#!/usr/bin/env python3 import argparse import os import SimpleITK as sitk import numpy as np import imageio from PIL import Image, ImageFilter import torch import nibabel as nib def window_image(img: sitk.Image, window_min: float, window_max: float) -> sitk.Image: return sitk.IntensityWindowing( img, windowMinimum=window_min, windowMaximum=window_max, outputMinimum=0.0, outputMaximum=255.0 ) def create_overlay(img: sitk.Image, lbl: sitk.Image, alpha: float) -> sitk.Image: return sitk.LabelOverlay(image=img, labelImage=lbl, opacity=alpha, backgroundValue=0) def get_resample_filter(name: str): from PIL import Image as PILImage mapping = { 'nearest': PILImage.NEAREST, 'bilinear': PILImage.BILINEAR, 'lanczos': PILImage.LANCZOS } return mapping.get(name, PILImage.NEAREST) def resize_image(arr: np.ndarray, width: int, height: int, resample_mode) -> np.ndarray: return np.array(Image.fromarray(arr).resize((width, height), resample_mode)) def sharpen_image(arr: np.ndarray, radius: float, percent: int, threshold: int) -> np.ndarray: return np.array( Image.fromarray(arr).filter( ImageFilter.UnsharpMask(radius=radius, percent=percent, threshold=threshold) ) ) def apply_outline_slice(slice_rgb: np.ndarray, label_slice: np.ndarray, dark_factor: float) -> np.ndarray: label_img = sitk.GetImageFromArray(label_slice.astype(np.uint8)) contour = sitk.LabelContour(label_img) contour_arr = sitk.GetArrayFromImage(contour) > 0 dark = (slice_rgb.astype(np.float32) * dark_factor).astype(np.uint8) result = slice_rgb.copy() result[contour_arr] = dark[contour_arr] return result def save_slices(slices: np.ndarray, label_arr: np.ndarray, output_dir: str, width: int, height: int, resample_mode, sharpen: bool, sharpen_params: dict, outline: bool, outline_factor: float): os.makedirs(output_dir, exist_ok=True) for i, slice_rgb in enumerate(slices): img = slice_rgb.astype(np.uint8) if outline and label_arr is not None: img = apply_outline_slice(img, label_arr[i], outline_factor) if width and height: img = resize_image(img, width, height, resample_mode) if sharpen: img = sharpen_image( img, radius=sharpen_params['radius'], percent=sharpen_params['percent'], threshold=sharpen_params['threshold'] ) filepath = os.path.join(output_dir, f"slice_{i:03d}.png") imageio.imwrite(filepath, img) def main(): parser = argparse.ArgumentParser(description="Generate overlay PNGs with optional mask and outline") parser.add_argument('image', help="Input NIfTI image file (.nii or .nii.gz)") parser.add_argument('--label', help="Optional NIfTI label file (.nii or .nii.gz)", default=None) parser.add_argument('--alpha', type=float, default=0.5, help="Overlay opacity (0.0-1.0)") parser.add_argument('--window-min', type=float, default=-450.0, help="Window minimum intensity") parser.add_argument('--window-max', type=float, default=250.0, help="Window maximum intensity") parser.add_argument('--width', type=int, default=1024, help="Output image width") parser.add_argument('--height', type=int, default=1024, help="Output image height") parser.add_argument('--resample', choices=['nearest','bilinear','lanczos'], default='nearest', help="Resize interpolation mode") parser.add_argument('--sharpen', action='store_true', help="Apply sharpening filter after resizing and outlining") parser.add_argument('--sharpen-radius', type=float, default=1.0, help="Sharpen radius") parser.add_argument('--sharpen-percent', type=int, default=150, help="Sharpen percent") parser.add_argument('--sharpen-threshold', type=int, default=3, help="Sharpen threshold") parser.add_argument('--outline', action='store_true', help="Overlay dark outline on mask edges if label provided") parser.add_argument('--outline-factor', type=float, default=0.7, help="Darkening factor for outline (0.0-1.0)") parser.add_argument('--output-dir', type=str, default='output', help="Directory to save images") args = parser.parse_args() if args.image.endswith('.pt'): image, *_ = torch.load(args.image, map_location='cpu') image = image.squeeze().cpu().numpy().transpose((2, 1, 0)) image = sitk.GetImageFromArray(image) else: image = nib.load(args.image) image_data = image.get_fdata() image = sitk.GetImageFromArray(np.transpose(image_data, (2, 1, 0))) if args.label: if args.label.endswith('.pt'): *_, label, _ = torch.load(args.label, map_location='cpu') background_mask = torch.where((label <= 0.02).sum(dim=1) > 0, torch.tensor(0.0), torch.tensor(1.0)) label = torch.where(background_mask == 0, torch.argmin(label, dim=1)+1, torch.tensor(0.0)) label = label.squeeze().cpu().numpy().transpose((2, 1, 0)) label_img = sitk.GetImageFromArray(label) label_img = sitk.Cast(label_img, sitk.sitkUInt8) else: label_img = nib.load(args.label) label_data = label_img.get_fdata() label_img = sitk.GetImageFromArray(np.transpose(label_data, (2, 1, 0))) label_img = sitk.Cast(label_img, sitk.sitkUInt8) windowed = window_image(image, args.window_min, args.window_max) windowed_uint8 = sitk.Cast(windowed, sitk.sitkUInt8) if args.label: overlay = create_overlay(windowed_uint8, label_img, alpha=args.alpha) slices = sitk.GetArrayFromImage(overlay) label_arr = sitk.GetArrayFromImage(label_img) else: arr = sitk.GetArrayFromImage(windowed_uint8) slices = np.stack([arr, arr, arr], axis=-1) # expand to RGB label_arr = None resample_mode = get_resample_filter(args.resample) sharpen_params = {'radius': args.sharpen_radius, 'percent': args.sharpen_percent, 'threshold': args.sharpen_threshold} save_slices( slices, label_arr, args.output_dir, args.width, args.height, resample_mode, args.sharpen, sharpen_params, args.outline, args.outline_factor ) print(f"Images saved to: {args.output_dir}") if __name__ == '__main__': main() ''' python GenMedVis.py "C:/Users/gwd200/Desktop/MSN/MMWHS-CT/MMWHS-CT/MMWHS-CT-resize-try-test/MMWHS-CT-010-image.nii.gz" --label "C:/Users/gwd200/Desktop/MSN/MMWHS-CT/MMWHS-CT/MMWHS-CT-resize-try-test/MMWHS-CT-010-label.nii.gz" --alpha 0.3 --window-min -800 --window-max +600 --outline '''