Source code for tensorly.datasets.synthetic

import numpy as np
from .. import backend as T


[docs]def gen_image(region='swiss', image_height=20, image_width=20, n_channels=None, weight_value=1): """Generates an image for regression testing Parameters ---------- region : {'swiss', 'rectangle'} image_height : int, optional image_width : int, optional weight_value : float, optional n_channels : int or None, optional if not None, the resulting image will have a third dimension Returns ------- ndarray array of shape ``(image_height, image_width)`` or ``(image_height, image_width, n_channels)`` array for which all values are zero except the region specified """ weight = np.zeros((image_height, image_width), dtype=float) if region == "swiss": slim_width = (image_width // 2) - (image_width // 10 + 1) large_width = (image_width // 2) - (image_width // 3 + 1) slim_height = (image_height // 2) - (image_height // 10 + 1) large_height = (image_height // 2) - (image_height // 3 + 1) weight[large_height:-large_height, slim_width:-slim_width] = weight_value weight[slim_height:-slim_height, large_width:-large_width] = weight_value elif region == "rectangle": large_height = (image_height // 2) - (image_height // 4) large_width = (image_width // 2) - (image_width // 4) weight[large_height:-large_height, large_width:-large_width] = weight_value elif region == "circle": radius = image_width // 3 cy = image_width // 2 cx = image_height // 2 y, x = np.ogrid[-radius: radius, -radius: radius] index = x**2 + y**2 <= radius**2 weight[cy - radius:cy + radius, cx - radius:cx + radius][index] = 1 if n_channels is not None and weight.ndim == 2: weight = np.concatenate([weight[..., None]] * n_channels, axis=-1) return T.tensor(weight)