allModelsCreatePredNpys.py 13 KB
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import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import tkinter as tk
import os
import matplotlib
from tkinter import filedialog
from skimage import transform
import SimpleITK as sitk
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import nibabel as nib
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import argparse
#import os
#import pydot
#from graphviz import Digraph
#import shutil
#from tensorflow.keras import layers, models
#from utils.dataLoader import *
from utils.other_functions import *
from nets.Unet import *

def main():
    folds = ["12", "34", "56", "78", "910", "1112", "1314", "1516"]
    folder_path = "finalResults/complete_"
    unet_models_paths = ["ctfgt2/unet_cv_ctfgt2/",
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                         "seg/unet_cv_seg/",
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                         "thresh/unet_cv_thresh/",
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                         "segmr/mr_unet_cv_segmr/",
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                         "segmr/mr_unet_cv_ctseg/",
                         "segmr/mr_unet_cv_ctthresh/",
                         "segmr/mr_unet_cv_unetpred/"]
    cluster_models_paths = ["ctfgt2/ae_noclass_cv_ctfgt2/",
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                             "seg/ae_noclass_cv_seg/",
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                             "thresh/ae_noclass_cv_thresh/"]
    class_models_paths = ["ctfgt2/ae_class_cv_ctfgt2/",
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                         "seg/ae_class_cv_seg/",
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                         "thresh/ae_class_cv_thresh/"]
    clusterclass_models_paths = ["ctfgt2/ae_class_cv_ctfgt2/",
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                                 "seg/ae_class_cv_seg/",
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                                 "thresh/ae_class_cv_thresh/"]
    modeltypes = ["Unet_", "Cluster_", "Class_", "Cluster_class_"]

    for modeltype in modeltypes:
        if modeltype == "Unet_": model_paths = unet_models_paths
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        elif modeltype == "Cluster_": model_paths = cluster_models_paths
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        elif modeltype == "Class_": model_paths = class_models_paths
        elif modeltype == "Cluster_class_": model_paths = clusterclass_models_paths

        print("!!!!!!!!!! Lets start the ", modeltype, " models !!!!!!!!!!")

        for model_path in model_paths:

            for fold in folds:
                print("Starting fold ", fold, "in ", model_path)

                file_path = folder_path+model_path+"TPs"+fold+modeltype
                print(file_path)

                ### read out files ###
                weights = np.load(file_path+"model.npy", allow_pickle=True)
                [test_patients,
                 val_patients,
                 number_patients,
                 img_path,
                 shrink_data,
                 newSize,
                 lr,
                 batch_size,
                 num_epochs,
                 e,
                 augment,
                 save_path,
                 gt_type,
                 filter_multiplier] = np.load(file_path+"params.npy", allow_pickle=True)
                #  autoencoder_model__e100_switchclass1024_nohiddenclusternet needs to comment out gt_type and val_patients
                '''print('Training Parameters:')
                print('-----------------')
                print('Number of Patients: ', number_patients)
                print('Number of epochs: ', num_epochs)
                print('Test Patient number: ', test_patients)
                print('Image Size: ', newSize)
                print('Filter Multiplier: ', filter_multiplier)
                print('Data Augmentation: ', augment)
                print('Learning rate: ', lr)
                print('Image Path: ', img_path)
                print('Save Path: ', save_path)
                print('GT Type:', gt_type)'''
        
                ### Load test patient
                if gt_type == "thresh" or gt_type == "ctthresh_gt" or gt_type == "_mr_ctunetpred":
                    img_path = "data/npy_thresh/"
                else:
                    img_path = "data/npy/"
                full_list = os.listdir(img_path)
                seg_list = os.listdir("data/npy/")

                X_img_list = []
                GT_img_list = []
                ytrue_img_list = []
                # thresh_img_list = []
                if "mr" in save_path:
                    for elem in full_list:
                        if elem.endswith("T1.gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            X_img_list.append(elem)
                            if gt_type == "ctthresh_gt":
                                X_img_list.append(elem)
                                X_img_list.append(elem)

                        elif elem.endswith(gt_type + ".gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            GT_img_list.append(elem)
                    for elem in seg_list:
                        if elem.endswith("segmr.gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            ytrue_img_list.append(elem)
                            if gt_type == "ctthresh_gt":
                                ytrue_img_list.append(elem)
                                ytrue_img_list.append(elem)

                else:
                    for elem in full_list:
                        if elem.endswith("ct.gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            X_img_list.append(elem)
                            if gt_type == "thresh":
                                X_img_list.append(elem)
                                X_img_list.append(elem)

                        elif elem.endswith(gt_type + ".gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            GT_img_list.append(elem)
                    for elem in seg_list:
                        if elem.endswith("seg.gipl.npy") and (
                                elem.startswith('P' + str(test_patients[0]).zfill(2)) or elem.startswith(
                                'P' + str(test_patients[1]).zfill(2))):
                            ytrue_img_list.append(elem)
                            if gt_type == "thresh":
                                ytrue_img_list.append(elem)
                                ytrue_img_list.append(elem)
                list.sort(X_img_list)
                list.sort(GT_img_list)
                list.sort(ytrue_img_list)
                '''print("Input Image List", X_img_list)
                print("GT Image List", GT_img_list)
                print("True Segmentation Image List", ytrue_img_list)'''
        
        
                for j in range(2):
                    if gt_type == "thresh" or gt_type == "ctthresh_gt":
                        X_img_npys = np.load(img_path + X_img_list[j*3])
                        GT_img_npys = np.load(img_path + GT_img_list[j*3])
                        ytrue_img_npys = np.load(img_path + ytrue_img_list[j*3])
        
                        X_img_npys = np.append(X_img_npys, np.load(img_path + X_img_list[j * 3+1]), axis=0)
                        GT_img_npys = np.append(GT_img_npys, np.load(img_path + GT_img_list[j * 3+1]), axis=0)
                        ytrue_img_npys = np.append(ytrue_img_npys, np.load(img_path + ytrue_img_list[j * 3+1]), axis=0)
        
                        X_img_npys = np.append(X_img_npys, np.load(img_path + X_img_list[j * 3+2]), axis=0)
                        GT_img_npys = np.append(GT_img_npys, np.load(img_path + GT_img_list[j * 3+2]), axis=0)
                        ytrue_img_npys = np.append(ytrue_img_npys, np.load(img_path + ytrue_img_list[j * 3+2]), axis=0)

        
                    else:
                        X_img_npys = np.load(img_path + X_img_list[j])
                        GT_img_npys = np.load(img_path + GT_img_list[j])
                        ytrue_img_npys = np.load(img_path + ytrue_img_list[j])
        
        
        
                    X_img_npys = transform.resize(X_img_npys, (X_img_npys.shape[0], newSize[0], newSize[1]), order=0,
                                                  preserve_range=True, mode='constant', anti_aliasing=False,
                                                  anti_aliasing_sigma=None)
                    GT_img_npys = transform.resize(GT_img_npys, (GT_img_npys.shape[0], newSize[0], newSize[1]), order=0,
                                                   preserve_range=True, mode='constant', anti_aliasing=False,
                                                   anti_aliasing_sigma=None)
                    ytrue_img_npys = transform.resize(ytrue_img_npys, (ytrue_img_npys.shape[0], newSize[0], newSize[1]),
                                                      order=0,
                                                      preserve_range=True, mode='constant', anti_aliasing=False,
                                                      anti_aliasing_sigma=None)
                    X_test = np.reshape(X_img_npys, (X_img_npys.shape[0], X_img_npys.shape[1], X_img_npys.shape[2], 1))
                    GT_test = np.reshape(GT_img_npys, (GT_img_npys.shape[0], GT_img_npys.shape[1], GT_img_npys.shape[2], 1))
                    ytrue = np.reshape(ytrue_img_npys,
                                       (ytrue_img_npys.shape[0], ytrue_img_npys.shape[1], ytrue_img_npys.shape[2], 1))
        
        
                    test_dataset = tf.data.Dataset.from_tensor_slices((X_test, ytrue))
                    test_dataset = test_dataset.batch(batch_size=1)
                    TP_num = test_patients[j]
        
                    ###################################################################################
        
                    detailed_images = False
                    npys3d          = True
        
                    ###################################################################################

                    test_loss = []
                    test_loss_hdd = []
                    test_loss_hdd2 = []
                    y_pred3d = []
                    for features in test_dataset:
                        image, y_true = features
                        y_true = onehotencode(y_true)
                        y_pred = Unet(image, weights, filter_multiplier, training=False)
        
                        loss = dice_loss(y_pred, y_true)
                        loss = tf.make_ndarray(tf.make_tensor_proto(loss))
                        test_loss.append(loss)
                        #print(test_loss)
                        try:
                            y_true_np = np.squeeze(y_true[0, :, :, 0].numpy() > 0.5)
                            y_true_np = y_true_np.astype(np.float_)
                            pred_np = np.squeeze(y_pred[0, :, :, 0].numpy() > 0.5)
                            pred_np = pred_np.astype(np.float_)
                            hausdorff_distance_filter = sitk.HausdorffDistanceImageFilter()
                            hausdorff_distance_filter.Execute(sitk.GetImageFromArray(y_true_np), sitk.GetImageFromArray(pred_np))
                            test_loss_hdd.append(hausdorff_distance_filter.GetHausdorffDistance())
        
                            hausdorff_distance_filter2 = sitk.HausdorffDistanceImageFilter()
                            hausdorff_distance_filter2.Execute(sitk.GetImageFromArray(pred_np), sitk.GetImageFromArray(y_true_np))
                            test_loss_hdd2.append(hausdorff_distance_filter2.GetHausdorffDistance())

                            if y_pred3d == []:
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                                y_pred3d = np.reshape(pred_np, newshape=(1, 512, 512))
                                y_true3d = np.reshape(y_true_np, newshape=(1, 512, 512))
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                            else:
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                                y_pred3d = np.append(np.reshape(pred_np, newshape=(1, 512, 512)), y_pred3d, axis=0)
                                y_true3d = np.append(np.reshape(y_true_np, newshape=(1, 512, 512)), y_true3d, axis=0)
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                        except:
                            pass
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                    npy_savepath = "finalResults/3dnpys/" + model_path + "P" + str(TP_num).zfill(2)
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                    if not os.path.exists(npy_savepath):
                        os.makedirs(npy_savepath)
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                    np.save(npy_savepath + "_pred3D", y_pred3d)

                    output_volume = np.squeeze(y_pred3d)
                    output_nifty = nib.Nifti1Image(output_volume, np.eye(4))

                    fname = npy_savepath+'_Scan.nii.gz'
                    nib.save(output_nifty, fname)

                    output_label = np.argmax(output_volume, axis=-1)
                    output_label = output_label.astype(np.uint8)
                    output_label_nifty = nib.Nifti1Image(output_label, np.eye(4))

                    fname = npy_savepath+'_Label.nii.gz'
                    nib.save(output_label_nifty, fname)

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          #####

if __name__ == "__main__":
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    main()