# How timeline_view.py Generates Visual Composites for Critical Decision Points in Video-Use

> Discover how timeline_view.py creates visual composites for critical decision points. Learn about frame extraction, waveform generation, and transcript overlay for efficient video review.

- Repository: [Browser Use/video-use](https://github.com/browser-use/video-use)
- Tags: internals
- Published: 2026-07-07

---

**The [`timeline_view.py`](https://github.com/browser-use/video-use/blob/main/timeline_view.py) script generates visual composite PNGs by orchestrating frame extraction, audio envelope computation, and transcript overlay through the `render_timeline` function, creating a single image that combines film-strips, waveforms, and silence indicators for manual review of specific time ranges.**

The [`timeline_view.py`](https://github.com/browser-use/video-use/blob/main/timeline_view.py) helper in the `browser-use/video-use` repository provides on-demand visualization for video analysis workflows. This self-contained script creates detailed visual composites that help reviewers identify critical decision points by correlating video frames with audio waveforms and transcription data.

## Architecture of the Visual Composite Pipeline

The generation process is orchestrated by the `render_timeline` function, which coordinates multiple helper functions to extract media assets and render them onto a unified canvas. The script operates within a temporary directory managed by `tempfile.TemporaryDirectory` to ensure intermediate frame files are automatically cleaned up after the PNG is written.

### Frame Extraction and Audio Processing

The process begins with `extract_frames`, which creates a temporary directory and computes `n` evenly spaced timestamps across the requested range. It invokes `ffmpeg` to grab a single JPEG per timestamp, storing these intermediate files for later compositing.

Simultaneously, `compute_envelope` handles the audio visualization layer. This function extracts the audio segment using `ffmpeg`, reads the raw PCM data, and computes a windowed RMS (Root Mean Square) envelope. The envelope values are normalized to the range `[0, 1]` to ensure consistent waveform rendering regardless of input volume.

### Transcript Analysis and Silence Detection

For textual overlay, `words_in_range` loads a JSON transcript file and filters for words whose time spans overlap the requested video range. The `find_silences` function then analyzes these word timestamps to detect gaps of **400ms or greater**, marking these as silence regions that will be visually highlighted in the final composite.

The script handles font loading through `load_font`, which attempts to use system monospaced fonts before falling back to Pillow's default renderer. This ensures text labels remain readable across different operating systems.

### Canvas Layout and Rendering

The `render_timeline` function initializes an RGB canvas and pre-loads three distinct font objects: `header_font`, `label_font`, and `small_font`. It calculates dimensions for the film-strip, waveform, and time ruler components before beginning the rendering sequence.

**Film-Strip Rendering (lines 108-154):** Each extracted frame is opened with Pillow, resized to a uniform height, and pasted side-by-side. If the total width of the assembled frames exceeds the canvas dimensions, the entire strip is scaled down proportionally to fit.

**Waveform and Silence Visualization (lines 164-191):** The script draws a dark rectangular background beneath the film-strip to serve as the waveform canvas. For each detected silence interval, `time_to_x` converts temporal coordinates to horizontal pixel positions, and the script draws semi-transparent blue bands to indicate these gaps. The RMS envelope is then plotted as two mirrored poly-lines (top and bottom halves), with the area between them filled using a semi-transparent blue-ish color.

**Word Labels and Time Ruler (lines 192-219):** Words longer than 50ms are rendered above the waveform with small tick marks. Labels are spaced at least **28 pixels** apart to prevent overlap. Below the waveform, six evenly spaced ticks and timestamps provide temporal reference points.

**Final Composition (lines 220-227):** If silences were detected, a legend is added to the composite. The final image is saved as an optimized PNG to the user-specified path or a default location under `…/edit/verify/`.

## Command-Line Usage Examples

The script accepts `<video> <start> <end>` arguments along with optional flags for customization.

Generate a basic composite for a 30-second window:

```bash
python helpers/timeline_view.py my_video.mp4 83.0 113.0

```

Specify the number of frames and custom output location:

```bash
python helpers/timeline_view.py my_video.mp4 30.0 45.0 \
    --n-frames 12 \
    --output ./reports/decision_point.png

```

Incorporate a pre-generated transcript for word overlay:

```bash
python helpers/timeline_view.py my_video.mp4 0.0 10.0 \
    --transcript ./transcripts/my_video.json

```

The script outputs progress messages indicating extraction status and final file size:

```

extracting 12 frames from 30.00s to 45.00s
saved: ./reports/decision_point.png  (214 KB)

```

## Summary

- **[`timeline_view.py`](https://github.com/browser-use/video-use/blob/main/timeline_view.py)** generates visual composites by combining **film-strip frames**, **audio waveforms**, and **transcript overlays** into a single PNG file.
- The **`render_timeline`** function orchestrates the entire pipeline, from `ffmpeg`-based extraction to Pillow-based rendering.
- **Silence detection** identifies gaps ≥400ms, visualized as semi-transparent blue bands beneath the waveform.
- The script operates within a **temporary directory** to manage intermediate JPEG files, ensuring automatic cleanup after the composite is saved.
- Command-line flags `--n-frames`, `--transcript`, and `--output` provide flexibility for analyzing specific decision points.

## Frequently Asked Questions

### What video formats does timeline_view.py support?

The script relies on `ffmpeg` for frame and audio extraction, inheriting support for any format that `ffmpeg` can decode, including MP4, MOV, AVI, and MKV. As long as the input file is readable by the system's `ffmpeg` installation, [`timeline_view.py`](https://github.com/browser-use/video-use/blob/main/timeline_view.py) can process it.

### How does the silence detection threshold work?

The `find_silences` function analyzes word timestamps from the JSON transcript and marks gaps of **400 milliseconds or greater** as silence regions. These intervals are converted to pixel coordinates via `time_to_x` and rendered as semi-transparent blue overlays on the waveform background, making pauses immediately visible to reviewers.

### Can I adjust the number of frames in the visual composite?

Yes. Use the `--n-frames` flag to specify how many evenly spaced frames should be extracted from the time range. The `extract_frames` function automatically calculates the timestamp intervals and scales the final film-strip to fit the canvas width if the combined frames exceed the available space.

### Where are intermediate files stored during processing?

The script creates a temporary directory using `tempfile.TemporaryDirectory` to store the JPEG frames extracted by `ffmpeg`. These intermediate files are automatically deleted when the script completes, regardless of success or failure, ensuring no residual files clutter the workspace.