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# Extract frames from videos
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| Video |
Duration |
Frames at 1 FPS |
| interview.mp4 |
30 min |
1,800 frames |
| product_demo.mp4 |
5 min |
300 frames |
| surveillance.mp4 |
1 hour |
3,600 frames |
`, `
`, `
`, `
| Parameter |
Description |
fps=1.0 |
Extract at 1 frame per second |
fps=0.5 |
Extract every 2 seconds |
num_frames=10 |
Extract exactly 10 evenly-spaced frames |
keyframes_only=True |
Extract only I-frames (fastest) |
`];
Pull frames from video files at specified intervals for analysis,
thumbnails, or training data.
## Problem
You have video files and need to extract frames for:
* Object detection on video content
* Creating thumbnails or previews
* Building training datasets
* Scene analysis and classification
## Solution
**What’s in this recipe:**
* Extract frames at a fixed rate (FPS)
* Extract a specific number of frames
* Extract only keyframes for efficiency
You create a view with a `frame_iterator` that automatically extracts
frames from each video. New videos are processed without extra code.
### Setup
```python theme={null}
%pip install -qU pixeltable
```
```python theme={null}
import pixeltable as pxt
from pixeltable.functions.video import frame_iterator
```
### Load videos
```python theme={null}
# Create a fresh directory
pxt.drop_dir('video_demo', force=True)
pxt.create_dir('video_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'video\_demo'.
\
```python theme={null}
# Create table for videos
videos = pxt.create_table('video_demo/videos', {'video': pxt.Video})
```
Created table 'videos'.
```python theme={null}
# Insert a sample video
videos.insert(
[
{
'video': 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/bangkok.mp4'
}
]
)
```
Inserting rows into \`videos\`: 1 rows \[00:00, 212.90 rows/s]
Inserted 1 row with 0 errors.
1 row inserted, 2 values computed.
### Extract frames at fixed rate
Create a view that extracts 1 frame per second:
```python theme={null}
# Extract 1 frame per second
frames = pxt.create_view(
'video_demo/frames',
videos,
iterator=frame_iterator(
videos.video,
fps=1.0, # 1 frame per second
),
)
```
Inserting rows into \`frames\`: 19 rows \[00:00, 8687.65 rows/s]
```python theme={null}
# View extracted frames
frames.select(frames.frame, frames.pos).head(3)
```
### Extract keyframes only
For faster processing, extract only keyframes (I-frames):
```python theme={null}
# Extract only keyframes (much faster for long videos)
keyframes = pxt.create_view(
'video_demo/keyframes',
videos,
iterator=frame_iterator(videos.video, keyframes_only=True),
)
keyframes.select(keyframes.frame).head(3)
```
Inserting rows into \`keyframes\`: 7 rows \[00:00, 3277.53 rows/s]
## Explanation
**Extraction options:**
Only one of `fps`, `num_frames`, or `keyframes_only` can be specified.
**When to use keyframes:**
* Quick video scanning and thumbnails
* Initial content classification
* Processing very long videos
**Frame metadata:**
Each frame includes:
* `frame`: The extracted image
* `pos`: Frame position in the video
* `pts`: Presentation timestamp
## See also
* [Iterators
documentation](/platform/iterators)
* [Analyze images in
batch](/howto/cookbooks/images/vision-batch-analysis)
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