tta, paths_to_ridge_polygons from lib.vis import compute_bullseye_sector_mask_for_slice from lib.dataset import load_npy_file from lib.windows import normalize_data from lib.cmaps import default_cmap CONFIG = "experiments/036_mini.yaml" POSE_MODELPATH = r"E:\Dropbox\Work\Other projects\T1T2\output\models\036_mini\70_0.0010686.pt" # CONFIG = "experiments/030.yaml" # POSE_MODELPATH = r"E:\Dropbox\Work\Other projects\T1T2\output\models\030\154_0.0004970.pt" LANDMARK_MODELPATH = "./data/models/landmark_model.pts" TEST_DICOM_DIR = r"E:\Dropbox\Work\Other projects\T1T2\data\dicoms\mini_test" FOV = 256 DEVICE = "cuda" cfg, _ = load_config(CONFIG) dates_for_studies = glob(os.path.join(TEST_DICOM_DIR, "**/*.npy"), recursive=True) dates_for_studies = { os.path.basename(os.path.dirname(f)): os.path.basename(os.path.dirname(os.path.dirname(f))) for f in dates_for_studies } # LOAD MODELS model = get_hrnet_model(get_hrnet_cfg(cfg)).to(DEVICE) model = model.eval() model.load_state_dict(torch.load(POSE_MODELPATH)['state_dict']) # OUTLIERS = ['T1T2_141613_54120998_54121006_116_20201113-103051__T1_T2_PD_SLC1_CON0_PHS0_REP0_SET0_AVE0_2.npy',
import numpy as np import skimage.io import matplotlib.pyplot as plt from glob import glob from tqdm import tqdm from collections import defaultdict from lib.landmarks import load_landmark_model, perform_cmr_landmark_detection from lib.cfg import load_config from lib.transforms import get_segmentation_transforms from lib.inference import center_crop, pad_if_needed from lib.vis import compute_bullseye_sector_mask_for_slice CONFIG = "./experiments/030.yaml" cfg, model_dir = load_config(CONFIG) TEST_DICOM_DIR = cfg['export']['dicom_path_test'] LANDMARK_MODELPATH = cfg['export']['landmark_model_path'] LABEL_ROOT_DIR = cfg['export']['label_path_test'] FOV = 256 WRITE_PNGS = True # Load config sequences = cfg['export']['source_channels'] label_classes = cfg['export']['label_classes'] gaussian_sigma = cfg['export']['gaussian_sigma'] n_channels_keep_img = len( sequences) # May have exported more channels to make PNG n_channels_keep_lab = len(label_classes)