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ComfyUI FOV Estimator
A ComfyUI custom node that estimates the Field of View (FOV) and tilt angle (horizon angle) of images using computer vision techniques.
Features
- FOV Estimation: Estimates the camera's field of view in degrees by detecting vanishing points
- Tilt Detection: Detects the horizon angle and tilt of the camera in degrees
- Visual Debugging: Optionally overlays detected lines, vanishing points, and measurements on the output image
- Configurable Parameters: Adjust edge detection and line detection thresholds for different image types
Installation
-
Navigate to your ComfyUI custom nodes directory:
cd ComfyUI/custom_nodes/ -
Clone this repository:
git clone https://github.com/gitcapoom/comfyui_fovestimator.git -
Install dependencies:
cd comfyui_fovestimator pip install -r requirements.txt -
Restart ComfyUI
Usage
The node will appear in ComfyUI under the image/analysis category as "FOV & Tilt Estimator".
Inputs
- image (required): The input image to analyze
- edge_threshold_low (optional, default: 50): Lower threshold for Canny edge detection (0-255)
- edge_threshold_high (optional, default: 150): Upper threshold for Canny edge detection (0-255)
- line_threshold (optional, default: 100): Threshold for Hough line detection (10-500)
- visualize (optional, default: True): Whether to draw detected features on the output image
Outputs
- annotated_image: The input image with visualization overlays (if enabled)
- fov_degrees: Estimated field of view in degrees (float)
- tilt_degrees: Estimated tilt/horizon angle in degrees (float)
- info: Text string with formatted results
Example Workflow
- Load an image
- Connect it to the FOV & Tilt Estimator node
- View the results:
- The annotated image shows detected horizon lines (blue) and vanishing points (green)
- FOV and tilt values are displayed as text overlay and available as outputs
How It Works
Tilt Detection
- Converts image to grayscale
- Applies Gaussian blur to reduce noise
- Uses Canny edge detection to find edges
- Applies Hough line transform to detect lines
- Filters for near-horizontal lines
- Calculates median angle as the tilt
FOV Estimation
- Detects edge lines in the image
- Finds intersections between lines to locate vanishing points
- Calculates the angular separation between vanishing points
- Estimates FOV based on vanishing point geometry
- Falls back to aspect ratio-based estimation if vanishing points aren't found
Tips
- For architecture/buildings: Default settings work well
- For outdoor landscapes: Reduce line_threshold to 50-70
- For noisy images: Increase edge_threshold_low to 70-100
- For subtle edges: Decrease edge_threshold_high to 100-120
Limitations
- Works best on images with clear linear features (buildings, roads, horizons)
- May be less accurate on images with:
- Extreme wide-angle or fisheye distortion
- Very cluttered scenes
- No clear horizon or parallel lines
Technical Details
- Uses OpenCV for computer vision operations
- Implements Canny edge detection and Hough transforms
- Performs vanishing point analysis for FOV estimation
- Median-based robust estimation for tilt angle
License
MIT License
Contributing
Contributions are welcome! Please feel free to submit issues or pull requests.