| import numpy as np |
| from PIL import Image |
|
|
|
|
| def label_to_onehot(label, colormap): |
| """ |
| Converts a segmentation label (H, W, C) to (H, W, K) where the last dim is a one |
| hot encoding vector, C is usually 1 or 3, and K is the number of class. |
| """ |
| semantic_map = [] |
| for colour in colormap: |
| equality = np.equal(label, colour) |
| class_map = np.all(equality, axis=-1) |
| semantic_map.append(class_map) |
| semantic_map = np.stack(semantic_map, axis=-1).astype(np.float32) |
| return semantic_map |
|
|
|
|
| def onehot_to_label(semantic_map, colormap): |
| """ |
| Converts a mask (H, W, K) to (H, W, C) |
| """ |
| x = np.argmax(semantic_map, axis=-1) |
| colour_codes = np.array(colormap) |
| label = np.uint8(colour_codes[x.astype(np.uint8)]) |
| return label |
|
|
|
|
| def onehot2mask(semantic_map): |
| """ |
| Converts a mask (K, H, W) to (H,W) |
| """ |
| _mask = np.argmax(semantic_map, axis=0).astype(np.uint8) |
| return _mask |
|
|
|
|
| def mask2onehot(mask, num_classes): |
| """ |
| Converts a segmentation mask (H,W) to (K,H,W) where the last dim is a one |
| hot encoding vector |
| |
| """ |
| semantic_map = [mask == i for i in range(num_classes)] |
| return np.array(semantic_map).astype(np.uint8) |
|
|
|
|
| def vis_trun(image, mask, weight=0.3): |
| """ |
| :param image: shape [3,H,W] |
| :param mask: shape [H,W] or [1,H,W] |
| :param weight: |
| :return: |
| """ |
| assert image.ndim == 3 and image.shape[0] == 3 and (mask.ndim == 2 or mask.ndim == 3) |
| if mask.shape[0] == 1: |
| mask = mask[0] |
| if mask.shape[-1] == 1: |
| mask = mask[..., 0] |
| semantic_map = mask2onehot(mask, 2) |
| color = np.array([106, 206, 235])[:, None, None] |
| color_a = semantic_map[1][None, ...].astype(np.float) * color.astype(np.float) |
| color_b = (image * 255).astype(np.uint8).astype(np.float) |
| color_c = color_a * mask * weight + color_b * (1 - mask) |
| color_c = color_c + mask * (1 - weight) * color_b |
| color_c = color_c.astype(np.uint8) |
| return color_c |
|
|
