Claude
1ddd2ead66
Replaced the previous angular separation method with a more accurate focal length-based approach using the pinhole camera model. New FOV estimation algorithm: - Filters non-horizontal lines (converging lines like buildings/roads) - Uses RANSAC-like clustering to find dominant vanishing point - Calculates focal length from distance between image center and VP - Converts focal length to FOV: hfov = 2 × arctan(width / (2 × f)) - More conservative aspect ratio-based fallback defaults Key improvements: - Much more accurate FOV estimation (tested: real 39° vs previous 65°) - Robust to outliers through clustering and voting - Based on well-established pinhole camera model - Better handling of images without clear vanishing points Technical details: - Performance optimization: limits pairwise line intersections - Dynamic clustering threshold based on image size - Requires minimum 3 supporting intersections for reliability - Removed old _calculate_fov_from_vanishing_points method Updated README with detailed explanation of new algorithm and improved technical documentation.
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
FOV Estimation
Uses the pinhole camera model and vanishing point analysis:
- Detects edge lines in the image using Canny edge detection and Hough transform
- Filters for non-horizontal lines (converging lines from buildings, roads, etc.)
- Finds intersections between line pairs to identify potential vanishing points
- Uses RANSAC-like clustering to find the dominant vanishing point (most supported by line intersections)
- Calculates focal length from the distance between the image center and vanishing point
- Converts focal length to horizontal FOV using:
hfov = 2 × arctan(width / (2 × focal_length)) - Falls back to aspect ratio-based estimation if no good vanishing point is found
Key Principle: The distance from the image center (principal point) to a vanishing point created by receding parallel lines approximates the camera's focal length in pixels. This relationship allows accurate FOV estimation from image geometry.
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 horizon lines
- Filters for near-horizontal lines
- Calculates where the horizon intersects the center of the frame
- Converts horizontal FOV to vertical FOV using the image aspect ratio
- Measures tilt based on horizon position relative to frame center using the estimated vertical FOV:
- Tilt = 0° when horizon is at the center of the frame
- Tilt < 0° when horizon is above center (camera looking up)
- Tilt > 0° when horizon is below center (camera looking down)
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
- FOV estimation works best on images with clear converging lines (buildings, roads, railroad tracks)
- Tilt estimation works best on images with visible horizons
- May be less accurate on images with:
- Extreme wide-angle or fisheye distortion (non-linear distortion)
- Very cluttered scenes with few clear lines
- No clear horizon or parallel lines
- Mostly organic/natural scenes without geometric features
Technical Details
- Computer Vision: Uses OpenCV for all image processing operations
- Edge Detection: Canny edge detection with configurable thresholds
- Line Detection: Hough line transform for finding straight lines
- FOV Estimation:
- RANSAC-like clustering for robust vanishing point detection
- Pinhole camera model for focal length estimation
- Handles images without clear vanishing points via aspect ratio fallback
- Tilt Estimation:
- Horizon line detection and position analysis
- Converts horizontal to vertical FOV using aspect ratio
- Median-based robust estimation resistant to outliers
License
MIT License
Contributing
Contributions are welcome! Please feel free to submit issues or pull requests.