source_code/SegMamba/6_visualize_predictions.py

import argparse
import os
import sys

import numpy as np
import SimpleITK as sitk

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.patches import Patch


MODALITY_MAP = {
    "t2w": 0,
    "t2f": 1,
    "t1n": 2,
    "t1c": 3,
}


def _parse_cases(s: str):
    if not s:
        return []
    return [p.strip() for p in s.split(",") if p.strip()]


def _normalize_slice(img2d: np.ndarray) -> np.ndarray:
    p1, p99 = np.percentile(img2d, (1, 99))
    if p99 <= p1:
        return np.zeros_like(img2d, dtype=np.float32)
    img = (img2d - p1) / (p99 - p1)
    return np.clip(img, 0.0, 1.0).astype(np.float32)


def _pred_to_three_channels(pred: np.ndarray) -> np.ndarray:
    # Accept either 4D (3, D, H, W) or 3D label map (D, H, W) with labels 0-3.
    if pred.ndim == 4:
        return pred
    if pred.ndim != 3:
        raise ValueError(f"unexpected pred shape: {pred.shape}")
    # label -> TC/WT/ET
    labels = pred
    tc = (labels == 1) | (labels == 3)
    wt = (labels == 1) | (labels == 2) | (labels == 3)
    et = labels == 3
    return np.stack([tc, wt, et], axis=0).astype(np.uint8)


def _pick_slices(mask_3c: np.ndarray, num_slices: int) -> list[int]:
    # mask_3c: (3, D, H, W)
    mask_sum = mask_3c.sum(axis=0)  # (D, H, W)
    per_slice = mask_sum.reshape(mask_sum.shape[0], -1).sum(axis=1)
    if per_slice.max() == 0:
        # fallback: evenly spaced slices
        return sorted(set(np.linspace(0, mask_sum.shape[0] - 1, num_slices, dtype=int).tolist()))
    idx = np.argsort(per_slice)[::-1]
    chosen = []
    for i in idx:
        if len(chosen) >= num_slices:
            break
        chosen.append(int(i))
    return sorted(chosen)


def _overlay_mask(gray: np.ndarray, masks: list[np.ndarray]) -> np.ndarray:
    # gray: (H, W), masks: [tc, wt, et]
    rgb = np.stack([gray, gray, gray], axis=-1)
    colors = [
        (1.0, 0.0, 0.0),  # TC - red
        (0.0, 1.0, 0.0),  # WT - green
        (1.0, 1.0, 0.0),  # ET - yellow
    ]
    alphas = [0.5, 0.25, 0.5]
    for mask, color, alpha in zip(masks, colors, alphas):
        m = mask.astype(bool)
        if m.any():
            rgb[m] = rgb[m] * (1.0 - alpha) + np.array(color) * alpha
    return rgb


def _load_processed_image(processed_dir: str, case_name: str, modality: int) -> np.ndarray:
    img_path = os.path.join(processed_dir, f"{case_name}.npy")
    if not os.path.isfile(img_path):
        raise FileNotFoundError(f"processed image not found: {img_path}")
    arr = np.load(img_path, mmap_mode="r")
    if arr.ndim != 4:
        raise ValueError(f"unexpected image shape: {arr.shape}")
    return np.asarray(arr[modality], dtype=np.float32)  # (D, H, W)


def _load_prediction(pred_dir: str, case_name: str) -> np.ndarray:
    pred_path = os.path.join(pred_dir, f"{case_name}.nii.gz")
    if not os.path.isfile(pred_path):
        raise FileNotFoundError(f"prediction not found: {pred_path}")
    pred_itk = sitk.ReadImage(pred_path)
    pred_arr = sitk.GetArrayFromImage(pred_itk)
    return _pred_to_three_channels(np.asarray(pred_arr))

def _load_gt(processed_dir: str, case_name: str) -> np.ndarray:
    seg_path = os.path.join(processed_dir, f"{case_name}_seg.npy")
    if not os.path.isfile(seg_path):
        raise FileNotFoundError(f"gt seg not found: {seg_path}")
    seg = np.load(seg_path, mmap_mode="r")
    seg = np.asarray(seg)
    if seg.ndim == 4 and seg.shape[0] == 1:
        seg = seg[0]
    return _pred_to_three_channels(seg)


def visualize_case(case_name: str, pred_dir: str, processed_dir: str, modality: int, num_slices: int, out_dir: str, show_gt: bool = True):
    img = _load_processed_image(processed_dir, case_name, modality)  # (D, H, W)
    pred = _load_prediction(pred_dir, case_name)  # (3, D, H, W)
    gt = None
    if show_gt:
        try:
            gt = _load_gt(processed_dir, case_name)
        except FileNotFoundError:
            gt = None

    if pred.shape[1:] != img.shape:
        raise ValueError(f"shape mismatch for {case_name}: img={img.shape}, pred={pred.shape}")
    if gt is not None and gt.shape[1:] != img.shape:
        raise ValueError(f"shape mismatch for {case_name}: img={img.shape}, gt={gt.shape}")

    slice_ids = _pick_slices(pred, num_slices)

    ncols = 3 if gt is not None else 2
    fig, axes = plt.subplots(nrows=len(slice_ids), ncols=ncols, figsize=(4 * ncols, 3 * len(slice_ids)))
    if len(slice_ids) == 1:
        axes = np.array([axes])
    if ncols == 2 and axes.ndim == 1:
        axes = axes[None, :]

    for row, z in enumerate(slice_ids):
        img2d = img[z]
        gray = _normalize_slice(img2d)
        tc = pred[0, z]
        wt = pred[1, z]
        et = pred[2, z]

        axes[row, 0].imshow(gray, cmap="gray")
        axes[row, 0].set_title(f"{case_name} z={z} (raw)")
        axes[row, 0].axis("off")

        overlay = _overlay_mask(gray, [tc, wt, et])
        axes[row, 1].imshow(overlay)
        axes[row, 1].set_title(f"{case_name} z={z} (pred)")
        axes[row, 1].axis("off")

        if gt is not None:
            gt_tc = gt[0, z]
            gt_wt = gt[1, z]
            gt_et = gt[2, z]
            gt_overlay = _overlay_mask(gray, [gt_tc, gt_wt, gt_et])
            axes[row, 2].imshow(gt_overlay)
            axes[row, 2].set_title(f"{case_name} z={z} (gt)")
            axes[row, 2].axis("off")

    legend = [
        Patch(color=(1.0, 0.0, 0.0), label="TC"),
        Patch(color=(0.0, 1.0, 0.0), label="WT"),
        Patch(color=(1.0, 1.0, 0.0), label="ET"),
    ]
    fig.legend(handles=legend, loc="lower center", ncol=3)
    fig.tight_layout(rect=[0, 0.05, 1, 1])

    os.makedirs(out_dir, exist_ok=True)
    out_path = os.path.join(out_dir, f"{case_name}_overlay.png")
    fig.savefig(out_path, dpi=150)
    plt.close(fig)

    return out_path


def main():
    parser = argparse.ArgumentParser(description="Visualize SegMamba predictions (overlay on processed images).")
    parser.add_argument("--pred_dir", type=str, required=True, help="Prediction folder containing case_name.nii.gz.")
    parser.add_argument("--processed_dir", type=str, required=True, help="Processed data dir containing case_name.npy.")
    parser.add_argument("--out_dir", type=str, default="./prediction_results/visualizations")
    parser.add_argument("--modality", type=str, default="t2f", help="t2w|t2f|t1n|t1c or an int index.")
    parser.add_argument("--num_cases", type=int, default=5)
    parser.add_argument("--num_slices", type=int, default=3)
    parser.add_argument("--cases", type=str, default="", help="Comma-separated case names to visualize.")
    parser.add_argument("--no_gt", action="store_true", help="Disable GT overlay (prediction only).")
    args = parser.parse_args()

    if args.modality.isdigit():
        modality = int(args.modality)
    else:
        modality = MODALITY_MAP.get(args.modality.lower(), 1)
    if modality < 0 or modality > 3:
        raise ValueError("modality index must be 0..3")

    cases = _parse_cases(args.cases)
    if not cases:
        pred_files = sorted([f for f in os.listdir(args.pred_dir) if f.endswith(".nii.gz")])
        cases = [os.path.splitext(os.path.splitext(f)[0])[0] for f in pred_files][: args.num_cases]

    if not cases:
        print("No cases found.")
        sys.exit(0)

    print(f"Visualizing {len(cases)} cases, modality={modality}")
    for case_name in cases:
        out_path = visualize_case(
            case_name=case_name,
            pred_dir=args.pred_dir,
            processed_dir=args.processed_dir,
            modality=modality,
            num_slices=args.num_slices,
            out_dir=args.out_dir,
            show_gt=not args.no_gt,
        )
        print(f"saved: {out_path}")


if __name__ == "__main__":
    main()