Tools for creating and manipulating computer vision datasets
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 versions 3.6 and 3.7, and the installation methods below
assume that the package will be installed into a Python 3.6 or 3.7 virtual environment.
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
This package can be installed into the active Python environment as source from its git repository. We'll first clone/download from GitHub and then install the package into the active Python environment:
$ git clone git@github.com:monocongo/cvdata.git
$ cd cvdata
$ pip install -e .
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
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
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
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
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
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
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
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
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 --format darknet \
--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
The module/script cvdata/relabel.py
or the corresponding script entry point cvdata_relabel
can be used to filter the number of image/annotation files of a dataset. For example,
to relabel all PASCAL annotation files in a directory from "dog" to "beagle":
$ cvdata_relabel --labels_dir /data/cvdata/pascal \
--old dog --new beagle --format pascal
Since Darknet (YOLO) annotation files use index values that correspond to entries
in a class labels file we would use integer values for the --old
and --new
arguments:
$ cvdata_relabel --labels_dir /data/cvdata/darknet \
--old 1 --new 4 --format darknet
This function currently supports darknet
, kitti
, and pascal
formats.
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
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
Basic statistics about a dataset are available via the script cvdata/analyze.py
or the corresponding script entry point cvdata_analyze
.
For example, we can count the number of examples in a collection of TFRecord files (specify a directory containing only TFRecod files):
$ cvdata_analyze --format tfrecord --annotations /data/animals/tfrecord
Total number of examples: 100
The above functionality can be utilized within Python code like so:
from cvdata.analyze import count_tfrecord_examples
tfrecords_dir = "/data/animals/tfrecord"
number_of_examples = count_tfrecord_examples(tfrecords_dir)
print(f"Number of examples: {number_of_examples}")
For datasets containing annotation files in COCO, Darknet (YOLO), KITTI, or PASCAL formats we can get the number of images per class label. For example:
$ cvdata_analyze --format kitti --annotations /data/scissors/kitti --images /data/scissors/images
Label: scissors Count: 100
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
Tests are based on pytest
and are launched in stand-alone virtual environments
via tox:
$ tox
@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--"
}