Class PCAAug

core/utils/flow_transforms.py:250–398

Chromatic Eigen Augmentation: https://github.com/lmb-freiburg/flownet2/blob/master/src/caffe/layers/data_augmentation_layer.cu

class PCAAug(object):
    """
    Chromatic Eigen Augmentation: https://github.com/lmb-freiburg/flownet2/blob/master/src/caffe/layers/data_augmentation_layer.cu
    """
    def __init__(self,  lmult_pow  =[0.4, 0,-0.2],
                        lmult_mult =[0.4, 0,0,  ],
                        lmult_add  =[0.03,0,0,  ],
                        sat_pow    =[0.4, 0,0,  ],
                        sat_mult   =[0.5, 0,-0.3],
                        sat_add    =[0.03,0,0,  ],
                        col_pow    =[0.4, 0,0,  ],
                        col_mult   =[0.2, 0,0,  ],
                        col_add    =[0.02,0,0,  ],
                        ladd_pow   =[0.4, 0,0,  ],
                        ladd_mult  =[0.4, 0,0,  ],
                        ladd_add   =[0.04,0,0,  ],
                        col_rotate =[1.,  0,0,  ],
                        schedule_coeff=1):
        # no mean
        self.pow_nomean = [1,1,1]
        self.add_nomean = [0,0,0]
        self.mult_nomean = [1,1,1]
        self.pow_withmean = [1,1,1]
        self.add_withmean = [0,0,0]
        self.mult_withmean = [1,1,1]
        self.lmult_pow = 1
        self.lmult_mult = 1
        self.lmult_add = 0
        self.col_angle = 0
        if not ladd_pow is None: 
            self.pow_nomean[0] =np.exp(np.random.normal(ladd_pow[2], ladd_pow[0]))
        if not col_pow is None:     
            self.pow_nomean[1] =np.exp(np.random.normal(col_pow[2], col_pow[0]))
            self.pow_nomean[2] =np.exp(np.random.normal(col_pow[2], col_pow[0]))
        
        if not ladd_add is None: 
            self.add_nomean[0] =np.random.normal(ladd_add[2], ladd_add[0])
        if not col_add is None:     
            self.add_nomean[1] =np.random.normal(col_add[2], col_add[0])
            self.add_nomean[2] =np.random.normal(col_add[2], col_add[0])
        
        if not ladd_mult is None:
            self.mult_nomean[0] =np.exp(np.random.normal(ladd_mult[2], ladd_mult[0]))
        if not col_mult is None:     
            self.mult_nomean[1] =np.exp(np.random.normal(col_mult[2], col_mult[0]))
            self.mult_nomean[2] =np.exp(np.random.normal(col_mult[2], col_mult[0]))

        # with mean
        if not sat_pow is None:     
            self.pow_withmean[1]   =np.exp(np.random.uniform(sat_pow[2]-sat_pow[0], sat_pow[2]+sat_pow[0]))
            self.pow_withmean[2]   =self.pow_withmean[1]
        if not sat_add is None:     
            self.add_withmean[1]  =np.random.uniform(sat_add[2]-sat_add[0], sat_add[2]+sat_add[0])
            self.add_withmean[2]  =self.add_withmean[1]
        if not sat_mult is None:     
            self.mult_withmean[1] = np.exp(np.random.uniform(sat_mult[2]-sat_mult[0], sat_mult[2]+sat_mult[0]))
            self.mult_withmean[2] = self.mult_withmean[1]