add files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/* filter=lfs diff=lfs merge=lfs -text

.python-version

@@ -0,0 +1 @@
+3.10

README.md

@@ -1,8 +1,8 @@
 ---
 title: Bouncing Target Reference
-emoji: 📉
-colorFrom: pink
-colorTo: green
+emoji: 🏃
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.36.2
 app_file: app.py

app-download-image.py

@@ -0,0 +1,256 @@
+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
+
+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="green", alpha=0.2
+):
+    """
+    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 = {
+        "red": (1.0, 0.0, 0.0),
+        "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 = 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()

app.py

@@ -0,0 +1,87 @@
+import random
+
+import gradio as gr
+
+choices = [
+    "A_001_frames",
+    "A_003_frames",
+    "A_004_frames",
+    "A_005_frames",
+    "A_006_frames",
+    "A_007_frames",
+    "A_008_frames",
+    "A_010_frames",
+    "A_011_frames",
+    "A_012_frames",
+    "A_013_frames",
+    "A_014_frames",
+    "A_016_frames",
+    "A_019_frames",
+    "A_020_frames",
+    "A_021_frames",
+    "A_022_frames",
+    "A_023_frames",
+    "A_024_frames",
+    "A_025_frames",
+    "A_026_frames",
+    "A_027_frames",
+    "A_028_frames",
+    "A_029_frames",
+    "A_032_frames",
+    "B_002_frames",
+    "B_003_frames",
+    "B_006_frames",
+    "B_007_frames",
+    "B_008_frames",
+    "B_010_frames",
+    "B_012_frames",
+    "B_017_frames",
+    "B_019_frames",
+    "B_021_frames",
+    "B_022_frames",
+    "B_023_frames",
+    "B_024_frames",
+    "B_025_frames",
+    "B_026_frames",
+    "C_001_frames",
+    "C_004_frames",
+    "C_005_frames",
+    "C_006_frames",
+    "C_008_frames",
+    "C_009_frames",
+    "C_010_frames",
+    "C_011_frames",
+    "C_012_frames",
+    "C_016_frames",
+]
+
+
+def load_video(choice: str):
+    return f"assets/{choice}.mp4"
+
+
+demo = gr.Interface(
+    load_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()

assets/A_001_frames.mp4

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