def __getitem__(self, item):
# Get parameter
gp = GlobalProperties()
camMat = gp.getCamMat()
# Sampling for reprojection error image
sampling = cnn.stochasticSubSample(self.imgBGR,
targetsize=self.objInputSize,
patchsize=self.rgbInputSize)
estObj = cnn.getCoordImg(self.imgBGR, sampling, self.rgbInputSize,
self.model)
# Produce GroundTruth Label
poseGT = Hypothesis()
poseGT.Info(self.info)
driftLevel = np.random.randint(0, 3)
if not driftLevel:
poseNoise = poseGT * getRandHyp(2, 2)
else:
poseNoise = poseGT * getRandHyp(10, 100)
data = cnn.getDiffMap(
TYPE.our2cv([poseNoise.getRotation(),
poseNoise.getTranslation()]), estObj, sampling,
camMat)
data = self.transform(data)
label = -1 * self.temperature * max(
poseGT.calcAngularDistance(poseNoise),
np.linalg.norm(poseGT.getTranslation() -
poseNoise.getTranslation()) / 10.0)
return data, label
def __getitem__(self, item):
# Get raw data
imgBGR = self.dataset.rand_getBGR(item)
info = self.dataset.rand_getInfo(item)
poseGT = Hypothesis()
poseGT.Info(info)
# Sampling for reprojection error image
sampling = cnn.stochasticSubSample(imgBGR,
targetsize=self.objInputSize,
patchsize=self.rgbInputSize)
estObj = cnn.getCoordImg1(imgBGR, sampling, self.rgbInputSize,
self.model)
return imgBGR, poseGT, sampling, estObj
