When Does Uniform Sampling Fallback Trigger Over Scene Detection?

Uniform sampling automatically replaces scene detection when a video produces fewer than 8 distinct scene cuts, ensuring usable frame extraction from static or low-motion content.

The bradautomates/claude-video repository implements an intelligent frame extraction pipeline that switches between scene-based detection and uniform sampling based on content analysis. When visual changes are insufficient, the system triggers a uniform sampling fallback to guarantee meaningful frame coverage across the entire clip. This behavior is hardcoded in the extraction logic and requires no manual configuration.

The 8-Frame Minimum Threshold

The fallback mechanism relies on a hardcoded constant defined in skills/watch/scripts/frames.py. At line 26, the code sets SCENE_MIN_FRAMES = 8, establishing the minimum threshold for meaningful scene detection.

When processing a video, the system first attempts to identify scene changes using extract_scene_candidates. If the resulting frame count—including the mandatory first frame—falls below this threshold, the algorithm classifies the content as effectively static. This condition captures videos with zero scene changes (such as solid-color clips) or minimal motion that fails to generate distinct visual boundaries.

How the Fallback Logic Works

The decision logic resides in the extract_scene_or_uniform function within skills/watch/scripts/frames.py (lines 521–543). The implementation follows this sequence:

1. Extract candidate frames using scene detection
2. Count the candidates (scene_count = len(scene_frames))
3. Compare against SCENE_MIN_FRAMES
4. Execute the appropriate extraction path

scene_frames = extract_scene_candidates(...)
scene_count = len(scene_frames)

# ---- Uniform-fallback guard ----

if scene_count >= SCENE_MIN_FRAMES:
    # Normal scene engine path

    ...
else:
    # Uniform fallback: sample across the whole range

    frames = extract(...)
    return frames, {
        "engine": "uniform",
        "candidate_count": scene_count,
        "fallback": True,
    }

When the fallback activates, the function returns metadata with "engine": "uniform" and "fallback": True, using the requested fps and target_frames parameters to generate evenly-spaced samples across the entire video duration.

Practical Code Examples

Detecting When Fallback Occurs

You can identify fallback activation by inspecting the metadata returned from extract_scene_or_uniform. Static content triggers the uniform engine automatically:

from pathlib import Path
from skills.watch.scripts import frames

video = "static_clip.mp4"          # A clip with no scene changes

out_dir = Path("/tmp/frames")
fps = 1.0
target = 30                       # Desired number of frames

frames_out, meta = frames.extract_scene_or_uniform(
    video_path=video,
    out_dir=out_dir,
    fps=fps,
    target_frames=target,
    resolution=512,
    max_frames=None,
)

print(meta)

# => {

#      "engine": "uniform",

#      "candidate_count": 0,

#      "fallback": True,

#      "selected_count": 30,

#    }

The meta["fallback"] flag confirms that uniform sampling was used because the scene engine located insufficient cuts.

Normal Scene Detection Operation

Content with sufficient visual changes bypasses the fallback entirely:

# A clip with many cuts (e.g., a fast-editing music video)

video = "cut_heavy_clip.mp4"

frames_out, meta = frames.extract_scene_or_uniform(
    video_path=video,
    out_dir=out_dir,
    fps=1.0,
    target_frames=30,
    resolution=512,
    max_frames=30,
)

print(meta["engine"])   # → "scene"

print(meta["fallback"]) # → False

Because scene_count >= 8, the scene engine executes without intervention.

Key Source Files and Testing

The fallback implementation spans the following files:

• skills/watch/scripts/frames.py — Contains extract_scene_or_uniform, extract_scene_candidates, and the SCENE_MIN_FRAMES constant (line 26)
• tests/test_frames.py — Unit tests verifying both scene engine and uniform fallback behavior
• tests/test_watch.py — Integration tests confirming the watch command reports correct engine and fallback status

Summary

• Eight frames minimum: The SCENE_MIN_FRAMES constant (set to 8) defines the threshold below which uniform sampling replaces scene detection
• Automatic activation: No CLI flag exists to override the fallback; the system decides based on content analysis
• Metadata transparency: The return dictionary includes "engine": "uniform" and "fallback": True when the fallback triggers
• Even distribution: Fallback frames use the requested fps and target_frames to sample uniformly across the entire clip duration

Frequently Asked Questions

What is the minimum number of scene cuts required to avoid uniform sampling fallback?

You need at least 8 distinct scene cuts (including the first frame) to prevent the fallback from triggering. This threshold is defined by the SCENE_MIN_FRAMES constant in skills/watch/scripts/frames.py at line 26.

Can I disable the uniform sampling fallback and force scene detection?

No. According to the source code in bradautomates/claude-video, the fallback is automatic and unconditional. There is no configuration option or CLI flag to force scene detection when the shot count falls below the threshold.

How can I verify which extraction engine was used?

Inspect the metadata dictionary returned by extract_scene_or_uniform. The "engine" key will show either "scene" or "uniform", while the "fallback" boolean indicates whether the uniform sampling fallback was activated due to insufficient scene cuts.

Does the fallback affect the number of frames returned?

No. The uniform sampling fallback respects your requested target_frames and fps parameters. Instead of returning frames at scene boundaries, it returns the same quantity of frames distributed evenly across the entire video duration.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}