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