admincer 1.2.0

Tool for managing datasets for visual ad detection

AdMincer

AdMincer is a command line tool for enriching datasets of screenshots used in ML-based ad detection. It can probably be used with other object-detection datasets, but ad detection is the main use case we're after.

Installation

Clone this repository, then:

$ cd admincer/
$ pip install .

Usage

From the command line, run $ admincer <command> to get information on command options and usage. The current available options are place, extract, slice, find, and convert.

Place

This command places fragment images into the regions of source images. It takes a directory with source images that have regions marked on them and multiple mappings of region type to fragment directory:

$ admincer place -f ad=ads/dir -f label=labels:other/labels -n 5 source target

This will take images with marked regions from source/, place images from ads/dir/ into the regions of type ad and images from labels/ and other/labels/ into the label regions. It will generate 5 images and store them in target/.

The placements are performed in the order of region types on the command line. In the example above first all ad regions will be placed and then all label regions.

Region marking

Regions of the images can be defined via a CSV file in the following format (the numbers are X and Y coordinates of the top left corner followed by the bottom right corner, and the headings in the first line are required):

image,xmin,ymin,xmax,ymax,label
image1.png,50,50,80,90,region_type1
image2.gif,10,10,20,20,region_type2

They can also be defined via TXT files of the same name as the image. The TXT files should be in the format commonly used with YOLO object detector. The numbers are: <object-class> <x> <y> <width> <height> Where:

• <object-class> is an integer representing the box's label
• <x> <y> are the float coordinates at the center of the rectangle
• <width> <height> are the ratio of the box's width/height relative to the size of the whole image, from (0.0 to 1.0].

E.g. <height> = <box_height> / <image_height>

0 0.075 0.15 0.05 0.1
1 0.225 0.15 0.05 0.1

It's possible to provide names for the region type numbers via placing a file with .names extension into the directory. It should simply contian the names in the successive lines:

region_type1
region_type2

When the names file is provided it's also possible to mix CSV and TXT region definitions but not for the same image.

Note: regions that extend beyond the boundaries of the image will be clipped.

Resize modes

When the fragments placed into the regions are not of the same size as the regions, there are several possible options for resizing them. The default is to scale the fragment to match the size of the region. Another option is to cut off the part of the fragment that doesn't fit and place the rest into the part of the region that it would cover. Yet another approach is to cut off some parts and pad the remaining image to the size of the region. These modes are called scale, crop and pad respectively and they can be configured via --resize-mode command line option. Example:

$ admincer place -f ad=ads/dir -f label=labels -r pad -r label=crop ...

Here the first -r sets the default resize mode and the second one overrides it for label region type.

Extract

This command extracts the contents of marked regions from source images. It takes a directory with source images with marked regions (see above) and multiple mappings of region type to target directory:

$ admincer extract --target-dir ad=ads/dir -t label=labels source

This will load the images and region maps from source and will extract the contents of the regions labeled ad and label into ads/dir and labels directories respectively.

Slice

This command produces viewport-sized square screenshots from page-sized tall rectangular screenshots. It remaps the regions of the original images to the produced part (as long as sufficient part of the region is inside the part).

$ admincer slice --step=10 --min-part=50 source target

If additional --no-empty option is specified, slices that don't contain any regions will not be produced.

Find

This command finds source images that have regions of specific types and sizes. For example the following command will find all images in source directory that have regions of the type ad 100 pixels wide by 50 pixels high.

$ admincer find --region=ad:100x50 source

There's certain tolerance for size mismatches. Normally it's +25% and -20%. Tolerance can be configured via an additional parameter of the region query:

$ admincer find -r ad:100x50:100 source

Here height and width can be up to 100% larger and up to 50% smaller. In general the tolerance value X allows the region to be X% larger than specified or the specification to be X% larger than the region.

Multiple --region/-r options can be given. In this case images that contain at least one region matching each query will be found (i.e. multiple queries are combined using and operator).

Convert

This command will convert annotations from a CVAT-format .xml file into YOLO- format .txt files, placing the .txt files alongside their images:

$ admincer convert source.xml

Multiple .xml files can also be provided, either as a list, or by using shell expansions:

$ admincer convert *.xml

Optionally, a --target-dir can be specified. This will place the .txt annotations into the specified target directory, along with a class.names file indicating the <object-class> order. If no target-dir is given, class.names will be written to the image directories.

$ admincer convert *.xml --target-dir path/to/target/

Additionally, the -m or -c flags may be given, which will either move or copy the images to the --target-dir, respectively.

Notes:

• Each image's name tag in the .xml file should contain the image's path, relative to the xml file.
• A .names file may be provided. If multiple image folders are combined into a --target-dir, their .names files will be combined and written to <target_dir>/class.names. If new labels are found, the .names file will be overwritten to include all labels.

Questions

• Fragment matching policy (current one allows scaling by 80% to 125%).
• What to do if there are no valid candidate fragments for placement? Right now we bomb out with an exception.
• Do we want sampling with/without replacement? Or maybe some kind of deterministic selection? Right now it's with replacement.