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HALOWERK Dense Farneback Optical Flow
x402
HALOWERK Dense Farneback Optical Flow is a paid API for AI agents from medien.halowerk.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-28).
Computes dense Farneback optical flow between two consecutive grayscale frames, returning per-pixel horizontal and vertical displacement vectors for the entire image.
Price / call$0.004
Success—
StatusUnknown
How this service works
Computes dense Farneback optical flow between two consecutive grayscale frames and returns the horizontal and vertical displacement of every pixel. Unlike sparse tracking this covers the whole image, which is what you want for measuring motion in regions with no distinct features to track — smoke, water, crowds, a panning background. Both frames must be the same size and should come from the same camera at a short interval.
Output
Returns a dense optical flow field covering every pixel in the input frames: a horizontal displacement map (flow_x) and a vertical displacement map (flow_y), both the same spatial dimensions as the input images, representing how far each pixel has moved between the two frames in the x and y directions.
Example use cases
Smoke plume motion analysis
“I have two grayscale frames from an industrial camera capturing a smoke plume — can you compute dense optical flow between them so I can measure how the smoke is moving across the whole frame, even where there are no sharp edges or features to track?”
Crowd flow estimation in surveillance
“I've got two consecutive frames from a crowd surveillance camera. Can you run dense Farneback optical flow on them to get the full pixel-by-pixel motion field so I can see how the crowd is moving across the entire scene?”
Camera pan background motion compensation
“I have two back-to-back grayscale frames from a panning camera and I need the dense per-pixel displacement vectors for the whole image to understand the background motion before I try to isolate moving objects.”
Technical details
Endpoint
POSThttps://medien.halowerk.com/v1/oss-opencv-dense-frame-optical-flow?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need motion information for every pixel in an image, especially in regions lacking distinct visual features (smoke, water, fog, crowds, uniform textures, panning backgrounds). It is preferable to sparse feature tracking (e.g. Lucas-Kanade on keypoints) when the regions of interest have no trackable corners or edges, and when a complete dense flow field is required for downstream analysis such as motion segmentation, action recognition, or video stabilization.
Side effects
- Frames are different sizes — both inputs must have identical dimensions
- Non-grayscale or multi-channel images may be rejected or produce incorrect results
- Frames taken too far apart in time may produce noisy or unreliable flow due to large displacements
- Empty or malformed image data in the request body returns a 400/422 error
- Payment not included or insufficient USDC triggers a 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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