{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qm-z9XQTrqMf"
      },
      "outputs": [],
      "source": [
        "!pip install -qqq soccernet huggingface_hub datasets lsq-ellipse\n",
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "6n_f8AG1rqKQ"
      },
      "outputs": [],
      "source": [
        "#@title Download data\n",
        "from SoccerNet.Downloader import SoccerNetDownloader as SNdl\n",
        "soccerNetDownloader = SNdl(LocalDirectory=\"SoccerNet\")\n",
        "soccerNetDownloader.downloadDataTask(task=\"calibration-2023\", split=[\"train\",\"valid\",\"test\"])\n",
        "!unzip SoccerNet/calibration-2023/train.zip\n",
        "!unzip SoccerNet/calibration-2023/test.zip\n",
        "!unzip SoccerNet/calibration-2023/valid.zip\n",
        "!rm SoccerNet/calibration-2023/train.zip\n",
        "!rm SoccerNet/calibration-2023/test.zip\n",
        "!rm SoccerNet/calibration-2023/valid.zip\n",
        "!git clone https://github.com/Spiideo/soccersegcal.git\n",
        "!git clone https://github.com/NikolasEnt/soccernet-calibration-sportlight.git\n",
        "%cd soccersegcal\n",
        "!pip install -r requirements.txt\n",
        "!pip install vi3o"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "cellView": "form",
        "id": "ZKhHdQw7trpJ"
      },
      "outputs": [],
      "source": [
        "#@title Constants\n",
        "\n",
        "LINES_CLASSES = [\n",
        "    'Big rect. left bottom',\n",
        "    'Big rect. left main',\n",
        "    'Big rect. left top',\n",
        "    'Big rect. right bottom',\n",
        "    'Big rect. right main',\n",
        "    'Big rect. right top',\n",
        "    'Circle central',\n",
        "    'Circle left',\n",
        "    'Circle right',\n",
        "    'Goal left crossbar',\n",
        "    'Goal left post left ',\n",
        "    'Goal left post right',\n",
        "    'Goal right crossbar',\n",
        "    'Goal right post left',\n",
        "    'Goal right post right',\n",
        "    'Middle line',\n",
        "    'Side line bottom',\n",
        "    'Side line left',\n",
        "    'Side line right',\n",
        "    'Side line top',\n",
        "    'Small rect. left bottom',\n",
        "    'Small rect. left main',\n",
        "    'Small rect. left top',\n",
        "    'Small rect. right bottom',\n",
        "    'Small rect. right main',\n",
        "    'Small rect. right top'\n",
        "]\n",
        "\n",
        "CLASS2ID = dict(zip(LINES_CLASSES, list(range(len(LINES_CLASSES)))))\n",
        "ID2CLASS = dict(zip(list(range(len(LINES_CLASSES))), LINES_CLASSES))\n",
        "\n",
        "# https://github.com/NikolasEnt/soccernet-calibration-sportlight/blob/8255f5044bc7f2ef4f77e9c1dc67cf0861045290/src/datatools/intersections.py\n",
        "LINE_INTERSECTIONS = {\n",
        "    0: ('Goal left crossbar', 'Goal left post left '),\n",
        "    1: ('Goal left crossbar', 'Goal left post right'),\n",
        "    2: ('Side line left', 'Goal left post left '),\n",
        "    3: ('Side line left', 'Goal left post right'),\n",
        "    4: ('Small rect. left main', 'Small rect. left bottom'),\n",
        "    5: ('Small rect. left main', 'Small rect. left top'),\n",
        "    6: ('Side line left', 'Small rect. left bottom'),\n",
        "    7: ('Side line left', 'Small rect. left top'),\n",
        "    8: ('Big rect. left main', 'Big rect. left bottom'),\n",
        "    9: ('Big rect. left main', 'Big rect. left top'),\n",
        "    10: ('Side line left', 'Big rect. left bottom'),\n",
        "    11: ('Side line left', 'Big rect. left top'),\n",
        "    12: ('Side line left', 'Side line bottom'),\n",
        "    13: ('Side line left', 'Side line top'),\n",
        "    14: ('Middle line', 'Side line bottom'),\n",
        "    15: ('Middle line', 'Side line top'),\n",
        "    16: ('Big rect. right main', 'Big rect. right bottom'),\n",
        "    17: ('Big rect. right main', 'Big rect. right top'),\n",
        "    18: ('Side line right', 'Big rect. right bottom'),\n",
        "    19: ('Side line right', 'Big rect. right top'),\n",
        "    20: ('Small rect. right main', 'Small rect. right bottom'),\n",
        "    21: ('Small rect. right main', 'Small rect. right top'),\n",
        "    22: ('Side line right', 'Small rect. right bottom'),\n",
        "    23: ('Side line right', 'Small rect. right top'),\n",
        "    24: ('Goal right crossbar', 'Goal right post left'),\n",
        "    25: ('Goal right crossbar', 'Goal right post right'),\n",
        "    26: ('Side line right', 'Goal right post left'),\n",
        "    27: ('Side line right', 'Goal right post right'),\n",
        "    28: ('Side line right', 'Side line bottom'),\n",
        "    29: ('Side line right', 'Side line top'),\n",
        "}\n",
        "\n",
        "ELLIPSE_INTERSECTIONS = {\n",
        "    \"Circle left\": 'Big rect. left main',\n",
        "    \"Circle right\": 'Big rect. right main'\n",
        "}\n",
        "\n",
        "FIELD_AREAS = {\n",
        "        'Goal right': {\n",
        "            'index': 5,\n",
        "            'border': [\n",
        "                'Goal right crossbar',\n",
        "                'Goal right post right',\n",
        "                'Side line right',\n",
        "                'Goal right post left',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Goal right crossbar',\n",
        "                'Goal right post right',\n",
        "                'Goal right post left',\n",
        "            ],\n",
        "        },\n",
        "        'Goal left': {\n",
        "            'index': 5,\n",
        "            'border': [\n",
        "                'Goal left crossbar',\n",
        "                'Goal left post right',\n",
        "                'Side line left',\n",
        "                'Goal left post left ',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Goal left crossbar',\n",
        "                'Goal left post right',\n",
        "                'Goal left post left ',\n",
        "            ],\n",
        "        },\n",
        "        'Small rect. right': {\n",
        "            'index': 4,\n",
        "            'border': [\n",
        "                'Small rect. right top',\n",
        "                'Small rect. right main',\n",
        "                'Small rect. right bottom',\n",
        "                'Side line right',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Small rect. right bottom',\n",
        "                'Small rect. right main',\n",
        "                'Small rect. right top',\n",
        "            ],\n",
        "        },\n",
        "        'Small rect. left': {\n",
        "            'index': 4,\n",
        "            'border': [\n",
        "                'Small rect. left top',\n",
        "                'Small rect. left main',\n",
        "                'Small rect. left bottom',\n",
        "                'Side line left',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Small rect. left bottom',\n",
        "                'Small rect. left main',\n",
        "                'Small rect. left top',\n",
        "            ],\n",
        "        },\n",
        "        'Circle left': {\n",
        "            'index': 3,\n",
        "            'border': ['Big rect. left main', 'Circle left'],\n",
        "            'contains': ['Circle left'],\n",
        "        },\n",
        "        'Circle right': {\n",
        "            'index': 3,\n",
        "            'border': ['Big rect. right main', 'Circle right'],\n",
        "            'contains': ['Circle right'],\n",
        "        },\n",
        "        'Big rect. right': {\n",
        "            'index': 2,\n",
        "            'border': [\n",
        "                'Big rect. right top',\n",
        "                'Big rect. right main',\n",
        "                'Big rect. right bottom',\n",
        "                'Side line right',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Big rect. right top',\n",
        "                'Big rect. right main',\n",
        "                'Big rect. right bottom',\n",
        "                'Small rect. right bottom',\n",
        "                'Small rect. right main',\n",
        "                'Small rect. right top',\n",
        "            ],\n",
        "        },\n",
        "        'Big rect. left': {\n",
        "            'index': 2,\n",
        "            'border': [\n",
        "                'Big rect. left top',\n",
        "                'Big rect. left main',\n",
        "                'Big rect. left bottom',\n",
        "                'Side line left',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Big rect. left top',\n",
        "                'Big rect. left main',\n",
        "                'Big rect. left bottom',\n",
        "                'Small rect. left bottom',\n",
        "                'Small rect. left main',\n",
        "                'Small rect. left top',\n",
        "            ],\n",
        "        },\n",
        "        'Circle central': {\n",
        "            'index': 1,\n",
        "            'border': ['Circle central'],\n",
        "            'contains': ['Circle central'],\n",
        "        },\n",
        "        'Full field': {\n",
        "            'index': 0,\n",
        "            'border': [\n",
        "                'Side line bottom',\n",
        "                'Side line left',\n",
        "                'Side line top',\n",
        "                'Side line right',\n",
        "            ],\n",
        "            'contains': [\n",
        "                'Side line bottom',\n",
        "                'Side line left',\n",
        "                'Side line top',\n",
        "                'Side line right',\n",
        "                'Big rect. left bottom',\n",
        "                'Big rect. left main',\n",
        "                'Big rect. left top',\n",
        "                'Big rect. right bottom',\n",
        "                'Big rect. right main',\n",
        "                'Big rect. right top',\n",
        "                'Circle central',\n",
        "                'Circle left',\n",
        "                'Circle right',\n",
        "                'Middle line',\n",
        "                'Small rect. left bottom',\n",
        "                'Small rect. left main',\n",
        "                'Small rect. left top',\n",
        "                'Small rect. right bottom',\n",
        "                'Small rect. right main',\n",
        "                'Small rect. right top'\n",
        "            ],\n",
        "        }\n",
        "    }\n",
        "\n",
        "\n",
        "# https://github.com/Spiideo/soccersegcal/blob/main/soccersegcal/dataloader.py\n",
        "BAD = {\"valid\": {11, 32, 60, 64, 71, 82, 85, 108, 129, 151, 176, 200, 280, 284, 295, 310, 315, 367, 374, 381, 407, 411, 420, 458, 459, 465, 521, 525, 527, 532, 536, 538, 544, 553, 572, 579, 592, 595, 614, 617, 638, 651, 710, 756, 780, 786, 790, 799, 801, 809, 813, 847, 873, 876, 883, 884, 914, 920, 927, 962, 965, 993, 1002, 1012, 1015, 1026, 1040, 1046, 1066, 1067, 1078, 1080, 1088, 1096, 1104, 1119, 1139, 1141, 1143, 1148, 1152, 1225, 1244, 1249, 1258, 1264, 1265, 1268, 1284, 1285, 1286, 1300, 1301, 1316, 1318, 1325, 1338, 1341, 1357, 1379, 1394, 1424, 1439, 1444, 1457, 1458, 1467, 1468, 1477, 1493, 1498, 1509, 1545, 1577, 1600, 1619, 1649, 1650, 1737, 1761, 1763, 1768, 1789, 1814, 1816, 1841, 1865, 1870, 1905, 1912, 1920, 1922, 1931, 1945, 1956, 2026, 2056, 2057, 2074, 2096, 2097, 2111, 2112, 2121, 2138, 2143, 2147, 2153, 2155, 2186, 2187, 2194, 2199, 2202, 2232, 2244, 2262, 2282, 2297, 2306, 2317, 2327, 2334, 2358, 2363, 2364, 2375, 2393, 2394, 2397, 2504, 2518, 2521, 2525, 2541, 2554, 2557, 2558, 2562, 2564, 2573, 2580, 2588, 2604, 2613, 2618, 2650, 2671, 2756, 2768, 2800, 2829, 2830, 2832, 2835, 2838, 2871, 2886, 2928, 2941, 2995, 3007, 3015, 3026, 3046, 3049, 3071, 3091, 3106, 3124, 3131, 3132, 3133, 3134, 3149, 3161, 3162, 3179, 3182, 3207},\n",
        "            \"test\": {23, 33, 75, 92, 100, 112, 119, 129, 141, 181, 188, 258, 294, 304, 314, 321, 342, 343, 482, 487, 496, 501, 508, 518, 545, 582, 586, 589, 591, 609, 615, 646, 649, 697, 713, 747, 748, 756, 787, 829, 834, 839, 850, 863, 869, 872, 941, 984, 1007, 1020, 1025, 1028, 1030, 1039, 1042, 1071, 1086, 1098, 1115, 1118, 1121, 1125, 1131, 1136, 1158, 1169, 1191, 1223, 1224, 1227, 1245, 1262, 1268, 1269, 1279, 1281, 1307, 1311, 1323, 1326, 1328, 1341, 1357, 1394, 1413, 1414, 1500, 1546, 1584, 1616, 1631, 1640, 1644, 1663, 1698, 1700, 1702, 1707, 1759, 1763, 1788, 1795, 1817, 1886, 1889, 1907, 1930, 1934, 1970, 2051, 2058, 2062, 2070, 2072, 2130, 2173, 2181, 2185, 2188, 2208, 2216, 2268, 2276, 2280, 2286, 2294, 2305, 2307, 2317, 2330, 2346, 2389, 2439, 2453, 2462, 2493, 2517, 2527, 2564, 2570, 2592, 2608, 2621, 2627, 2631, 2637, 2647, 2708, 2713, 2732, 2786, 2850, 2862, 2888, 2899, 2902, 2920, 2925, 2962, 2963, 2972, 2998, 3014, 3029, 3033, 3039, 3040, 3046, 3053, 3056, 3057, 3060, 3064, 3077, 3082, 3086, 3089, 3104, 3122, 3137},\n",
        "            \"train\": {81, 87, 89, 109, 128, 153, 154, 186, 195, 198, 227, 274, 280, 284, 288, 296, 300, 336, 339, 340, 350, 351, 372, 395, 400, 402, 409, 419, 426, 431, 432, 433, 435, 451, 544, 609, 644, 658, 711, 723, 724, 794, 840, 877, 901, 903, 972, 983, 1004, 1036, 1081, 1087, 1089, 1110, 1257, 1339, 1342, 1370, 1498, 1516, 1518, 1522, 1532, 1563, 1600, 1643, 1645, 1655, 1666, 1678, 1706, 1712, 1739, 1753, 1844, 1919, 1931, 1937, 1961, 1968, 2029, 2037, 2041, 2052, 2056, 2085, 2115, 2125, 2168, 2236, 2244, 2260, 2261, 2280, 2287, 2307, 2328, 2346, 2367, 2369, 2373, 2380, 2410, 2426, 2443, 2465, 2466, 2511, 2543, 2591, 2629, 2631, 2651, 2656, 2661, 2674, 2692, 2694, 2698, 2707, 2719, 2730, 2740, 2744, 2747, 2751, 2753, 2766, 2776, 2780, 2812, 2821, 2860, 2890, 2930, 2969, 2971, 2973, 2981, 2997, 3019, 3052, 3082, 3168, 3186, 3218, 3247, 3264, 3278, 3366, 3385, 3392, 3434, 3451, 3460, 3486, 3535, 3539, 3556, 3564, 3660, 3662, 3670, 3696, 3743, 3798, 3825, 3841, 3871, 3873, 3894, 3930, 3947, 3948, 3968, 3987, 4005, 4017, 4039, 4053, 4061, 4063, 4102, 4113, 4123, 4144, 4147, 4157, 4178, 4191, 4204, 4216, 4254, 4264, 4273, 4294, 4316, 4337, 4355, 4364, 4386, 4396, 4435, 4485, 4493, 4575, 4599, 4605, 4628, 4672, 4688, 4744, 4758, 4778, 4783, 4784, 4786, 4826, 4861, 4894, 4896, 4902, 4909, 4918, 4923, 4928, 4929, 4932, 4934, 4958, 4959, 4960, 4976, 4977, 4984, 5018, 5035, 5045, 5048, 5053, 5066, 5067, 5070, 5073, 5087, 5089, 5142, 5161, 5169, 5172, 5190, 5194, 5195, 5240, 5241, 5268, 5281, 5293, 5303, 5319, 5360, 5365, 5394, 5395, 5415, 5416, 5446, 5481, 5484, 5528, 5575, 5581, 5626, 5670, 5691, 5721, 5732, 5746, 5762, 5780, 5786, 5796, 5806, 5814, 5927, 5946, 6135, 6150, 6156, 6157, 6164, 6168, 6178, 6203, 6246, 6247, 6250, 6278, 6279, 6315, 6316, 6321, 6323, 6338, 6371, 6388, 6393, 6396, 6404, 6425, 6459, 6462, 6486, 6508, 6518, 6522, 6523, 6553, 6565, 6600, 6614, 6617, 6665, 6674, 6712, 6727, 6756, 6765, 6775, 6781, 6787, 6812, 6855, 6892, 6936, 6940, 6943, 6944, 6955, 6959, 6969, 6981, 6998, 7008, 7025, 7041, 7049, 7061, 7064, 7065, 7092, 7121, 7128, 7146, 7155, 7161, 7164, 7218, 7224, 7241, 7251, 7276, 7290, 7293, 7294, 7299, 7310, 7315, 7334, 7338, 7343, 7344, 7432, 7441, 7442, 7472, 7475, 7529, 7538, 7548, 7554, 7590, 7592, 7623, 7624, 7632, 7644, 7692, 7695, 7702, 7710, 7712, 7719, 7745, 7757, 7781, 7787, 7832, 7845, 7857, 7866, 7867, 7869, 7885, 7896, 7962, 7991, 7996, 8012, 8052, 8085, 8115, 8133, 8137, 8142, 8144, 8158, 8164, 8190, 8214, 8215, 8222, 8250, 8262, 8280, 8312, 8329, 8336, 8338, 8350, 8351, 8378, 8445, 8450, 8455, 8457, 8461, 8474, 8476, 8486, 8499, 8506, 8507, 8516, 8525, 8534, 8556, 8565, 8568, 8571, 8584, 8590, 8595, 8597, 8598, 8638, 8675, 8688, 8700, 8705, 8713, 8732, 8733, 8859, 8881, 8903, 8922, 8924, 8929, 8930, 8952, 8991, 9033, 9040, 9078, 9122, 9158, 9159, 9176, 9198, 9219, 9256, 9261, 9275, 9287, 9371, 9396, 9397, 9423, 9451, 9452, 9455, 9462, 9469, 9482, 9527, 9533, 9538, 9568, 9635, 9640, 9654, 9664, 9667, 9689, 9694, 9718, 9728, 9740, 9750, 9753, 9768, 9771, 9824, 9869, 9876, 9888, 9892, 9894, 9896, 9922, 9923, 9993, 10039, 10067, 10076, 10103, 10104, 10113, 10129, 10157, 10159, 10183, 10193, 10204, 10233, 10237, 10273, 10277, 10282, 10294, 10309, 10311, 10319, 10321, 10336, 10346, 10358, 10363, 10395, 10419, 10455, 10468, 10469, 10471, 10480, 10523, 10534, 10557, 10562, 10593, 10626, 10627, 10686, 10705, 10712, 10724, 10745, 10797, 10799, 10804, 10820, 10821, 10822, 10827, 10860, 10865, 10873, 10874, 10875, 10882, 10916, 10951, 10964, 10973, 10991, 11039, 11054, 11076, 11086, 11098, 11113, 11126, 11137, 11172, 11181, 11219, 11221, 11223, 11237, 11243, 11246, 11247, 11253, 11262, 11304, 11327, 11345, 11346, 11348, 11356, 11366, 11397, 11400, 11411, 11415, 11478, 11513, 11543, 11561, 11569, 11583, 11590, 11597, 11614, 11621, 11640, 11650, 11666, 11677, 11680, 11695, 11700, 11703, 11716, 11731, 11755, 11758, 11764, 11809, 11816, 11817, 11861, 11862, 11878, 11887, 11918, 11946, 11951, 11959, 11975, 11980, 11993, 12008, 12011, 12016, 12044, 12063, 12077, 12093, 12095, 12108, 12113, 12131, 12173, 12177, 12178, 12183, 12184, 12205, 12239, 12248, 12277, 12284, 12285, 12286, 12300, 12318, 12327, 12329, 12333, 12336, 12354, 12357, 12377, 12379, 12380, 12394, 12395, 12399, 12400, 12403, 12406, 12419, 12425, 12431, 12440, 12444, 12482, 12497, 12503, 12507, 12523, 12529, 12540, 12554, 12581, 12611, 12648, 12694, 12727, 12743, 12838, 12850, 12856, 12858, 12870, 12875, 12889, 12890, 12927, 12937, 12949, 12985, 13035, 13050, 13089, 13111, 13128, 13146, 13153, 13168, 13213, 13391, 13398, 13400, 13404, 13408, 13414, 13434, 13492, 13503, 13508, 13541, 13542, 13577, 13578, 13584, 13588, 13649, 13651, 13752, 13756, 13757, 13767, 13771, 13792, 13818, 13847, 13853, 13858, 13879, 13883, 13889, 13896, 13904, 13919, 13928, 13960, 13962, 13973, 13981, 14010, 14021, 14024, 14029, 14076, 14107, 14142, 14182, 14188, 14189, 14201, 14208, 14217, 14229, 14306, 14313, 14317, 14335, 14354, 14357, 14378, 14381, 14414, 14486, 14494, 14509, 14511, 14528, 14529, 14536, 14538, 14550, 14554, 14563, 14575, 14580, 14583, 14601, 14616, 14647, 14657, 14659, 14704, 14756, 14779, 14783, 14812, 14824, 14832, 14842, 14852, 14863, 14869, 14881, 14894, 14901, 14911, 14981, 14997, 15008, 15009, 15023, 15060, 15067, 15070, 15073, 15087, 15090, 15098, 15100, 15107, 15110, 15111, 15117, 15130, 15141, 15154, 15162, 15167, 15170, 15175, 15210, 15212, 15229, 15233, 15241, 15248, 15261, 15262, 15270, 15297, 15315, 15318, 15324, 15339, 15342, 15364, 15368, 15377, 15383, 15392, 15400, 15402, 15403, 15427, 15437, 15471, 15472, 15496, 15498, 15516, 15517, 15521, 15529, 15542, 15545, 15550, 15560, 15564, 15571, 15572, 15578, 15630, 15635, 15660, 15693, 15709, 15718, 15740, 15743, 15755, 15812, 15822, 15831, 15853, 15858, 15860, 15869, 15877, 15884, 15890, 15893, 15895, 15899, 15904, 15919, 15921, 15938, 15970, 15973, 15986, 15998, 15999, 16040, 16070, 16072, 16076, 16091, 16098, 16115, 16128, 16169, 16172, 16173, 16185, 16189, 16191, 16193, 16197, 16203, 16211, 16232, 16233, 16247, 16250, 16255, 16262, 16267, 16305, 16309, 16315, 16320, 16327, 16342, 16356, 16362, 16373, 16380, 16392}\n",
        "       }\n",
        "\n",
        "MASK_CLASS_NAMES = ['FullField', 'CircleCentral', 'BigRect', 'CircleSide', 'SmallRect', 'Goal']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVw7qhazu7jh",
        "outputId": "75a01d85-c51f-428b-f83d-5626528fd0db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/soccernet-calibration-sportlight\n"
          ]
        }
      ],
      "source": [
        "#@title Imports\n",
        "\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "# %cd soccersegcal\n",
        "from sncalib.detect_extremities import join_points\n",
        "\n",
        "%cd ../soccernet-calibration-sportlight\n",
        "import os\n",
        "import json\n",
        "import numpy as np\n",
        "import cv2\n",
        "import math\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image, ImageDraw\n",
        "from ellipse import LsqEllipse\n",
        "from typing import Tuple, List, Dict\n",
        "from scipy.optimize import fsolve, root\n",
        "from scipy.ndimage import convolve\n",
        "from shapely.geometry import LineString, Point\n",
        "\n",
        "from src.datatools.geom import point_within_img\n",
        "from src.datatools.line import find_closest_points\n",
        "from src.datatools.ellipse import add_conic_points, ellipse_line_intersect\n",
        "from src.datatools.intersections import intersection\n",
        "\n",
        "ROOT_PATH = \"../\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "l0BkeZ5C1Uyx"
      },
      "outputs": [],
      "source": [
        "#@title Visualisation Helpers\n",
        "\n",
        "def show_example(id: str, key = None, verbose = True):\n",
        "    with open(f\"{ROOT_PATH}{id}.json\", \"r\") as f:\n",
        "        data = json.load(f)\n",
        "    if verbose:\n",
        "        print(data.keys())\n",
        "    img = cv2.imread(f\"{ROOT_PATH}{id}.jpg\")\n",
        "    height, width, _ = img.shape\n",
        "\n",
        "    if key:\n",
        "        for item in data[key]:\n",
        "            val = (item['x'] * width, item['y'] * height)\n",
        "            val = int(val[0]), int(val[1])\n",
        "            cv2.circle(img, val, 10, (255, 255, 0), 2)\n",
        "    else:\n",
        "        for p in data:\n",
        "            coords = data[p]\n",
        "            for item in coords:\n",
        "                val = (item['x'] * width, item['y'] * height)\n",
        "                val = int(val[0]), int(val[1])\n",
        "                cv2.circle(img, val, 10, (255, 255, 0), 2)\n",
        "    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "def overlay_mask(img: Image, mask: Image):\n",
        "    img = img.convert(\"RGBA\")\n",
        "    if isinstance(mask, list):\n",
        "        mask = np.array(mask)\n",
        "    if isinstance(mask, np.ndarray):\n",
        "        mask = Image.fromarray(mask.astype(np.uint8))\n",
        "    mask = mask.convert(\"RGBA\")\n",
        "    newData = []\n",
        "    for item in mask.getdata():\n",
        "        if item[0] == 0 and item[1] == 0 and item[2] == 0:\n",
        "            newData.append((255, 255, 255, 0))\n",
        "        else:\n",
        "            newData.append(item)\n",
        "    mask.putdata(newData)\n",
        "    img.paste(mask, (0, 0), mask)\n",
        "    return img\n",
        "\n",
        "def show_segments(segments):\n",
        "    class_map = np.argmax(np.flip(segments, axis = 0), axis =0) # flip for the smaller\n",
        "    rgb_image = np.zeros((class_map.shape[0], class_map.shape[1], 3), dtype=np.float32)\n",
        "    colors = np.array([\n",
        "        [0, 0, 0],      # Black\n",
        "        [255, 0, 0],    # Red\n",
        "        [0, 255, 0],    # Green\n",
        "        [0, 0, 255],    # Blue\n",
        "        [255, 255, 0],  # Yellow\n",
        "        [255, 0, 255],  # Magenta\n",
        "        [0, 255, 255]   # Cyan\n",
        "    ]) / 255.0\n",
        "\n",
        "    rgb_image = colors[class_map]\n",
        "    return rgb_image\n",
        "\n",
        "def show_item(item):\n",
        "    fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (20, 4))\n",
        "    axs[0].imshow(item['image'])\n",
        "    axs[0].set_title(\"Image\")\n",
        "    axs[0].axis('off')\n",
        "\n",
        "    axs[1].imshow(overlay_mask(item['image'], item['outlines']))\n",
        "    axs[1].set_title(\"Outlines\")\n",
        "    axs[1].axis('off')\n",
        "\n",
        "    axs[2].imshow(show_segments(item['segments']))\n",
        "    axs[2].set_title(\"Segments\")\n",
        "    axs[2].axis('off')\n",
        "            # PART 3: GET MASK OUTLINES\n",
        "    kernel = np.array([[0, 1, 0],\n",
        "                        [1, -4, 1],\n",
        "                        [0, 1, 0]])\n",
        "    segments = np.array(item['segments']).astype(np.uint8)\n",
        "    class_edges = np.zeros(segments.shape[1:], dtype=int)\n",
        "\n",
        "    for i in range(segments.shape[0]):\n",
        "        edge = convolve(segments[i], kernel, mode='constant', cval=0)\n",
        "        edge_detected = edge != 0\n",
        "        class_edges[edge_detected] = i\n",
        "\n",
        "    axs[3].imshow(overlay_mask(item['image'], class_edges))\n",
        "    axs[3].set_title(\"Segments Outlines\")\n",
        "    axs[3].axis('off')\n",
        "    if item['is_bad']:\n",
        "        s = f\"Bad ID: {item['id']}\"\n",
        "    else:\n",
        "        s = f\"ID: {item['id']}\"\n",
        "    fig.suptitle(s, fontsize = 8)\n",
        "    plt.subplots_adjust(hspace = -0.2, wspace = -0.05)\n",
        "    plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "cellView": "form",
        "id": "lvdFJcIVOoOi"
      },
      "outputs": [],
      "source": [
        "#@title Math Helpers\n",
        "def fit_ellipse(points) -> Dict:\n",
        "    ellipse = cv2.fitEllipse(points)\n",
        "    (xc, yc), (major_axis, minor_axis), rotation_angle = ellipse\n",
        "    return {\n",
        "        \"center\": (int(xc), int(yc)),\n",
        "        \"axes\": (int(major_axis/2) , int(minor_axis/2)),\n",
        "        \"deg_angle\": rotation_angle,\n",
        "        \"rad_angle\": np.deg2rad(rotation_angle)\n",
        "    }\n",
        "\n",
        "def get_angle_on_ellipse(curr_point: Tuple, center: Tuple, axes: Tuple , rad_angle):\n",
        "    x, y = curr_point\n",
        "    x0, y0 = center\n",
        "    a, b = axes\n",
        "    dx = x - x0\n",
        "    dy = y - y0\n",
        "\n",
        "    rotated_dx = np.cos(rad_angle) * dx + np.sin(rad_angle) * dy\n",
        "    rotated_dy = -np.sin(rad_angle) * dx + np.cos(rad_angle) * dy\n",
        "    return np.degrees(np.arctan2(a * rotated_dy, b * rotated_dx))\n",
        "\n",
        "def get_furthest_points(line_points):\n",
        "    p1 = np.array(line_points[0])\n",
        "    p2 = np.array(line_points[1])\n",
        "    max_dist = np.linalg.norm(p2 - p1)\n",
        "    for i in range(len(line_points)):\n",
        "        for j in range(i + 1, len(line_points)):\n",
        "            p_test_1 = np.array(line_points[i])\n",
        "            p_test_2 = np.array(line_points[j])\n",
        "            dist = np.linalg.norm(p_test_2 - p_test_1)\n",
        "            if dist > max_dist:\n",
        "                p1, p2 = p_test_1, p_test_2\n",
        "                max_dist = dist\n",
        "    return p1, p2\n",
        "\n",
        "def ellipse_line_intersection(center, major, minor, angle_rad, line_points):\n",
        "\n",
        "    # Define the parametric form of the ellipse\n",
        "    def ellipse(x, y):\n",
        "        cos_a = np.cos(angle_rad)\n",
        "        sin_a = np.sin(angle_rad)\n",
        "        x0, y0 = center\n",
        "        term1 = ((x - x0) * cos_a + (y - y0) * sin_a) ** 2 / major ** 2\n",
        "        term2 = ((x - x0) * sin_a - (y - y0) * cos_a) ** 2 / minor ** 2\n",
        "        return term1 + term2 - 1\n",
        "\n",
        "    # Define the line in parametric form, assuming line_points are [point1, point2]\n",
        "    p1, p2 = get_furthest_points(line_points)\n",
        "    def line(s):\n",
        "        return p1 + (p2 - p1) * s\n",
        "\n",
        "    def find_intersection(t):\n",
        "        x, y = line(t)\n",
        "        return ellipse(x, y)\n",
        "\n",
        "    # Find initial t values that might be close to intersections\n",
        "    t_values = np.linspace(0, 1, 1000)\n",
        "    initial_ts = [t for t in t_values if np.abs(find_intersection(t)) < 0.1]\n",
        "\n",
        "    # Find precise intersection points\n",
        "    intersections = []\n",
        "    for t in initial_ts:\n",
        "        t_solution, = fsolve(find_intersection, t)\n",
        "        if 0 <= t_solution <= 1:\n",
        "            intersections.append(line(t_solution))\n",
        "\n",
        "    # Filter unique solutions considering a tolerance\n",
        "    unique_intersections = np.unique(np.array(intersections).astype(np.int32), axis=0)\n",
        "\n",
        "    return unique_intersections\n",
        "\n",
        "def direction_vector(points):\n",
        "    i = 1\n",
        "    points = [np.array(x) for x in points]\n",
        "    while i < len(points) - 1 and np.linalg.norm(points[0] - points[i]) < 10:\n",
        "        i += 1\n",
        "    return points[0] - points[i]\n",
        "\n",
        "def find_extreme_points(points):\n",
        "    slope = (points[-1, 1] - points[0, 1]) / (points[-1, 0] - points[0, 0]) if points[-1, 0] != points[0, 0] else float('inf')\n",
        "\n",
        "    # Find extreme points based on the direction of the line\n",
        "    if abs(slope) > 1:\n",
        "        # Line is closer to vertical, use y to find extremes\n",
        "        min_point = points[points[:, 1].argmin()]\n",
        "        max_point = points[points[:, 1].argmax()]\n",
        "    else:\n",
        "        # Line is closer to horizontal, use x to find extremes\n",
        "        min_point = points[points[:, 0].argmin()]\n",
        "        max_point = points[points[:, 0].argmax()]\n",
        "\n",
        "    return min_point, max_point"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "cellView": "form",
        "id": "9MWQ3o5jvI04"
      },
      "outputs": [],
      "source": [
        "#@title Processing Helpers\n",
        "ELLIPSE_INTERSECTIONS = {\n",
        "    \"Circle left\": 'Big rect. left main',\n",
        "    \"Circle right\": 'Big rect. right main'\n",
        "}\n",
        "def check_orient(annotations): # check mislabelled\n",
        "    # for pair in [(\"Circle left\",'Big rect. left main'), (\"Circle right\" 'Big rect. right main')]:\n",
        "    #     if (pair[0] in annotations) ^ (pair[1] in annotations):\n",
        "    left = []\n",
        "    right = []\n",
        "    for k in annotations:\n",
        "        if 'goal' not in k.lower():\n",
        "            if 'left' in k:\n",
        "                left.append(k)\n",
        "            if 'right' in k:\n",
        "                right.append(k)\n",
        "    left_count = len(left)\n",
        "    right_count = len(right)\n",
        "\n",
        "    if abs(left_count-right_count) > 2: # something is probably mislabelled\n",
        "        if left_count == 1:\n",
        "            src = left[0]\n",
        "            dst = src.replace(\"left\", \"right\")\n",
        "        elif right_count == 1:\n",
        "            src = right[0]\n",
        "            dst = src.replace(\"right\", \"left\")\n",
        "        else:\n",
        "            return annotations\n",
        "            # continue\n",
        "        if src in annotations:\n",
        "            if dst in annotations:\n",
        "                annotations[dst] += annotations[src]\n",
        "            else:\n",
        "                annotations[dst] = annotations[src]\n",
        "            del annotations[src]\n",
        "    return annotations\n",
        "\n",
        "def read_annotations(id: str, img_size: Tuple = (960, 540), derive_intersections:bool = True) -> Tuple[Dict, Dict]:\n",
        "    w, h = img_size\n",
        "    with open(f\"{ROOT_PATH}{id}.json\", \"r\") as f:\n",
        "        annotations = json.load(f)\n",
        "\n",
        "    res = {}\n",
        "    for cls_id in annotations:\n",
        "        if len(annotations[cls_id]) <= 1:\n",
        "            continue\n",
        "        res[cls_id] = []\n",
        "        for point in annotations[cls_id]:\n",
        "            res[cls_id].append((point['x'], point['y']))\n",
        "    annotations = res\n",
        "    annotations = check_orient(annotations)\n",
        "    if derive_intersections:\n",
        "        inters = {}\n",
        "        for i, (line1_name, line2_name) in LINE_INTERSECTIONS.items():\n",
        "            if line1_name in annotations and line2_name in annotations:\n",
        "                if len(annotations[line1_name]) > 1 and len(annotations[line2_name]) > 1:\n",
        "                    inters[(line1_name, line2_name)] = intersection(\n",
        "                                                            np.array(annotations[line1_name]) * img_size,\n",
        "                                                            np.array(annotations[line2_name]) * img_size)\n",
        "\n",
        "\n",
        "        inters = {i: point_within_img(inters[i], img_size, margin=0)\n",
        "           for i in inters}\n",
        "        for key, value in inters.items():\n",
        "            a, b = key\n",
        "            if value:\n",
        "                normalized = (value[0]/ img_size[0], value[1]/ img_size[1])\n",
        "                annotations[a].append(normalized)\n",
        "                annotations[b].append(normalized)\n",
        "\n",
        "    abs_annotations = {}\n",
        "    for class_name, raw_points in annotations.items():\n",
        "        abs_annotations[class_name] = np.array([(x[0] * w, x[1] * h) for x in raw_points]).astype(np.float32)\n",
        "\n",
        "    return annotations, abs_annotations\n",
        "\n",
        "def default_drawer(mask: np.array, points: np.array, class_number: int, sort: bool = True, close: bool = False):\n",
        "    if sort:\n",
        "        centroid = np.mean(points, axis=0)\n",
        "        angles = np.arctan2(points[:, 1] - centroid[1], points[:, 0] - centroid[0])\n",
        "        sort_order = np.argsort(angles)\n",
        "        points = points[sort_order]\n",
        "    mask = cv2.polylines(mask, [points.astype(np.int32)], close, class_number + 1, 2)\n",
        "    return mask\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "cellView": "form",
        "id": "XiA8HmQlak5q"
      },
      "outputs": [],
      "source": [
        "#@title Problematic examples\n",
        "\n",
        "def swap(fpath: str, src, dst):\n",
        "\n",
        "    with open(f\"../{fpath}\" + \".json\", \"r\") as f:\n",
        "        data = json.load(f)\n",
        "    if src in data:\n",
        "        if dst in data:\n",
        "            data[dst] += data[src]\n",
        "        else:\n",
        "            data[dst] = data[src]\n",
        "        del data[src]\n",
        "    with open(f\"../{fpath}\" + \".json\", \"w\") as f:\n",
        "        json.dump(data, f)\n",
        "\n",
        "# src is incorrect\n",
        "to_fix = [\n",
        "        {\n",
        "            \"fpath\": \"train/15019\",\n",
        "            \"src\": 'Circle right',\n",
        "            \"dst\": 'Circle central'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/08785\",\n",
        "            \"src\": 'Middle line',\n",
        "            \"dst\": 'Circle central'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/02426\",\n",
        "            \"src\": 'Big rect. left main',\n",
        "            \"dst\": 'Big rect. right main'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"test/00823\",\n",
        "            \"src\": 'Line unknown',\n",
        "            \"dst\": 'Goal right post right'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/07449\",\n",
        "            \"src\": 'Circle right',\n",
        "            \"dst\": 'Circle left'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/09740\",\n",
        "            \"src\": 'Big rect. left main',\n",
        "            \"dst\": 'Big rect. right main'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/09674\",\n",
        "            \"src\": 'Circle left',\n",
        "            \"dst\": 'Circle right'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/09674\",\n",
        "            \"src\": 'Circle right',\n",
        "            \"dst\": 'Circle central'\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/13537\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Middle line\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/03820\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Big rect. right bottom\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/14948\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Big rect. right bottom\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/04434\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Big rect. left bottom\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/16302\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Side line top\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"train/14244\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Big rect. right bottom\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"valid/03102\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Side line right\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"test/01960\",\n",
        "            \"src\": \"Goal unknown\",\n",
        "            \"dst\": \"Goal left crossbar\"\n",
        "        },\n",
        "        {\n",
        "            \"fpath\": \"test/03134\",\n",
        "            \"src\": \"Line unknown\",\n",
        "            \"dst\": \"Big rect. right top\"\n",
        "        },\n",
        "]\n",
        "\n",
        "for item in to_fix:\n",
        "    swap(**item)\n",
        "\n",
        "def del_unknown(fpath, name: str = 'Line unknown'):\n",
        "    with open(f\"../{fpath}\" + \".json\", \"r\") as f:\n",
        "        data = json.load(f)\n",
        "    if name in data:\n",
        "        del data[name]\n",
        "    with open(f\"../{fpath}\" + \".json\", \"w\") as f:\n",
        "        json.dump(data, f)\n",
        "\n",
        "\n",
        "to_del = ['train/12506', 'train/05777', \"train/05752\", \"train/12675\", \"train/02208\", \"train/03352\",\n",
        "          \"train/07810\", \"train/13496\", \"train/01548\", \"train/02539\", \"train/06971\", \"train/00703\",\n",
        "          \"train/12715\", \"train/01199\", \"train/16302\", \"train/07907\", \"train/02556\", \"valid/00616\",\n",
        "          \"valid/00262\", \"valid/02702\", \"valid/00249\", \"test/02461\", \"test/00292\", \"test/02405\",\n",
        "          \"test/02587\", \"train/03353\", \"train/04438\"]\n",
        "for item in to_del:\n",
        "    del_unknown(item)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "cellView": "form",
        "id": "VVlTbAqol17C"
      },
      "outputs": [],
      "source": [
        "#@title SoccerSegCal\n",
        "\n",
        "class Line:\n",
        "    def __init__(self, pkts, image_shape):\n",
        "        self.image_shape = image_shape\n",
        "        self.missing = False\n",
        "        pkts = [np.array((p[0] * image_shape[1], p[1] * image_shape[0])) for p in pkts]\n",
        "        pkts = np.array(join_points(pkts, float('Inf'))[0])\n",
        "        self.original = LineString(pkts)\n",
        "        p1 = self.connect_to_image_border(pkts[0], self.direction(pkts))\n",
        "        p2 = self.connect_to_image_border(pkts[-1], self.direction(pkts[::-1]))\n",
        "        self.line_string = LineString(np.vstack([[p1], pkts, [p2]]))\n",
        "        self.padding = [LineString([p1, pkts[0]]), LineString([pkts[-1], p2])]\n",
        "\n",
        "    def direction(self, pkts):\n",
        "        i = 1\n",
        "        while i < len(pkts) - 1 and np.linalg.norm(pkts[0] - pkts[i]) < 10:\n",
        "            i += 1\n",
        "        return pkts[0] - pkts[i]\n",
        "\n",
        "\n",
        "    def connect_to_image_border(self, p, v):\n",
        "        if p is None:\n",
        "            return 0, None\n",
        "        v = v / np.linalg.norm(v)\n",
        "        tt  = [-p[0] / v[0], -p[1] / v[1], (self.image_shape[1] - 1 - p[0]) / v[0], (self.image_shape[0] - 1 - p[1]) / v[1]]\n",
        "        tt = [t for t in tt if t >= 0 and np.isfinite(t)]\n",
        "        if len(tt) == 0:\n",
        "            return p\n",
        "        t = min(tt)\n",
        "        return p + t * v\n",
        "\n",
        "\n",
        "class MisingLine(Line):\n",
        "    def __init__(self, image_shape) -> None:\n",
        "        self.image_shape = image_shape\n",
        "        self.pkts = [None, None]\n",
        "        self.v1 = self.v2 = None\n",
        "        self.missing = True\n",
        "\n",
        "    def connect(self, other):\n",
        "        pass\n",
        "\n",
        "    def match_direction(self, other):\n",
        "        pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "0r8PvR97zpNy"
      },
      "outputs": [],
      "source": [
        "\n",
        "def process_indiv(id: str) -> Dict:\n",
        "    img = Image.open(f\"{ROOT_PATH}/{id}.jpg\")\n",
        "    width, height = img.size\n",
        "    tmp = id.split(\"/\")\n",
        "    s = tmp[0]\n",
        "    num_id = int(tmp[1])\n",
        "    annotations, abs_annotations = read_annotations(id, (width, height))\n",
        "    if len(abs_annotations) == 0:\n",
        "        return None\n",
        "\n",
        "    ### PART 1: OUTLINE MASK\n",
        "    outline_mask = np.zeros((height, width), dtype=np.uint8) # numpy reads (height, width, channels)\n",
        "    for class_name, abs_points in abs_annotations.items():\n",
        "        class_number = CLASS2ID[class_name]\n",
        "        if 'circle' in class_name.lower():\n",
        "            if len(abs_points) < 5: # unable to fit a regression\n",
        "                outline_mask = default_drawer(outline_mask, abs_points, class_number, sort=True)\n",
        "            else:\n",
        "                reg_res = fit_ellipse(abs_points)\n",
        "                if 'central' in class_name.lower():\n",
        "                    outline_mask = cv2.ellipse(outline_mask, reg_res['center'], reg_res['axes'], reg_res['deg_angle'], 0, 360, class_number + 1, 2)\n",
        "                else: # Half Ellipse\n",
        "                    other_name = ELLIPSE_INTERSECTIONS[class_name]\n",
        "                    out = []\n",
        "                    if other_name in abs_annotations and (len(abs_annotations[other_name]) > 1):\n",
        "                        out = ellipse_line_intersection(reg_res['center'], reg_res['axes'][0], reg_res['axes'][1], reg_res['rad_angle'], abs_annotations[other_name])\n",
        "                    if len(out) == 0: # unable to find intersection\n",
        "                        outline_mask = default_drawer(outline_mask, abs_points, class_number, sort=True)\n",
        "                    else:\n",
        "                        if len(out) == 1:\n",
        "                            out = [out[0], abs_points[-1]]\n",
        "                        extreme_angles = []\n",
        "                        for p in out:\n",
        "                            extreme_angles.append(get_angle_on_ellipse(p, reg_res['center'], reg_res['axes'], reg_res['rad_angle']))\n",
        "                        outline_mask = cv2.ellipse(outline_mask, reg_res['center'], reg_res['axes'], reg_res['deg_angle'],\n",
        "                                                    extreme_angles[0], extreme_angles[1], class_number + 1, 2)\n",
        "        else:\n",
        "            outline_mask = default_drawer(outline_mask, abs_points, class_number, sort=False)\n",
        "\n",
        "        # PART 2: GET FULL MASKS\n",
        "        # https://github.com/Spiideo/soccersegcal/blob/main/soccersegcal/dataloader.py\n",
        "        segments = np.zeros((6,) + (height, width), np.uint8)\n",
        "        for name, area in FIELD_AREAS.items():\n",
        "            all_lines = [Line(annotations[n], (height, width)).original.coords\n",
        "                         for n in area['contains'] if n in annotations]\n",
        "            if len(all_lines) == 0:\n",
        "                continue\n",
        "            center_x, center_y = map(int, np.round(np.vstack(all_lines).mean(0)))\n",
        "            center_x = min(max(center_x, 0), width - 1)\n",
        "            center_y = min(max(center_y, 0), height - 1)\n",
        "\n",
        "            rim = []\n",
        "            for n in area['border']:\n",
        "                if n in annotations:\n",
        "                    rim.append(Line(annotations[n], (height, width)))\n",
        "                else:\n",
        "                    rim.append(MisingLine((height, width)))\n",
        "\n",
        "            segs = np.zeros((height, width), np.uint8)\n",
        "            plot_lines = [np.int32(l.line_string.coords) for l in rim if not l.missing]\n",
        "            cv2.polylines(segs, plot_lines, False, 255, 1)\n",
        "\n",
        "            if segs[max(center_y-1, 0):min(center_y+2, height), max(center_x-1, 0):min(center_x+2, width)].max() == 255:  # If the center is on the line try moving it towards the long border not contained\n",
        "                for n in area['border']:\n",
        "                    if n not in area['contains'] and n in annotations:\n",
        "                        l = Line(annotations[n], (height, width)).line_string\n",
        "                        p = np.array(l.interpolate(l.project(Point([center_x, center_y]))).coords)[0]\n",
        "                        p -= (center_x, center_y)\n",
        "                        l = np.linalg.norm(p)\n",
        "                        if l > 0:\n",
        "                            p /= l\n",
        "                            center_x, center_y = map(int, (center_x, center_y) + 2 * p)\n",
        "                            center_x = min(max(center_x, 0), width - 1)\n",
        "                            center_y = min(max(center_y, 0), height - 1)\n",
        "\n",
        "            cv2.floodFill(segs, None, (center_x, center_y), 255)\n",
        "            cv2.polylines(segs, plot_lines, False, 0, 1)\n",
        "\n",
        "            segments[area['index']][segs>0] = segs[segs>0]\n",
        "\n",
        "        background = np.logical_not(np.sum(segments, axis=0, keepdims=True).astype(bool))\n",
        "        segments = np.concatenate((segments, background), axis=0)\n",
        "\n",
        "    return {\n",
        "        'image': img,\n",
        "        'outlines': outline_mask.astype(np.uint8),\n",
        "        'segments': np.array(segments, dtype=bool),\n",
        "        'id': num_id,\n",
        "        'is_bad': num_id in BAD[s]\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_3S_4hVnrqHy"
      },
      "outputs": [],
      "source": [
        "def generator(img_paths):\n",
        "    for p in img_paths:\n",
        "        id = p.replace(\".jpg\", \"\")\n",
        "        try:\n",
        "            out = process_indiv(id)\n",
        "            if out is None:\n",
        "                pass\n",
        "            else:\n",
        "                yield out\n",
        "        except Exception as e:\n",
        "            print(\"ERROR: \", id, e)\n",
        "            pass\n",
        "\n",
        "\n",
        "from datasets import Dataset, Features, Array3D, Array2D, Sequence, DatasetDict, Value\n",
        "import datasets\n",
        "\n",
        "\n",
        "train_paths = [r'train/' + x for x in os.listdir(ROOT_PATH + 'train') if x.endswith('.jpg')]\n",
        "train_dataset = Dataset.from_generator(generator, gen_kwargs={\"img_paths\": train_paths}, writer_batch_size = 500, num_proc = 1)\n",
        "!rm -rf 'content/train/'\n",
        "\n",
        "val_paths = [r'valid/' + x for x in os.listdir(ROOT_PATH + 'valid') if x.endswith('.jpg')]\n",
        "val_dataset = Dataset.from_generator(generator, gen_kwargs={\"img_paths\": val_paths}, writer_batch_size = 500, num_proc = 1)\n",
        "!rm -rf 'content/valid/'\n",
        "\n",
        "\n",
        "test_paths = [r'test/' + x for x in os.listdir(ROOT_PATH + 'test') if x.endswith('.jpg')]\n",
        "test_dataset = Dataset.from_generator(generator, gen_kwargs={\"img_paths\": test_paths}, writer_batch_size = 500, num_proc = 1)\n",
        "!rm -rf 'content/test/'\n",
        "\n",
        "dataset = DatasetDict({\n",
        "    'train': train_dataset,\n",
        "    'val': val_dataset,\n",
        "    'test': test_dataset\n",
        "})\n",
        "\n",
        "dataset.push_to_hub(\"nreHieW/SoccerNet_Field_Segmentation\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "PLyn7URTMnzf",
        "outputId": "4a27f43e-8a51-4bc5-c23c-2f5040da9bdd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x400 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "show_item(dataset['train'][99])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFAkzzDEWY7A"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}