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urban_mask_detection
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urban_mask_detection
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Algorithme détection tâche urbaine 0.1\n",
"\n",
"Trop instable pour le moment:\n",
"à faire:\n",
"- Accès à des données plus propres; plus complètes pour les données temporelles\n",
"\n",
"Input:\n",
"* Liste d'images à la même échelle de la même ville. Le nom des images doit pouvoir être classé dans l'ordre temporel\n",
"* Un masque de la tâche urbaine pour la plus vieille image\n",
"\n",
"Output:\n",
"* Masque de la tâche urbaine pour les n autres photos aériennes\n",
"\n",
"Limites:\n",
"* Ne marche que dans le cas où la couleur de la tâche urbaine est nette par rapport au milieu rural (vrai dans le cas des villes d'afrique équatoriale)\n",
"* Est susceptible d'être lent avec des images hautes résolutions (Solution conseillée: réduire la qualité des images car pas nécessaire pour une approsimation de la tâche urbaine)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from skimage import io\n",
"import skimage as sk\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import os\n",
"\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"\n",
"msk = io.imread(\"masque2000.png\")\n",
"\n",
"# Quelques fonctions pour mettre le masque dans le bon format\n",
"def convert_mask(msk, shape=msk.shape):\n",
"#msk must be a numpy matrix\n",
" from skimage import color\n",
" from skimage import transform\n",
" import numpy as np\n",
" return np.bool_(transform.resize (color.rgb2grey(msk), [shape[0], shape[1]]))\n",
"\n",
"def apply_mask(msk, img):\n",
" result = np.uint8(np.zeros(img.shape))\n",
" for i in range(img.shape[2]):\n",
" result[:,:,i] = msk*img[:,:,i]\n",
" return result\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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6OAKbVTafWKzOy608B3qrXVMnResaJGvVsq26s3CgWk1fTroPibtubCX5ronp1nadG+O8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\nj/Q8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b91c0b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3e8ad2b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b920b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Kk9eNiJzGRsh/VlU/V91+trLfb+34p6r7J4Dza6/vqe5lQ4oFzxxpegTMhe9OxLbFz7bd\ntCkJ0aZycVNNRQ7pzEEuxEiDi9eNADcDj6vqJ2tf3QVcW11fC9xZu3+NiJwuIhcCe4EHwyV5OjkJ\niKUy1oc41POlsVZtNidSt7GY9e9iunk38I+Ab4jItiR+H7gRuF1ErgOeAq4GUNVHReR24DHgReD6\nPo8bIw9CN7IpXiSh34nN2BlRjnlZO6kG29jxZnvWzZqJfcZHDvGGEHKht6unxLT59KSaVU3xTrPT\nKwtn6R0/pPmsdCFv5MGS+5wJ+gxZcoNrMiavKRaQjeWTqj01nQ2Sed0Y62Jur6Sp57osgSXmKSW2\n2P9KTNAbwPiGnupMb5cT/oxySO3yHNMTr563VHk0QW/Mdv63Ha1rxKbPoSD1YJLShba4Hx6ZQg6+\nyjmkoYtmulwGgClml5zLYm6sHMKUwdBJpylo61dzp2VVGr11pnT0lX1qTctIz5r6Zoq8LlKjn3vE\nTOX3HoquH1ao05XHqWYfE/AvE/u0z9zIbUaXW3pCskiNPsUPUbjGubQfklhqx5ib+kBqR3TMz5KF\nPCxU0C+VuRZKt3GFEDjNsOsnWi65Y43B95eHSqe5s3mKCW+qErT0fRkm6FtwrfBcTyscQ6hfg2q6\nkdUFetfZ9GsR+n15LXUvQUj32vrJpM2/oXhDlF/MNpi6vkzQd+CiXUzVAnIRbm129y6/X58z5bs6\nU198S6arXMbuDs6BmOmIOYOdm9T1ZYJ+gCFBl7oCx+AqWGN3iLUI+Dou2umacZ3hldjvUrJIr5up\nNDX17ULNUhZsQuZh7BR4zcKt3r6213NsWkvJ1L4z5v0upWyJ5TuEafQd9LkRGhu6ysTKapi1rEts\nmZrXse+XUMZz7Bg3jb5BXXMIqcUvZTYAJsinUp8hbllK2xhL6L0opZVtcyE6dJpN0DeI5eKWurHl\nsKnLBgijjRjHYqTub77EHpjMdGNEYYy3ztooTRjNxVSf+tJonkcfg+IE/VoqPzQpbMKpj2YthbUL\nfBdBZ21oGsWZbkrqFLnY5X3MNqE7lHXQDXUbbP1//TqHtpKSEBvJ5iCHfr1tMzt2uz0/qNGLyBki\n8qCI/LWIPCoif1Dd3yki94jIE9X/c2rvHBKRYyJyVESuHJUTYxCfKa65PKbFytaNKeU0Vxm3ucTm\njovp5gXgN1T17wGXAh8QkXcBB4H7VHUvcF/1GRG5GLgGuAT4APCnIrLDN2GlFWQbc26pHnJ1LL0s\nl0BfHaTWEEPj23998t/cud2cEcU+miQHjd7XFDso6HXD/64+nlb9KbAfOFLdPwJcVV3vB25T1RdU\n9UngGHCZc4qwqewQLkK9raP1HWtgA0Fa+up06fUzNn9d74TamLWkMney0Vca+cPA3wX+vao+ICK7\nVPVk9cgzwK7q+jzg/trrx6t7zTAPAAcAzuDMV3xnAr6dPhcsV83dNPwyKbFPzHF0d5MQ2vY2jK59\nNCXuknfyulHVl1T1UmAPcJmIvKXxvbLR8p1R1cOquk9V953G6T6vrg6XLfIlNTrj1fTV31Dd5jho\nNzcA+bw35UTTEDtwXbyAhryDcjvTyMvrRlV/IiJfZGN7f1ZEdqvqSRHZDZyqHjsBnF97bU91z3Ck\nT1sYEghtA0KOgsDY0FafITbP+Hpahd6ROuVkV5eZa850lWdKZczF6+ZNInJ2df064P3At4C7gGur\nx64F7qyu7wKuEZHTReRCYC/wYOiErwXfTt+mDZm2v2ymavxTB5O2dYQQQr4edv27ZtvOqX37pGfO\nQUs2VpeeB0TeymaxdQebgeF2Vf2EiLwRuB34VeAp4GpVfa565+PA7wIvAh9V1S/0xXGW7NR3yhVT\n82IMEKJh2SwhDnXh0HYWTvMZX9rqK/RsIUR6x7arnIS9K20Lv775uFfveFhV9w095+J183VVfZuq\nvlVV36Kqn6ju/0hVr1DVvar6vq2Qr777Q1X9O6r65iEhb8zHFNvnlqV7gORA13G6U46ViCUIXWec\n1mbSUtwRCEYYYh3WFmIwWSuuWvfQYFs3pYR0QXQZaOp1P3atoWug63o21Omycw9Gcw6MxR2BUDop\n9gjEjnNoQTE2Uxb+csF1QXSoLmN6e4xxK/R93ieOkO25rw2FNKENhR+rr5qgn5m5BLxL4xwSkEP2\n+BwOoFrizGHIcyXEwBrbD3zK4NvmSNBWJl15GJu3MZ5upWCCfuVM1Yabnap0zToVbYNqLCHffM/H\nSySEySeUEF6yYA6NCfoZaPOi8NHWxlAXHC62QJcNO2022tRCfkkDi09eutqOizCey7PF1Qd+jC3f\nJa8h9iPMRbOPhZ5x2WJsZLp2ysUU8j5hTlnMyr3zlISr8G1bNKzXYcl14jtADQn5ksoi9jqAafSR\nGbJjl9QY65SabqPcuvP1xnF9NhdiygQT9IkoqQE2KTntJeJqhptCc/NOl2lkShq2YbYtrLp6HOXQ\n9kIL5JALyl2Y6WYCTX9l20xk+OC756DpcRKKtiMGQvrfT6GZtpT9q9m/Y6clZPiDRyDMQclHIJS0\n4BMbG+TcGXJd7Xp+TobsxqG8cPriaHsvhW3eZ+CbkgZfW32wIxCMlwm907A0+vJvQj48qe3Mc+7c\ndAl37k1UzXCbYcc0udhibI25FzR9fHmXjMsU32jHd1dpamJ6grVpr0N5zr2/TU1f22axEKxSo8+h\nAy0BK0c/chdSPoTSWpvrFKnLKGdT2hSK1ejnEjJmg38ZX7tyrDSUOqNoera4UGqbc51tj5mVj90x\n3OZJ1Iw/pXloiClmoeI1+lDbqUOGv1Ry8CqKvbEkNjmU4RxM2Yg3Bpdyddm0OESoPM0dd1FeN33+\nvbEpfYPTVHIVTjnMMkJTShvrOtpj+13zXl84Ls+1vdOkGb/vrC922YeWI65eN8WYblIKeSin88Ug\nZyGac9rWyJQZl0/f7hLgPlp7mwknJm0++C6DYAjZU4zpZs2CNjVW9tPw3RS1JfdBLES7mDuPXZvU\n5mjjzUVn1/WAECanokw3RjpyETqlLcBu6RLg9VnqkJaXyxEAbbQtcvYd3RDahOHSJlJtOosZr6vp\nxgS90UuJQjVHYgu6EmmWQYydtnXWLOidTTciskNEviYin68+7xSRe0Tkier/ObVnD4nIMRE5KiJX\njsuCYSyDUNvhl0qIxdi5vHz6dobPfRaODz42+o8Aj9c+HwTuU9W9wH3VZ0TkYuAa4BLgA8CfisiO\nMMk15maJGmdOeZq65X9MuLlQF84+6a0fcNYl8GMJfpcwx7jQxq4vJ0EvInuA3wJuqt3eDxypro8A\nV9Xu36aqL6jqk8Ax4LIwyTWMafguhsVOB7z6FNS10ayLNZZDl2kvFK7ulX8CfAx4Q+3eLlU9WV0/\nA+yqrs8D7q89d7y69wpE5ABwAOAMzvRIcr7kvFg2liV3uBR5G6Ppxd4UmBtDaW9b2O7y24958FhX\nmprpaYt7aJ/B0LrF9v0du93SNyjoReRDwClVfVhELm97RlVVRLxWdVX1MHAYNouxPu/mSsmdq4tS\nvVy6SOHON+X5JbapUAwJ0y7mcqVs0jYoudCWn5c/H3MKw0Wjfzfw2yLyQeAM4CwR+QzwrIjsVtWT\nIrIbOFU9fwI4v/b+nuqeUShLE/axGTpTZYkzP1+mlEHpJ1zWcZkJhGgzg4JeVQ8BhwAqjf5fquqH\nReSPgWuBG6v/d1av3AXcIiKfBM4F9gIPjk6hYRRE3+FYJuQ3hFYaXPYgpMZ312/9vea9MUw5AuFG\n4HYRuQ54CrgaQFUfFZHbgceAF4HrVfWlSak0krOEM2Viz0zazlmpf+7bVLQmpmjyXfb33MsxxDk+\nU7ANUysk9sYUo5vcBVIJ5K69T2FoR3ET+ylBo5exGztSuyXGYol5ypUQCkOp60Y+6wsh26Rp9MYo\nSuxkczN1a//SsHIIj2n0RjRyFPIhBYjvrGXoeRNuG8xcmA4T9IYXPj8oMSchBYLvzsy1C6O+/E/d\n8ZtTGyuZYn54xMiDUm2jsVlzmdhsJn9MozeMmVnzoGCkwQS9MYoxW/VL1+6maq6pDuuaO87Y+bSB\n0h8T9IY3JQjsOd1Axy4yzv07pUvAPHfGYe6VKyD2AmpuwqTrZ/tChtvGmLjadhybIAvH0gcGc680\nfkHshh4r/CnH88Y4T8XlGZ+ZxHZhu5nWvlMZSyKH9OYk5FP+7oAJ+gSEdgVMFfdYYg8MY4+DHQp3\nyvttfz70/apSLsyZrlzLoIu2X8KaExP0CQhdyW0aYSiBkLJDjYk7dnp9w3f5EY2m4Hdd2B26n/Og\nMIVSzTFjB/kQmI1+RlI10K5DoFzS03eAVN/Z2SHI7cRMn2OGYxy85VIOudn8Y7X5JR9s5oOrjd42\nTK2Avs7g8ms8rjbk0OQi4KH/nPk56BPcTS0+h7Taxrq8yEbQlzodcyXH/I0xEfR14hD5i+UxM4ap\nZ7OEPn+nGX5bPE1zja/m23Zmvs/79Wdza+9rJhsb/dIbRc75C3F06hgzy9BMYwypzRRzpMPHhOPz\nTtt7uR5LnWOaciYbQW+kY8yOT1cBkEJQpFwHmYMx2jW0/wZBfQaQegblQ0lpzQFbjC0EX9PPmOeb\ntJlougRHKJNLatvumMXMvvTG8LDKyaRkpGX1i7GxbMgpCWXnbwvH1fSSm/tiTvGPNZf4MqUdlN4H\njHEsVqOfU8uKQWpXzC1dbpV1zbupzQ9p5c3vc3Oj3OKzgOnz/Fh84slx8d8IT9AjEETkuyLyDRF5\nREQequ7tFJF7ROSJ6v85tecPicgxETkqIleOz8Z0XP2/c6C50SnXdNa9KrpmBlOOL/B5PoYwGxNu\nTkLe5zljHThp9CLyXWCfqv6wdu+PgOdU9UYROQico6o3iMjFwK3AZcC5wL3ARar6Ulf4UzV6l07g\na6Mc87zr1H3s4BPbk6OpWYeKr22WUI+jLe4hYppJpuZ7Tg2/a2aVOm25s5QZzxw2+v3A5dX1EeBL\nwA3V/dtU9QXgSRE5xkbof2VCXJOpd4ihSs7ljJQ5Sb0RaGyZ5zjrmassx86aciyzuSmpb4bAVdAr\ncK+IvAT8R1U9DOxS1ZPV988Au6rr84D7a+8er+69AhE5ABwAOIMzRyT9ZXyms2M156bW2Way8NES\nQp+Zkitt+RwjaObIf6wZTWx8hXcp+YJXzlp8+5hLuGvB1Y/+Pap6KfCbwPUi8t76l7qx/3it6qrq\nYVXdp6r7TuN0n1cnMdU1bSiMsaaHUM+uheYO0DUzpRxyLcNt/TbXfEL1hbX1KSdBr6onqv+ngP/K\nxhTzrIjsBqj+n6oePwGcX3t9T3Vv0TQ3o3Rpsr6mijmPBKinL5TWtGW7wDlloTPEIvXQzt7t9ZT8\nzzUIhfCNn1vg5TqwLJ3BxVgReT3wS6r6fHV9D/AJ4ArgR7XF2J2q+jERuQS4hZcXY+8D9k5ZjPXR\nqFPiOh1MNc3uOsckRpxDdda3QNuVHt9F26EwQw1qSzAD5JSHnNKSO66LsS6C/tfYaPGwsenfoqp/\nKCJvBG4HfhV4CrhaVZ+r3vk48LvAi8BHVfULfXG4eN2ErPxcbLAhzDyufu9tz/gKelcN0nWzWtPz\nZigtrgOVL6nbgWGMJZign4O5j0DIRdCD34Fivu+6hNEV1tCAEULQj3knhrDPoR0YG0yb98N+M7aH\nXHdi+jBlgBj7/Fgbuy8+cXRt2DLKxOovDos962aIPlfJuQjhO96X9jl2d7qaXMaEMRRuLuSihQ6t\nYYzd+GeUz2oF/ZYcvA58/LdTmp3a4m7a14fs+yF3tLostA7hmv6hMHKgWS91XMqj5Bmu0c/qBf1c\nhPBwaW55T8HUGYSLxuijVbouPvcNsD5l2hwUStGAXU1zEMZt08gLE/Sk05J97MshfMhzYYpwHGMG\n6nqnmQ6f2UjoDTyGEZNVet00KUGDyXUvga/Q7pqVuGjmTVw1cV/fe8MoBXOv9CBXIbqlzVywvS4V\nH1dNH/fQoUEkxhEVhpGK1f/C1FhK6uCl2Ifr+A6qUxdHQ5m6ljC4Gutl9YK+BGHZZXrIPd1NYq4v\n9J1hM8U7ZwlrIoaxekFfkrAsKa0uxM7PlPNw1i7gbQazLLIV9FM1bWuo+RGjLtraia+QdlmstXZk\nlEy2gt52mV7/AAAJh0lEQVQ61oa23Y6llk0MM1lbeGOPuAixeSo3xixm1/+3PdPE5UwiIy2rPOum\nBO7+fv4/El6nL43bvOTU8dtmAUsT8uCfj3qba1sTahsIms+V0F7Xhgn6Qihdm8893bmnbyxtArsN\nH0+lUJvFbEAYJlQZLdqPPjct0oeStcuxB2rFwNdTqcRydzlHaMqBdaWUwxqxY4rJu4EOmTrA77je\nHEl9TMCaNMaQh8U1maMc11RXKVi0Rl8qS9CkhrTMOfLmO7MIcfDcWhgzWy55hj1EqryZRm9kSc6z\nlFzTFQtX+30bsQ6li0mKDXu5YILeiEqqjt20T489SiH3DhyCueoodVmmjj8lToJeRM4WkTtE5Fsi\n8riI/LqI7BSRe0Tkier/ObXnD4nIMRE5KiJXxkv+Mllag5yiOYZkaeU6le1A5nv66FRitIe2MOds\nc7m08S6cbPQicgT4n6p6k4i8FjgT+H3gOVW9UUQOAueo6g0icjFwK3AZcC5wL3CRqr7UFb7Z6F9J\n6bbM0tJvtnk/xtbvHOszObe9GPkPZqMXkV8B3gvcDKCqP1PVnwD7gSPVY0eAq6rr/cBtqvqCqj4J\nHGMj9A0PctYOhsi1oxnpid02cm57KU2BLqabC4EfAH8mIl8TkZtE5PXALlU9WT3zDLCruj4PeLr2\n/vHq3isQkQMi8pCIPPRzXhifgwVT0s5YyH/62kVbB4ytdZbOmFM/p8wCtm1rCWWXApezbl4DvB34\nPVV9QET+LXCw/oCqqoh4+Wmq6mHgMGxMNz7vLp3mLsWctZQtJXfAudNeQn260Nc2t+cGTc1r6r0Y\nS8FFoz8OHFfVB6rPd7AR/M+KyG6A6v+p6vsTwPm19/dU9wxH6ppLCcfobjt06V4qPrtmjX6srF5N\nyjIZ1OhV9RkReVpE3qyqR4ErgMeqv2uBG6v/d1av3AXcIiKfZLMYuxd4MEbi10CbJ0FToMbQ+n1O\nPUwp3Id+VGTsBqm+d9tOFO0LZ4hcBscxi4VtxyzU76VuHzmRshxc/eh/D/isiHwduBT4V2wE/PtF\n5AngfdVnVPVR4HY2A8FfAtf3edwYr6Z5SmC9gXQdyxtaWyi9c/oImKHydfnO9TnXuHJmKN1LyOPS\nyPYIhDVqAl15XlJZzH0EwhQfcZ9Z05LqyIWcDq5zwfdwO59wU+bbfhw8MCErtEvY5d5RQqRvO/uY\no4O4hu9ywNzYuEo6t2hKneScv5TCeOoAEyrtJugdmXq2R91+6XKcbPO5VI01hqBqy/+U8g1dLr6m\nh77jgHMWgE3GHhORM1PWmsbMCLtMqyEZ0+YXfdZNDiv/Qzb2Es8/d8G37HOoqzrms71Mmv74zev6\nc23fd/nz++x56Yqva89A8/8YOWEafSKGRuWSNUPfhjklbzHKpSRzy9y4zD5zZkjjbqv7Ic+iMWmo\n95HmXoGumfyUvQmL1uhzpulVM9fipE8DbbpyjmncITrGFKakOXS4SybX8phzH4prP25zkY7d/7PV\n6EMt/OWIr8YYKh++bpgus4q2vMS0U/pqNL7xtmlVzfjXTFOr7BKiOfW9tnbf1JKb97vq2XXhfmhG\n4BNWCLJ1r1wyZhrIB1+viLFeOLkJP5jufgrrbcO51Kf9wlQCctX2ck1Xbgz5ybvucs1BALgwVcj3\n3V8iJefVBH0A5vIL9yXWzKHL82CJhMpnbm3Dl670T3U7Lom6mae0+jRBH4CxWlwpjT3WrsLcmFof\nrvbXUvGdAXS5IJZMqW3fBH1CYjWa0B1qjk0gOTLVhW5p+C7k+7gPG3ExQT8zc0z9hjxHXBnq2KV3\n1jY3t/p3U8JdgvbaZGp9l6LYLBET9JHo200Xc5NPiZSSdlcTXUkLsr64DopLHexKJQv3ShF5Hjia\nOh0z8LeAH6ZOxAxYPpfDGvII5ebzb6vqm4YeymXD1FEXX9DSEZGHLJ/LYQ35XEMeYfn5NNONYRjG\nwjFBbxiGsXByEfSHUydgJiyfy2IN+VxDHmHh+cxiMdYwDMOIRy4avWEYhhEJE/SGYRgLJ7mgF5EP\niMhRETkmIgdTp2csInK+iHxRRB4TkUdF5CPV/Z0ico+IPFH9P6f2zqEq30dF5Mp0qfdHRHaIyNdE\n5PPV58XlU0TOFpE7RORbIvK4iPz60vIpIv+8aq/fFJFbReSMJeRRRD4tIqdE5Ju1e975EpF3iMg3\nqu/+nYjI3HkJgqom+wN2AN8Bfg14LfDXwMUp0zQhL7uBt1fXbwC+DVwM/BFwsLp/EPjX1fXFVX5P\nBy6symFH6nx45PdfALcAn68+Ly6fwBHgn1TXrwXOXlI+gfOAJ4HXVZ9vB35nCXkE3gu8Hfhm7Z53\nvoAHgXcBAnwB+M3UeRvzl1qjvww4pqp/o6o/A24D9idO0yhU9aSq/lV1/TzwOJuOtJ+NwKD6f1V1\nvR+4TVVfUNUngWNsyiN7RGQP8FvATbXbi8qniPwKG2FxM4Cq/kxVf8LC8slm0+TrROQ1wJnA91lA\nHlX1y8Bzjdte+RKR3cBZqnq/bqT+n9feKYrUgv484Ona5+PVvaIRkQuAtwEPALtU9WT11TPAruq6\n5Lz/CfAx4P/V7i0tnxcCPwD+rDJR3SQir2dB+VTVE8C/Ab4HnAT+l6r+dxaUxwa++Tqvum7eL47U\ngn5xiMgvA38BfFRVf1r/rtIKivZnFZEPAadU9eGuZ5aQTzaa7tuB/6CqbwP+D5vp/i8oPZ+VjXo/\nm0HtXOD1IvLh+jOl57GLpeari9SC/gRwfu3znupekYjIaWyE/GdV9XPV7WerKSDV/1PV/VLz/m7g\nt0Xku2xMbb8hIp9hefk8DhxX1Qeqz3ewEfxLyuf7gCdV9Qeq+nPgc8DfZ1l5rOObrxPVdfN+caQW\n9F8F9orIhSLyWuAa4K7EaRpFtRp/M/C4qn6y9tVdwLXV9bXAnbX714jI6SJyIbCXzcJP1qjqIVXd\no6oXsKmv/6GqH2Z5+XwGeFpE3lzdugJ4jGXl83vAu0TkzKr9XsFmbWlJeazjla/KzPNTEXlXVT7/\nuPZOWaReDQY+yMZD5TvAx1OnZ0I+3sNmKvh14JHq74PAG4H7gCeAe4GdtXc+XuX7KAWu5gOX87LX\nzeLyCVwKPFTV6X8DzllaPoE/AL4FfBP4z2w8T4rPI3Arm3WHn7OZnV03Jl/AvqpsvgN8iuo0gdL+\n7AgEwzCMhZPadGMYhmFExgS9YRjGwjFBbxiGsXBM0BuGYSwcE/SGYRgLxwS9YRjGwjFBbxiGsXD+\nP0qbLp3+rJ7TAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b97d5f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b4bff98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b5e5d68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b778da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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3aVu45JxlaFu3ro+gb7/BkulKZeEsFxf6TMk1lr8PY9chJqJmuzwklUK1o3gN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c3b5d5898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#get list of images\n",
"liste_dir = sorted(os.listdir(\"yaounde/\"))\n",
"\n",
"#initialize prediction\n",
"img_train= io.imread(\"yaounde/\"+liste_dir[0])\n",
"\n",
"msk = convert_mask(msk, img_train.shape)\n",
"#Transform mask in a list of pixel\n",
"label = np.reshape(msk, msk.shape[0]*msk.shape[1])\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"for i in range(len(liste_dir)-1):\n",
" img_test= io.imread(\"yaounde/\"+liste_dir[i+1])\n",
" \n",
" #transform RGB image (x,y,3) to an list of RGB value \n",
" train = np.reshape(img_train, [img_train.shape[0]*img_train.shape[1],img_train.shape[2] ])\n",
"\n",
" #Standardisation des données: normalement améliore les résultats mais là était mauvais.\n",
" #scaler = StandardScaler()\n",
" #scaler.fit(train) \n",
" #train = scaler.transform(train)\n",
"\n",
" n_iter = np.ceil(10**6 / len(train))\n",
" mask_predict = np.zeros([img_test.shape[0], img_test.shape[1]])\n",
" \n",
" #several repetitions to avoid local minimum, (SGD algorithm can easily be trapped in a local minima)\n",
" for j in range(10):\n",
" #Train classifier (SGD because fast); no work on parameters, we just want a first, dirty model\n",
" clf = SGDClassifier(loss=\"hinge\", penalty=\"l2\", n_iter = n_iter, shuffle = True)\n",
" clf.fit(train, label)\n",
"\n",
" #transform RGB image (x,y,3) to an list of RGB value \n",
" list_test = np.reshape(img_test, [img_test.shape[0]*img_test.shape[1],img_test.shape[2] ])\n",
" #list_test = scaler.transform(list_test) # apply same transformation to test data\n",
" \n",
" #prediction of urban area\n",
" list_predict = clf.predict(list_test)\n",
" mask_predict += np.reshape(list_predict, [img_test.shape[0], img_test.shape[1]])\n",
" mask_predict[mask_predict > 0]= 1 \n",
" plt.imshow(mask_predict)\n",
" plt.show()\n",
" io.imsave(\"tache/tache\"+liste_dir[i+1], np.uint8(mask_predict*255))\n",
" img_train = img_test\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Conclusion du premier test\n",
"\n",
"* La couleur est super utile pour détecter la tâche urbaine, de façon intuitive. Le fait de traiter les trois bandes séparement permet d'éviter de prendre en compte les nuages\n",
"* Sur certaines années, il y a des \"stries\", qui sont dus à un défaut du capteur. Ce sont ces années où le détecteur semble le plus mauvais. Elles sont plus gênantes que les nuages. B&B était aussi gêné par ce facteur dans leur étude. A priori une réflexion sur la normalisation de l'image peut largement améliorer les performances.\n",
"\n",
"# Suite\n",
"\n",
"* On ne peut pas continuer avec des données en copier-coller, le travail avec des données accessibles par des API doit se poser maintenant.\n",
"* Un pré-traitement pour normaliser l'image, surtout à des pas de temps différents, ou au moins apporter des descri"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}