def train(self, trainSet):
pntNum = trainSet.meanShape.shape[0]
treeNum = int(self.maxTreeNum / pntNum)
# Train the random forests
for i in range(pntNum):
rf = RandForest(treeDepth=self.treeDepth,
treeNum=treeNum,
feaNum=self.feaNum,
radius=self.radius,
binNum=self.binNum,
feaRange=self.feaRange)
rf.train(trainSet, i)
self.rfs.append(rf)
# Extract the local binary features
feas = self.genFeaOnTrainset(trainSet)
# Global regression
y = trainSet.residuals
y = y.reshape(y.shape[0], y.shape[1] * y.shape[2])
for i in range(pntNum * 2):
# TODO Show the training result
reg = LinearSVR(epsilon=0.0,
C=1.0 / feas.shape[0],
loss='squared_epsilon_insensitive',
fit_intercept=True)
reg.fit(feas, y[:, i])
self.regs.append(reg)
# Update the initshapes
for i in range(pntNum):
regX = self.regs[2 * i]
regY = self.regs[2 * i + 1]
x = regX.predict(feas)
y = regY.predict(feas)
delta = np.squeeze(np.dstack((x, y)))
delta = Affine.transPntsForwardWithDiffT(delta,
trainSet.ms2reals)
delta = np.multiply(delta,
trainSet.bndBoxs[:, [2, 3]])
trainSet.initShapes[:, i, :] = trainSet.initShapes[:, i, :] + delta
