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# ------------------------------------------------------------ #
#
# file : preprocessing/threshold.py
# author : CM
# Preprocess function for Bullitt dataset
#
# ------------------------------------------------------------ #
import os
import sys
import numpy as np
import nibabel as nib
from utils.io.write import npToNii
from utils.config.read import readConfig
# Get the threshold for preprocessing
def getThreshold(dataset_mra, dataset_gd):
threshold = dataset_mra.max()
for i in range(0,len(dataset_mra)):
mra = dataset_mra[i]
gd = dataset_gd[i]
for x in range(0, mra.shape[0]):
for y in range(0, mra.shape[1]):
for z in range(0, mra.shape[2]):
if(gd[x,y,z] == 1 and mra[x,y,z] < threshold):
threshold = mra[x,y,z]
return threshold
# Apply threshold to an image
def thresholding(data, threshold):
output = np.copy(data)
for x in range(0, data.shape[0]):
for y in range(0, data.shape[1]):
for z in range(0, data.shape[2]):
if data[x,y,z] > threshold:
output[x,y,z] = data[x,y,z]
else:
output[x,y,z] = 0
return output
config_filename = sys.argv[1]
if(not os.path.isfile(config_filename)):
sys.exit(1)
config = readConfig(config_filename)
output_folder = sys.argv[2]
if(not os.path.isdir(output_folder)):
sys.exit(1)
print("Loading training dataset")
train_mra_dataset = np.empty((30, config["image_size_x"], config["image_size_y"], config["image_size_z"]))
i = 0
files = os.listdir(config["dataset_train_mra_path"])
files.sort()
for filename in files:
if(i>=30):
break
print(filename)
train_mra_dataset[i, :, :, :] = nib.load(os.path.join(config["dataset_train_mra_path"], filename)).get_data()
i = i + 1
train_gd_dataset = np.empty((30, config["image_size_x"], config["image_size_y"], config["image_size_z"]))
i = 0
files = os.listdir(config["dataset_train_gd_path"])
files.sort()
for filename in files:
if(i>=30):
break
print(filename)
train_gd_dataset[i, :, :, :] = nib.load(os.path.join(config["dataset_train_gd_path"], filename)).get_data()
i = i + 1
print("Compute threshold")
threshold = getThreshold(train_mra_dataset, train_gd_dataset)
train_mra_dataset = None
train_gd_dataset = None
print("Apply preprocessing to test image")
files = os.listdir(config["dataset_test_mra_path"])
files.sort()
for filename in files:
print(filename)
data = nib.load(os.path.join(config["dataset_test_mra_path"], filename)).get_data()
print(np.average(data))
preprocessed = thresholding(data, threshold)
print(np.average(preprocessed))
npToNii(preprocessed,os.path.join(output_folder+'/test_Images', 'pre_'+filename))
print("Apply threshold to train image : ", threshold)
files = os.listdir(config["dataset_train_mra_path"])
files.sort()
for filename in files:
print(filename)
data = nib.load(os.path.join(config["dataset_train_mra_path"], filename)).get_data()
print(np.average(data))
preprocessed = thresholding(data, threshold)
print(np.average(preprocessed))
npToNii(preprocessed,os.path.join(output_folder+'/train_Images', 'pre_'+filename)) |