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import random
from pathlib import Path
import cv2
import gradio as gr
import numpy as np
import SimpleITK
from huggingface_hub import hf_hub_download, list_repo_files
from scipy import ndimage
IMAGES_REPO = "LMUK-RADONC-PHYS-RES/TrackRAD2025"
DATASET_REPO_TYPE = "dataset"
LABELED_FOLDER = "trackrad2025_labeled_training_data"
OUT_DIR = Path("tmp/videos")
def get_images() -> list[str]:
images_repo_files = list_repo_files(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
)
image_files = [
fname
for fname in images_repo_files
if fname.startswith(LABELED_FOLDER) and fname.endswith("frames.mha")
]
return image_files
def download_image_files(image_file: str) -> dict[str, str]:
filename = image_file.split("/")[-1]
patient = filename.rsplit("_", 1)[0] # e.g., "A_033_frames.mha" -> "A_033"
frames_idx = ""
frames_file = hf_hub_download(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
filename=f"{LABELED_FOLDER}/{patient}/images/{patient}_frames{frames_idx}.mha",
)
labels_file = hf_hub_download(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
filename=f"{LABELED_FOLDER}/{patient}/targets/{patient}_labels{frames_idx}.mha",
)
field_strength_file = hf_hub_download(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
filename=f"{LABELED_FOLDER}/{patient}/b-field-strength.json",
)
scanned_region_file = hf_hub_download(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
filename=f"{LABELED_FOLDER}/{patient}/scanned-region{frames_idx}.json",
)
frame_rate_file = hf_hub_download(
repo_id=IMAGES_REPO,
repo_type=DATASET_REPO_TYPE,
filename=f"{LABELED_FOLDER}/{patient}/frame-rate{frames_idx}.json",
)
return {
"frames_file": frames_file,
"labels_file": labels_file,
"field_strength_file": field_strength_file,
"scanned_region_file": scanned_region_file,
"frame_rate_file": frame_rate_file,
}
def overlay_labels_on_frames(
frames_array, labels_array, overlay_color="cyan", alpha=1.0
):
"""
Overlay binary labels on grayscale frames with a bright color.
Parameters:
-----------
frames_array : numpy.ndarray
Grayscale image sequence of shape [X, Y, T]
labels_array : numpy.ndarray
Binary labels of shape [X, Y, T]
overlay_color : str or tuple
Color for the overlay ('red', 'green', 'blue', 'yellow', 'cyan', 'magenta')
or RGB tuple (r, g, b) with values 0-1
alpha : float
Transparency of the overlay (0=transparent, 1=opaque)
Returns:
--------
overlaid_frames : numpy.ndarray
RGB frames with labels overlaid, shape [X, Y, T, 3]
"""
# Normalize frames to 0-1 range if not already
frames_norm = frames_array.astype(np.float32)
if frames_norm.max() > 1.0:
frames_norm = frames_norm / frames_norm.max()
# Convert grayscale to RGB by repeating across 3 channels
rgb_frames = np.stack([frames_norm] * 3, axis=-1) # Shape: [X, Y, T, 3]
# Define color mapping
color_map = {
"green": (0.0, 1.0, 0.0),
"blue": (0.0, 0.0, 1.0),
"yellow": (1.0, 1.0, 0.0),
"cyan": (0.0, 1.0, 1.0),
"magenta": (1.0, 0.0, 1.0),
}
if overlay_color in color_map:
r, g, b = color_map[overlay_color]
else:
raise ValueError(
f"Unknown color '{overlay_color}'. Use: {list(color_map.keys())} or RGB tuple"
)
# Create the overlaid frames
overlaid_frames = rgb_frames.copy()
# Apply overlay where labels are True (assuming binary labels are 0/1 or False/True)
mask = (ndimage.binary_erosion(labels_array) ^ labels_array).astype(bool)
# Blend the colors using alpha blending
overlaid_frames[mask, 0] = (1 - alpha) * rgb_frames[mask, 0] + alpha * r
overlaid_frames[mask, 1] = (1 - alpha) * rgb_frames[mask, 1] + alpha * g
overlaid_frames[mask, 2] = (1 - alpha) * rgb_frames[mask, 2] + alpha * b
return overlaid_frames
def overlay_video(files: dict[str, str]):
frames = SimpleITK.ReadImage(files["frames_file"])
frames_array = SimpleITK.GetArrayFromImage(frames)
# frames_array = [X,Y,T]
frames_array = np.flip(frames_array, axis=0)
labels = SimpleITK.ReadImage(files["labels_file"])
labels_array = SimpleITK.GetArrayFromImage(labels)
# labels_array = [X,Y,T]
labels_array = np.flip(labels_array, axis=0)
overlaid_array = overlay_labels_on_frames(frames_array, labels_array)
output_path = numpy_to_video_opencv(overlaid_array, "tmp_video", fps=8)
return output_path
def numpy_to_video_opencv(array: np.ndarray, output_prefix: str, fps: int) -> str:
limit = 10 * fps
array_clip = array[:, :, :limit] # 10s of video
p99: float = np.percentile(array_clip, 99) # type: ignore
array_clip_normalized = cv2.convertScaleAbs(array_clip, alpha=(255.0 / p99))
OUT_DIR.mkdir(parents=True, exist_ok=True)
output_path = str((OUT_DIR / output_prefix).with_suffix(".webm"))
# Define codec and create VideoWriter
# VP90 is supported by browsers and is available in the pip-installed opencv
fourcc = cv2.VideoWriter.fourcc(*"VP90")
X, Y, T, _ = array_clip.shape
bgr_frames = array_clip_normalized[:, :, :, [2, 1, 0]]
out = cv2.VideoWriter(output_path, fourcc, fps, (X, Y))
# Write frames
for t in range(T):
frame = bgr_frames[:, :, t, :]
# OpenCV expects frames in BGR format, but for grayscale we can use as-is
out.write(frame)
out.release()
return output_path
choices = [
"trackrad2025_labeled_training_data/A_001/images/A_001_frames.mha",
"trackrad2025_labeled_training_data/A_003/images/A_003_frames.mha",
"trackrad2025_labeled_training_data/A_004/images/A_004_frames.mha",
"trackrad2025_labeled_training_data/A_005/images/A_005_frames.mha",
"trackrad2025_labeled_training_data/A_006/images/A_006_frames.mha",
"trackrad2025_labeled_training_data/A_007/images/A_007_frames.mha",
"trackrad2025_labeled_training_data/A_008/images/A_008_frames.mha",
"trackrad2025_labeled_training_data/A_010/images/A_010_frames.mha",
"trackrad2025_labeled_training_data/A_011/images/A_011_frames.mha",
"trackrad2025_labeled_training_data/A_012/images/A_012_frames.mha",
"trackrad2025_labeled_training_data/A_013/images/A_013_frames.mha",
"trackrad2025_labeled_training_data/A_014/images/A_014_frames.mha",
"trackrad2025_labeled_training_data/A_016/images/A_016_frames.mha",
"trackrad2025_labeled_training_data/A_019/images/A_019_frames.mha",
"trackrad2025_labeled_training_data/A_020/images/A_020_frames.mha",
"trackrad2025_labeled_training_data/A_021/images/A_021_frames.mha",
"trackrad2025_labeled_training_data/A_022/images/A_022_frames.mha",
"trackrad2025_labeled_training_data/A_023/images/A_023_frames.mha",
"trackrad2025_labeled_training_data/A_024/images/A_024_frames.mha",
"trackrad2025_labeled_training_data/A_025/images/A_025_frames.mha",
"trackrad2025_labeled_training_data/A_026/images/A_026_frames.mha",
"trackrad2025_labeled_training_data/A_027/images/A_027_frames.mha",
"trackrad2025_labeled_training_data/A_028/images/A_028_frames.mha",
"trackrad2025_labeled_training_data/A_029/images/A_029_frames.mha",
"trackrad2025_labeled_training_data/A_032/images/A_032_frames.mha",
"trackrad2025_labeled_training_data/B_002/images/B_002_frames.mha",
"trackrad2025_labeled_training_data/B_003/images/B_003_frames.mha",
"trackrad2025_labeled_training_data/B_006/images/B_006_frames.mha",
"trackrad2025_labeled_training_data/B_007/images/B_007_frames.mha",
"trackrad2025_labeled_training_data/B_008/images/B_008_frames.mha",
"trackrad2025_labeled_training_data/B_010/images/B_010_frames.mha",
"trackrad2025_labeled_training_data/B_012/images/B_012_frames.mha",
"trackrad2025_labeled_training_data/B_017/images/B_017_frames.mha",
"trackrad2025_labeled_training_data/B_019/images/B_019_frames.mha",
"trackrad2025_labeled_training_data/B_021/images/B_021_frames.mha",
"trackrad2025_labeled_training_data/B_022/images/B_022_frames.mha",
"trackrad2025_labeled_training_data/B_023/images/B_023_frames.mha",
"trackrad2025_labeled_training_data/B_024/images/B_024_frames.mha",
"trackrad2025_labeled_training_data/B_025/images/B_025_frames.mha",
"trackrad2025_labeled_training_data/B_026/images/B_026_frames.mha",
"trackrad2025_labeled_training_data/C_001/images/C_001_frames.mha",
"trackrad2025_labeled_training_data/C_004/images/C_004_frames.mha",
"trackrad2025_labeled_training_data/C_005/images/C_005_frames.mha",
"trackrad2025_labeled_training_data/C_006/images/C_006_frames.mha",
"trackrad2025_labeled_training_data/C_008/images/C_008_frames.mha",
"trackrad2025_labeled_training_data/C_009/images/C_009_frames.mha",
"trackrad2025_labeled_training_data/C_010/images/C_010_frames.mha",
"trackrad2025_labeled_training_data/C_011/images/C_011_frames.mha",
"trackrad2025_labeled_training_data/C_012/images/C_012_frames.mha",
"trackrad2025_labeled_training_data/C_016/images/C_016_frames.mha",
]
def play_video(fname: str):
files = download_image_files(fname)
output_path = overlay_video(files)
return output_path
demo = gr.Interface(
play_video,
[
gr.Dropdown(
choices=choices,
label="Select an MR sequence",
value=random.choice(choices),
)
],
gr.Video(
height=500,
autoplay=True,
loop=True,
label="MR Sequence",
),
live=True,
title="TrackRAD2025 Labeled Data Viewer",
examples=[[random.choice(choices)]],
cache_examples=True,
preload_example=0,
flagging_mode="never",
)
demo.launch()
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