import os import argparse import re import warnings import nibabel as nib import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from matplotlib.colors import ListedColormap, LinearSegmentedColormap, to_rgba, CSS4_COLORS import tikzplotlib from ddmr.utils.misc import segmentation_ohe_to_cardinal # segm_cm = np.asarray([to_rgba(CSS4_COLORS[c], 1) for c in CSS4_COLORS.keys()]) # # segm_cm.sort() # segm_cm = segm_cm[np.linspace(0, len(segm_cm), 4, endpoint=False).astype(int), ...] segm_cm = cm.get_cmap('jet').reversed() segm_cm = segm_cm(np.linspace(0, 1, 30)) segm_cm[0, :] = np.asarray([0, 0, 0, 0]) segm_cm = ListedColormap(segm_cm) DICT_MODEL_NAMES = {'BASELINE': 'BL', 'SEGGUIDED': 'SG', 'UW': 'UW'} DICT_METRICS_NAMES = {'NCC': 'N', 'SSIM': 'S', 'DICE': 'D', 'DICE_MACRO': 'D', 'HD': 'H', } def get_model_name(in_path: str): model = re.search('((UW|SEGGUIDED|BASELINE).*)_\d+-\d+', in_path) if model: model = model.group(1).rstrip('_') model = model.replace('_Lsim', '') model = model.replace('_Lseg', '') model = model.replace('_L', '') model = model.replace('_', ' ') model = model.upper() elements = model.split() model = elements[0] metrics = list() model = DICT_MODEL_NAMES[model] for m in elements[1:]: if m != 'MACRO': metrics.append(DICT_METRICS_NAMES[m]) return '{}-{}'.format(model, ''.join(metrics)) else: try: model = re.search('(SyNCC|SyN)', in_path).group(1) except AttributeError: raise ValueError('Unknown folder name/model: '+ in_path) return model def load_segmentation(file_path) -> np.ndarray: segm = np.asarray(nib.load(file_path).dataobj) if segm.shape[-1] > 1: segm = segmentation_ohe_to_cardinal(segm) return segm if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-d', '--dir', type=str, help='Directories where the models are stored', default=None) parser.add_argument('-o', '--output', type=str, help='Output directory', default=os.getcwd()) parser.add_argument('--overwrite', type=bool, default=True) parser.add_argument('--fileno', type=int, default=2) parser.add_argument('--tikz', type=bool, default=False) args = parser.parse_args() assert args.dir is not None, "No directories provided. Stopping" os.makedirs(args.output, exist_ok=True) list_fix_img = list() list_mov_img = list() list_fix_seg = list() list_mov_seg = list() list_pred_img = list() list_pred_seg = list() print('Fetching data...') init_lvl = args.dir.count(os.sep) for r, d, f in os.walk(args.dir): current_lvl = r.count(os.sep) - init_lvl if current_lvl < 3: for name in f: if re.search('^{:03d}'.format(args.fileno), name) and name.endswith('nii.gz'): if re.search('fix_img', name) and name.endswith('nii.gz'): list_fix_img.append(os.path.join(r, name)) elif re.search('mov_img', name): list_mov_img.append(os.path.join(r, name)) elif re.search('fix_seg', name): list_fix_seg.append(os.path.join(r, name)) elif re.search('mov_seg', name): list_mov_seg.append(os.path.join(r, name)) elif re.search('pred_img', name): list_pred_img.append(os.path.join(r, name)) elif re.search('pred_seg', name): list_pred_seg.append(os.path.join(r, name)) # Figure: all coronal views # Fix img | Mov img # BASELINE 1 | BASELINE 2 | SEGGUIDED # UW 1 | UW 2 | UW 3 list_fix_img.sort() list_fix_seg.sort() list_mov_img.sort() list_mov_seg.sort() list_pred_img.sort() list_pred_seg.sort() print('Making Test_data.png...') selected_slice = 64 fix_img = np.asarray(nib.load(list_fix_img[0]).dataobj)[selected_slice, ..., 0].T mov_img = np.asarray(nib.load(list_mov_img[0]).dataobj)[selected_slice, ..., 0].T fix_seg = load_segmentation(list_fix_seg[0])[selected_slice, ..., 0].T mov_seg = load_segmentation(list_mov_seg[0])[selected_slice, ..., 0].T fig, ax = plt.subplots(nrows=1, ncols=4, figsize=(9, 3), dpi=200) for i, (img, title) in enumerate(zip([(fix_img, fix_seg), (mov_img, mov_seg)], [('Fixed image', 'Fixed segms.'), ('Moving image', 'Moving segms.')])): ax[i].imshow(img[0], origin='lower', cmap='Greys_r') ax[i+2].imshow(img[0], origin='lower', cmap='Greys_r') ax[i+2].imshow(img[1], origin='lower', cmap=segm_cm, alpha=0.6) ax[i].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) ax[i+2].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) ax[i].set_xlabel(title[0], fontsize=16) ax[i+2].set_xlabel(title[1], fontsize=16) plt.tight_layout() if not args.overwrite and os.path.exists(os.path.join(args.output, 'Test_data.png')): warnings.warn('File Test_data.png already exists. Skipping') else: plt.savefig(os.path.join(args.output, 'Test_data.png'), format='png') if args.tikz: tikzplotlib.save(os.path.join(args.output, 'Test_data.tex')) plt.close() print('Making Pred_data.png...') fig, ax = plt.subplots(nrows=2, ncols=len(list_pred_img), figsize=(9, 3), dpi=200) for i, (pred_img_path, pred_seg_path) in enumerate(zip(list_pred_img, list_pred_seg)): img = np.asarray(nib.load(pred_img_path).dataobj)[selected_slice, ..., 0].T seg = load_segmentation(pred_seg_path)[selected_slice, ..., 0].T ax[0, i].imshow(img, origin='lower', cmap='Greys_r') ax[1, i].imshow(img, origin='lower', cmap='Greys_r') ax[1, i].imshow(seg, origin='lower', cmap=segm_cm, alpha=0.6) ax[0, i].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) ax[1, i].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) model = get_model_name(pred_img_path) ax[1, i].set_xlabel(model, fontsize=9) plt.tight_layout() if not args.overwrite and os.path.exists(os.path.join(args.output, 'Pred_data.png')): warnings.warn('File Pred_data.png already exists. Skipping') else: plt.savefig(os.path.join(args.output, 'Pred_data.png'), format='png') if args.tikz: tikzplotlib.save(os.path.join(args.output, 'Pred_data.tex')) plt.close() print('Making Pred_data_large.png...') fig, ax = plt.subplots(nrows=2, ncols=len(list_pred_img) + 2, figsize=(9, 3), dpi=200) list_pred_img = [list_mov_img[0]] + list_pred_img list_pred_img = [list_fix_img[0]] + list_pred_img list_pred_seg = [list_mov_seg[0]] + list_pred_seg list_pred_seg = [list_fix_seg[0]] + list_pred_seg for i, (pred_img_path, pred_seg_path) in enumerate(zip(list_pred_img, list_pred_seg)): img = np.asarray(nib.load(pred_img_path).dataobj)[selected_slice, ..., 0].T seg = load_segmentation(pred_seg_path)[selected_slice, ..., 0].T ax[0, i].imshow(img, origin='lower', cmap='Greys_r') ax[1, i].imshow(img, origin='lower', cmap='Greys_r') ax[1, i].imshow(seg, origin='lower', cmap=segm_cm, alpha=0.6) ax[0, i].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) ax[1, i].tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False) if i > 1: model = get_model_name(pred_img_path) elif i == 0: model = 'Moving image' else: model = 'Fixed image' ax[1, i].set_xlabel(model, fontsize=7) plt.tight_layout() if not args.overwrite and os.path.exists(os.path.join(args.output, 'Pred_data_large.png')): warnings.warn('File Pred_data.png already exists. Skipping') else: plt.savefig(os.path.join(args.output, 'Pred_data_large.png'), format='png') if args.tikz: tikzplotlib.save(os.path.join(args.output, 'Pred_data_large.png')) plt.close() print('...done!')