Images

mlop supports logging many different image types to the platform, you can current log imagees that are of the format of

NumPy arrays
PILLOW images
PyTorch tensors
Matplotlib plots or figures

To use image logging you first need to instantiate the mlop.Image class

img = mlop.Image(
    data: Union[str, PILImage.Image, matplotlib.figure.Figure, torch.Tensor, np.ndarray],
    caption: str | None = None,
)

Parameter	Type	Description
`data`	`Union[str, PILImage.Image, matplotlib.figure.Figure, torch.Tensor, np.ndarray]`	The image data to log. Can be a path to an image file, a PIL image, a Matplotlib figure, a PyTorch tensor, or a NumPy array.
`caption`	`str`	A caption for the image.

Then log the image as standard

mlop.log({"image/file/0-0": img})

This provides you with the visualization as such

:::tip mlop uses PILLOW as its backend for image logging, with the following supported formats. :::

Examples

Create a PIL image

from PIL import Image
 
img = Image.new("RGB", (100, 100), color = (255,255,255))
filename = "pil_image.png"
img.save(filename)
 
mlop.log({"img": mlop.Image(img)})

File Path

path = "pil_image.png"
mlop.log({"img": mlop.Image(path)})

NumPy Array

data = np.random.randint(low=0, high=256, size=(100, 100, 3))
mlop.log({"image": mlop.Image(data)})

Matplotlib

 
noise = np.random.rand(*size)
x = np.linspace(0, 1, size[1])
y = np.linspace(0, 1, size[0])
X, Y = np.meshgrid(x, y)
gradient = X + Y
image = (noise + gradient) / 2
fig, ax = plt.subplots(figsize=(1, 1), dpi=100)
ax.imshow(image, cmap="viridis")
ax.axis("off")
 
mlop.log({"image": mlop.Image(fig)})