cutcutcodec.core.analysis.video.metric.lpips

cutcutcodec.core.analysis.video.metric.lpips(ref: Tensor, dis: Tensor, *args, **kwargs) → Tensor[source]

Compute the Learned Perceptual Image Patch Similarity.

It uses the module pip install lpips in backend, based on torch.

Parameters

ref, disarraylike: The 2 images to be compared, of shape ([*batch], height, width, channels). The frames are assumed to be in RGB in range [0, 1]. Gamut and EOTF must be standard rgb.
netstr, default=”alex”: The neuronal network used, “alex” or “vgg”.
threadsint, optional: Defines the number of threads. The value -1 means that the function uses as many calculation threads as there are cores. The default value (0) allows the same behavior as (-1) if the function is called in the main thread, otherwise (1) to avoid nested threads. Any other positive value corresponds to the number of threads used.

Returns

lpipsarraylike: The learned perceptual image patch similarity of each layers.

Examples

>>> import numpy as np
>>> from cutcutcodec.core.analysis.video.metric import lpips
>>> np.random.seed(0)
>>> ref = np.random.random((720, 1080, 3))  # It could also be a torch array list...
>>> dis = 0.8 * ref + 0.2 * np.random.random((720, 1080, 3))
>>> lpips(ref, dis).round(1)
np.float64(0.0)
>>>