Metadata-Version: 2.1
Name: cvdata
Version: 0.0.4
Summary: Tools for creating and manipulating computer vision datasets
Home-page: https://github.com/monocongo/cvdata
Author: James Adams
Author-email: monocongo@gmail.com
License: UNKNOWN
Description: # 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.
        ```bash
        $ 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:
        ```bash
        $ git clone git@github.com:monocongo/cvdata.git
        $ cd cvdata
        $ pip install -r requirements.txt
        $ python setup.py install
        ```
        
        ## OpenImages
        To download various image classes from [OpenImages](https://storage.googleapis.com/openimages/web/index.html) 
        use the script `cvdata/openimages.py`. This script currently only supports writing 
        annotations in PASCAL VOC format. For example:
        ```bash
        $ 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`. 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:
        ```bash
        $ python resize.py --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:
        ```bash
        $ python resize.py --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.py`. 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` (width of the enumeration part of the new 
        file names) arguments. For example: 
        ```bash
        $ python rename.py --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:
        ```bash
        $ python rename.py --annotations_dir ~/datasets/handgun/kitti \
        >  --images_dir ~/datasets/handgun/images \
        > --prefix handgun --start 100 --digits 6 \
        > --format kitti --kitti_ids_file file_ids.txt
        ```
        
        ## Convert annotation formats
        In order to convert from one annotation format to another use the script 
        `cvdata/convert.py`. 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: 
        ```bash
        $ python convert.py --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
        
        $ python convert.py --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`. For example:
        ```bash
        $ python convert.py --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`. 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:
        ```bash
        $ python 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`. For example: 
        ```bash
        $ python exclude.py --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: 
        ```bash
        $ python exclude.py --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` 
        which will convert the images to JPG (if any are in PNG format), (optionally) replace 
        labels, (optionally) remove bounding boxes that contain 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:
        ```bash
        $ python clean.py --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`. 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: 
        ```bash
        $ python split.py --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:
        ```bash
        $ python split.py --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` 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: 
        ```bash
        $ python filter.py --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` 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 target directory is specified. 
        For example:
        ```bash
        $ python duplicates.py --images_dir /data/trucks/ups/images \
        >      --annotations_dir /data/trucks/ups/pascal \
        >      --dups_dir /data/trucks/ups/dups
        ```
        
        ## Visualize annotations
        In order to visualize images and corresponding annotations use the script 
        `cvdata/visualize.py`. This script currently supports annotations in COCO (*.json), 
        Darknet (*.txt), KITTI (*.txt), 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:
        ```bash
        $ python cvdata/visualize.py --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--"
        }
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Provides: cvdata
Requires-Python: ==3.7.*
Description-Content-Type: text/markdown
