# 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
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###################################################################################

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()