CBT_project/imaging/preprocessing.py

import os
import SimpleITK as sitk
from imaging.resample import resample_img
from imaging.affine import standardize_affine
from imaging.segmentation import seg_bone
import json
import glob
from config.constant import LABEL_MAP
from imaging.nifti_io import sitk_to_nibabel, nibabel_to_sitk

PROGRESS_FILE = "progress.json"

def load_progress():
    if os.path.exists(PROGRESS_FILE):
        with open(PROGRESS_FILE, "r") as f:
            return json.load(f)
    return {}

def save_progress(progress):
    with open(PROGRESS_FILE, "w") as f:
        json.dump(progress, f, indent=2)
        
def process_single_image(image_path, label_path, output_dir_base=None):

    image = sitk.ReadImage(image_path)
    label = sitk.ReadImage(label_path)
    file_name = os.path.basename(image_path)
    name = file_name.replace(".nii.gz", "")

    # LabelStatisticsImageFilter computes statistics (e.g., mean, minimum, maximum, median) of pixel values in an image, segmented by labels in a corresponding label image.
    lsif = sitk.LabelStatisticsImageFilter()
    lsif.Execute(image, label)

    # Assume to have some sitk image (itk_image) and label (itk_label)
    resampled_sitk_img = resample_img(image, out_spacing=[0.5, 0.5, 0.5], is_label=False)
    resampled_sitk_lbl = resample_img(label, out_spacing=[0.5, 0.5, 0.5], is_label=True)
    
    # 取得現有 label
    lssif = sitk.LabelShapeStatisticsImageFilter()
    lssif.Execute(label)
    existing_labels = lssif.GetLabels()  # 這會回傳 list，例如 [1,2,3,20,21]
    
    print(f"Existing labels in {os.path.basename(label_path)}: {existing_labels}")
    
    # 建立每個檔案的輸出資料夾
    file_name = os.path.basename(image_path)
    name = file_name.replace(".nii.gz", "")
    output_dir = os.path.join(output_dir_base, name)
    os.makedirs(output_dir, exist_ok=True)
    
    # 存現有 label 到 txt
    txt_path = os.path.join(output_dir, f"{name}_labels.txt")
    with open(txt_path, "w") as f:
        for lab in existing_labels:
            f.write(f"{lab}\t{LABEL_MAP.get(lab, 'Unknown')}\n")
    
    # 遍歷現有 label 做分割
    for n in existing_labels:
        try:
            roi_path, binary_path, roi2_path, cortical_path = seg_bone(n, name, resampled_sitk_img, resampled_sitk_lbl, output_dir, label_map=LABEL_MAP)
            for path in [roi_path, binary_path, roi2_path, cortical_path]:
                standardize_affine(path, output_dir)
        except RuntimeError as e:
            print(f"Label {n} could not be processed, skipping. Error: {e}")

    return {
        "processed_labels": [lab for lab in existing_labels],
        "missing_labels": []
    }

def process_dataset(image_dir, label_dir, output_dir, labels_to_process=None):
    image_files = sorted(glob.glob(os.path.join(image_dir, "*.nii.gz")))
    total_files = len(image_files)
    print(f"Total files: {total_files}")

    progress = load_progress()
    all_file_summary = []

    for idx, image_path in enumerate(image_files, 1):
        file_name = os.path.basename(image_path)
        name = file_name.replace(".nii.gz", "")
        label_path = os.path.join(label_dir, file_name.replace(".nii.gz", "_seg.nii.gz"))

        file_summary = {
            "file_name": file_name,
            "current_labels": [],
            "missing_labels": []
        }

        if progress.get(name, {}).get("finished", False):
            print(f"[{idx}/{total_files}] Already finished: {file_name}")
            file_summary["current_labels"] = progress[name].get("processed_labels", [])
            file_summary["missing_labels"] = progress[name].get("missing_labels", [])
            all_file_summary.append(file_summary)
            continue

        if not os.path.exists(label_path):
            print(f"[{idx}/{total_files}] Warning: label not found for {file_name}")
            file_summary["note"] = "Label file not found"
            all_file_summary.append(file_summary)
            continue

        try:
            result = process_single_image(image_path, label_path, output_dir_base=output_dir)
            # print(result)
            # exit()
        except Exception as e:
            print(f"[{idx}/{total_files}] Error processing {file_name}: {e}")
            file_summary["note"] = f"Error: {e}"
            all_file_summary.append(file_summary)
            continue

        file_summary["current_labels"] = result["processed_labels"]
        file_summary["missing_labels"] = result["missing_labels"]
        all_file_summary.append(file_summary)

        progress[name] = {
            "finished": True,
            "processed_labels": result["processed_labels"],
            "missing_labels": result["missing_labels"]
        }
        save_progress(progress)

        print(f"[{idx}/{total_files}] Finished: {file_name} | Missing labels: {result['missing_labels'] or 'None'}")

    # --- Summary ---
    summary_path = os.path.join(output_dir, "all_files_label_summary.txt")
    os.makedirs(os.path.dirname(summary_path), exist_ok=True)
    print(f"\nWriting summary to {summary_path}...")

    with open(summary_path, "w") as f:
        f.write("--- CTSpine1K Dataset Label Summary ---\n")
        f.write(f"Total files processed: {total_files}\n\n")

        for summary in all_file_summary:
            f.write("================================================\n")
            f.write(f"File: {summary['file_name']}\n")

            processed_labels_str = ", ".join([str(l) for l in summary['current_labels']])
            f.write(f"Labels processed: {processed_labels_str}\n")

            if summary['missing_labels']:
                missing_str = ", ".join([f"{l} ({LABEL_MAP.get(l, 'Unknown')})" for l in summary['missing_labels']])
                f.write(f"🚨 Missing Labels: {missing_str}\n")
            else:
                f.write("✅ Missing Labels: None\n")

            if "note" in summary:
                f.write(f"Note: {summary['note']}\n")
            f.write("================================================\n\n")

    print("All done! Summary file created.")