cvdata 0.0.6

Tools for creating and manipulating computer vision datasets

cvdata

Tools for creating and manipulating computer vision datasets

Installation

This package can be installed into the active Python environment, making the cvdata module available for import within other Python codes and available for utilization at the command line as illustrated in the usage examples below. This package is currently supported for Python version 3.7, and the installation methods below assume that the package will be installed into a Python 3.7 virtual environment.

From PyPI

This package can be installed into the active Python environment from PyPI via pip. In addition to installing this package from PyPI, users will also need to install the TensorFlow Object Detection API from that project's GitHub repository.

$ pip install cvdata
$ pip install -e git+https://github.com/tensorflow/models.git#egg=object_detection\&subdirectory=research

From Source

This package can be installed into the active Python environment as source from its git repository. We'll first clone/download from GitHub, install the dependencies specified in requirements.txt, and finally install the package into the active Python environment:

$ git clone [e-mail]:monocongo/cvdata.git
$ cd cvdata
$ pip install -r requirements.txt
$ python setup.py install

OpenImages

To download various image classes from OpenImages use the script cvdata/openimages.py or the corresponding script entry point cvdata_openimages. This script currently only supports writing annotations in PASCAL VOC format. For example:

$ cvdata_openimages --label Handgun Shotgun Rifle \
>   --exclusions /home/james/git/cvdata/exclusions/exclusions_weapons.txt \
>   --base_dir /data/cvdata/weapons --format pascal \
>   --csv_dir /data/openimages

The above will save each image class in a separate subdirectory under the base directory, with images in a subdirectory named "images" and the PASCAL VOC format annotations in a subdirectory named "pascal".

NOTE:

If you'll use this command more than once then be sure to utilize the --csv_dir option that specifies where to save the (rather large) CSV file containing bounding box information etc., as this will save you from having to redownload this large file in subsequent usages.

Resize images

In order to resize images and update the associated annotations use the script cvdata/resize.py or the corresponding script entry point cvdata_resize. This script currently supports annotations in KITTI (.txt) and PASCAL VOC (.xml) formats. For example to resize images to 1024x768 and update the associated annotations in KITTI format:

$ cvdata_resize --input_images /ssd_training/kitti/image_2 \
    --input_annotations /ssd_training/kitti/label_2 \
    --output_images /ssd_training/kitti/image_2 \
    --output_annotations /ssd_training/kitti/label_2 \
    --width 1024 --height 768 --format kitti

We can also resize all images in a directory by using the same command as above but without an annotation directory or format specified:

$ cvdata_resize --input_images /ssd_training/kitti/image_2 \
    --output_images /ssd_training/kitti/image_2 \
    --width 1024 --height 768

Rename files

In order to perform bulk renaming of image files we provide the script cvdata/rename or the corresponding script entry point cvdata_rename. This allows us to specify a directory containing image files, all of which will be renamed according to the --prefix (the prefix used for the resulting file names), --start (the initial number in the enumeration part of the new file names), and --digits (the width of the enumeration part of the new file names) arguments. For example:

$ cvdata_rename --images_dir ~/datasets/handgun/images --prefix handgun --start 100 --digits 6

In a future release we'll support renaming of image and corresponding annotation files. For example:

$ cvdata_rename --annotations_dir ~/datasets/handgun/kitti \
>  --images_dir ~/datasets/handgun/images \
> --prefix handgun --start 100 --digits 6 \
> --format kitti --kitti_ids_file file_ids.txt

Annotation format conversion

In order to convert from one annotation format to another use the script cvdata/convert.py or the corresponding script entry point cvdata_convert. This script currently supports converting annotations from PASCAL to KITTI, from PASCAL to TFRecord, from PASCAL to OpenImages, from KITTI to Darknet, and from KITTI to TFRecord. For example:

$ cvdata_convert --in_format pascal --out_format kitti \
    --annotations_dir /data/handgun/pascal \
    --images_dir /data/handgun/images \
    --out_dir /data/handgun/kitti \
    --kitti_ids_file handgun.txt

$ cvdata_convert --in_format kitti --out_format tfrecord \
    --annotations_dir /data/kitti \ 
    --images_dir /data/images \
    --out_dir /data/tfrecord/dataset.tfrecord \
    --tf_label_map /data/tfrecord/label_map.pbtxt \
    --tf_shards 2

Image format conversion

In order to convert all images in a directory from PNG to JPG we can use the script cvdata/convert.py or the corresponding script entry point cvdata_convert. For example:

$ cvdata_convert --in_format png --out_format jpg --images_dir /datasets/vehicle

Rename annotation labels

In order to rename the image class labels of annotations use the script cvdata/rename.py or the corresponding script entry point cvdata_rename. This script currently supports annotations in KITTI (.txt) and PASCAL VOC (.xml) formats. It is used to replace the label name for all annotation files of the specified format in the specified directory. For example:

$ cvdata_rename.py --labels_dir /data/cvdata/pascal --old handgun --new firearm --format pascal

Exclusion of unwanted images/annotations

Unwanted images and (optionally) their corresponding annotations can be excluded (removed) from a dataset using the script cvdata/exclude.py or the corresponding script entry point cvdata_exclude. For example:

$ cvdata_exclude --format pascal \
>  --exclusions /data/handgun/exclusions.txt
>  --images /data/handgun/images \
>  --annotations /data/handgun/pascal \

The script can also be used to filter out only corresponding image files by omitting the --annotations argument and corresponding --format argument. For example:

$ cvdata_exclude --exclusions /data/handgun/exclusions.txt --images /data/handgun/images

Sanitize dataset

In order to clean a dataset's annotations we can utilize the script cvdata/clean.py or the corresponding script entry point cvdata_clean which will convert the images to JPG (if any are in PNG format), (optionally) replace labels, (optionally) remove bounding boxes that contain specified labels, and update the annotation files so that all bounding boxes are within reasonable ranges. If specified then offending/problematic files can be moved into a "problems" directory, otherwise they will be removed. For example:

$ cvdata_clean --format pascal \
>    --annotations_dir /data/datasets/delivery_truck/pascal \
>    --images_dir /data/datasets/delivery_truck/images \
>    --problems_dir /data/datasets/delivery_truck/problem \
>    --replace_labels deivery:delivery truck:ups \
>    --remove_labels bus train

Split dataset into training, validation, and test subsets

In order to split a dataset into training, validation, and test subsets we can utilize the script cvdata/split.py or the corresponding script entry point cvdata_split. This script's CLI contains options for specifying the source dataset's images and annotations directories and the destination images and annotations directories for the respective train/valid/test subset splits. The default split ratio is 70% training, 20% validation, and 10% testing but can be modified with the --split argument (these are colon-separated float values and should sum to 1). For example:

$ cvdata_split --annotations_dir /data/rifle/kitti/label_2 \
> --images_dir /data/rifle/kitti/image_2 \
> --train_annotations_dir /data/rifle/split/kitti/trainval/label_2 \
> --train_images_dir /data/rifle/split/kitti/trainval/image_2 \
> --val_annotations_dir /data/rifle/split/kitti/trainval/label_2 \
> --val_images_dir /data/rifle/split/kitti/trainval/image_2 \
> --test_annotations_dir /data/rifle/split/kitti/test/label_2 \
> --test_images_dir /data/rifle/split/kitti/test/image_2 \
> --format kitti --split 0.65:0.25:0.1 --move

In the case where only images are required to be split, we can omit the annotations related arguments from the command:

$ cvdata_split --images_dir /data/rifle/kitti/image_2 \
> --train_images_dir /data/rifle/split/kitti/train/image_2 \
> --val_images_dir /data/rifle/split/kitti/valid/image_2 \
> --test_images_dir /data/rifle/split/kitti/test/image_2 \
> --move

Filtering

The module/script cvdata/filter.py or the corresponding script entry point cvdata_filter can be used to filter the number of image/annotation files of a dataset. It currently supports limiting the number of bounding boxes per class type. The filtered dataset will contain annotation files with bounding boxes only for the class labels specified and limited to the number of boxes specified for each class label. For example:

$ cvdata_filter --src_annotations /data/darknet --dest_annotations /data/filtered_darknet \
    --src_images /data/images --dest_images /data/filtered_images \
    --darknet_labels /data/darknet/labels.txt \
    --boxes_per_class car:6000 truck:6000

Remove duplicates

The module/script cvdata/duplicates.py or the corresponding script entry point cvdata_duplicates can be used to remove duplicate images from a directory. This works on images that are similar, i.e. images don't need to be exactly the same. Optionally the module can remove corresponding annotation files, assuming that the annotation file names correspond to the image file names (for example abc.jpg and abc.xml). Also we can move the duplicate files into a separate directory rather than removing the files if a directory for duplicates is specified. For example:

$ cvdata_duplicates --images_dir /data/trucks/ups/images \
>   --annotations_dir /data/trucks/ups/pascal \
>   --dups_dir /data/trucks/ups/dups

Masks

Create masks from region polygons described in an annotation JSON file created by the VGG Image Annotator tool:

$ cvdata_mask --images /data/images \
>   --annotations /data/via_annotations.json \
>   --masks /data/masks \
>   --format vgg \
>   --classes /data/class_labels.txt

Masks will be written with the mask value corresponding to the class ID. For example, if we have a class labels file with a single label, then the only class ID is 1 and so the masks will have a pixel value of (1, 1, 1) where pixels are masked.

By default each mask described in the annotations file will result in a separate mask file. So, for example, if the annotation for image file "abc.jpg" includes two mask regions then the resulting mask files will be named "abc_0_segmentation.png" and "abc_0_segmentation.png". However, if the --combine option is used then all masks for an images will be included in a single mask file, so the single mask file corresponding to image file named "abc.jpg" will be "abc_segmentation.png".

We can also use the cvdata_mask script entry point to create TFRecord files from an input dataset of JPG images and corresponding PNG masks. For this scenario we expect the mask files to have the same base file name as the images files, and for the image and mask files to be present in their own separate directories. For example:

$ cvdata_mask --images /data/images --masks /data/masks \
>       --in_format png --out_format tfrecord \
>       --tfrecords /data/tfrecords \
>       --shards 4 -- train_pct 0.8

Visualize annotations

In order to visualize images and corresponding annotations use the script cvdata/visualize.py or the corresponding script entry point cvdata_visualize. This script currently supports annotations in COCO (.json), Darknet (.txt), KITTI (.txt), TFRecords, and PASCAL VOC (.xml) formats. It will display bounding boxes and labels for all images/annotations in the specified images and annotations directories. For example:

$ cvdata_visualize --format pascal --images_dir /data/weapons/images --annotations_dir /data/weapons/pascal

Citation

@misc {cvdata,
    author = "James Adams",
    title  = "cvdata, an open source Python library for manipulating computer vision datasets",
    url    = "https://github.com/monocongo/cvdata",
    month  = "october",
    year   = "2019--"
}