> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Image
export const GitHubLink = ({url}) =>
GitHub source
;
## class `Image`
A class for logging images to W\&B.
### method `Image.__init__`
```python theme={null}
__init__(
data_or_path: 'ImageDataOrPathType',
mode: 'str | None' = None,
caption: 'str | None' = None,
grouping: 'int | None' = None,
classes: 'Classes | Sequence[dict] | None' = None,
boxes: 'dict[str, BoundingBoxes2D] | dict[str, dict] | None' = None,
masks: 'dict[str, ImageMask] | dict[str, dict] | None' = None,
file_type: 'str | None' = None,
normalize: 'bool' = True
) → None
```
Initialize a `wandb.Image` object.
This class handles various image data formats and automatically normalizes pixel values to the range \[0, 255] when needed, ensuring compatibility with the W\&B backend.
* Data in range \[0, 1] is multiplied by 255 and converted to uint8 \* Data in range \[-1, 1] is rescaled from \[-1, 1] to \[0, 255] by mapping
-1 to 0 and 1 to 255, then converted to uint8 \* Data outside \[-1, 1] but not in \[0, 255] is clipped to \[0, 255] and converted to uint8 (with a warning if values fall outside \[0, 255]) \* Data already in \[0, 255] is converted to uint8 without modification
**Args:**
* `data_or_path`: Accepts NumPy array/pytorch tensor of image data, a PIL image object, or a path to an image file. If a NumPy array or pytorch tensor is provided, the image data will be saved to the given file type. If the values are not in the range \[0, 255] or all values are in the range \[0, 1], the image pixel values will be normalized to the range \[0, 255] unless `normalize` is set to `False`.
* pytorch tensor should be in the format (channel, height, width)
* NumPy array should be in the format (height, width, channel)
* `mode`: The PIL mode for an image. Most common are "L", "RGB", "RGBA".
* `Full Pillow docs for more information https`: //pillow\.readthedocs.io/en/stable/handbook/concepts.html#modes
* `caption`: Label for display of image.
* `grouping`: The grouping number for the image.
* `classes`: A list of class information for the image, used for labeling bounding boxes, and image masks.
* `boxes`: A dictionary containing bounding box information for the image.
* `see https`: //docs.wandb.ai/models/ref/python/data-types/boundingboxes2d
* `masks`: A dictionary containing mask information for the image.
* `see https`: //docs.wandb.ai/models/ref/python/data-types/imagemask
* `file_type`: The file type to save the image as. This parameter has no effect if `data_or_path` is a path to an image file.
* `normalize`: If `True`, normalize the image pixel values to fall within the range of \[0, 255]. Normalize is only applied if `data_or_path` is a numpy array or pytorch tensor.
**Examples:**
Create a wandb.Image from a numpy array
```python theme={null}
import numpy as np
import wandb
with wandb.init() as run:
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
image = wandb.Image(pixels, caption=f"random field {i}")
examples.append(image)
run.log({"examples": examples})
```
Create a wandb.Image from a PILImage
```python theme={null}
import numpy as np
from PIL import Image as PILImage
import wandb
with wandb.init() as run:
examples = []
for i in range(3):
pixels = np.random.randint(
low=0, high=256, size=(100, 100, 3), dtype=np.uint8
)
pil_image = PILImage.fromarray(pixels, mode="RGB")
image = wandb.Image(pil_image, caption=f"random field {i}")
examples.append(image)
run.log({"examples": examples})
```
Log .jpg rather than .png (default)
```python theme={null}
import numpy as np
import wandb
with wandb.init() as run:
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
image = wandb.Image(
pixels, caption=f"random field {i}", file_type="jpg"
)
examples.append(image)
run.log({"examples": examples})
```
***
### property Image.image
***