def main():
cube = img_as_float(DataReader.PaviauRaw().cube)
image = cube[:, :, [40, 17, 1]]
i_sp = image.shape
image = np.reshape(image, (-1, i_sp[-1]))
image = StandardScaler().fit_transform(image)
image = np.reshape(image, i_sp)
numSegments = 2500
num_sample = 3
num_epoch = 1000
train_csv = pd.read_csv("data/splitDataset/train/splitPavia_{}.csv".format(num_sample))
test_csv = pd.read_csv("data/splitDataset/test/splitPavia_{}.csv".format(num_sample))
training_set = train_csv.loc[:, ["row_0", "col_0", "label_0"]].to_numpy()
testing_set = test_csv.loc[:, ["row_0", "col_0", "label_0"]].to_numpy()
global_train_mask = getGlobalMask(DataReader.PaviauRaw().truth, training_set)
global_test_mask = getGlobalMask(DataReader.PaviauRaw().truth, testing_set)
segments, edge_index = getSuperpixelGraph(image, num_segments=numSegments, compactness=2.5, sigma=2)
train_mask, test_mask, y = getMaskAndLable(training_set, DataReader.PaviauRaw().truth, segments)
sp_feature = getSuperpixelFeature(DataReader.PaviauRaw().cube, segments)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(103, 9).to(device)
# x = torch.Tensor(PCA(5).fit_transform(sp_feature))
x = torch.Tensor(sp_feature)
edge_index = torch.tensor(edge_index).t().contiguous()
showSuperpixel(image, segments)
data = Data(x=x, edge_index=edge_index, test_mask=test_mask, train_mask=train_mask, y=y)
data.test_mask = torch.tensor(test_mask)
data.train_mask = torch.tensor(train_mask)
data.y = torch.tensor(y)
data = data.to(device)
gpu_segments = torch.tensor(segments).flatten().to(device)
global_train_mask = torch.tensor(global_train_mask).to(device)
gpu_truth = torch.tensor(DataReader.PaviauRaw().truth, dtype=torch.long).flatten().to(device)
evaluate(model, gpu_truth, gpu_segments, global_test_mask)
print(os.getcwd())
# construct the argument parser and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required = True, help = "Path to the image")
# args = vars(ap.parse_args())
# # load the image and convert it to a floating point data type
# image = img_as_float(io.imread(args["image"]))
# loop over the number of segments
image = img_as_float(DataReader.KSCRaw().cube)[:, :, [40, 17, 1]]
image = (image - np.min(image)) / (np.max(image) - np.min(image))
i_sp = image.shape
# apply SLIC and extract (approximately) the supplied number
# of segments
# paviau
image = img_as_float(DataReader.PaviauRaw().cube)[:, :, [100, 65, 3]]
image = (image - np.min(image)) / (np.max(image) - np.min(image))
i_sp = image.shape
# apply SLIC and extract (approximately) the supplied number
# of segments
numSegments = 2600
segments = slic(image,
n_segments=numSegments,
compactness=3,
sigma=1.5,
enforce_connectivity=True,
multichannel=True,
convert2lab=True)
train_csv = pd.read_csv("data/splitDataset/train/splitPavia_3.csv")
test_csv = pd.read_csv("data/splitDataset/test/splitPavia_3.csv")
