 1/100\n2155/2155 [==============================] - 1s 392us/step - loss: 0.2892\nEpoch 2/100\n2155/2155 [==============================] - 1s 339us/step - loss: 0.2581\nEpoch 3/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2831\nEpoch 4/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.3040\nEpoch 5/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.3325\nEpoch 6/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.2836\nEpoch 7/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2442\nEpoch 8/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.2675\nEpoch 9/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.3188\nEpoch 10/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.3344\nEpoch 11/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.2934\nEpoch 12/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2562\nEpoch 13/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.2797\nEpoch 14/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2922\nEpoch 15/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2457\nEpoch 16/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.2246\nEpoch 17/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2313\nEpoch 18/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2320\nEpoch 19/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.2728\nEpoch 20/100\n2155/2155 [==============================] - 1s 352us/step - loss: 0.2624\nEpoch 21/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2457\nEpoch 22/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2280\nEpoch 23/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2181\nEpoch 24/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.1848\nEpoch 25/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.1908\nEpoch 26/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2392\nEpoch 27/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2277\nEpoch 28/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.2472\nEpoch 29/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2068\nEpoch 30/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2098\nEpoch 31/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.2394\nEpoch 32/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.2428\nEpoch 33/100\n2155/2155 [==============================] - 1s 340us/step - loss: 0.2215\nEpoch 34/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.2070\nEpoch 35/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2052\nEpoch 36/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2431\nEpoch 37/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2107\nEpoch 38/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.2432\nEpoch 39/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1957\nEpoch 40/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2346\nEpoch 41/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.3313\nEpoch 42/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2395\nEpoch 43/100\n2155/2155 [==============================] - 1s 346us/step - loss: 0.1869\nEpoch 44/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.2153\nEpoch 45/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1925\nEpoch 46/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.1655\nEpoch 47/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2221\nEpoch 48/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2028\nEpoch 49/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.1739\nEpoch 50/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.1922\nEpoch 51/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1864\nEpoch 52/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1836\nEpoch 53/100\n2155/2155 [==============================] - 1s 339us/step - loss: 0.1762\nEpoch 54/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2178\nEpoch 55/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.1641\nEpoch 56/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1599\nEpoch 57/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2189\nEpoch 58/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1759\nEpoch 59/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1981\nEpoch 60/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1940\nEpoch 61/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1814\nEpoch 62/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1698\nEpoch 63/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1542\nEpoch 64/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.2137\nEpoch 65/100\n2155/2155 [==============================] - 1s 346us/step - loss: 0.1546\nEpoch 66/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.1505\nEpoch 67/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.1732\nEpoch 68/100\n2155/2155 [==============================] - 1s 342us/step - loss: 0.1851\nEpoch 69/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.1706\nEpoch 70/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2240\nEpoch 71/100\n2155/2155 [==============================] - 1s 345us/step - loss: 0.1826\nEpoch 72/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.2330\nEpoch 73/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.2062\nEpoch 74/100\n2155/2155 [==============================] - 1s 341us/step - loss: 0.1612\nEpoch 75/100\n2155/2155 [==============================] - 1s 346us/step - loss: 0.1313\nEpoch 76/100\n2155/2155 [==============================] - 1s 346us/step - loss: 0.1318\nEpoch 77/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1699\nEpoch 78/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1984\nEpoch 79/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1907\nEpoch 80/100\n2155/2155 [==============================] - 1s 344us/step - loss: 0.1567\nEpoch 81/100\n2155/2155 [==============================] - 1s 346us/step - loss: 0.1402\nEpoch 82/100\n2155/2155 [==============================] - 1s 349us/step - loss: 0.1300\nEpoch 83/100\n2155/2155 [==============================] - 1s 377us/step - loss: 0.1354\nEpoch 84/100\n2155/2155 [==============================] - 1s 369us/step - loss: 0.1749\nEpoch 85/100\n2155/2155 [==============================] - 1s 374us/step - loss: 0.1644\nEpoch 86/100\n2155/2155 [==============================] - 1s 371us/step - loss: 0.1772\nEpoch 87/100\n2155/2155 [==============================] - 1s 374us/step - loss: 0.1771\nEpoch 88/100\n2155/2155 [==============================] - 1s 374us/step - loss: 0.1690\nEpoch 89/100\n2155/2155 [==============================] - 1s 371us/step - loss: 0.1348\nEpoch 90/100\n2155/2155 [==============================] - 1s 371us/step - loss: 0.1597\nEpoch 91/100\n2155/2155 [==============================] - 1s 375us/step - loss: 0.1698\nEpoch 92/100\n2155/2155 [==============================] - 1s 376us/step - loss: 0.1864\nEpoch 93/100\n2155/2155 [==============================] - 1s 371us/step - loss: 0.1614\nEpoch 94/100\n2155/2155 [==============================] - 1s 373us/step - loss: 0.1413\nEpoch 95/100\n","name":"stdout"},{"output_type":"stream","text":"2155/2155 [==============================] - 1s 358us/step - loss: 0.1592\nEpoch 96/100\n2155/2155 [==============================] - 1s 380us/step - loss: 0.1909\nEpoch 97/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1568\nEpoch 98/100\n2155/2155 [==============================] - 1s 348us/step - loss: 0.1472\nEpoch 99/100\n2155/2155 [==============================] - 1s 343us/step - loss: 0.1330\nEpoch 100/100\n2155/2155 [==============================] - 1s 390us/step - loss: 0.1327\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Xtest1.shape","execution_count":281,"outputs":[{"output_type":"execute_result","execution_count":281,"data":{"text/plain":"(10, 96, 96, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ypred=model1.predict(Xtest1)","execution_count":282,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x=Xtest1[5].reshape((96,96))*255","execution_count":283,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(x)","execution_count":284,"outputs":[{"output_type":"execute_result","execution_count":284,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f92952c0320>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"m=ypred[5]","execution_count":285,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"m","execution_count":286,"outputs":[{"output_type":"execute_result","execution_count":286,"data":{"text/plain":"array([59.761623, 35.88294 , 76.583   , 37.20068 , 37.089962, 36.58521 ,\n       20.645063, 38.518692, 56.17257 , 29.98824 , 81.05498 , 28.374134,\n       39.587017, 29.91998 , 14.599173, 30.877968, 65.07676 , 74.86875 ,\n       32.320324, 76.66035 , 48.85681 , 71.412415], dtype=float32)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x1=Image.fromarray(x)","execution_count":287,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"draw=ImageDraw.Draw(x1)","execution_count":288,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(m)//2):\n    if i==len(m)-1:\n        continue\n    draw.ellipse((m[2*i]-1,m[2*i+1]-1,m[2*i]+1,m[2*i+1]+1),fill=255)\n    \nplt.imshow(x1) ","execution_count":289,"outputs":[{"output_type":"execute_result","execution_count":289,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f929527ff28>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df=pd.read_csv('../input/IdLookupTable.csv')","execution_count":292,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df","execution_count":295,"outputs":[{"output_type":"execute_result","execution_count":295,"data":{"text/plain":"       RowId  ImageId                FeatureName  Location\n0          1        1          left_eye_center_x       NaN\n1          2        1          left_eye_center_y       NaN\n2          3        1         right_eye_center_x       NaN\n3          4        1         right_eye_center_y       NaN\n4          5        1    left_eye_inner_corner_x       NaN\n5          6        1    left_eye_inner_corner_y       NaN\n6          7        1    left_eye_outer_corner_x       NaN\n7          8        1    left_eye_outer_corner_y       NaN\n8          9        1   right_eye_inner_corner_x       NaN\n9         10        1   right_eye_inner_corner_y       NaN\n10        11        1   right_eye_outer_corner_x       NaN\n11        12        1   right_eye_outer_corner_y       NaN\n12        13        1   left_eyebrow_inner_end_x       NaN\n13        14        1   left_eyebrow_inner_end_y       NaN\n14        15        1   left_eyebrow_outer_end_x       NaN\n15        16        1   left_eyebrow_outer_end_y       NaN\n16        17        1  right_eyebrow_inner_end_x       NaN\n17        18        1  right_eyebrow_inner_end_y       NaN\n18        19        1  right_eyebrow_outer_end_x       NaN\n19        20        1  right_eyebrow_outer_end_y       NaN\n20        21        1                 nose_tip_x       NaN\n21        22        1                 nose_tip_y       NaN\n22        23        1        mouth_left_corner_x       NaN\n23        24        1        mouth_left_corner_y       NaN\n24        25        1       mouth_right_corner_x       NaN\n25        26        1       mouth_right_corner_y       NaN\n26        27        1     mouth_center_top_lip_x       NaN\n27        28        1     mouth_center_top_lip_y       NaN\n28        29        1  mouth_center_bottom_lip_x       NaN\n29        30        1  mouth_center_bottom_lip_y       NaN\n...      ...      ...                        ...       ...\n27094  27095     1780         right_eye_center_x       NaN\n27095  27096     1780         right_eye_center_y       NaN\n27096  27097     1780                 nose_tip_x       NaN\n27097  27098     1780                 nose_tip_y       NaN\n27098  27099     1780  mouth_center_bottom_lip_x       NaN\n27099  27100     1780  mouth_center_bottom_lip_y       NaN\n27100  27101     1781          left_eye_center_x       NaN\n27101  27102     1781          left_eye_center_y       NaN\n27102  27103     1781         right_eye_center_x       NaN\n27103  27104     1781         right_eye_center_y       NaN\n27104  27105     1781                 nose_tip_x       NaN\n27105  27106     1781                 nose_tip_y       NaN\n27106  27107     1781  mouth_center_bottom_lip_x       NaN\n27107  27108     1781  mouth_center_bottom_lip_y       NaN\n27108  27109     1782          left_eye_center_x       NaN\n27109  27110     1782          left_eye_center_y       NaN\n27110  27111     1782         right_eye_center_x       NaN\n27111  27112     1782         right_eye_center_y       NaN\n27112  27113     1782                 nose_tip_x       NaN\n27113  27114     1782                 nose_tip_y       NaN\n27114  27115     1782  mouth_center_bottom_lip_x       NaN\n27115  27116     1782  mouth_center_bottom_lip_y       NaN\n27116  27117     1783          left_eye_center_x       NaN\n27117  27118     1783          left_eye_center_y       NaN\n27118  27119     1783         right_eye_center_x       NaN\n27119  27120     1783         right_eye_center_y       NaN\n27120  27121     1783                 nose_tip_x       NaN\n27121  27122     1783                 nose_tip_y       NaN\n27122  27123     1783  mouth_center_bottom_lip_x       NaN\n27123  27124     1783  mouth_center_bottom_lip_y       NaN\n\n[27124 rows x 4 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RowId</th>\n      <th>ImageId</th>\n      <th>FeatureName</th>\n      <th>Location</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>left_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>left_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>right_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>right_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1</td>\n      <td>left_eye_inner_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>1</td>\n      <td>left_eye_inner_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>1</td>\n      <td>left_eye_outer_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>1</td>\n      <td>left_eye_outer_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>1</td>\n      <td>right_eye_inner_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>1</td>\n      <td>right_eye_inner_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>1</td>\n      <td>right_eye_outer_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>1</td>\n      <td>right_eye_outer_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13</td>\n      <td>1</td>\n      <td>left_eyebrow_inner_end_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>1</td>\n      <td>left_eyebrow_inner_end_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>1</td>\n      <td>left_eyebrow_outer_end_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>1</td>\n      <td>left_eyebrow_outer_end_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>1</td>\n      <td>right_eyebrow_inner_end_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>1</td>\n      <td>right_eyebrow_inner_end_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19</td>\n      <td>1</td>\n      <td>right_eyebrow_outer_end_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20</td>\n      <td>1</td>\n      <td>right_eyebrow_outer_end_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21</td>\n      <td>1</td>\n      <td>nose_tip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>22</td>\n      <td>1</td>\n      <td>nose_tip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>23</td>\n      <td>1</td>\n      <td>mouth_left_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>24</td>\n      <td>1</td>\n      <td>mouth_left_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>25</td>\n      <td>1</td>\n      <td>mouth_right_corner_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>26</td>\n      <td>1</td>\n      <td>mouth_right_corner_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>27</td>\n      <td>1</td>\n      <td>mouth_center_top_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28</td>\n      <td>1</td>\n      <td>mouth_center_top_lip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29</td>\n      <td>1</td>\n      <td>mouth_center_bottom_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>30</td>\n      <td>1</td>\n      <td>mouth_center_bottom_lip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>27094</th>\n      <td>27095</td>\n      <td>1780</td>\n      <td>right_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27095</th>\n      <td>27096</td>\n      <td>1780</td>\n      <td>right_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27096</th>\n      <td>27097</td>\n      <td>1780</td>\n      <td>nose_tip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27097</th>\n      <td>27098</td>\n      <td>1780</td>\n      <td>nose_tip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27098</th>\n      <td>27099</td>\n      <td>1780</td>\n      <td>mouth_center_bottom_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27099</th>\n      <td>27100</td>\n      <td>1780</td>\n      <td>mouth_center_bottom_lip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27100</th>\n      <td>27101</td>\n      <td>1781</td>\n      <td>left_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27101</th>\n      <td>27102</td>\n      <td>1781</td>\n      <td>left_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27102</th>\n      <td>27103</td>\n      <td>1781</td>\n      <td>right_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27103</th>\n      <td>27104</td>\n      <td>1781</td>\n      <td>right_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27104</th>\n      <td>27105</td>\n      <td>1781</td>\n      <td>nose_tip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27105</th>\n      <td>27106</td>\n      <td>1781</td>\n      <td>nose_tip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27106</th>\n      <td>27107</td>\n      <td>1781</td>\n      <td>mouth_center_bottom_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27107</th>\n      <td>27108</td>\n      <td>1781</td>\n      <td>mouth_center_bottom_lip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27108</th>\n      <td>27109</td>\n      <td>1782</td>\n      <td>left_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27109</th>\n      <td>27110</td>\n      <td>1782</td>\n      <td>left_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27110</th>\n      <td>27111</td>\n      <td>1782</td>\n      <td>right_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27111</th>\n      <td>27112</td>\n      <td>1782</td>\n      <td>right_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27112</th>\n      <td>27113</td>\n      <td>1782</td>\n      <td>nose_tip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27113</th>\n      <td>27114</td>\n      <td>1782</td>\n      <td>nose_tip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27114</th>\n      <td>27115</td>\n      <td>1782</td>\n      <td>mouth_center_bottom_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27115</th>\n      <td>27116</td>\n      <td>1782</td>\n      <td>mouth_center_bottom_lip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27116</th>\n      <td>27117</td>\n      <td>1783</td>\n      <td>left_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27117</th>\n      <td>27118</td>\n      <td>1783</td>\n      <td>left_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27118</th>\n      <td>27119</td>\n      <td>1783</td>\n      <td>right_eye_center_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27119</th>\n      <td>27120</td>\n      <td>1783</td>\n      <td>right_eye_center_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27120</th>\n      <td>27121</td>\n      <td>1783</td>\n      <td>nose_tip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27121</th>\n      <td>27122</td>\n      <td>1783</td>\n      <td>nose_tip_y</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27122</th>\n      <td>27123</td>\n      <td>1783</td>\n      <td>mouth_center_bottom_lip_x</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27123</th>\n      <td>27124</td>\n      <td>1783</td>\n      <td>mouth_center_bottom_lip_y</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>27124 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"traindf.columns","execution_count":296,"outputs":[{"output_type":"execute_result","execution_count":296,"data":{"text/plain":"Index(['left_eye_center_x', 'left_eye_center_y', 'right_eye_center_x',\n       'right_eye_center_y', 'left_eye_inner_corner_x',\n       'left_eye_inner_corner_y', 'left_eye_outer_corner_x',\n       'left_eye_outer_corner_y', 'right_eye_inner_corner_x',\n       'right_eye_inner_corner_y', 'right_eye_outer_corner_x',\n       'right_eye_outer_corner_y', 'left_eyebrow_inner_end_x',\n       'left_eyebrow_inner_end_y', 'left_eyebrow_outer_end_x',\n       'left_eyebrow_outer_end_y', 'right_eyebrow_inner_end_x',\n       'right_eyebrow_inner_end_y', 'right_eyebrow_outer_end_x',\n       'right_eyebrow_outer_end_y', 'nose_tip_x', 'nose_tip_y',\n       'mouth_left_corner_x', 'mouth_left_corner_y', 'mouth_right_corner_x',\n       'mouth_right_corner_y', 'mouth_center_top_lip_x',\n       'mouth_center_top_lip_y', 'mouth_center_bottom_lip_x',\n       'mouth_center_bottom_lip_y', 'Image'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}