def load_test(data_file_names):
# load test data
masks_path = '/media/stim-scratch/berisha/00-annotations/hd/right/com-right/'
classimages = sorted(glob.glob(masks_path +
'*.png')) # load the class file names
C = classify.filenames2class(
classimages) # generate the class images for testing
C = C.astype(np.uint32)
bool_mask = np.sum(C.astype(np.uint32), 0)
# get number of classes
num_classes = C.shape[0]
for i in range(1, num_classes):
C[i, :, :] *= i + 1
total_mask = np.sum(C.astype(np.uint32), 0) # validation mask
Etest = envi.envi(data_file_names[0], mask=total_mask)
N = np.count_nonzero(total_mask) # set the batch size
Tv = [] # initialize the target array to empty
x_test = Etest.loadbatch(N)
y_test = total_mask.flat[np.flatnonzero(
total_mask)] # get the indices of valid pixels
Etest.close()
return x_test, y_test
def load_train(data_file_names):
# load train data
masks_path = '/media/stim-scratch/berisha/00-annotations/hd/left/com-left/'
classimages = sorted(glob.glob(masks_path +
'*.png')) # load the class file names
C = classify.filenames2class(
classimages) # generate the class images for training
# open the ENVI file for reading, use the validation image for batch access
Etrain = envi.envi(data_file_names[0])
# x_train, y_train = Etrain.loadtrain(C)
x_train, y_train = Etrain.loadtrain_balance(C, num_samples=100000)
Etrain.close()
return x_train, y_train
=====================
Optimal params comparison
=====================
A comparison of several classifiers using ('optimal' params) from scikit-learn on FTIR data.
Code source: Sebastian Berisha
"""
# load train data
data_path = '/media/stim-processed/berisha/breast-processing/lm/br1003/no-mnf/new-cnn/'
masks_path = '/media/stim-processed/berisha/breast-processing/lm/br1003/masks/no-mnf-bcemn/'
classimages = sorted(glob.glob(masks_path +
'*.png')) # load the class file names
C = classify.filenames2class(
classimages) # generate the class images for training
# open the ENVI file for reading, use the validation image for batch access
Etrain = envi.envi(data_path + 'br1003-br2085b-bas-nor-fin-bip-pca16')
#x_train, y_train = Etrain.loadtrain(C)
x_train, y_train = Etrain.loadtrain_balance(C, num_samples=60000)
Etrain.close()
# load test data
data_path = '/media/stim-processed/berisha/breast-processing/lm/br1003/no-mnf/brc961-proj/brc961-proj/new-cnn/'
masks_path = '/media/stim-processed/berisha/breast-processing/lm/brc961/masks/no-mnf-bcemn/'
classimages = sorted(glob.glob(masks_path +
'*.png')) # load the class file names
C = classify.filenames2class(
classimages) # generate the class images for testing
