# Media formats Datumaro supports the following media types: - 2D RGB(A) images - KITTI Point Clouds To create an unlabelled dataset from an arbitrary directory with images use `image_dir` and `image_zip` formats: ``` bash datum create -o datum import -p -f image_dir ``` or, if you work with Datumaro API: - for using with a project: ```python from datumaro.components.project import Project project = Project.init() project.import_source('source1', format='image_dir', url='directory/path/') dataset = project.working_tree.make_dataset() ``` - for using as a dataset: ```python from datumaro.components.dataset import Dataset dataset = Dataset.import_from('directory/path/', 'image_dir') ``` This will search for images in the directory recursively and add them as dataset entries with names like `//`. The list of formats matches the list of supported image formats in OpenCV: ``` .jpg, .jpeg, .jpe, .jp2, .png, .bmp, .dib, .tif, .tiff, .tga, .webp, .pfm, .sr, .ras, .exr, .hdr, .pic, .pbm, .pgm, .ppm, .pxm, .pnm ``` Once there is a `Dataset` instance, its items can be split into subsets, renamed, filtered, joined with annotations, exported in various formats etc. To import frames from a video, you can split the video into frames with the [`split_video` command](/docs/user-manual/command-reference/util#split-video) and then use the `image_dir` format described above. In more complex cases, consider using [FFmpeg](https://ffmpeg.org/) and other tools for video processing. Alternatively, you can use the `video_frames` format directly: > Note, however, that it can produce different results if the system > environment changes. If you want to obtain reproducible results, consider > splitting the video into frames by any method. ``` bash datum create -o datum import -p -f video_frames