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159 lines
5.3 KiB
Markdown
159 lines
5.3 KiB
Markdown
---
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title: Masks
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---
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Masks are source data used in deep learning for image segmentation. Mask URIs are a property of a SingleFrame.
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ClearML applies the masks in one of two modes:
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* [Pixel segmentation](#pixel-segmentation-masks) - Pixel RGB values are each mapped to segmentation labels.
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* [Alpha channel](#alpha-channel-masks) - Pixel RGB values are interpreted as opacity levels.
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In the WebApp's [frame viewer](webapp/webapp_datasets_frames.md#frame-viewer), you can select how to apply a mask over
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a frame.
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## Pixel Segmentation Masks
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For pixel segmentation, mask RGB pixel values are mapped to labels.
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Mask-label mapping is defined at the dataset level, through the `mask_labels` property in a version's metadata.
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`mask_labels` is a list of dictionaries, where each dictionary includes the following keys:
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* `value` - Mask's RGB pixel value
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* `labels` - Label associated with the value.
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See how to manage dataset version mask labels pythonically [here](dataset.md#managing-version-mask-labels).
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In the UI, you can view the mapping in a dataset version's [Metadata](webapp/webapp_datasets_versioning.md#metadata) tab.
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When viewing a frame with a mask corresponding with the version's mask-label mapping, the UI arbitrarily assigns a color
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to each label. The color assignment can be [customized](webapp/webapp_datasets_frames.md#labels).
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For example:
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* Original frame image:
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* Frame image with the semantic segmentation mask enabled. Labels are applied according to the dataset version's
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mask-label mapping:
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The frame's sources array contains a masks list of dictionaries that looks something like this:
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```editorconfig
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{
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"id": "<framegroup_id>",
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"timestamp": "<timestamp>",
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"context_id": "car_1",
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"sources": [
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{
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"id": "<source_id>",
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"content_type": "<type>",
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"uri": "<image_uri>",
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"timestamp": 1234567889,
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...
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"masks": [
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{
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"id": "<mask_id>",
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"content_type": "video/mp4",
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"uri": "<mask_uri>",
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"timestamp": 123456789
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}
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]
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}
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]
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}
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```
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The masks dictionary includes the frame's masks' URIs and IDs.
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## Alpha Channel Masks
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For alpha channel, mask RGB pixel values are interpreted as opacity values so that when the mask is applied, only the
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desired sections of the source are visible.
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For example:
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* Original frame:
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* Same frame with an alpha channel mask, emphasizing the troll doll:
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The frame's sources array contains a masks list of dictionaries that looks something like this:
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```editorconfig
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{
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"sources" : [
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{
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"id" : "321"
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"uri" : "https://i.ibb.co/bs7R9k6/troll.png"
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"masks" : [
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{
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"id" : "troll",
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"uri" : "https://i.ibb.co/TmJ3mvT/troll-alpha.png"
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}
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]
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"timestamp" : 0
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}
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]
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}
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```
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Note that for alpha channel masks, no labels are used.
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## Usage
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### Register Frames with a Masks
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To register frames with a mask, create a frame and specify the frame's mask file's URI.
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```python
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# create dataset version
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version = DatasetVersion.create_version(
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dataset_name="Example",
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version_name="Registering frame with mask"
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)
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# create frame with mask
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frame = SingleFrame(
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source='https://s3.amazonaws.com/allegro-datasets/cityscapes/leftImg8bit_trainvaltest/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png',
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mask_source='https://s3.amazonaws.com/allegro-datasets/cityscapes/gtFine_trainvaltest/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelIds.png'
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)
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# add frame to version
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version.add_frames([frame])
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```
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To use the mask for pixel segmentation, define the pixel-label mapping for the DatasetVersion:
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```python
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version.set_masks_labels(
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{(0,0,0): ["background"], (1,1,1): ["person", "sitting"], (2,2,2): ["cat"]}
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)
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```
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The relevant label is applied to all masks in the version according to the version's mask-label mapping dictionary.
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### Registering Frames with Multiple Masks
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Frames can contain multiple masks. To add multiple masks, use the SingleFrame's `masks_source` property. Input one of
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the following:
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* A dictionary with mask string ID keys and mask URI values
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* A list of mask URIs. Number IDs are automatically assigned to the masks ("00", "01", etc.)
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```python
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frame = SingleFrame(source='https://s3.amazonaws.com/allegro-datasets/cityscapes/leftImg8bit_trainvaltest/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png',)
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# add multiple masks
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# with dictionary
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frame.masks_source={"ID 1 ": "<mask_URI_1>", "ID 2": "<mask_URI_2>"}
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# with list
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frame.masks_source=[ "<mask_URI_1>", "<mask_URI_2>"]
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```
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