# LeapImage

Used to visualize a grayscale/RGB image

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

@dataclass
class LeapImage:
    data: Union[npt.NDArray[np.float32], npt.NDArray[np.uint8]]
    type: LeapDataType = LeapDataType.Image
    compress: Optional[bool] = True
```

<table><thead><tr><th width="167.39065467110788">Args</th><th></th></tr></thead><tbody><tr><td><code>data</code></td><td>np.ndarray uint8/float32 representation of the image. The expected image format is [H,W,1] OR [H,W,3]. For uint8, values should be in [0,255]. For float32, values should be in [0,1].</td></tr><tr><td><code>compress</code></td><td>(boolean, optional). Images are automatically compressed to jpg in the platform. For visualization that require no compression, set compress=False to get a png.</td></tr></tbody></table>

## Examples

#### Basic Usage

```python
from code_loader.contract.visualizer_classes import LeapImage
import cv2
...

@tensorleap_custom_visualizer(name='bgr2rgb_vis',
                              visualizer_type=LeapDataType.Image)
def bgr2rgb_visualizer(data: np.ndarray) -> LeapImage:
    im_rgb = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
    return LeapImage(im_rgb)
```

#### Resize Image and Heat-map

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

def resized_image_visualizer_heatmap(data: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]:
    # data is the heatmap with original size (origin_W, origin_H)
    return np.resize(data, (256, 512))    # we reshape to the resized shape
    
@tensorleap_custom_visualizer(name='image_visualizer',
                              visualizer_type=LeapDataType.Image,
                              heatmap_function=resized_image_visualizer_heatmap)
def resized_image_visualizer(data: npt.NDArray[np.float32]) -> LeapImage:
    return LeapImage(np.resize(data, (256, 512, 3)))
```


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