113 lines
3.9 KiB
Python
Executable File
113 lines
3.9 KiB
Python
Executable File
import argparse
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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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import numpy as np
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from sfransen.utils_quintin import *
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from sfransen.DWI_exp import preprocess
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from sfransen.DWI_exp.helpers import *
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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.Saliency.base import *
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from sfransen.Saliency.integrated_gradients import *
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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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args = parser.parse_args()
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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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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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froc_metrics = read_yaml_to_dict(f'{YAML_DIR}/froc_metrics.yml')
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top_10_idx = np.argsort(froc_metrics['roc_pred'])[-1 :]
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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_top10 = [TEST_INDEX[i] for i in top_10_idx]
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TEST_INDEX_image = [371]
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N_CPUS = 12
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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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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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# create pool of workers
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pool = multiprocessing.Pool(processes=N_CPUS)
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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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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_image]
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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_image]
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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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########### 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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}
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reconstructed_model = load_model(MODEL_PATH, custom_objects=dependencies)
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# reconstructed_model.layers[-1].activation = tf.keras.activations.linear
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######### Build Saliency heatmap ##############
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print(' >>>>>>> Build saliency map <<<<<<<<<')
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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],(1,192,192,24,len(SERIES)))
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saliency_map.append(ig.get_mask(input_img).numpy())
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print("size saliency map",np.shape(saliency_map))
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np.save(f'{YAML_DIR}/saliency',saliency_map)
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np.save(f'{YAML_DIR}/images_list',images_list)
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np.save(f'{YAML_DIR}/segmentations',segmentations) |