# LeapImageMask

Used to visualize a semantic segmentation mask

```python
import numpy.typing as npt
from code_loader.contract.enums import LeapDataType

@dataclass
class LeapImageMask:
    mask: npt.NDArray[np.uint8]
    image: npt.NDArray[np.float32]
    labels: List[str]
    type: LeapDataType = LeapDataType.ImageMask
```

<table><thead><tr><th width="167.39065467110788">Args</th><th></th></tr></thead><tbody><tr><td><code>image</code></td><td>np.ndarray uint8/float32 representation of the image. The expected image format is [H,W,1] OR [H,W,3] and is expected to be in [0,255].</td></tr><tr><td><code>mask</code></td><td>np.ndarray uint8 representation of the mask. The expected format is [H,W] where each pixel value signifies its label</td></tr><tr><td><code>labels</code></td><td>A list of strings mapping pixel values to their labels</td></tr></tbody></table>

## Examples

#### Basic Usage

```python
import numpy as np
from code_loader.contract.visualizer_classes import LeapImageMask
from code_loader import leap_binder
from code_loader.contract.enums import LeapDataType
...

@tensorleap_custom_visualizer(name='seg_visualizer',
                              visualizer_type=LeapDataType.ImageMask,
                              heatmap_function=None)
def segmentation_visualizer(image: np.ndarray, segmentation_prediction: np.ndarray) -> LeapImageMask:
    labels = ['background', 'vehicle', 'tree', 'road', 'pavement']
    return LeapImageMask(mask=segmentation_prediction.squeeze(0), image=image.squeeze(0), labels=labels)
```


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