opschonen van 2.froc.py
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Stefan
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be5b392456
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02d5b371d6
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@ -1,63 +1,56 @@
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import sys
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from os import path
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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 json
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import matplotlib.pyplot as plt
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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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sys.path.append('./../code')
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from utils_quintin import *
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sys.path.append('./../code/DWI_exp')
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from helpers import *
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from preprocessing_function import preprocess
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from callbacks import dice_coef
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sys.path.append('./../code/FROC')
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from blob_preprocess import *
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from cal_froc_from_np import *
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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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parser = argparse.ArgumentParser(
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description='Train a U-Net model for segmentation/detection tasks.' +
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'using cross-validation.')
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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, must correspond with' +
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"path files in ./data/")
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help='List of series to include')
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args = parser.parse_args()
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######## parsed inputs #############
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# SERIES = ['b50', 'b400', 'b800'] #can be parsed
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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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# Import model
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# MODEL_PATH = f'./../train_output/train_10h_{series_}/models/train_10h_{series_}.h5'
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# YAML_DIR = f'./../train_output/train_10h_{series_}'
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MODEL_PATH = f'./../train_output/train_n0.001_{series_}/models/train_n0.001_{series_}.h5'
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print(MODEL_PATH)
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YAML_DIR = f'./../train_output/train_n0.001_{series_}'
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################ constants ############
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EXPERIMENT = args.experiment
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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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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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# import val_indx
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DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
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TEST_INDEX = DATA_SPLIT_INDEX['val_set0']
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N_CPUS = 12
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########## load images ##############
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images, image_paths = {s: [] for s in SERIES}, {}
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segmentations = []
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########## load images in parrallel ##############
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print_(f"> Loading images into RAM...")
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# read paths from txt
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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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@ -65,90 +58,33 @@ 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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# Read and preprocess each of the paths for each SERIES, and the segmentations.
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from typing import List
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def load_images(
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image_paths: str,
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seq: str,
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target_shape: List[int],
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target_space = List[float]):
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img_s = sitk.ReadImage(image_paths, sitk.sitkFloat32)
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#resample
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mri_tra_s = resample(img_s,
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min_shape=target_shape,
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method=sitk.sitkNearestNeighbor,
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new_spacing=target_space)
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#center crop
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mri_tra_s = center_crop(mri_tra_s, shape=target_shape)
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#normalize
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if seq != 'seg':
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filter = sitk.NormalizeImageFilter()
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mri_tra_s = filter.Execute(mri_tra_s)
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else:
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filter = sitk.BinaryThresholdImageFilter()
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filter.SetLowerThreshold(1.0)
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mri_tra_s = filter.Execute(mri_tra_s)
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return sitk.GetArrayFromImage(mri_tra_s).T
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N_CPUS = 12
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# create pool of workers
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pool = multiprocessing.Pool(processes=N_CPUS)
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partial_f = partial(load_images,
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partial_images = partial(load_images_parrallel,
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seq = 'images',
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target_shape=IMAGE_SHAPE,
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target_space = TARGET_SPACING)
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images_2 = []
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for s in SERIES:
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image_paths_seq = image_paths[s]
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image_paths_index = np.asarray(image_paths_seq)[TEST_INDEX]
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data_list = pool.map(partial_f,image_paths_index)
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data = np.stack(data_list, axis=0)
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images_2.append(data)
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# print(s)
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# print(np.shape(data))
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print(np.shape(images_2))
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partial_f = partial(load_images,
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partial_seg = partial(load_images_parrallel,
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seq = 'seg',
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target_shape=IMAGE_SHAPE,
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target_space = TARGET_SPACING)
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#load images
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images = []
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for s in SERIES:
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image_paths_seq = image_paths[s]
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image_paths_index = np.asarray(image_paths_seq)[TEST_INDEX]
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data_list = pool.map(partial_images,image_paths_index)
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data = np.stack(data_list, axis=0)
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images.append(data)
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images_list = np.transpose(images, (1, 2, 3, 4, 0))
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#load segmentations
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seg_paths_index = np.asarray(seg_paths)[TEST_INDEX]
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data_list = pool.map(partial_f,seg_paths_index)
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data_list = pool.map(partial_seg,seg_paths_index)
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segmentations = np.stack(data_list, axis=0)
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# print("segmentations pool",np.shape(segmentations_2))
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# for img_idx in TEST_INDEX: #for less images
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# img_s = {s: sitk.ReadImage(image_paths[s][img_idx], sitk.sitkFloat32)
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# for s in SERIES}
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# seg_s = sitk.ReadImage(seg_paths[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[seq].append(img_n[seq])
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# segmentations.append(seg_n)
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# print("segmentations old",np.shape(segmentations))
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# # from dict to list
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# # images_list = [img nmbr, [INPUT_SHAPE]]
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# images_list = [images[s] for s in images.keys()]
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# images_list = np.transpose(images_list, (1, 2, 3, 4, 0))
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images_list = np.transpose(images_2, (1, 2, 3, 4, 0))
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print("images size ",np.shape(images_list))
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print("size segmentation",np.shape(segmentations))
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# print("images size pool",np.shape(images_list_2))
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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########### load module ##################
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print(' >>>>>>> LOAD MODEL <<<<<<<<<')
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@ -158,21 +94,18 @@ dependencies = {
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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 val_indx
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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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# print("The shape of the predictions list is: ",np.shape(predictions_blur))
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# print(type(predictions))
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# np.save('predictions',predictions)
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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) # Joeran has his numpy arrays ordered differently.
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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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@ -192,8 +125,8 @@ metrics = evaluate(y_true=segmentations, y_pred=predictions)
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dump_dict_to_yaml(metrics, YAML_DIR, "froc_metrics", verbose=True)
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# save one image
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############## save image as example #################
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# save image nmr 3
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IMAGE_DIR = f'./../train_output/train_10h_{series_}'
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img_s = sitk.GetImageFromArray(predictions_blur[3].squeeze())
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sitk.WriteImage(img_s, f"{IMAGE_DIR}/predictions_blur_001.nii.gz")
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@ -202,16 +135,4 @@ img_s = sitk.GetImageFromArray(predictions[3].squeeze())
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sitk.WriteImage(img_s, f"{IMAGE_DIR}/predictions_001.nii.gz")
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img_s = sitk.GetImageFromArray(segmentations[3].squeeze())
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sitk.WriteImage(img_s, f"{IMAGE_DIR}/segmentations_001.nii.gz")
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# create plot
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# json_path = './../scripts/metrics.json'
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# f = open(json_path)
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# data = json.load(f)
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# x = data['fpr']
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# y = data['tpr']
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# auroc = data['auroc']
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# plt.plot(x,y)
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sitk.WriteImage(img_s, f"{IMAGE_DIR}/segmentations_001.nii.gz")
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@ -11,24 +11,16 @@ import numpy as np
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from sfransen.Saliency.base import *
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from sfransen.Saliency.integrated_gradients import *
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# from tensorflow.keras.vis.visualization import visualize_saliency
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sys.path.append('./../code')
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from utils_quintin import *
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sys.path.append('./../code/DWI_exp')
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# from preprocessing_function import preprocess
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from sfransen.utils_quintin import *
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from sfransen.DWI_exp import preprocess
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print("done step 1")
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from sfransen.DWI_exp.helpers import *
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# from helpers import *
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from callbacks import dice_coef
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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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sys.path.append('./../code/FROC')
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from blob_preprocess import *
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from cal_froc_from_np import *
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quit()
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# train_10h_t2_b50_b400_b800_b1400_adc
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SERIES = ['t2','b50','b400','b800','b1400','adc']
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MODEL_PATH = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc/models/train_10h_t2_b50_b400_b800_b1400_adc.h5'
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@ -46,7 +38,7 @@ IMAGE_SHAPE = INPUT_SHAPE[:3]
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experiment_path = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc/froc_metrics.yml'
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experiment_metrics = read_yaml_to_dict(experiment_path)
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DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
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TEST_INDEX = DATA_SPLIT_INDEX['val_set0']
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TEST_INDEX = DATA_SPLIT_INDEX['test_set0']
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top_10_idx = np.argsort(experiment_metrics['roc_pred'])[-10:]
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TEST_INDEX = [TEST_INDEX[i] for i in top_10_idx]
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@ -90,7 +82,6 @@ print('START prediction')
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ig = IntegratedGradients(reconstructed_model)
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saliency_map = []
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for img_idx in range(len(images_list)):
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# input_img = np.resize(images_list[img_idx],(1,48,48,8,8))
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input_img = np.resize(images_list[img_idx],(1,192,192,24,len(SERIES)))
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saliency_map.append(ig.get_mask(input_img).numpy())
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print("size saliency map is:",np.shape(saliency_map))
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