def train_bbox_regressor(X, bbox, gt):
config = Data()
config.min_overlap = 0.6
config.delta = 1000
config.method = 'ridge_reg_chol'
# get training groundtruth
Y, O = get_examples(bbox, gt)
X = X[O > config.min_overlap]
Y = Y[O > config.min_overlap]
# add bias
X = np.c_[X, np.ones([X.shape[0], 1])]
# center and decorrelate targets
mu = np.mean(Y, axis=0).reshape(1, -1)
Y = Y - mu
S = dot(Y.T, Y) / Y.shape[0]
D, V = eig(S)
T = dot(dot(V, diag(1.0 / sqrt(D + 0.001))), V.T)
T_inv = dot(dot(V, diag(sqrt(D + 0.001))), V.T)
Y = dot(Y, T)
model = Data()
model.mu = mu
model.T = T
model.T_inv = T_inv
model.Beta = np.c_[solve(X, Y[:, 0], config.delta, config.method),
solve(X, Y[:, 1], config.delta, config.method),
solve(X, Y[:, 2], config.delta, config.method),
solve(X, Y[:, 3], config.delta, config.method)]
# pack
bbox_reg = Data()
bbox_reg.model = model
bbox_reg.config = config
return bbox_reg
