This project is a set of tools to detect objects in panoramic images and apply blur operation to them. It supports currently input images in EQR (equirectangular) projection and outputs coordinates of detected objects as well as a blurred version of the input panoramic image.
It also contains a tool to evaluate the performance of a given object detection algorithm / model.
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Vanilla haar cascades: by using a gnomonic projection window from the eqr image, the default face detection model can be applied to the undistorted image.
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Trained haar cascades (todo): the goal is to try to bypass the linear reprojection step by training a model against variations of faces when subjected to eqr projection. This could work for the main area (near the equatorial line) but probably not for the poles. It's possible to compute eqr projection centered at the poles to achieve detection in 3 passes (north, center, south).
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Trained latent svm (todo): by following same approach as above, but using a different classifier based on a trained non-linear svm model. This should improve detection accuracy directly within eqr compared to haar cascades.
- Compilation
- Usage
- Main programs
- Tools
- Controls
- Object blurring
- Performance validation
- Copyright
- License
git submodule init
git submodule update --init --recursive
make clean
make
sudo make install
yafdb-detect --algorithm algo input-image.tiff output-objects.yaml
Detects objects within input image. Detected objects are written to a yaml file.
General options:
--full-invalid : Mark all detected objects as invalid
--merge-valid-objects : Merge overlapping valid objects rectangles
--merge-min-overlap 1 : Minimum occurrence of overlap to keep detected objects
--algorithm algo : algorithm to use for object detection ('haar')
Gnomonic projection options:
--gnomonic : activate task
--gnomonic-width 2048 : projection window width
--gnomonic-aperture-x 60 : horizontal projection aperture
--gnomonic-aperture-y 60 : vertical projection aperture
Filtering options:
--filters-disable : Disable filtering
--flter-ratio-min 0.7 : Minimum detected object ratio filtering threshold
--flter-ratio-max 1.3 : Maximum detected object ratio filtering threshold
--flter-size-max-width 3000 : Maximum detected object width filtering threshold
--flter-size-max-height 3000 : Maximum detected object height filtering threshold
Haar-cascades options:
--haar-model class:file.xml[:minChild:maxChild] : parent haar model file with class name (allowed multiple times)
--haar-model class:file.xml:parentclass[:min:max] : child haar model file with class name (allowed multiple times)
--haar-scale 1.1 : haar reduction scale factor
--haar-min-overlap 3 : haar minimum detection overlap
yafdb-export input-image.tiff input-objects.yaml output-path/
Export detected objects from source image into output folder (yaml+images).
General options:
--merge-disable: don't merge overlapping rectangles
--merge-min-overlap 1 : minimum occurrence of overlap to keep detected objects
--format png : set exported object image format ('png', 'jpeg', 'tiff')
yafdb-preview input-image.tiff input-objects.yaml
Preview detected objects in source image.
General options:
--merge-disable: don't merge overlapping rectangles
--merge-min-overlap 1 : minimum occurrence of overlap to keep detected objects
--export output-image.tiff : export detected objects in image
yafdb-validate input-image.tiff [input-objects.yaml] output-objects.yaml
Validate detected objects in source image.
General options:
--image-width 7134 : Default image width during validation
--fullscreen : Start validation window in fullscreen
--show-invalid-objects : Display invalid objects
--merge-disable: don't merge overlapping rectangles
--merge-min-overlap 1 : minimum occurrence of overlap to keep detected objects
--auto-validate : enable auto-validation instead of manual validation
Gnomonic projection options:
--gnomonic : activate gnomonic reprojection for visualization
Create default directory structure
yafdb-create-directories [Base path]
Perform blurring on all images
yafdb-batch-blur [Base path]
Perform object detection on all images
yafdb-batch-detect <HAAR cascades folder> [Base Path]
Perform objects extraction on all images
yafdb-batch-extract [Base Path]
Perform batch validation on all images
yafdb-batch-validate [OPTIONS]
-h --help Prints this
-i --ignore Ignore validated images
-c --convert Write all yml files timestamps to state file
-d --dir Base directory
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Panorama view
Left click on a rectangle => Edit Right click on a rectangle => Invalidate Left click then right click => Create a new zone (defined by the two previous selected points) -
Edit view
Left click => Change rectangle position (Upper left point) Right click => Change rectangle size (Bottom right point) "t" => Edit class type "y" | "Enter" => Validate "n" | "Escape" => Invalidate -
Type config view
"Escape" => Don't change the class type and close the window "y" | "Enter" => Validate
yafdb-blur --algorithm algo input-image.tiff input-objects.yaml output-image.tiff
Blurs detected objects and write modified image as output.
General options:
--algorithm algo : algorithm to use for blurring ('gaussian', 'progressive')
--merge-min-overlap 1 : minimum occurrence of overlap to keep detected objects
Gaussian options:
--gaussian-kernel 65 : gaussian kernel size
--gaussian-steps 1 : gaussian blurring steps
Resizing options:
--resize-width 800: Resizing width
--resize-height 600: Resizing height
yafdb-test input-objects.yaml mask-image.png
Compute the detection error rate by comparing optimal area given in
reference bitmap mask (black=none, white=object) to the detected area
given in input text file.
General options:
--merge-disable: don't merge overlapping rectangles
--merge-min-overlap 1 : minimum occurrence of overlap to keep detected objects
--preview input-image.tiff: preview detection errors
Copyright (c) 2014 FOXEL SA - http://foxel.ch
This program is part of the FOXEL project <http://foxel.ch>.
Please read the COPYRIGHT.md file for more information.
This program is licensed under the terms of the GNU Affero General Public License v3 (GNU AGPL), with two additional terms. The content is licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) license.
You must read <http://foxel.ch/license> for more information about our Licensing terms and our Usage and Attribution guidelines.