# 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()
plt.subplot(1,2,1)
plt.imshow(img[:,:,10],cmap="gray")
plt.title("Click on the image to exit")
plt.subplot(1,2,2)
plt.imshow(pred[:,:,10],cmap="gray")
plt.title("Click on the image to exit")
plt.ginput(1)
