192 lines
7.4 KiB
Python
Executable File
192 lines
7.4 KiB
Python
Executable File
from inspect import _ParameterKind
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import SimpleITK as sitk
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from focal_loss import BinaryFocalLoss
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import numpy as np
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import multiprocessing
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from functools import partial
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import os
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from os import path
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from tqdm import tqdm
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import argparse
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from sfransen.utils_quintin import *
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from sfransen.DWI_exp.helpers import *
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from sfransen.DWI_exp.preprocessing_function import preprocess
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from sfransen.DWI_exp.callbacks import dice_coef
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#from sfransen.FROC.blob_preprocess import *
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from sfransen.FROC.cal_froc_from_np import *
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from sfransen.load_images import load_images_parrallel
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from sfransen.DWI_exp.losses import weighted_binary_cross_entropy
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from umcglib.froc import *
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from umcglib.binarize import dynamic_threshold
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parser = argparse.ArgumentParser(
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description='Calculate the froc metrics and store in froc_metrics.yml')
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parser.add_argument('-experiment',
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help='Title of experiment')
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parser.add_argument('--series', '-s',
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metavar='[series_name]', required=True, nargs='+',
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help='List of series to include')
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parser.add_argument('-fold',
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default='',
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help='List of series to include')
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args = parser.parse_args()
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# if __name__ = '__main__':
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# bovenstaande nodig om fork probleem op te lossen (windows cs linux)
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######## CUDA ################
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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######## constants #############
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SERIES = args.series
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series_ = '_'.join(args.series)
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EXPERIMENT = args.experiment
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# fold = args.fold
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predictions_added = []
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segmentations_added = []
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for fold in range(0,5):
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MODEL_PATH = f'./../train_output/{EXPERIMENT}_{series_}_{fold}/models/{EXPERIMENT}_{series_}_{fold}.h5'
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YAML_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}'
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IMAGE_DIR = f'./../train_output/{EXPERIMENT}_{series_}_{fold}'
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# MODEL_PATH = f'./../train_output/{EXPERIMENT}_{series_}/models/{EXPERIMENT}_{series_}.h5'
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# YAML_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
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# IMAGE_DIR = f'./../train_output/{EXPERIMENT}_{series_}'
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DATA_DIR = "./../data/Nijmegen paths/"
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TARGET_SPACING = (0.5, 0.5, 3)
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INPUT_SHAPE = (192, 192, 24, len(SERIES))
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IMAGE_SHAPE = INPUT_SHAPE[:3]
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DATA_SPLIT_INDEX = read_yaml_to_dict(f'./../data/Nijmegen paths/train_val_test_idxs_{fold}.yml')
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# DATA_SPLIT_INDEX = read_yaml_to_dict(f'./../data/Nijmegen paths/train_val_test_idxs.yml')
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TEST_INDEX = DATA_SPLIT_INDEX['test_set0']
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N_CPUS = 12
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########## test with old method #############
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print_(f"> Loading images into RAM...")
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image_paths = {}
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for s in SERIES:
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with open(path.join(DATA_DIR, f"{s}.txt"), 'r') as f:
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image_paths[s] = [l.strip() for l in f.readlines()]
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with open(path.join(DATA_DIR, f"seg.txt"), 'r') as f:
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seg_paths = [l.strip() for l in f.readlines()]
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num_images = len(seg_paths)
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images = []
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images_list = []
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segmentations = []
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# Read and preprocess each of the paths for each series, and the segmentations.
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for img_idx in tqdm(range(len(TEST_INDEX))): #[:40]): #for less images
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# print('images number',[TEST_INDEX[img_idx]])
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img_s = {f'{s}': sitk.ReadImage(image_paths[s][TEST_INDEX[img_idx]], sitk.sitkFloat32) for s in SERIES}
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seg_s = sitk.ReadImage(seg_paths[TEST_INDEX[img_idx]], sitk.sitkFloat32)
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img_n, seg_n = preprocess(img_s, seg_s,
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shape=IMAGE_SHAPE, spacing=TARGET_SPACING)
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for seq in img_n:
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images.append(img_n[f'{seq}'])
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images_list.append(images)
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images = []
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segmentations.append(seg_n)
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images_list = np.transpose(images_list, (0, 2, 3, 4, 1))
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print('>>>>> size image_list nmr 2:', np.shape(images_list), '. equal to: (5, 192, 192, 24, 3)?')
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########### load module ##################
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print(' >>>>>>> LOAD MODEL <<<<<<<<<')
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dependencies = {
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'dice_coef': dice_coef,
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'weighted_cross_entropy_fn':weighted_binary_cross_entropy
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}
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reconstructed_model = load_model(MODEL_PATH, custom_objects=dependencies)
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# reconstructed_model.summary(line_length=120)
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# make predictions on all TEST_INDEX
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print(' >>>>>>> START prediction <<<<<<<<<')
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predictions_blur = reconstructed_model.predict(images_list, batch_size=1)
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############# preprocess #################
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# preprocess predictions by removing the blur and making individual blobs
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print('>>>>>>>> START preprocess')
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def move_dims(arr):
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# UMCG numpy dimensions convention: dims = (batch, width, heigth, depth)
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# Joeran numpy dimensions convention: dims = (batch, depth, heigth, width)
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arr = np.moveaxis(arr, 3, 1)
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arr = np.moveaxis(arr, 3, 2)
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return arr
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# Joeran has his numpy arrays ordered differently.
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predictions_blur = move_dims(np.squeeze(predictions_blur))
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segmentations = move_dims(np.squeeze(segmentations))
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# predictions = [preprocess_softmax(pred, threshold="dynamic")[0] for pred in predictions_blur]
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predictions = predictions_blur
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print("the size of predictions is:",np.shape(predictions))
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# Remove outer edges
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zeros = np.zeros(np.shape(predictions))
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test = np.squeeze(predictions)[:,2:-2,2:190,2:190]
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zeros[:,2:-2,2:190,2:190] = test
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predictions = zeros
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# # perform Froc method joeran
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# metrics = evaluate(y_true=segmentations, y_pred=predictions)
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# dump_dict_to_yaml(metrics, YAML_DIR, "froc_metrics_focal_10_test", verbose=True)
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############# save image as example #################
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# save image nmr 6
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# img_s = sitk.GetImageFromArray(predictions_blur[6].squeeze())
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# sitk.WriteImage(img_s, f"{IMAGE_DIR}/predictions_blur_006_dyn_0.6.nii.gz")
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# img_s = sitk.GetImageFromArray(predictions[6].squeeze())
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# sitk.WriteImage(img_s, f"{IMAGE_DIR}/predictions_006_dyn_0.6.nii.gz")
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# img_s = sitk.GetImageFromArray(segmentations[6].squeeze())
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# sitk.WriteImage(img_s, f"{IMAGE_DIR}/segmentations_006_dyn_0.6.nii.gz")
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# img_s = sitk.GetImageFromArray(np.transpose(images_list[6,:,:,:,0].squeeze()))
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# sitk.WriteImage(img_s, f"{IMAGE_DIR}/t2_006_dyn_0.6.nii.gz")
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if fold == 0:
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segmentations_added = segmentations
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predictions_added = predictions
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else:
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segmentations_added = np.append(segmentations_added,segmentations,axis=0)
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predictions_added = np.append(predictions_added,predictions,axis=0)
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# Froc method umcglib
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stats = calculate_froc(y_true=segmentations_added,
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y_pred=predictions_added,
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preprocess_func=dynamic_threshold,
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dynamic_threshold_factor=0.5,
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minimum_confidence=0.1,
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bootstrap = 1000,
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min_overlap = 0.01,
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overlap_function = 'dsc')
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dump_stats_to_yaml(stats, YAML_DIR, "umcglib_stats_overlap_0.01", verbose=True)
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quit()
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plot_multiple_froc(
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sensitivities=[np.array(stats['sensitivity'])],
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fp_per_patient=[np.array(stats["fp_per_patient"])],
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ci_low=[np.array(stats['sens_95_boot_ci_low'])],
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ci_high=[np.array(stats["sens_95_boot_ci_high"])],
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model_names=["test only"],
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title="testtest",
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height=12, width=15,
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save_as="froc_test_0.5_conf_0.1_overlap_0.1_dsc.png",
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xlims=(0.1, 5)
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) |