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 segm_cm = cm.get_cmap('Dark2', 256) segm_cm = segm_cm(np.linspace(0, 1, 28)) segm_cm[0, :] = np.asarray([0, 0, 0, 0]) segm_cm = ListedColormap(segm_cm) 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) args = parser.parse_args() assert args.dir is not None, "No directories provided. Stopping" 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...') for r, d, f in os.walk(args.dir): if os.path.split(r)[1] == 'Evaluation_paper': for name in f: if re.search('^050', 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 = 30 fix_img = np.asarray(nib.load(list_fix_img[0]).dataobj)[..., selected_slice, 0] mov_img = np.asarray(nib.load(list_mov_img[0]).dataobj)[..., selected_slice, 0] fix_seg = np.asarray(nib.load(list_fix_seg[0]).dataobj)[..., selected_slice, 0] mov_seg = np.asarray(nib.load(list_mov_seg[0]).dataobj)[..., selected_slice, 0] 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') plt.close() print('Making Pred_data.png...') fig, ax = plt.subplots(nrows=2, ncols=6, 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] seg = np.asarray(nib.load(pred_seg_path).dataobj)[..., selected_slice, 0] 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 = re.search('((UW|SEGGUIDED|BASELINE).*)_{2,}MET', pred_img_path).group(1).rstrip('_') model = model.replace('_Lsim', ' ') model = model.replace('_Lseg', ' ') model = model.replace('_L', ' ') model = model.replace('_', ' ') model = model.upper() model = ' '.join(model.split()) 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') plt.close() print('Making Pred_data_large.png...') fig, ax = plt.subplots(nrows=2, ncols=8, 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] seg = np.asarray(nib.load(pred_seg_path).dataobj)[..., selected_slice, 0] 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 = re.search('((UW|SEGGUIDED|BASELINE).*)_{2,}MET', pred_img_path).group(1).rstrip('_') model = model.replace('_Lsim', ' ') model = model.replace('_Lseg', ' ') model = model.replace('_L', ' ') model = model.replace('_', ' ') model = model.upper() model = ' '.join(model.split()) 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') plt.close() print('...done!')