Anchors.swift

/**
 * Anchors
 *
 * SSD Anchor boxes for SSD/Mobilenet architectures
 * num_layers    = 6
 * min_scale     = 0.2
 * max_scale     = 0.95
 * aspect_ratios = [1.0, 2.0, 0.5, 3.0, 0.3333]
 *
 * See: https://github.com/tensorflow/models/blob/master/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py#L248
 */
struct Anchors {
  static let numAnchors = 1917
  static var ssdAnchors: [[Float32]] {
    var arr: [[Float32]] = Array(repeating: Array(repeating: 0.0, count: 4), count: numAnchors)

    arr[0] = [ -0.02368421, -0.02368421,  0.07631579,  0.07631579 ]
    arr[1] = [ -0.04439489, -0.11510557,  0.09702647,  0.16773716 ]
    arr[2] = [ -0.11510557, -0.04439489,  0.16773716,  0.09702647 ]
    arr[3] = [ -0.02368421,  0.02894736,  0.07631579,  0.12894736 ]
    arr[4] = [ -0.04439489, -0.06247400,  0.09702647,  0.22036873 ]
    arr[5] = [ -0.11510557,  0.00823668,  0.16773716,  0.14965805 ]
    arr[6] = [ -0.02368421,  0.08157896,  0.07631579,  0.18157895 ]
    arr[7] = [ -0.04439489, -0.00984241,  0.09702647,  0.27300030 ]
    arr[8] = [ -0.11510557,  0.06086827,  0.16773716,  0.20228964 ]
    arr[9] = [ -0.02368421,  0.13421053,  0.07631579,  0.23421052 ]
    arr[10] = [ -0.04439489,  0.04278916,  0.09702647,  0.32563189 ]
    arr[11] = [ -0.11510557,  0.11349984,  0.16773716,  0.25492120 ]
    arr[12] = [ -0.02368421,  0.18684211,  0.07631579,  0.28684211 ]
    arr[13] = [ -0.04439489,  0.09542075,  0.09702647,  0.37826347 ]
    arr[14] = [ -0.11510557,  0.16613144,  0.16773716,  0.30755278 ]
    arr[15] = [ -0.02368421,  0.23947369,  0.07631579,  0.33947369 ]
    arr[16] = [ -0.04439489,  0.14805232,  0.09702647,  0.43089503 ]
    arr[17] = [ -0.11510557,  0.21876299,  0.16773716,  0.36018437 ]
    arr[18] = [ -0.02368421,  0.29210523,  0.07631579,  0.39210525 ]
    arr[19] = [ -0.04439489,  0.20068388,  0.09702647,  0.48352659 ]
    arr[20] = [ -0.11510557,  0.27139455,  0.16773716,  0.41281593 ]
    arr[21] = [ -0.02368421,  0.34473681,  0.07631579,  0.44473684 ]
    arr[22] = [ -0.04439489,  0.25331545,  0.09702647,  0.53615820 ]
    arr[23] = [ -0.11510557,  0.32402614,  0.16773716,  0.46544752 ]
    arr[24] = [ -0.02368421,  0.39736840,  0.07631579,  0.49736843 ]
    arr[25] = [ -0.04439489,  0.30594707,  0.09702647,  0.58878976 ]
    arr[26] = [ -0.11510557,  0.37665772,  0.16773716,  0.51807910 ]
    arr[27] = [ -0.02368421,  0.44999999,  0.07631579,  0.55000001 ]
    arr[28] = [ -0.04439489,  0.35857862,  0.09702647,  0.64142138 ]
    arr[29] = [ -0.11510557,  0.42928931,  0.16773716,  0.57071066 ]
    arr[30] = [ -0.02368421,  0.50263160,  0.07631579,  0.60263163 ]
    arr[31] = [ -0.04439489,  0.41121024,  0.09702647,  0.69405299 ]
    arr[32] = [ -0.11510557,  0.48192093,  0.16773716,  0.62334228 ]
    arr[33] = [ -0.02368421,  0.55526316,  0.07631579,  0.65526319 ]
    arr[34] = [ -0.04439489,  0.46384180,  0.09702647,  0.74668455 ]
    arr[35] = [ -0.11510557,  0.53455251,  0.16773716,  0.67597383 ]
    arr[36] = [ -0.02368421,  0.60789472,  0.07631579,  0.70789474 ]
    arr[37] = [ -0.04439489,  0.51647335,  0.09702647,  0.79931611 ]
    arr[38] = [ -0.11510557,  0.58718407,  0.16773716,  0.72860539 ]
    arr[39] = [ -0.02368421,  0.66052634,  0.07631579,  0.76052636 ]
    arr[40] = [ -0.04439489,  0.56910497,  0.09702647,  0.85194772 ]
    arr[41] = [ -0.11510557,  0.63981569,  0.16773716,  0.78123701 ]
    arr[42] = [ -0.02368421,  0.71315789,  0.07631579,  0.81315792 ]
    arr[43] = [ -0.04439489,  0.62173653,  0.09702647,  0.90457928 ]
    arr[44] = [ -0.11510557,  0.69244725,  0.16773716,  0.83386856 ]
    arr[45] = [ -0.02368421,  0.76578951,  0.07631579,  0.86578953 ]
    arr[46] = [ -0.04439489,  0.67436814,  0.09702647,  0.95721090 ]
    arr[47] = [ -0.11510557,  0.74507886,  0.16773716,  0.88650018 ]
    arr[48] = [ -0.02368421,  0.81842107,  0.07631579,  0.91842109 ]
    arr[49] = [ -0.04439489,  0.72699970,  0.09702647,  1.00984240 ]
    arr[50] = [ -0.11510557,  0.79771042,  0.16773716,  0.93913174 ]
    arr[51] = [ -0.02368421,  0.87105262,  0.07631579,  0.97105265 ]
    arr[52] = [ -0.04439489,  0.77963126,  0.09702647,  1.06247401 ]
    arr[53] = [ -0.11510557,  0.85034198,  0.16773716,  0.99176329 ]
    arr[54] = [ -0.02368421,  0.92368424,  0.07631579,  1.02368426 ]
    arr[55] = [ -0.04439489,  0.83226287,  0.09702647,  1.11510563 ]
    arr[56] = [ -0.11510557,  0.90297359,  0.16773716,  1.04439497 ]
    arr[57] = [  0.02894736, -0.02368421,  0.12894736,  0.07631579 ]
    arr[58] = [  0.00823668, -0.11510557,  0.14965805,  0.16773716 ]
    arr[59] = [ -0.06247400, -0.04439489,  0.22036873,  0.09702647 ]
    arr[60] = [  0.02894736,  0.02894736,  0.12894736,  0.12894736 ]
    arr[61] = [  0.00823668, -0.06247400,  0.14965805,  0.22036873 ]
    arr[62] = [ -0.06247400,  0.00823668,  0.22036873,  0.14965805 ]
    arr[63] = [  0.02894736,  0.08157896,  0.12894736,  0.18157895 ]
    arr[64] = [  0.00823668, -0.00984241,  0.14965805,  0.27300030 ]
    arr[65] = [ -0.06247400,  0.06086827,  0.22036873,  0.20228964 ]
    arr[66] = [  0.02894736,  0.13421053,  0.12894736,  0.23421052 ]
    arr[67] = [  0.00823668,  0.04278916,  0.14965805,  0.32563189 ]
    arr[68] = [ -0.06247400,  0.11349984,  0.22036873,  0.25492120 ]
    arr[69] = [  0.02894736,  0.18684211,  0.12894736,  0.28684211 ]
    arr[70] = [  0.00823668,  0.09542075,  0.14965805,  0.37826347 ]
    arr[71] = [ -0.06247400,  0.16613144,  0.22036873,  0.30755278 ]
    arr[72] = [  0.02894736,  0.23947369,  0.12894736,  0.33947369 ]
    arr[73] = [  0.00823668,  0.14805232,  0.14965805,  0.43089503 ]
    arr[74] = [ -0.06247400,  0.21876299,  0.22036873,  0.36018437 ]
    arr[75] = [  0.02894736,  0.29210523,  0.12894736,  0.39210525 ]
    arr[76] = [  0.00823668,  0.20068388,  0.14965805,  0.48352659 ]
    arr[77] = [ -0.06247400,  0.27139455,  0.22036873,  0.41281593 ]
    arr[78] = [  0.02894736,  0.34473681,  0.12894736,  0.44473684 ]
    arr[79] = [  0.00823668,  0.25331545,  0.14965805,  0.53615820 ]
    arr[80] = [ -0.06247400,  0.32402614,  0.22036873,  0.46544752 ]
    arr[81] = [  0.02894736,  0.39736840,  0.12894736,  0.49736843 ]
    arr[82] = [  0.00823668,  0.30594707,  0.14965805,  0.58878976 ]
    arr[83] = [ -0.06247400,  0.37665772,  0.22036873,  0.51807910 ]
    arr[84] = [  0.02894736,  0.44999999,  0.12894736,  0.55000001 ]
    arr[85] = [  0.00823668,  0.35857862,  0.14965805,  0.64142138 ]
    arr[86] = [ -0.06247400,  0.42928931,  0.22036873,  0.57071066 ]
    arr[87] = [  0.02894736,  0.50263160,  0.12894736,  0.60263163 ]
    arr[88] = [  0.00823668,  0.41121024,  0.14965805,  0.69405299 ]
    arr[89] = [ -0.06247400,  0.48192093,  0.22036873,  0.62334228 ]
    arr[90] = [  0.02894736,  0.55526316,  0.12894736,  0.65526319 ]
    arr[91] = [  0.00823668,  0.46384180,  0.14965805,  0.74668455 ]
    arr[92] = [ -0.06247400,  0.53455251,  0.22036873,  0.67597383 ]
    arr[93] = [  0.02894736,  0.60789472,  0.12894736,  0.70789474 ]
    arr[94] = [  0.00823668,  0.51647335,  0.14965805,  0.79931611 ]
    arr[95] = [ -0.06247400,  0.58718407,  0.22036873,  0.72860539 ]
    arr[96] = [  0.02894736,  0.66052634,  0.12894736,  0.76052636 ]
    arr[97] = [  0.00823668,  0.56910497,  0.14965805,  0.85194772 ]
    arr[98] = [ -0.06247400,  0.63981569,  0.22036873,  0.78123701 ]
    arr[99] = [  0.02894736,  0.71315789,  0.12894736,  0.81315792 ]
    arr[100] = [  0.00823668,  0.62173653,  0.14965805,  0.90457928 ]
    arr[101] = [ -0.06247400,  0.69244725,  0.22036873,  0.83386856 ]
    arr[102] = [  0.02894736,  0.76578951,  0.12894736,  0.86578953 ]
    arr[103] = [  0.00823668,  0.67436814,  0.14965805,  0.95721090 ]
    arr[104] = [ -0.06247400,  0.74507886,  0.22036873,  0.88650018 ]
    arr[105] = [  0.02894736,  0.81842107,  0.12894736,  0.91842109 ]
    arr[106] = [  0.00823668,  0.72699970,  0.14965805,  1.00984240 ]
    arr[107] = [ -0.06247400,  0.79771042,  0.22036873,  0.93913174 ]
    arr[108] = [  0.02894736,  0.87105262,  0.12894736,  0.97105265 ]
    arr[109] = [  0.00823668,  0.77963126,  0.14965805,  1.06247401 ]
    arr[110] = [ -0.06247400,  0.85034198,  0.22036873,  0.99176329 ]
    arr[111] = [  0.02894736,  0.92368424,  0.12894736,  1.02368426 ]
    arr[112] = [  0.00823668,  0.83226287,  0.14965805,  1.11510563 ]
    arr[113] = [ -0.06247400,  0.90297359,  0.22036873,  1.04439497 ]
    arr[114] = [  0.08157896, -0.02368421,  0.18157895,  0.07631579 ]
    arr[115] = [  0.06086827, -0.11510557,  0.20228964,  0.16773716 ]
    arr[116] = [ -0.00984241, -0.04439489,  0.27300030,  0.09702647 ]
    arr[117] = [  0.08157896,  0.02894736,  0.18157895,  0.12894736 ]
    arr[118] = [  0.06086827, -0.06247400,  0.20228964,  0.22036873 ]
    arr[119] = [ -0.00984241,  0.00823668,  0.27300030,  0.14965805 ]
    arr[120] = [  0.08157896,  0.08157896,  0.18157895,  0.18157895 ]
    arr[121] = [  0.06086827, -0.00984241,  0.20228964,  0.27300030 ]
    arr[122] = [ -0.00984241,  0.06086827,  0.27300030,  0.20228964 ]
    arr[123] = [  0.08157896,  0.13421053,  0.18157895,  0.23421052 ]
    arr[124] = [  0.06086827,  0.04278916,  0.20228964,  0.32563189 ]
    arr[125] = [ -0.00984241,  0.11349984,  0.27300030,  0.25492120 ]
    arr[126] = [  0.08157896,  0.18684211,  0.18157895,  0.28684211 ]
    arr[127] = [  0.06086827,  0.09542075,  0.20228964,  0.37826347 ]
    arr[128] = [ -0.00984241,  0.16613144,  0.27300030,  0.30755278 ]
    arr[129] = [  0.08157896,  0.23947369,  0.18157895,  0.33947369 ]
    arr[130] = [  0.06086827,  0.14805232,  0.20228964,  0.43089503 ]
    arr[131] = [ -0.00984241,  0.21876299,  0.27300030,  0.36018437 ]
    arr[132] = [  0.08157896,  0.29210523,  0.18157895,  0.39210525 ]
    arr[133] = [  0.06086827,  0.20068388,  0.20228964,  0.48352659 ]
    arr[134] = [ -0.00984241,  0.27139455,  0.27300030,  0.41281593 ]
    arr[135] = [  0.08157896,  0.34473681,  0.18157895,  0.44473684 ]
    arr[136] = [  0.06086827,  0.25331545,  0.20228964,  0.53615820 ]
    arr[137] = [ -0.00984241,  0.32402614,  0.27300030,  0.46544752 ]
    arr[138] = [  0.08157896,  0.39736840,  0.18157895,  0.49736843 ]
    arr[139] = [  0.06086827,  0.30594707,  0.20228964,  0.58878976 ]
    arr[140] = [ -0.00984241,  0.37665772,  0.27300030,  0.51807910 ]
    arr[141] = [  0.08157896,  0.44999999,  0.18157895,  0.55000001 ]
    arr[142] = [  0.06086827,  0.35857862,  0.20228964,  0.64142138 ]
    arr[143] = [ -0.00984241,  0.42928931,  0.27300030,  0.57071066 ]
    arr[144] = [  0.08157896,  0.50263160,  0.18157895,  0.60263163 ]
    arr[145] = [  0.06086827,  0.41121024,  0.20228964,  0.69405299 ]
    arr[146] = [ -0.00984241,  0.48192093,  0.27300030,  0.62334228 ]
    arr[147] = [  0.08157896,  0.55526316,  0.18157895,  0.65526319 ]
    arr[148] = [  0.06086827,  0.46384180,  0.20228964,  0.74668455 ]
    arr[149] = [ -0.00984241,  0.53455251,  0.27300030,  0.67597383 ]
    arr[150] = [  0.08157896,  0.60789472,  0.18157895,  0.70789474 ]
    arr[151] = [  0.06086827,  0.51647335,  0.20228964,  0.79931611 ]
    arr[152] = [ -0.00984241,  0.58718407,  0.27300030,  0.72860539 ]
    arr[153] = [  0.08157896,  0.66052634,  0.18157895,  0.76052636 ]
    arr[154] = [  0.06086827,  0.56910497,  0.20228964,  0.85194772 ]
    arr[155] = [ -0.00984241,  0.63981569,  0.27300030,  0.78123701 ]
    arr[156] = [  0.08157896,  0.71315789,  0.18157895,  0.81315792 ]
    arr[157] = [  0.06086827,  0.62173653,  0.20228964,  0.90457928 ]
    arr[158] = [ -0.00984241,  0.69244725,  0.27300030,  0.83386856 ]
    arr[159] = [  0.08157896,  0.76578951,  0.18157895,  0.86578953 ]
    arr[160] = [  0.06086827,  0.67436814,  0.20228964,  0.95721090 ]
    arr[161] = [ -0.00984241,  0.74507886,  0.27300030,  0.88650018 ]
    arr[162] = [  0.08157896,  0.81842107,  0.18157895,  0.91842109 ]
    arr[163] = [  0.06086827,  0.72699970,  0.20228964,  1.00984240 ]
    arr[164] = [ -0.00984241,  0.79771042,  0.27300030,  0.93913174 ]
    arr[165] = [  0.08157896,  0.87105262,  0.18157895,  0.97105265 ]
    arr[166] = [  0.06086827,  0.77963126,  0.20228964,  1.06247401 ]
    arr[167] = [ -0.00984241,  0.85034198,  0.27300030,  0.99176329 ]
    arr[168] = [  0.08157896,  0.92368424,  0.18157895,  1.02368426 ]
    arr[169] = [  0.06086827,  0.83226287,  0.20228964,  1.11510563 ]
    arr[170] = [ -0.00984241,  0.90297359,  0.27300030,  1.04439497 ]
    arr[171] = [  0.13421053, -0.02368421,  0.23421052,  0.07631579 ]
    arr[172] = [  0.11349984, -0.11510557,  0.25492120,  0.16773716 ]
    arr[173] = [  0.04278916, -0.04439489,  0.32563189,  0.09702647 ]
    arr[174] = [  0.13421053,  0.02894736,  0.23421052,  0.12894736 ]
    arr[175] = [  0.11349984, -0.06247400,  0.25492120,  0.22036873 ]
    arr[176] = [  0.04278916,  0.00823668,  0.32563189,  0.14965805 ]
    arr[177] = [  0.13421053,  0.08157896,  0.23421052,  0.18157895 ]
    arr[178] = [  0.11349984, -0.00984241,  0.25492120,  0.27300030 ]
    arr[179] = [  0.04278916,  0.06086827,  0.32563189,  0.20228964 ]
    arr[180] = [  0.13421053,  0.13421053,  0.23421052,  0.23421052 ]
    arr[181] = [  0.11349984,  0.04278916,  0.25492120,  0.32563189 ]
    arr[182] = [  0.04278916,  0.11349984,  0.32563189,  0.25492120 ]
    arr[183] = [  0.13421053,  0.18684211,  0.23421052,  0.28684211 ]
    arr[184] = [  0.11349984,  0.09542075,  0.25492120,  0.37826347 ]
    arr[185] = [  0.04278916,  0.16613144,  0.32563189,  0.30755278 ]
    arr[186] = [  0.13421053,  0.23947369,  0.23421052,  0.33947369 ]
    arr[187] = [  0.11349984,  0.14805232,  0.25492120,  0.43089503 ]
    arr[188] = [  0.04278916,  0.21876299,  0.32563189,  0.36018437 ]
    arr[189] = [  0.13421053,  0.29210523,  0.23421052,  0.39210525 ]
    arr[190] = [  0.11349984,  0.20068388,  0.25492120,  0.48352659 ]
    arr[191] = [  0.04278916,  0.27139455,  0.32563189,  0.41281593 ]
    arr[192] = [  0.13421053,  0.34473681,  0.23421052,  0.44473684 ]
    arr[193] = [  0.11349984,  0.25331545,  0.25492120,  0.53615820 ]
    arr[194] = [  0.04278916,  0.32402614,  0.32563189,  0.46544752 ]
    arr[195] = [  0.13421053,  0.39736840,  0.23421052,  0.49736843 ]
    arr[196] = [  0.11349984,  0.30594707,  0.25492120,  0.58878976 ]
    arr[197] = [  0.04278916,  0.37665772,  0.32563189,  0.51807910 ]
    arr[198] = [  0.13421053,  0.44999999,  0.23421052,  0.55000001 ]
    arr[199] = [  0.11349984,  0.35857862,  0.25492120,  0.64142138 ]
    arr[200] = [  0.04278916,  0.42928931,  0.32563189,  0.57071066 ]
    arr[201] = [  0.13421053,  0.50263160,  0.23421052,  0.60263163 ]
    arr[202] = [  0.11349984,  0.41121024,  0.25492120,  0.69405299 ]
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    arr[1876] = [  0.47277826,  0.13944489,  1.19388843,  0.86055511 ]
    arr[1877] = [  0.60352367,  0.04038063,  1.06314301,  0.95961940 ]
    arr[1878] = [  0.37371397,  0.27019030,  1.29295278,  0.72980970 ]
    arr[1879] = [  0.64569455, -0.06291646,  1.02097225,  1.06291652 ]
    arr[1880] = [  0.27038872,  0.31237054,  1.39627802,  0.68762946 ]
    arr[1881] = [  0.50833338,  0.50833338,  1.15833330,  1.15833330 ]
    arr[1882] = [  0.47277826,  0.47277826,  1.19388843,  1.19388843 ]
    arr[1883] = [  0.60352367,  0.37371400,  1.06314301,  1.29295278 ]
    arr[1884] = [  0.37371397,  0.60352367,  1.29295278,  1.06314301 ]
    arr[1885] = [  0.64569455,  0.27041692,  1.02097225,  1.39624977 ]
    arr[1886] = [  0.27038872,  0.64570391,  1.39627802,  1.02096283 ]
    arr[1887] = [ -0.15000001, -0.15000001,  0.64999998,  0.64999998 ]
    arr[1888] = [ -0.18588990, -0.18588990,  0.68588990,  0.68588990 ]
    arr[1889] = [ -0.03284273, -0.31568545,  0.53284276,  0.81568545 ]
    arr[1890] = [ -0.31568545, -0.03284273,  0.81568545,  0.53284276 ]
    arr[1891] = [  0.01905988, -0.44282031,  0.48094010,  0.94282031 ]
    arr[1892] = [ -0.44285494,  0.01907143,  0.94285494,  0.48092857 ]
    arr[1893] = [ -0.15000001,  0.34999999,  0.64999998,  1.14999998 ]
    arr[1894] = [ -0.18588990,  0.31411010,  0.68588990,  1.18588996 ]
    arr[1895] = [ -0.03284273,  0.18431455,  0.53284276,  1.31568551 ]
    arr[1896] = [ -0.31568545,  0.46715727,  0.81568545,  1.03284276 ]
    arr[1897] = [  0.01905988,  0.05717969,  0.48094010,  1.44282031 ]
    arr[1898] = [ -0.44285494,  0.51907146,  0.94285494,  0.98092854 ]
    arr[1899] = [  0.34999999, -0.15000001,  1.14999998,  0.64999998 ]
    arr[1900] = [  0.31411010, -0.18588990,  1.18588996,  0.68588990 ]
    arr[1901] = [  0.46715727, -0.31568545,  1.03284276,  0.81568545 ]
    arr[1902] = [  0.18431455, -0.03284273,  1.31568551,  0.53284276 ]
    arr[1903] = [  0.51905990, -0.44282031,  0.98094010,  0.94282031 ]
    arr[1904] = [  0.05714506,  0.01907143,  1.44285488,  0.48092857 ]
    arr[1905] = [  0.34999999,  0.34999999,  1.14999998,  1.14999998 ]
    arr[1906] = [  0.31411010,  0.31411010,  1.18588996,  1.18588996 ]
    arr[1907] = [  0.46715727,  0.18431455,  1.03284276,  1.31568551 ]
    arr[1908] = [  0.18431455,  0.46715727,  1.31568551,  1.03284276 ]
    arr[1909] = [  0.51905990,  0.05717969,  0.98094010,  1.44282031 ]
    arr[1910] = [  0.05714506,  0.51907146,  1.44285488,  0.98092854 ]
    arr[1911] = [  0.02500001,  0.02500001,  0.97500002,  0.97500002 ]
    arr[1912] = [  0.01266029,  0.01266029,  0.98733974,  0.98733974 ]
    arr[1913] = [  0.16412428, -0.17175144,  0.83587575,  1.17175150 ]
    arr[1914] = [ -0.17175144,  0.16412428,  1.17175150,  0.83587575 ]
    arr[1915] = [  0.22575861, -0.32272410,  0.77424139,  1.32272410 ]
    arr[1916] = [ -0.32276523,  0.22577232,  1.32276523,  0.77422768 ]

    return arr
  }
}

SSDPostprocessor.swift

import CoreML
import UIKit

struct BoundingBox {
  let yMin: Double
  let xMin: Double
  let yMax: Double
  let xMax: Double
  let imgHeight = 300.0
  let imgWidth = 300.0
  
  init(yMin: Double, xMin: Double, yMax: Double, xMax: Double) {
    self.yMin = yMin
    self.xMin = xMin
    self.yMax = yMax
    self.xMax = xMax
  }
  
  init(fromAnchor: [Float32]) {
    self.yMin = Double(fromAnchor[0])
    self.xMin = Double(fromAnchor[1])
    self.yMax = Double(fromAnchor[2])
    self.xMax = Double(fromAnchor[3])
  }
  
  func toCGRect() -> CGRect {
    let height = imgHeight * (yMax - yMin)
    let width = imgWidth * (xMax - xMin)
    
    return CGRect(x: imgWidth * xMin, y: imgHeight * yMin, width: width, height: height)
  }
}

struct AnchorEncoding {
  let ty: Double
  let tx: Double
  let th: Double
  let tw: Double
}

struct Prediction {
  let index: Int
  let score: Double
  let anchor: BoundingBox
  let anchorEncoding: AnchorEncoding
  
  var finalPrediction: BoundingBox {
    get {
      let yACtr = (anchor.yMin + anchor.yMax) / 2.0
      let xACtr = (anchor.xMin + anchor.xMax) / 2.0
      let ha = (anchor.yMax - anchor.yMin)
      let wa = (anchor.xMax - anchor.xMin)
      
      let ty = anchorEncoding.ty / 10.0
      let tx = anchorEncoding.tx / 10.0
      let th = anchorEncoding.th / 5.0
      let tw = anchorEncoding.tw / 5.0
      
      let w = exp(tw) * wa
      let h = exp(th) * ha
      
      let yCtr = ty * ha + yACtr
      let xCtr = tx * wa + xACtr
      
      let yMin = yCtr - h / 2.0
      let xMin = xCtr - w / 2.0
      let yMax = yCtr + h / 2.0
      let xMax = xCtr + w / 2.0
      
      return BoundingBox(yMin: yMin, xMin: xMin, yMax: yMax, xMax: xMax)
    }
  }
}


class SSDPostProcessor {
  var numAnchors: Int
  var numClasses: Int
  var threshold: Double

  init(numAnchors: Int, numClasses: Int, threshold: Double = 0.01) {
    self.numAnchors = numAnchors
    self.numClasses = numClasses
    self.threshold = threshold
  }
  
  func postprocess(boxPredictions: MLMultiArray, classPredictions: MLMultiArray) -> [Prediction] {
    let prunedPredictions = pruneLowScoring(boxPredictions: boxPredictions, classPredictions: classPredictions)
    
    let foo = nonMaximumSupression(predictions: prunedPredictions)
    
    
    print(">>>", foo.count)
    
    return foo
  }
  
  private func nonMaximumSupression(predictions: [[Prediction]]) -> [Prediction] {
    var finalPredictions: [Prediction] = []
    
    for klass in 1...numClasses {
      let predictionsForClass = predictions[klass]
      
      let supressedPredictions = nonMaximumSupressionForClass(predictionsForClass)
      
      finalPredictions.append(contentsOf: supressedPredictions)
    }
    
    return finalPredictions.sorted(by: { return $0.score > $1.score })
  }
  
  private func nonMaximumSupressionForClass(_ predictions: [Prediction]) -> [Prediction] {
    return predictions
  }
  
  private func pruneLowScoring(boxPredictions: MLMultiArray, classPredictions: MLMultiArray) -> [[Prediction]] {
    var prunedPredictions: [[Prediction]] = Array(repeating: [], count: numClasses + 1)
    
    for klass in 1...numClasses {
      for box in 0...(numAnchors-1) {
        let score = classPredictions[offset(klass, box)].doubleValue
        if score > threshold {
          let anchor = BoundingBox.init(fromAnchor: Anchors.ssdAnchors[box])
          let anchorEncoding = AnchorEncoding(
            ty: boxPredictions[offset(0, box)].doubleValue,
            tx: boxPredictions[offset(1, box)].doubleValue,
            th: boxPredictions[offset(2, box)].doubleValue,
            tw: boxPredictions[offset(3, box)].doubleValue
          )
          
          let prediction = Prediction(index: box, score: score, anchor: anchor, anchorEncoding: anchorEncoding)
          
          prunedPredictions[klass].append(prediction)
        }
      }
    }
    
    return prunedPredictions
  }
  
  private func offset(_ i: Int, _ j: Int) -> Int {
    return i * numAnchors + j
  }
}