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import os
import glob
import matplotlib.pyplot as plt
import nibabel as nib
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=80,
linewidth=1.5,
)
for i, (x, y) in enumerate(zip(x_coords, y_coords), 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
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 plot_2Darray_wRASinfo(img_data, slice_dim, pixel_sizes, save_path):
"""Helper function to plot 2D image slices with RAS orientation info."""
# Create visualization
img_height, img_width = img_data.shape
aspect_ratio = img_width / img_height
base_size = 10
figsize = (
(base_size * aspect_ratio, base_size)
if aspect_ratio > 1
else (base_size, base_size / aspect_ratio)
)
# Calculate aspect ratio based on pixel sizes
aspect_ratio = pixel_sizes[1] / pixel_sizes[0]
# Plot image and landmarks with correct aspect ratio
plt.figure(figsize=figsize)
# Handle different slice orientations
if slice_dim == 0: # Sagittal
plt.imshow(
img_data.T,
cmap="gray",
origin="lower",
aspect=aspect_ratio,
)
plt.xlabel("Anterior →", fontsize=14)
plt.ylabel("Superior →", fontsize=14)
elif slice_dim == 1: # Coronal
plt.imshow(
img_data.T,
cmap="gray",
origin="lower",
aspect=aspect_ratio,
)
plt.xlabel("Right →", fontsize=14)
plt.ylabel("Superior →", fontsize=14)
else: # Axial
plt.imshow(img_data.T, cmap="gray", origin="lower", aspect=aspect_ratio)
plt.xlabel("Right →", fontsize=14)
plt.ylabel("Anterior →", fontsize=14)
plt.margins(0)
plt.savefig(save_path)
plt.close()
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