# Segmentation Masks and Metadata Celantur container can generate two different segmentation masks and metadata per processed image: * Binary Segmentation * Instance Segmentation It's activated with the `--save-mask {all, instance, binary}` parameter. The segmentation is saved as a PNG file.

Anonymization, binary segmentation and instance segmentation applied to an image with Celantur software.

## Binary Segmentation The binary segmentation mask consist of two colors: * Background is black * Anonymized segments are white The file will be saved as `image-name_bin_mask.png`. ## Instance Segmentation #### v26.02.1 and later In instance segmentation masks, the RGB color values are used to differentiate to individual instances/objects. * The R (red) channel is 0. * The G (green) channel encodes individual instances / objects. * The B (blue) channel encodes the object type: | Object type | Blue channel value | Blue channel value in binary | | ------------- | ------------------ | ---------------------------- | | Person | `128` | `1000 0000` | | License plate | `64` | `0100 0000` | | Face / head | `32` | `0010 0000` | | Vehicle | `16` | `0001 0000` | When objects overlap, their color codes are combined using the binary OR operation, e.g. overlap of license plate and vehicle results in 80 (16 + 64), overlap of two persons remains 128. E.g. `[0, 85, 16]` is a vehicle. 16 identifies the object type, 85 identifies the instance from others. The file will be saved as `image-name_ins_maks.png`. #### Pre v26.02.1 In instance segmentation masks, the RGB color values are used to differentiate to individual instances/objects. * The R (red) channel is 0. * The G (green) channel encodes individual instances / objects. * The B (blue) channel encodes the object type: * Person: `128` * License plate: `64` * Face: `192` * Vehicle: `255` E.g. `[0, 85, 192]` is a face. The file will be saved as `image-name_ins_maks.png`. ## Scale Down Mask Files By adding the optional `--mask-scale {0-100}` (CLI) or `/v1/file/1/instance-mask?mask-scale={0-100}` (Container API) parameter, mask files will be scaled down by the specified ratio. ## Metadata ### Image metadata Metadata about detected instances/objects are stored in the corresponding `image-name.json` file. ### Image metadata example ```json { "id": "image-name.jpg", "detections": [ { "id": 0, "parent_image": "image-name.jpg", "offset": [ 1560, 744 ], "bbox": [ 1957, 744, 3003, 1855 ], "type": 103, "score": 0.9993754029273987, "is_anonymised": true, "type_label": "face", "color": null }, "size": [ 3456, 5184 ], "duration": 1.8533296539999355, "filename": "image-name.jpg", "folder": null } ``` ### Video metadata Metadata is generated for individual frames and provided as a range of mulitple frames. * **Batch/Stream mode:** \ The information is stored in `filename-[startframe]-[endframe].json` in the output directory. The maximum number of frames covered by a single file is 500. * **REST API mode:** \ The infromation can be retrieved via the [#download-video-metadata-json](https://doc.celantur.com/container/rest-api-v1-mode#download-video-metadata-json "mention") endpoint. ### Video metadata example ```json [ { "id": 0, "detections": [ { "id": 0, "parent_image": 0, "offset": [ 3, 4 ], "bbox": [ 3, 4, 201, 171 ], "type": 103, "score": 0.8267934918403625, "is_anonymised": true, "type_label": "face", "color": null } ], "size": [ 178, 320 ], "duration": 0.35388135999892256 }, { "id": 1, "detections": [ { "id": 0, "parent_image": 1, "offset": [ 0, 6 ], "bbox": [ 0, 20, 137, 175 ], "type": 103, "score": 0.8945223689079285, "is_anonymised": true, "type_label": "face", "color": null } ], "size": [ 178, 320 ], "duration": 0.2979645720006374 }, ... ] ``` ### Metadata attribute reference #### Attributes of a image or video frame

Attribute	Description
id	The id of the image (file name) or video frame (sequential number)
detections	List of detections, see #detected-instances-objects-provided-as-a-list-under-the-detections-attribute
size	Size of the image or frame in [width, height]
duration	Duration of processing (inference and anonymization) an image or video frame. Does not include IO, e.g. read/write from hard drive.
filename	Name of the file
folder	Name of the folder (relative to root input folder)

#### Detected instances/objects provided as a list under the `detections` attribute

Attribute	Description
id	The id of the detection, a sequential number starting from 0.
parent_image	The name of the image or the id of the video frame the detected instance/object was found on
offset	The offset of the detection's bounding box from the upper left corner of the image (x/y coordinates in pixels)
bbox	The coordinates of the detection's bounding box (x1, y1, x2, y2)
score	The detection's confidence score. States how confident the model is about the detection being a specific label (see type_label) between 0.0 and 1.0.
is_anonymized	Specifies whether the detection was anonymized (or detected with `method = detect`).
type_label	The detection's label assigned by the model. E.g. face, license plates, etc.
type	Numerical representation of the `type_label`.
color	The detections color (RGB) in the instance segmentation mask.
duration	Processing duration for a video frame (only for videos).

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