# LeapImageWithHeatmap

Used to visualize attention maps or a similar float-based mask over an image.

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

@dataclass
class LeapImageWithHeatmap:
    image: npt.NDArray[np.float32]
    heatmaps: npt.NDArray[np.float32]
    labels: List[str]
    type: LeapDataType = LeapDataType.ImageWithHeatmap
```

<table><thead><tr><th width="167.39065467110788">Args</th><th></th></tr></thead><tbody><tr><td><code>image</code></td><td>np.ndarray 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>heatmaps</code></td><td>np.ndarray float32 representation of the heatmaps. The expected shape is [C,H,W], where C is the amount of heatmaps that are provided for the image.</td></tr><tr><td><code>labels</code></td><td>A list of labels of C length for the heatmap labels</td></tr></tbody></table>

## Examples

#### Basic Usage

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

@tensorleap_custom_visualizer("heatmap", LeapDataType.ImageWithHeatmap)
def heatmap(image_data: np.ndarray, heatmap_data: np.ndarray):
    labels = ["heatmap1"]
    return LeapImageWithHeatmap(image=image_data.squeeze(0), heatmaps=heatmap_data, labels=labels)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tensorleap.ai/tensorleap-integration/python-api/code_loader/visualizer_classes/leapimagewithheatmap.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.