def CNN_classification(test_id, valid_id):
input_size = 100
def LoadData(fileList_NF, fileList_F):
data = []
labels = []
for im_path in fileList_NF:
img = numpy.asarray(
image.load_img(im_path, target_size=(input_size, input_size)))
data.append(img)
labels.append('0')
for im_path in fileList_F:
img = numpy.asarray(image.load_img(im_path,
target_size=(input_size,
input_size)),
dtype='float64')
data.append(img)
labels.append('1')
return numpy.asarray(data), numpy.asarray(labels)
fileList_NF = sorted(glob.glob('medfilt5_label_0/*.png'))
fileList_F = sorted(glob.glob('medfilt5_label_1/*.png'))
testfileList_NF = filter(
lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(test_id),
fileList_NF)
testfileList_F = filter(
lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(test_id),
fileList_F)
validfileList_NF = filter(
lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(valid_id),
fileList_NF)
validfileList_F = filter(
lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(valid_id),
fileList_F)
trainfileList_NF = filter(
lambda x: x.split('/')[-1].split('E')[0] != 'U' + str(test_id) and x.
split('/')[-1].split('E')[0] != 'U' + str(valid_id), fileList_NF)
trainfileList_F = filter(
lambda x: x.split('/')[-1].split('E')[0] != 'U' + str(test_id) and x.
split('/')[-1].split('E')[0] != 'U' + str(valid_id), fileList_F)
train_data, train_labels = LoadData(trainfileList_NF, trainfileList_F)
test_data, test_labels = LoadData(testfileList_NF, testfileList_F)
valid_data, valid_labels = LoadData(validfileList_NF, validfileList_F)
print train_data.shape, test_data.shape, valid_data.shape
clf.CNNclassifier(train_data, train_labels, valid_data, valid_labels,
input_size)
