Ango to Mask Converter
Last updated
Last updated
The Ango to Mask Converter plugin converts various types of annotations—such as segmentation, polygon, bounding box, and more—from Ango format into masks. This plugin supports different data types and annotation tools, offering flexibility for both image and video processing.
Converts multiple annotation types (segmentation, polygon, bounding box, etc.) into masks.
Supports various data types, including images, videos, and multi-page assets.
Compatible with a wide range of annotation tools, offering extensive versatility.
Can overlay masks on original media with customizable opacity for enhanced visual analysis.
Customizable logging and detailed output options for monitoring large projects.
Image
Video
Multi-Page Assets
Polygon
Segmentation
Point
Polyline
Bounding Box
Rotated Bounding Box
Brush (For image assets only)
class_mapping_type: Defines how classes are mapped to the mask output.
Please note that the output mask image may appear fully black. This is because our eyes cannot easily distinguish between pixel values of 0, 1, and 2, as these values are very close to pure black.
Options:
single_channel_sequential: Assigns grayscale values sequentially in a single channel.
Example: Class-1: [1], Class-2: [2], Class-3: [3]
single_channel_diverging: Uses diverging grayscale values for enhanced distinction.
Example: Class-1: [256], Class-2: [128], Class-3: [64]
multi_channel_qualitative: Maps each class to matplotlibs color schemas: 'tab10', 'tab20', 'tab20b', 'tab20c', and ‘xkcd-colors’ respectively.
Example: Class-1: [31, 119, 180], Class-2: [255, 127, 14], Class-3: [44, 160, 44]
project_colors: Uses project-defined colors for mask generation.
Example: Class-1: [244, 67, 54], Class-2: [3, 169, 244], Class-3: [156, 39, 176]
instance_segmentation: Assigns a distinct RGB color to each instance.
Example: Cat-1: [31, 199, 180], Dog-1: [255, 127, 14], Dog-2: [44, 160, 44]
selected_schema_ids: A list of schema IDs to specify which classes should be processed. Only annotations matching these IDs will be considered.
prioritized_schema_ids: A list of schema IDs that defines which annotations take precedence when multiple annotations overlap. Note: By default, the tools are sorted according to their tool type. The sorting order from back to front is as follows: brush, bounding box, rotated bounding box, polygon, segmentation, polyline, point
add_boundary: Boolean value (true or false). If enabled, adds boundaries to annotations for better visualization in the generated masks. Supported for polygon, segmentation, bounding box, and rotated bounding box.
polyline_thickness: Specifies the thickness of the boundary lines (in pixels) for polyline or segmentation annotations.
verbose: Boolean flag (true or false). When enabled, detailed logs are produced during the mask generation process.
verbose_frequency: Integer value specifying how frequently logs should be generated during processing. For example, setting verbose_frequency=5 will log progress for every 5th asset.
overlay_over_image: Boolean value (true or false). When enabled, the generated mask will be overlaid on the original image or video for easy comparison.
overlay_opacity: Float value between 0 and 1 that controls the transparency of the overlay. A value of 0 means the mask is fully transparent, while 1 means it is fully opaque.
Image and Video Segmentation: Convert segmentation annotations into masks for use in model training or visual analysis.
Object Detection: Generate masks from bounding box annotations for object detection models.
Overlay Masks on Media: Use the overlay_over_image feature to analyze masks over the original image or video.
