# Test for the IIBoost wrapper class
###################################################################################
from iiboost import Booster
from sklearn.externals import joblib # to load data
# to show something
import matplotlib.pyplot as plt
# load data
gt = joblib.load("../../testData/gt.jlb")
img = joblib.load("../../testData/img.jlb")
model = Booster()
# Train: note that we pass a list of stacks
model.train([img], [gt], numStumps=100, debugOutput=True)
pred = model.predict(img)
# show image & prediction side by side
plt.ion()
plt.figure()
plt.subplot(1, 2, 1)
plt.imshow(img[:, :, 10], cmap="gray")
plt.title("Click on the image to exit")
import numpy as np
# to show something
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
###################################################################################
from iiboost import Booster, computeIntegralImage
from sklearn.externals import joblib # to load data
import numpy as np
# to show something
import matplotlib.pyplot as plt
# load data
gt = joblib.load("../../testData/gt.jlb")
img = joblib.load("../../testData/img.jlb")
model = Booster()
imgFloat = np.float32(img)
iiImage = computeIntegralImage( imgFloat )
# Train: note that we pass a list of stacks
model.trainWithChannel( [img], [gt], [iiImage], numStumps=100, debugOutput=True)
imgFloat = np.float32(img)
iiImage = computeIntegralImage( imgFloat )
pred = model.predictWithChannel( img, iiImage )
# show image & prediction side by side
plt.ion()
plt.figure()