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)