opschonen van scripts. Update van saliency visualisatie.
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Stefan
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02d5b371d6
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@ -42,7 +42,7 @@ 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('./../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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N_CPUS = 12
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@ -1,53 +1,60 @@
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import sys
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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 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 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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# 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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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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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['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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TEST_INDEX_top10 = [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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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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@ -55,45 +62,52 @@ 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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# 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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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 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_top10]
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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_top10]
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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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print('START prediction')
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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 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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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)
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@ -1,90 +1,57 @@
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import argparse
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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# import matplotlib.cm as cm
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heatmap = np.load('saliency.npy')
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print(np.shape(heatmap))
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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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########## 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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SALIENCY_DIR = f'./../train_output/{EXPERIMENT}_{series_}/saliency.npy'
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IMAGES_DIR = f'./../train_output/{EXPERIMENT}_{series_}/images_list.npy'
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SEGMENTATION_DIR = f'./../train_output/{EXPERIMENT}_{series_}/segmentations.npy'
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########## load saliency map ############
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heatmap = np.load(SALIENCY_DIR)
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heatmap = np.squeeze(heatmap)
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######### load images and segmentations ###########
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images_list = np.load(IMAGES_DIR)
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images_list = np.squeeze(images_list)
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segmentations = np.load(SEGMENTATION_DIR)
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######## take average ##########
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# len(heatmap) is smaller then maximum number of images
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# if len(heatmap) < 100:
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# heatmap = np.mean(abs(heatmap),axis=0)
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heatmap = abs(heatmap)
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fig, axes = plt.subplots(2,len(SERIES))
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print(np.shape(axes))
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print(np.shape(heatmap))
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### take average over 5 #########
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heatmap = np.mean(abs(heatmap),axis=0)
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print(np.shape(heatmap))
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SERIES = ['t2','b50','b400','b800','b1400','adc']
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fig, axes = plt.subplots(1,6)
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print(np.shape(images_list))
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max_value = np.amax(heatmap)
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pri
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min_value = np.amin(heatmap)
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# vmin vmax van hele heatmap voor scaling in imshow
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# cmap naar grey
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im = axes[0].imshow(np.squeeze(heatmap[:,:,12,0]))
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axes[1].imshow(np.squeeze(heatmap[:,:,12,1]), vmin=min_value, vmax=max_value)
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axes[2].imshow(np.squeeze(heatmap[:,:,12,2]), vmin=min_value, vmax=max_value)
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axes[3].imshow(np.squeeze(heatmap[:,:,12,3]), vmin=min_value, vmax=max_value)
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axes[4].imshow(np.squeeze(heatmap[:,:,12,4]), vmin=min_value, vmax=max_value)
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axes[5].imshow(np.squeeze(heatmap[:,:,12,5]), vmin=min_value, vmax=max_value)
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axes[0].set_title("t2")
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axes[1].set_title("b50")
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axes[2].set_title("b400")
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axes[3].set_title("b800")
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axes[4].set_title("b1400")
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axes[5].set_title("adc")
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for indx in range(len(SERIES)):
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print(indx)
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axes[0,indx].imshow(images_list[:,:,12,indx],cmap='gray')
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im = axes[1,indx].imshow(np.squeeze(heatmap[:,:,12,indx]),vmin=min_value, vmax=max_value)
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axes[0,indx].set_title(SERIES[indx])
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axes[0,indx].set_axis_off()
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axes[1,indx].set_axis_off()
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cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.5, orientation='horizontal')
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cbar.set_ticks([-0.1,0,0.1])
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cbar.set_ticklabels(['less importance', '0', 'important'])
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fig.suptitle('Average saliency maps over the 5 highest predictions', fontsize=16)
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plt.show()
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cbar.set_ticks([min_value,max_value])
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cbar.set_ticklabels(['less important', 'important'])
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fig.suptitle('Saliency map', fontsize=16)
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plt.savefig(f'./../train_output/{EXPERIMENT}_{series_}/saliency_map.png', dpi=300)
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quit()
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#take one image out
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heatmap = np.squeeze(heatmap[0])
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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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class IndexTracker:
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def __init__(self, ax, X):
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self.ax = ax
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ax.set_title('use scroll wheel to navigate images')
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self.X = X
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rows, cols, self.slices = X.shape
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self.ind = self.slices//2
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self.im = ax.imshow(self.X[:, :, self.ind], cmap='jet')
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self.update()
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def on_scroll(self, event):
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print("%s %s" % (event.button, event.step))
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if event.button == 'up':
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self.ind = (self.ind + 1) % self.slices
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else:
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self.ind = (self.ind - 1) % self.slices
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self.update()
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def update(self):
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self.im.set_data(self.X[:, :, self.ind])
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self.ax.set_ylabel('slice %s' % self.ind)
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self.im.axes.figure.canvas.draw()
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plt.figure(0)
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fig, ax = plt.subplots(1, 1)
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tracker = IndexTracker(ax, heatmap[:,:,:,5])
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fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
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plt.show()
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plt.figure(1)
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fig, ax = plt.subplots(1, 1)
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tracker = IndexTracker(ax, heatmap[:,:,:,3])
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fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
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plt.show()
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@ -0,0 +1,48 @@
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quit()
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#take one image out
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heatmap = np.squeeze(heatmap[0])
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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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class IndexTracker:
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def __init__(self, ax, X):
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self.ax = ax
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ax.set_title('use scroll wheel to navigate images')
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self.X = X
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rows, cols, self.slices = X.shape
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self.ind = self.slices//2
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self.im = ax.imshow(self.X[:, :, self.ind], cmap='jet')
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self.update()
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def on_scroll(self, event):
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print("%s %s" % (event.button, event.step))
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if event.button == 'up':
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self.ind = (self.ind + 1) % self.slices
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else:
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self.ind = (self.ind - 1) % self.slices
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self.update()
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def update(self):
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self.im.set_data(self.X[:, :, self.ind])
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self.ax.set_ylabel('slice %s' % self.ind)
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self.im.axes.figure.canvas.draw()
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plt.figure(0)
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fig, ax = plt.subplots(1, 1)
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tracker = IndexTracker(ax, heatmap[:,:,:,5])
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fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
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plt.show()
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plt.figure(1)
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fig, ax = plt.subplots(1, 1)
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tracker = IndexTracker(ax, heatmap[:,:,:,3])
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fig.canvas.mpl_connect('scroll_event', tracker.on_scroll)
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plt.show()
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