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Images.zip

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Landmarks-fig.zip

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Landmarks.zip

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+size 146629

get_dataset.py

@@ -0,0 +1,372 @@
+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/Cephalogram400
+# Format (original): bm
+# Format (HF): nii.gz
+# ====================================
+
+
+def convert_bmp_to_niigz(
+    bmp_dir,
+    niigz_dir,
+    slice_dim_type,
+    pseudo_voxel_size,
+    slip_x=False,
+    slip_y=False,
+    swap_xy=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:
+        in_dir (str): Input directory containing BMP files to convert
+        out_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]
+        swap_xy (bool, optional): If True, swap X and Y dimensions. Defaults to False.
+        slip_x (bool, optional): If True, flip image along X axis. Defaults to False.
+        slip_y (bool, optional): If True, flip image along Y axis. 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)
+            height_orig, width_orig = img_2d.shape
+
+            # Note: this is definitely correct, DO NOT SWAP the order of transformations
+            if slip_x:
+                img_2d = cv2.flip(img_2d, 0)  # 0 means flip vertically
+            if slip_y:
+                img_2d = cv2.flip(img_2d, 1)  # 1 means flip horizontally
+            if swap_xy:  # 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 height_orig, width_orig
+
+
+def process_landmarks_data(
+    landmarks_txt_dir: str,
+    landmarks_json_dir: str,
+    n: int,
+    height_width_orig,
+    slip_x=False,
+    slip_y=False,
+    swap_xy=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")):
+        result = {}
+        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 definitely correct, DO NOT SWAP idx_dim1 and idx_dim2
+                    # Assuming an image with height and width:
+                    # - The data array read from bmp file is of size (height, width) -- dim1 is height, dim2 is width
+                    # - The landmark coordinates are defined as the indices in width (coordinate 1) and height (coordinate 2) directions
+                    idx_dim2, idx_dim1 = map(int, line.split(","))
+
+                    # Apply transformations
+                    # Note: this is definitely correct, DO NOT SWAP the order of transformations
+                    if slip_x:
+                        idx_dim1 = height_width_orig[0] - idx_dim1
+                    if slip_y:
+                        idx_dim2 = height_width_orig[1] - idx_dim2
+                    if swap_xy:  # this line should be AFTER slip_x and slip_y
+                        idx_dim1, idx_dim2 = idx_dim2, idx_dim1
+
+                    result[f"P{i+1}"] = [1, idx_dim1, idx_dim2]
+
+            # Save to JSON
+            with open(json_path, "w") as f:
+                json.dump(result, 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_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.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
+    x_coords = []
+    y_coords = []
+    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]
+            x_coords.append(coords[1])
+            y_coords.append(coords[2])
+
+    # Add landmarks and labels
+    plt.scatter(
+        x_coords,
+        y_coords,
+        facecolors="#18A727",
+        edgecolors="black",
+        marker="o",
+        s=30,
+        linewidth=1,
+    )
+    for i, (x, y) in enumerate(zip(x_coords, y_coords), 1):
+        plt.annotate(
+            f"{i}", (x, y), xytext=(2, 2), textcoords="offset points", color="#FE9100"
+        )
+
+    # Configure plot appearance
+    plt.axis("on")
+    plt.xlabel("Posterior to Anterior")
+    plt.ylabel("Inferior to Superior")
+
+    # Save or display the plot
+    if fig_path:
+        plt.savefig(fig_path, bbox_inches="tight", dpi=300)
+        print(f"Plot saved to: {fig_path}")
+    else:
+        plt.show()
+
+    plt.close()
+
+
+def plot_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_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
+    height_orig, width_orig = convert_bmp_to_niigz(
+        "Images-raw",
+        "Images",
+        slice_dim_type=0,
+        pseudo_voxel_size=[0.1, 0.1, 0.1],
+        slip_x=True,
+        slip_y=False,
+        swap_xy=True,
+    )
+
+    # Read landmark points from txt files and save as JSON
+    process_landmarks_data(
+        "400_senior",
+        "Landmarks",
+        19,
+        height_width_orig=[height_orig, width_orig],
+        slip_x=True,
+        slip_y=False,
+        swap_xy=True,
+    )
+
+    # Plot slices with landmarks
+    plot_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)