--- a
+++ b/Preprocessing Medical Data Pipeline/main.py
@@ -0,0 +1,262 @@
+import subprocess
+
+required_libraries = [
+    "numpy", "os", "pydicom", "SimpleITK", "pandas", "trimesh",
+    "pyntcloud", "scikit-image", "matplotlib", "tqdm", "nibabel",
+    "re", "scipy", "opencv-python", 'segmentation_models', 'random',
+    'trimesh'
+]
+
+for lib in required_libraries:
+    try:
+        __import__(lib)
+    except ImportError:
+        print(f"{lib} is not installed. Installing...")
+        subprocess.check_call(["pip", "install", lib])
+        print(f"{lib} has been installed.")
+
+
+from preprocessing import preprocessing, VisualValidationMSK, transform_string
+from image_preprocessing import image_cropping, uniform_cropping, uniform_resizing
+from writeout_dataset import Export2CompressedNifiti
+from MSKMulticlass import CreateMasks4MulticlassMSK
+from createDirectories import createDirectoriesfunc
+from preparingTestInstance import preprocessTestScans
+from data_augmentation import DataAugmentation
+import os
+# import tensorflow
+# from tensorflow import keras
+# from keras.models import load_model
+# from keras.utils import to_categorical
+import nibabel as nib
+import numpy as np
+# import segmentation_models as sm
+# from keras.metrics import MeanIoU
+from skimage.measure import marching_cubes
+import matplotlib.pyplot as plt
+import trimesh
+import SimpleITK as sitk
+import nibabel as nib
+import numpy as np
+
+createDirectoriesfunc()
+
+def Preprocessing(scans_path, scan_data_folders, Cropping):
+    print('-'*30)
+    print('Loading and preprocessing training data...')
+    print('-'*30)
+
+    # /research\resmed202100086-tws ----> Address for raw data
+    # /research\resabi202200010-Friedlander
+    total_slices_raw_data = 0
+    DataOnlyAOI = False
+    ExportDatasets = True
+    multiclass_mask_output_dir = None
+
+    # Only set multiclassSegmentation to True once all the AOI masks are preprocessed or its onto the final AOI
+    if (Cropping == "256x256"):
+        Cropping = True
+    else:
+        Cropping = False
+
+    scan_data_folders = [scan_data_folders]
+
+    # Creates the preprocessed scan and mask data and exports to  Data folder
+    # Formats binary masks and applies preprocessing steps 
+    segmasks = []
+    paitent_id = (scan_data_folders[0].split('_'))[-1]
+    files = os.listdir(('{}/Raw NIFITI Segmentation Masks (3D Slicer Output)').format(scans_path))
+
+    for file in files:
+        file_id = file.split('_')[-1]
+        file_id = file_id.split('.')[0]
+        if (file_id == paitent_id):
+            segmasks.append(file)
+    print(segmasks)
+    for segmask in segmasks:
+        print(segmask)
+        imgs_train, imgs_mask_train, median_aoi_index = preprocessing(scans_path, segmask, scan_data_folders, total_slices_raw_data, DataOnlyAOI, Cropping)
+        orientation = (segmask.split('_'))[1]
+        colab_fname = [transform_string(segmask)]
+        Export2CompressedNifiti(imgs_train, scans_path, colab_fname, imgs_mask_train, orientation)
+
+    # User Input
+    # Multi-class mask creation
+    individual_mask_directory = ('{}/nnUNet Data/masks').format(scans_path)
+    multiclass_mask_output_dir = ('{}/nnUNet Data/multiclass_masks').format(scans_path)
+    scan_dir = ('{}/nnUNet Data/scans').format(scans_path)
+    input_scan_dir = ('{}/nnUNet Data/unprocessed_scans').format(scans_path)
+
+    TIBIA_encoding = 1
+    FEMUR_encoding = 2
+    FIBULA_encoding = 3
+    PELVIS_encoding = 4
+    mask_index = int((scan_data_folders[0].split('_'))[0])
+    AOIThresholding = True
+    FriedLanderDataset = False
+
+    print('Multi-Class Segmentation Task Data Preparation!')
+    CreateMasks4MulticlassMSK(input_scan_dir, scan_dir, individual_mask_directory, mask_index, TIBIA_encoding, FEMUR_encoding, FIBULA_encoding, PELVIS_encoding, multiclass_mask_output_dir, AOIThresholding, FriedLanderDataset)
+
+
+    print('-'*30)
+    print('Completed Preprocessing Stage!')
+    print('-'*30)
+
+def preprocessTestScansMain(cutoffSlice, seg_scan_dir, folders):
+    preprocessTestScans(cutoffSlice, seg_scan_dir, [folders])
+
+def VisualiseSlice(basedir, colab_fname, slice_idx_bin):
+    import nibabel as nib
+    import numpy as np
+    import matplotlib.pyplot as plt
+    def superimpose_images(image1, image2):
+        image1 = image1 / np.max(image1)
+        image2 = image2 / np.max(image2)
+        alpha = 0.5
+        superimposed_image = alpha * image1 + (1 - alpha) * image2
+        return superimposed_image
+    
+    if ((colab_fname.split('_'))[0] != 'msk'):
+        orientation = (colab_fname.split('_'))[1]
+        colab_fname = transform_string(colab_fname)
+        mask_index = '{:03d}'.format(int(((colab_fname).split('_'))[1]))
+        nii_img_scan = nib.load(('{}/nnUNet Data/scans/msk_{}.nii.gz').format(basedir, mask_index))
+        nii_img_mask = nib.load(('{}/nnUNet Data/masks/{}/{}_{}.nii.gz').format(basedir, ((colab_fname).split('_'))[0],colab_fname, orientation))
+
+    if ((colab_fname.split('_'))[0] == 'msk'):
+        nii_img_scan = nib.load(('{}/nnUNet Data/scans/{}.nii.gz').format(basedir, colab_fname))
+        nii_img_mask = nib.load(('{}/nnUNet Data/multiclass_masks/{}.nii.gz').format(basedir, colab_fname))
+        orientation = '_'
+
+
+    mask_data = nii_img_mask.get_fdata()
+    scan_data = nii_img_scan.get_fdata()
+    image1 = scan_data[int(slice_idx_bin), :, :, 0]
+    image2 = mask_data[int(slice_idx_bin), :, :, 0]
+    superimposed_image = superimpose_images(image1, image2)
+
+    plt.imshow(superimposed_image, cmap='gray')
+    plt.title(('Validating Scan & Mask on Slice {} of {}_{}').format(slice_idx_bin, colab_fname, orientation))
+    plt.axis('off')
+    plt.show()
+
+    plt.imshow(image2, cmap='gray')
+    plt.title(('Validating Mask on Slice {} of {}_{}').format(slice_idx_bin, colab_fname, orientation))
+    plt.axis('off')
+    plt.show()
+
+    plt.imshow(image1, cmap='gray')
+    plt.title(('Validating Scan on Slice {} of {}_{}').format(slice_idx_bin, colab_fname, orientation))
+    plt.axis('off')
+    plt.show()
+
+    print('-'*30)
+    print('Visulisations Generated!')
+    print('-'*30)
+
+def DataAug(basedir, aug_fname, num_augs):
+    imgs_train = nib.load(('{}/nnUNet Data/scans/{}.nii.gz').format(basedir, aug_fname))
+    imgs_mask_train = nib.load(('{}/nnUNet Data/multiclass_masks/{}.nii.gz').format(basedir, aug_fname))
+    imgs_train = imgs_train.get_fdata()
+    imgs_mask_train = imgs_mask_train.get_fdata()
+    num_train = len(imgs_train)
+    num_augs = int(num_augs)
+    augmented_images_train, augmented_masks_train = DataAugmentation(imgs_train, imgs_mask_train, num_augs)
+
+    augmented_images_train_sorted, augmented_masks_train_sorted = [], []
+    for i in range (int(num_augs)):
+        augmented_images_train_temp = augmented_images_train[i::num_augs]
+        augmented_masks_train_temp = augmented_masks_train[i::num_augs]
+        augmented_images_train_sorted.append(augmented_images_train_temp)  
+        augmented_masks_train_sorted.append(augmented_masks_train_temp)  
+    augmented_images_train_sorted = np.array(augmented_images_train_sorted)
+    augmented_masks_train_sorted = np.array(augmented_masks_train_sorted)
+
+    print('Augmented Training Scan Shape: ', augmented_images_train_sorted.shape)
+    print('Augmented Training Mask Shape: ',augmented_masks_train_sorted.shape)
+
+
+    for i in range (len(augmented_images_train_sorted)):
+        temp = np.expand_dims(augmented_images_train_sorted[i], axis=-1)
+        temp = temp.astype('float32')
+        np.savetxt('D:\MRI - Tairawhiti (User POV)/train.txt', temp[100,:,:,0], fmt="%d", delimiter=",")
+        # temp /= 255.  # scale scans to [0, 1]
+        nii_img_train = nib.Nifti1Image(temp, affine=np.eye(4))
+        output_file_path = ('{}/nnUNet Data/scans/{}_aug{}.nii.gz').format(basedir, aug_fname, i)
+        nib.save(nii_img_train, output_file_path)
+
+        temp = np.expand_dims(augmented_masks_train_sorted[i], axis=-1)
+        temp = temp.astype(int)
+        np.savetxt('D:\MRI - Tairawhiti (User POV)/train_mask.txt', temp[100,:,:,0], fmt="%d", delimiter=",")
+        nii_img_mask = nib.Nifti1Image(temp, affine=np.eye(4))
+        output_file_path = ('{}/nnUNet Data/multiclass_masks/{}_aug{}.nii.gz').format(basedir, aug_fname, i)
+        nib.save(nii_img_mask, output_file_path)  
+
+    print('-'*30)
+    print('Data Augmentation Completed & Exported!')
+    print('-'*30) 
+
+
+def AutoSegModel(subjectfname, base_dir):
+    model_dir = ('{}/Pre-Trained Models (Google Colab)/res34_backbone_20_epochs_dicefocal_256_4P_12batch_maxF1_pelvis_aug_best.hdf5').format(base_dir)
+    model = load_model(model_dir, compile=False)
+
+    n_classes = 5
+    BACKBONE = 'resnet34'
+    model_used = 'U-Net(resnet34)'
+
+    img_dir = ('{}/nnUNet Data/scans/{}.nii.gz').format(base_dir, subjectfname)
+    img = nib.load(img_dir)
+    img_data = img.get_fdata()
+    X_test = np.repeat(img_data, 3, axis=3)
+
+    print(('Fined-Tuned Model: {}').format((model_dir.split('/'))[-1]))
+    print('Prediction Scan Stack: ', subjectfname)
+    print('Number of Segmentation Classes: ', n_classes)
+    print('\n')
+
+    print("Test Images Shape: ", X_test.shape)
+    print('\n')
+
+    preprocess_input = sm.get_preprocessing(BACKBONE)
+    X_test_processed = preprocess_input(X_test)
+
+    # Prediction
+    y_pred=model.predict(X_test_processed)
+    y_pred_argmax=np.argmax(y_pred, axis=3)
+    y_pred_argmax = np.expand_dims(y_pred_argmax, axis = -1)
+    print('Pred Mask Shape: ', y_pred_argmax.shape)
+    print("Pred Mask Labels: ", np.unique(y_pred_argmax))
+    print('\n')
+
+    combined_mask = y_pred_argmax.astype(np.int32)
+    combined_img = nib.Nifti1Image(combined_mask, affine=np.eye(4), dtype=np.int32)
+    nib.save(combined_img, ("{}/{}_pred.nii.gz").format(model_dir, subjectfname), dtype = np.uint8)
+    print('Exported Prediction Segmentation: ', (("{}/{}_pred.nii.gz").format(model_dir, subjectfname)))
+    print('\n')
+
+    def Export3DStructure(segmentation_data, predfname, class_msk, model_used):
+        # Generate a surface mesh using marching cubes
+        vertices, faces, normals, _ = marching_cubes(segmentation_data, level=0)
+
+        # Create a Trimesh object
+        mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_normals=normals)
+
+        # Save the mesh as a PLY file
+        ply_path = ('{}/{}_{}_{}.ply').format(model_dir, predfname, class_msk, model_used)
+        mesh.export(ply_path)
+
+    segmentation_data_all = y_pred_argmax
+    segmentation_data_all = segmentation_data_all[:,:,:,0]
+    tibia_seg_data = np.where(segmentation_data_all != 1, 0, 1)
+    femur_seg_data = np.where(segmentation_data_all != 2, 0, 2)
+    fibula_seg_data = np.where(segmentation_data_all != 3, 0, 3)
+    pelvis_seg_data = np.where(segmentation_data_all != 4, 0, 4)
+
+    segmentation_data = [segmentation_data_all, tibia_seg_data, femur_seg_data, fibula_seg_data, pelvis_seg_data]
+    class_msk = ['ALL', 'TIBIA', 'FEMUR', 'FIBULA', 'PELVIS']
+
+    for i in range (len(segmentation_data)):
+        Export3DStructure(segmentation_data[i], subjectfname, class_msk[i], model_used)
+