def compute_for_channel(output_channel, input_channel):
input_roi = numpy.array( (roi.start, roi.stop) )
input_roi[:,-1] = (input_channel, input_channel+1)
input_req = self.Input(*input_roi)
# If possible, use the result array itself as a scratch area
if self.Input.meta.dtype == result.dtype:
input_req.writeInto( result[...,output_channel:output_channel+1] )
input_data = input_req.wait()
input_data = input_data.astype(numpy.float32, order='C', copy=False)
input_data = input_data[...,0] # drop channel axis
result[..., output_channel] = computeIntegralImage(input_data)
def compute_for_channel(output_channel, input_channel):
input_roi = numpy.array( (roi.start, roi.stop) )
input_roi[:,-1] = (input_channel, input_channel+1)
input_req = self.Input(*input_roi)
# If possible, use the result array itself as a scratch area
if self.Input.meta.dtype == result.dtype:
input_req.writeInto( result[...,output_channel:output_channel+1] )
input_data = input_req.wait()
input_data = input_data.astype(numpy.float32, order='C', copy=False)
input_data = input_data[...,0] # drop channel axis
result[..., output_channel] = computeIntegralImage(input_data)
import matplotlib.pyplot as plt
# load data
gt = joblib.load("../../testData/gt.jlb")
img = joblib.load("../../testData/img.jlb")
# let's pretend we have 3 image stacks with different number of ROIs
# with its corresponding gt and 2 feature channels
img3 = img2 = img1 = img
gt3 = gt2 = gt1 = gt
model = Booster()
imgFloat = np.float32(img)
iiImage = computeIntegralImage( imgFloat )
# again, this is stupid, just presume the second channel is a different feature
channel1 = iiImage
channel2 = iiImage
channels3 = channels2 = channels1 = [channel1,channel2]
# anisotropy factor is the ratio between z voxel size and x/y voxel size.
# if Isotropic -> 1.0
zAnisotropyFactor = 1.0;
# this is typically a good value, but it depends on the voxel size of the data
hessianSigma = 3.5
eigV1 = computeEigenVectorsOfHessianImage( img1, zAnisotropyFactor, hessianSigma )
eigV2 = computeEigenVectorsOfHessianImage( img2, zAnisotropyFactor, hessianSigma )