import os import subprocess import shutil import nibabel as nib import matplotlib.pyplot as plt import glob import json import rarfile import numpy as np import cv2 from pathlib import Path import argparse # ==================================== # Dataset Info [!] # ==================================== # Dataset: Cephalogram400 # Data (original): https://figshare.com/s/37ec464af8e81ae6ebbf # Data (HF): https://huggingface.co/datasets/YongchengYAO/Ceph-Biometrics-400 # Format (original): bm # Format (HF): nii.gz # ==================================== def convert_bmp_to_niigz( bmp_dir, niigz_dir, slice_dim_type, pseudo_voxel_size, flip_dim0=False, flip_dim1=False, swap_dim01=False, ): """ Convert BMP image files to NIfTI (.nii.gz) format. This function converts 2D BMP images to 3D NIfTI volumes with specified slice orientation. The output NIfTI files will have RAS+ orientation with specified voxel size. Args: bmp_dir (str): Input directory containing BMP files to convert niigz_dir (str): Output directory where NIfTI files will be saved slice_dim_type (int): Slice dimension/orientation type: 0: Sagittal (YZ plane) 1: Coronal (XZ plane) 2: Axial (XY plane) pseudo_voxel_size (list): List of 3 floats specifying voxel dimensions in mm [x,y,z] flip_dim0 (bool, optional): If True, flip image along dimension 0. Defaults to False. flip_dim1 (bool, optional): If True, flip image along dimension 1. Defaults to False. swap_dim01 (bool, optional): If True, swap dimensions 0 and 1. Defaults to False. Returns: tuple: Original image dimensions (height, width) of the first converted BMP """ # Validate slice_dim_type if slice_dim_type not in [0, 1, 2]: raise ValueError("slice_dim_type must be 0, 1, or 2") # Convert pseudo_voxel_size to list if it's not already pseudo_voxel_size = list(pseudo_voxel_size) # Create output directory Path(niigz_dir).mkdir(parents=True, exist_ok=True) # Get all BMP files bmp_files = list(Path(bmp_dir).glob("*.bmp")) print(f"Found {len(bmp_files)} .bmp files") for bmp_file in bmp_files: try: print(f"Converting {bmp_file.name}") # Read BMP image img_2d = cv2.imread(str(bmp_file), cv2.IMREAD_GRAYSCALE) img_size_dim0, img_size_dim1 = img_2d.shape # Note: this is definitely correct, DO NOT SWAP the order of transformations if flip_dim0: img_2d = cv2.flip(img_2d, 0) # 0 means flip vertically if flip_dim1: img_2d = cv2.flip(img_2d, 1) # 1 means flip horizontally if swap_dim01: # this line should be AFTER slip_x and slip_y img_2d = np.swapaxes(img_2d, 0, 1) # Create 3D array based on slice_dim_type if slice_dim_type == 0: # Sagittal (YZ plane) img_3d = np.zeros( (1, img_2d.shape[0], img_2d.shape[1]), dtype=img_2d.dtype ) img_3d[0, :, :] = img_2d elif slice_dim_type == 1: # Coronal (XZ plane) img_3d = np.zeros( (img_2d.shape[0], 1, img_2d.shape[1]), dtype=img_2d.dtype ) img_3d[:, 0, :] = img_2d else: # Axial (XY plane) img_3d = np.zeros( (img_2d.shape[0], img_2d.shape[1], 1), dtype=img_2d.dtype ) img_3d[:, :, 0] = img_2d # Create affine matrix for RAS+ orientation # Set voxel size to 0.1mm in all dimensions affine = np.diag(pseudo_voxel_size + [1]) # Create NIfTI image nii_img = nib.Nifti1Image(img_3d, affine) # Set header information nii_img.header.set_zooms(pseudo_voxel_size) # Save as NIfTI file output_file = Path(niigz_dir) / f"{bmp_file.stem}.nii.gz" nib.save(nii_img, str(output_file)) print(f"Saved to {output_file}") except Exception as e: print(f"Error converting {bmp_file.name}: {e}") return img_size_dim0, img_size_dim1 def process_landmarks_data( landmarks_txt_dir: str, landmarks_json_dir: str, n: int, img_sizes, flip_dim0=False, flip_dim1=False, swap_dim01=False, ) -> None: """ Read landmark points from all txt files in a directory and save as JSON files. Args: in_dir (str): Directory containing the txt files out_dir (str): Directory where JSON files will be saved n (int): Number of lines to read from each file height_width_orig: Original height and width of the image swap_xy (bool): Whether to swap x and y coordinates slip_x (bool): Whether to flip coordinates along x-axis slip_y (bool): Whether to flip coordinates along y-axis """ ( os.makedirs(landmarks_json_dir, exist_ok=True) if not os.path.exists(landmarks_json_dir) else None ) for txt_file in glob.glob(os.path.join(landmarks_txt_dir, "*.txt")): landmarks = {} filename = os.path.basename(txt_file) json_path = os.path.join(landmarks_json_dir, filename.replace(".txt", ".json")) try: with open(txt_file, "r") as f: for i in range(n): line = f.readline().strip() if not line: break # Note: this is correct, DO NOT SWAP idx_dim0 and idx_dim1 # Assuming an image with height and width: # - The data array read from bmp file is of size (height, width) -- dim0 is height, dim1 is width # - The landmark coordinates are defined as the indices in width (dim1) and height (dim0) directions idx_dim1, idx_dim0 = map(int, line.split(",")) # Apply transformations # Note: this is correct # DO NOT SWAP the order of transformations if flip_dim0: idx_dim0 = img_sizes[0] - idx_dim0 if flip_dim1: idx_dim1 = img_sizes[1] - idx_dim1 if swap_dim01: # this line should be AFTER slip_x and slip_y idx_dim0, idx_dim1 = idx_dim1, idx_dim0 # Save landmark coordinates in 0-based indices landmarks[f"P{i+1}"] = [ coord - 1 for coord in [1, idx_dim0, idx_dim1] ] # This data structure is designed to be compatible with biometric data constructed from segmentation masks json_dict = { "slice_landmarks_x": [ { "slice_idx": 1, "landmarks": landmarks, }, ], "slice_landmarks_y": [], "slice_landmarks_z": [], } # Save to JSON with open(json_path, "w") as f: json.dump(json_dict, f, indent=4) except FileNotFoundError: print(f"Error: File {txt_file} not found") except ValueError: print(f"Error: Invalid format in file {txt_file}") except Exception as e: print(f"Error reading file {txt_file}: {str(e)}") def plot_sagittal_slice_with_landmarks( nii_path: str, json_path: str, fig_path: str = None ): """Plot first slice from NIfTI file and overlay landmarks from JSON file. Args: nii_path (str): Path to .nii.gz file json_path (str): Path to landmarks JSON file fig_path (str, optional): Path to save the plot. If None, displays plot """ # Load NIfTI image and extract first slice nii_img = nib.load(nii_path) slice_data = nii_img.get_fdata()[0, :, :] # Load landmark coordinates from JSON with open(json_path, "r") as f: landmarks_json = json.load(f) # Setup visualization plt.figure(figsize=(12, 12)) plt.imshow( slice_data.T, cmap="gray", origin="lower" ) # the transpose is necessary only for visualization # Extract and plot landmark coordinates coords_dim0 = [] coords_dim1 = [] landmarks = landmarks_json["slice_landmarks_x"][0]["landmarks"] for point_id, coords in landmarks.items(): if len(coords) == 3: # Check for valid [1, x, y] format # Note: this is definitely correct, DO NOT SWAP coords[1] and coords[2] coords_dim0.append(coords[1]) coords_dim1.append(coords[2]) # Add landmarks and labels plt.scatter( coords_dim0, coords_dim1, facecolors="#18A727", edgecolors="black", marker="o", s=80, linewidth=1.5, ) for i, (x, y) in enumerate(zip(coords_dim0, coords_dim1), 1): plt.annotate( f"$\\mathbf{{{i}}}$", (x, y), xytext=(2, 2), textcoords="offset points", color="#FE9100", fontsize=14, ) # Configure plot appearance plt.xlabel("Anterior →", fontsize=14) plt.ylabel("Superior →", fontsize=14) plt.margins(0) # Save or display the plot plt.savefig(fig_path, bbox_inches="tight", dpi=300) print(f"Plot saved to: {fig_path}") plt.close() def plot_sagittal_slice_with_landmarks_batch( image_dir: str, landmark_dir: str, fig_dir: str ): """Plot all cases from given directories. Args: image_dir (str): Directory containing .nii.gz files landmark_dir (str): Directory containing landmark JSON files fig_dir (str): Directory to save output figures """ # Create output directory if it doesn't exist os.makedirs(fig_dir, exist_ok=True) # Process each .nii.gz file for nii_path in glob.glob(os.path.join(image_dir, "*.nii.gz")): base_name = os.path.splitext(os.path.splitext(os.path.basename(nii_path))[0])[0] json_path = os.path.join(landmark_dir, f"{base_name}.json") fig_path = os.path.join(fig_dir, f"{base_name}.png") # Plot and save if os.path.exists(json_path): plot_sagittal_slice_with_landmarks(nii_path, json_path, fig_path) else: print(f"Warning: No landmark file found for {base_name}") def download_and_extract(dataset_dir, dataset_name): # Download files print(f"Downloading {dataset_name} dataset to {dataset_dir}...") # ==================================== # Add download logic here [!] # ==================================== # Download the file using curl url = "https://figshare.com/ndownloader/articles/3471833?private_link=37ec464af8e81ae6ebbf" output_file = "Cephalogram400.zip" subprocess.run(["curl", url, "-o", output_file], check=True) # Extract the ZIP file print("Extracting ZIP file...") subprocess.run(["unzip", output_file], check=True) # Find and extract all RAR files print("Extracting RAR files...") for file in os.listdir("."): if file.endswith(".rar"): with rarfile.RarFile(file) as rf: rf.extractall() # Create the Images-raw directory os.makedirs("Images-raw", exist_ok=True) # Move all BMP files from RawImage to Images-raw using glob for src_path in glob.glob(f"RawImage/**/*.bmp", recursive=True): shutil.move(src_path, os.path.join("Images-raw", os.path.basename(src_path))) # Convert BMP files to 3D nii.gz Flag_flip_dim0 = True Flag_flip_dim1 = False Flag_swap_dim01 = True img_size_dim0, img_size_dim1 = convert_bmp_to_niigz( "Images-raw", "Images", slice_dim_type=0, pseudo_voxel_size=[0.1, 0.1, 0.1], flip_dim0=Flag_flip_dim0, flip_dim1=Flag_flip_dim1, swap_dim01=Flag_swap_dim01, ) # Read landmark points from txt files and save as JSON process_landmarks_data( "400_senior", "Landmarks", 19, img_sizes=[img_size_dim0, img_size_dim1], flip_dim0=Flag_flip_dim0, flip_dim1=Flag_flip_dim1, swap_dim01=Flag_swap_dim01, ) # Plot slices with landmarks plot_sagittal_slice_with_landmarks_batch("Images", "Landmarks", "Landmarks-fig") # Clean up for dir_name in [ "RawImage", "400_junior", "400_senior", "Images-raw", "EvaluationCode", ]: shutil.rmtree(dir_name, ignore_errors=True) for file in os.listdir("."): if file.endswith((".rar", ".zip")): os.remove(file) # ==================================== print(f"Download and extraction completed for {dataset_name}") if __name__ == "__main__": # Set up argument parser parser = argparse.ArgumentParser(description="Download and extract dataset") parser.add_argument( "-d", "--dir_datasets_data", help="Directory path where datasets will be stored", required=True, ) parser.add_argument( "-n", "--dataset_name", help="Name of the dataset", required=True, ) args = parser.parse_args() # Create dataset directory dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) os.makedirs(dataset_dir, exist_ok=True) # Change to dataset directory os.chdir(dataset_dir) # Download and extract dataset download_and_extract(dataset_dir, args.dataset_name)