109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
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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 import keras
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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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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.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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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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YAML_DIR = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc'
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################ constants ############
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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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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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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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########## load images ##############
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images, image_paths = {s: [] for s in SERIES}, {}
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segmentations = []
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print_(f"> Loading images into RAM...")
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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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# Read and preprocess each of the paths for each SERIES, and the segmentations.
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for img_idx in TEST_INDEX[:5]: #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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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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########### load module ##################
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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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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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np.save('saliency',saliency_map)
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# Christian Roest, [11-3-2022 15:30]
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# input_img heeft dimensies (1, 48, 48, 8, 8)
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# reconstructed_model.summary(line_length=120)
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# make predictions on all val_indx
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print('START saliency')
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# predictions_blur = reconstructed_model.predict(images_list, batch_size=1)
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