# LeapImageWithBBox

Used to visualize an image overlayed with bounding boxes

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

@dataclass
class BoundingBox:
    x: float
    y: float
    width: float
    height: float
    confidence: float
    label: str
    rotation: float = 0.0
    metadata: Optional[Dict[str, Union[str, int, float]]] = None

@dataclass
class LeapImageWithBBox:
    data: Union[npt.NDArray[np.float32], npt.NDArray[np.uint8]]
    bounding_boxes: List[BoundingBox]
    type: LeapDataType = LeapDataType.ImageWithBBox
```

<table><thead><tr><th width="167.39065467110788">Args</th><th></th></tr></thead><tbody><tr><td><code>data</code></td><td>an 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>bounding_boxes</code></td><td><p>A list of bounding box. Each bounding_box has the following attributes:<br></p><ul><li>x - x center of the bounding box. Expected to be between [0,1]</li><li>y - y center of the bounding box. Expected to be between [0,1]</li><li>width - width of the bounding box. Expected to be between [0,1]</li><li>height - height of the bounding box. Expected to be between [0,1]</li><li>confidence - the confidence of the prediction. Expected to be between [0,1]. Ground Truth should be set to 1.</li><li>label - a str label of the prediction</li><li>rotation - a float between [0,360] that represents the degree of roation. Defaults to 0.</li><li>metadata (optional) - a metadata on the bounding box. A dictionary of property names (str) to their values (str/int/float)</li></ul></td></tr></tbody></table>

## Examples

#### Basic Usage

```python
from code_loader.contract.visualizer_classes import LeapImageWithBBox
import numpy as np
from code_loader.contract.enums import LeapDataType
...
@tensorleap_custom_visualizer("bb_gt_decoder", LeapDataType.ImageWithBBox)
def gt_bb_decoder(image: np.ndarray, bb_gt: np.ndarray) -> LeapImageWithBBox:
    bbox = [BoundingBox(x=bbx[0], y=bbx[1], width=bbx[2], height=bbx[3], confidence=1, label=all_clss.get(int(bbx[4]) if not np.isnan(bbx[4]) else -1, 'Unknown Class')) for bbx in bb_gt.squeeze(0)]
    image = image.squeeze(0)
    return LeapImageWithBBox(data=image, bounding_boxes=bbox)
```


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