from sklearn import svm
import numpy as np
from .video_edits import save_video_landmarks


class OneClassRbf():
    def __init__(self, video_analizer, rich_features=0, person=None, config=None, outliers=True, custom_params=None):
        self.video_analizer = video_analizer
        self.rich = rich_features
        self.config = config or self.video_analizer._get_config({'interval':[(0,1e10)], 'frames_per_sample':300})
        self.outliers = outliers
        custom_params = custom_params or dict(kernel='rbf', gamma=1e-05, degree=20, nu=0.1, shrinking=True)
        self.clf = svm.OneClassSVM(**custom_params)

    def set_video_analizer(self, video_analizer):
        """save association between classifier output and real class behind (eg. 1->Real, -1->Fake)"""
        self.video_analizer = video_analizer

    def get_sklearn_clf(self):
        return self.clf

    def predict_video(self, path_to_video, return_label=False, landmark_video=False):
        """
        input:
            - video path
        oputput:
            - integer that maps 'labels_map' into person is real or not
        """
        x, _ = self.video_analizer.process_video(files=[path_to_video], config=self.config, rich_features=1)
        if len(x)==0:      raise Exception(f'0 samples have been extracted from {path_to_video}')

        y = self.predict(x)
        y = [max(0,v) for v in y]
        y = sum(y)/len(y)
        label = (y>0.5 and 'real' or 'fake')

        if landmark_video:
            save_video_landmarks(path_to_video, label, self.video_analizer)

        return return_label and label or y

    def _get_x(self, x, rich_features, use_outliers=False):
        num = int(len(x)/20)
        if rich_features==0:
            tmp = [s[:190] for s in outliers[:num]]
            x   = [s[:190] for s in x]
        elif rich_features==2:
            tmp = [s[190:] for s in outliers[:num]]
            x   = [s[190:] for s in x]
        else:
            tmp = outliers[:num]
        if use_outliers:
            x += tmp
        return x

    def fit(self, x):
        x = self._get_x(x, self.rich, self.outliers)
        self.clf.fit(x)
        return self

    def predict(self, x):
        x = self._get_x(x, self.rich)
        return self.clf.predict(x)


class BoostedOneClassRbf(OneClassRbf):
    def __init__(self, video_analizer, rich_features=None, config=None, person=None):
        self.config = config or self.video_analizer._get_config({'interval':[(0,1e10)], 'frames_per_sample':300})
        self.video_analizer = video_analizer
        self.predictions = []
        self.person = person

        self.params = [
            # rich_features, outliers, params, weight
            (0, True, {'kernel':'poly', 'gamma':0.005, 'degree':3, 'nu':0.4,  'shrinking':True}, 0.6),
            (0, True, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.3, 'shrinking':True}, 0.95),
            (0, True, {'kernel':'rbf', 'gamma':0.01, 'degree':5, 'nu':0.05, 'shrinking':False}, 0.75),
            (1, True, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.3, 'shrinking':True}, 0.95),
            (1, True, {'kernel':'poly', 'gamma':'scale', 'degree':2, 'nu':0.8, 'shrinking':False}, 0.5),
            (2, True, {'kernel':'rbf', 'gamma':0.1, 'degree':10, 'nu':0.3, 'shrinking':True}, 0.75),
            (2, True, {'kernel':'poly', 'gamma':'scale', 'degree':2, 'nu':0.8, 'shrinking':False}, 0.6),
            (0, False, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.3, 'shrinking':True}, 0.85),
            (0, False, {'kernel':'rbf', 'gamma':0.01, 'degree':5, 'nu':0.05, 'shrinking':False}, 0.75),
            (0, False, {'kernel':'poly', 'gamma':0.005, 'degree':3, 'nu':0.4,  'shrinking':True}, 0.6),
            (0, False, {'kernel':'sigmoid', 'gamma':1e-05, 'degree':3, 'nu':0.2, 'shrinking':False}, 0.75),
            (1, False, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.1, 'shrinking':True}, 0.85),
            (1, True, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.1, 'shrinking':True}, 0.85),
            (0, False, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.1, 'shrinking':True}, 0.85),
            (0, True, {'kernel':'rbf', 'gamma':1e-05, 'degree':20, 'nu':0.1, 'shrinking':True}, 0.75)
        ]
        self.clfs = [
            OneClassRbf(video_analizer, rich_features=r, outliers=o, custom_params=p)   for r, o, p, w in self.params
        ]

    def get_sklearn_clf(self):
        return self.clfs[0]


    def fit(self, x):
        for clf in self.clfs:
            clf.fit(x)
        return self

    def predict(self, x):
        res=np.array([0]*len(x), dtype="float64")
        for index, clf in enumerate(self.clfs):
            p = np.array(clf.predict(x), dtype="float64")
            p *= [self.params[index][3]]
            res += p

        return [r>0 and 1 or 0 for r in res]


outliers = [[0.5668, 0.0, -0.0157, 0.0159, -0.0128, -0.0108, 0.0352, 0.0, -0.0119, -0.0419, -0.0559, -0.0499, -0.0184, -0.0287, -0.0027, -0.2077, -0.1195, -0.1129, 0.067, 0.0, -0.0157, -0.0175, -0.1148, -0.001, -0.0149, 0.0, -0.0066, 0.0031, -0.0136, -0.0333, -0.0102, -0.0888, 0.01, 0.1644, -0.1268, 0.0773, -0.14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.005, 0.068, -0.0158, 0.0013, 0.0, -0.0148, 0.0026, -0.0491, -0.044, -0.0244, 0.0719, 0.2119, -0.0618, 0.0447, -0.1166, 0.0815, 0.0654, -0.0058, 0.2217, 0.0, -0.0064, 0.037, 0.0263, -0.005, -0.0099, 0.0598, 0.0356, -0.0604, -0.0357, 0.0942, 0.0652, 0.0617, 0.0748, 0.0, -0.0051, -0.0415, -0.0099, -0.0119, -0.0643, 0.0718, 0.1946, -0.0796, -0.0051, -0.1582, 0.1313, 0.0993, 0.0, -0.0021, 0.281, 0.0098, 0.0054, 0.0402, 0.005, -0.0213, 0.1622, 0.0973, 0.1201, 0.0709, 0.0, -0.0052, -0.0252, -0.0162, 0.0064, -0.007, 0.1887, 0.0966, 0.0271, -0.0932, 0.0577, 0.1739, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0665, -0.0282, 0.1797, 0.0749, -0.0335, -0.0234, 0.0261, 0.0096, 0.1, -0.0854, 0.1831, 0.122, 0.0717, -0.1545, -0.0928, 0.1395, -0.0024, 0.2918, -0.2926, -0.1026, 0.023, -0.3698, -0.1945, 0.0023, 0.2053, 0.06, -0.3314, -0.0182, 0.1919, 0.0051, 0.1663, -0.1056, 0.1835, 0.0567, -0.0476, -0.0215, 0.1246, -0.056, 0.1493, -0.0944, 0.4661, -0.0183, -0.1699, -0.1496, 0.7366, 0.0444, -0.1673, -0.3661, 0.4099, -0.2899, 0.4021, -0.3306, 0.0177, -0.0941, -0.4351, 0.04096300733862513, 0.1651941224057379, 3.09, 0.01790474764115621, 0.13071123021028144, 2.62, 0.0, 0.0, 0.0, 0.025755578852778196, 0.07140381141131658, 0.77, 0.013258948629624083, 0.09924583468002997, 1.12, 0.8847865807997604, 0.38513136404597004, 2.24, 0.0019514752134192, 0.04468127567875761, 1.46, 0.004401677400029953, 0.040583092227565704, 1.16, 0.0, 0.0, 0.0, 0.0020682941440766816, 0.043004116430106404, 1.57, 0.050404373221506656, 0.21714876966537514, 3.54, 0.29953571963456643, 0.511564709015184, 2.85, 0.027073536019170282, 0.10682310242870138, 1.0, 0.004883929908641605, 0.06584303709030516, 1.38, 0.5023828066496929, 0.7206147207693282, 2.69, 0.31602965403624383, 0.6486453258470481, 2.76, -0.28748494833008836, 0.0575680279316928, 0.023, 0.050322450202186605, 0.024143624356788837, 0.17300000000000001, 50.8400273133606, 2.063035944966046, 69.68995623474017, 20.693244918392917, 3.2719748822137813, 30.804058174208155], [0.6711, -0.1493, 0.0, -0.0656, -0.1673, -0.04, 0.0328, -0.1032, 0.0397, 0.0042, -0.1259, -0.0548, -0.0858, -0.1219, -0.0069, 0.1284, 0.26, -0.1796, 0.0919, -0.0425, 0.0, 0.0457, -0.0637, -0.0297, -0.0574, 0.0059, 0.063, 0.013, -0.0127, 0.0488, -0.0636, 0.0122, 0.1106, 0.0965, 0.0625, -0.0359, 0.0889, 0.0, 0.0936, 0.1861, -0.0793, 0.4882, 0.0178, 0.3629, -0.0233, 0.34, -0.0371, -0.1102, 0.2411, 0.0021, 0.6132, -0.6469, 0.3316, 0.1159, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5336, -0.0181, 0.2415, 0.7633, -0.0341, -0.0418, 0.0276, -0.0256, 0.0498, 0.4847, 0.147, -0.0448, -0.0054, 0.4367, 0.1982, -0.0555, 0.2712, 0.4536, 0.0434, 0.0342, 0.332, 0.1732, 0.2231, 0.4081, 0.1446, 0.0135, -0.0582, 0.4888, 0.0857, 0.0828, 0.0173, -0.1316, -0.0219, -0.0634, -0.0331, -0.0204, 0.1612, 0.0992, -0.1754, 0.0408, 0.012, 0.1243, 0.2895, 0.1768, -0.0369, -0.0062, -0.1626, -0.2076, 0.5249, 0.1487, 0.478, -0.2397, 0.5716, 0.5592, -0.0813, -0.0523, -0.0755, -0.0678, 0.0111, 0.5262, 0.1542, -0.0631, 0.0086, 0.4531, 0.2636, 0.0844, 0.1611, 0.3212, 0.0623, -0.0772, -0.2062, 0.4351, -0.3982, 0.2898, -0.2788, 0.3213, 0.0225, 0.0821, -0.0715, -0.1175, 0.0154, -0.1399, 0.0317, -0.1914, 0.2137, 0.2834, -0.0535, -0.1627, 0.173, -0.2616, 0.2421, -0.421, 0.416, -0.0437, 0.0155, -0.0494, 0.0988, 0.092, -0.2675, -0.0116, -0.0429, -0.1159, 0.0975, 0.026, -0.2276, 0.4329, 0.1221, -0.0934, 0.4089, 0.6441, -0.0319, 0.0439, -0.1681, 0.5979, -0.366, 0.2663, 0.2186, -0.1617, 0.0461, 0.0953, 0.32237777777777776, 0.7855161866608306, 3.68, 0.07697777777777778, 0.25317929429897773, 1.86, 0.3645555555555556, 0.4481293491618764, 1.43, 0.0, 0.0, 0.0, 0.05486666666666666, 0.29531957078542714, 2.24, 0.22319999999999998, 0.3925528754193503, 1.89, 0.00791111111111111, 0.08120257856393537, 0.98, 0.8197333333333333, 0.7105002431776892, 2.32, 0.09764444444444444, 0.3515556005056861, 2.57, 0.9024555555555556, 0.6521707966665568, 2.41, 0.03256666666666667, 0.14499414164410615, 1.32, 0.4325444444444444, 0.6650873570209674, 2.55, 0.048, 0.1412326685532305, 0.94, 0.03514444444444445, 0.1682652708018642, 1.71, 0.3052888888888889, 0.5293482626871241, 3.01, 0.2893444444444444, 0.5535195602508071, 2.08, 0.07009666666666667, 0.047453399245997126, 0.204, -0.016556666666666667, 0.11641756315569876, 0.212, 59.602499589957766, 3.7370202577538416, 71.04653404635586, 22.538487290264968, 4.273633842775984, 36.083514241270905], [0.0604, -0.2092, 0.0, -0.1145, 0.0176, 0.0, 0.3706, -0.0728, -0.1666, -0.0393, 0.0059, -0.0887, 0.0, 0.1192, -0.0579, 0.1488, -0.1503, 0.1561, 0.2253, 0.1459, 0.0, -0.1383, 0.0356, 0.0, -0.0919, -0.0479, -0.1482, -0.0257, 0.0472, -0.0579, 0.0, -0.0435, 0.194, 0.0902, 0.1956, -0.352, -0.0303, 0.0, 0.5518, -0.2754, 0.0, -0.5802, -0.1717, -0.1496, -0.0997, -0.4293, 0.0483, 0.0, 0.0798, 0.1364, 0.0847, 0.812, -0.6585, -0.1052, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.077, 0.0, -0.1922, 0.0144, 0.1697, -0.2015, -0.5292, 0.3215, 0.0, 0.2462, -0.1541, -0.2192, 0.5318, -0.0501, 0.1113, 0.0, 0.2724, 0.3726, 0.3077, -0.0237, 0.2124, -0.0721, 0.0, -0.2894, -0.186, 0.4776, -0.5881, 0.4923, -0.0042, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1991, 0.0966, -0.0757, -0.0315, -0.1709, 0.0, 0.203, 0.0549, 0.1465, -0.5405, 0.662, 0.4547, 0.2514, -0.0305, 0.1133, -0.0446, 0.0, -0.065, -0.0868, 0.229, -0.2927, 0.3576, 0.0011, -0.0899, 0.279, 0.4974, 0.0, -0.311, -0.1909, 0.2391, -0.2996, 0.4124, -0.3951, 0.1435, -0.0288, 0.0, -0.0637, -0.0363, -0.1502, -0.0397, -0.0616, -0.0563, 0.1203, 0.0, -0.3806, -0.217, 0.233, -0.491, 0.2202, -0.5365, 0.0, -0.1396, -0.0819, -0.3198, 0.1586, -0.0662, -0.4151, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2985, -0.1467, 0.2208, 0.0104, 0.6458, -0.019, 0.2235, -0.3859, 0.271, -0.3629, 0.2145, 0.0096, -0.7343, 0.0182, 0.1756, 0.03946015424164525, 0.11353160700971149, 0.76, 0.027429305912596392, 0.12092009414018179, 0.95, 1.6944473007712082, 0.66615075485933, 2.88, 0.0, 0.0, 0.0, 0.876375321336761, 0.31342534649073966, 1.55, 2.0053213367609257, 0.3421957107486421, 2.89, 0.0, 0.0, 0.0, 0.2320051413881748, 0.34648004497719126, 1.2, 0.02344473007712082, 0.08690307311868854, 0.52, 0.1596401028277635, 0.2008679726921638, 0.86, 0.004061696658097687, 0.036001375269368203, 0.36, 0.35000000000000003, 0.5191212666228906, 2.05, 0.026118251928020562, 0.10259910952889222, 0.66, 0.0, 0.0, 0.0, 0.14724935732647815, 0.26150914994947894, 1.41, 0.07997429305912596, 0.24906973318122796, 1.26, 0.18956555269922878, 0.04344744123817184, 0.303, 0.15573521850899744, 0.08926117375499466, 0.309, 60.5111533915968, 3.964483264938189, 68.31149244453673, 18.13703051112263, 2.669339543020563, 25.413775791881072], [0.8283, -0.3906, 0.4145, 0.0119, -0.0753, -0.1646, 0.0026, 0.168, -0.0531, 0.0214, -0.3464, -0.0738, -0.1096, 0.0899, -0.0906, 0.0784, -0.2467, 0.1063, 0.1582, -0.3116, 0.4365, 0.017, -0.0925, -0.1138, 0.0158, 0.1604, -0.0584, -0.0696, -0.2612, -0.0433, -0.094, 0.1458, -0.0276, 0.0543, -0.1361, 0.0673, 0.1885, -0.1472, -0.0332, 0.1567, 0.2396, 0.0067, -0.1967, -0.0384, 0.0781, 0.4411, 0.0275, 0.2368, 0.0882, 0.1249, -0.0242, 0.1335, -0.1425, -0.0059, 0.033, 0.0272, -0.0805, 0.08, 0.1943, 0.0207, -0.0383, -0.1142, -0.0015, -0.0091, 0.143, 0.017, -0.0956, 0.0226, -0.0413, 0.1622, 0.2822, -0.0265, 0.3522, 0.6002, 0.4577, 0.0248, -0.1309, 0.2525, 0.0247, 0.3263, 0.031, 0.195, -0.1232, 0.3813, 0.1659, 0.0948, 0.146, 0.0961, 0.0822, 0.0335, 0.0188, 0.0459, 0.2733, 0.0623, 0.0327, -0.0361, -0.0629, 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