aot/utils/image.py

import numpy as np
from PIL import Image
import torch
import threading

_palette = [
    0, 0, 0, 128, 0, 0, 0, 128, 0, 128, 128, 0, 0, 0, 128, 128, 0, 128, 0, 128,
    128, 128, 128, 128, 64, 0, 0, 191, 0, 0, 64, 128, 0, 191, 128, 0, 64, 0,
    128, 191, 0, 128, 64, 128, 128, 191, 128, 128, 0, 64, 0, 128, 64, 0, 0,
    191, 0, 128, 191, 0, 0, 64, 128, 128, 64, 128, 22, 22, 22, 23, 23, 23, 24,
    24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30,
    30, 31, 31, 31, 32, 32, 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36,
    37, 37, 37, 38, 38, 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43,
    43, 43, 44, 44, 44, 45, 45, 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49,
    49, 50, 50, 50, 51, 51, 51, 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55,
    56, 56, 56, 57, 57, 57, 58, 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62,
    62, 62, 63, 63, 63, 64, 64, 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68,
    68, 69, 69, 69, 70, 70, 70, 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74,
    75, 75, 75, 76, 76, 76, 77, 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81,
    81, 81, 82, 82, 82, 83, 83, 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87,
    87, 88, 88, 88, 89, 89, 89, 90, 90, 90, 91, 91, 91, 92, 92, 92, 93, 93, 93,
    94, 94, 94, 95, 95, 95, 96, 96, 96, 97, 97, 97, 98, 98, 98, 99, 99, 99,
    100, 100, 100, 101, 101, 101, 102, 102, 102, 103, 103, 103, 104, 104, 104,
    105, 105, 105, 106, 106, 106, 107, 107, 107, 108, 108, 108, 109, 109, 109,
    110, 110, 110, 111, 111, 111, 112, 112, 112, 113, 113, 113, 114, 114, 114,
    115, 115, 115, 116, 116, 116, 117, 117, 117, 118, 118, 118, 119, 119, 119,
    120, 120, 120, 121, 121, 121, 122, 122, 122, 123, 123, 123, 124, 124, 124,
    125, 125, 125, 126, 126, 126, 127, 127, 127, 128, 128, 128, 129, 129, 129,
    130, 130, 130, 131, 131, 131, 132, 132, 132, 133, 133, 133, 134, 134, 134,
    135, 135, 135, 136, 136, 136, 137, 137, 137, 138, 138, 138, 139, 139, 139,
    140, 140, 140, 141, 141, 141, 142, 142, 142, 143, 143, 143, 144, 144, 144,
    145, 145, 145, 146, 146, 146, 147, 147, 147, 148, 148, 148, 149, 149, 149,
    150, 150, 150, 151, 151, 151, 152, 152, 152, 153, 153, 153, 154, 154, 154,
    155, 155, 155, 156, 156, 156, 157, 157, 157, 158, 158, 158, 159, 159, 159,
    160, 160, 160, 161, 161, 161, 162, 162, 162, 163, 163, 163, 164, 164, 164,
    165, 165, 165, 166, 166, 166, 167, 167, 167, 168, 168, 168, 169, 169, 169,
    170, 170, 170, 171, 171, 171, 172, 172, 172, 173, 173, 173, 174, 174, 174,
    175, 175, 175, 176, 176, 176, 177, 177, 177, 178, 178, 178, 179, 179, 179,
    180, 180, 180, 181, 181, 181, 182, 182, 182, 183, 183, 183, 184, 184, 184,
    185, 185, 185, 186, 186, 186, 187, 187, 187, 188, 188, 188, 189, 189, 189,
    190, 190, 190, 191, 191, 191, 192, 192, 192, 193, 193, 193, 194, 194, 194,
    195, 195, 195, 196, 196, 196, 197, 197, 197, 198, 198, 198, 199, 199, 199,
    200, 200, 200, 201, 201, 201, 202, 202, 202, 203, 203, 203, 204, 204, 204,
    205, 205, 205, 206, 206, 206, 207, 207, 207, 208, 208, 208, 209, 209, 209,
    210, 210, 210, 211, 211, 211, 212, 212, 212, 213, 213, 213, 214, 214, 214,
    215, 215, 215, 216, 216, 216, 217, 217, 217, 218, 218, 218, 219, 219, 219,
    220, 220, 220, 221, 221, 221, 222, 222, 222, 223, 223, 223, 224, 224, 224,
    225, 225, 225, 226, 226, 226, 227, 227, 227, 228, 228, 228, 229, 229, 229,
    230, 230, 230, 231, 231, 231, 232, 232, 232, 233, 233, 233, 234, 234, 234,
    235, 235, 235, 236, 236, 236, 237, 237, 237, 238, 238, 238, 239, 239, 239,
    240, 240, 240, 241, 241, 241, 242, 242, 242, 243, 243, 243, 244, 244, 244,
    245, 245, 245, 246, 246, 246, 247, 247, 247, 248, 248, 248, 249, 249, 249,
    250, 250, 250, 251, 251, 251, 252, 252, 252, 253, 253, 253, 254, 254, 254,
    255, 255, 255
]


def label2colormap(label):

    m = label.astype(np.uint8)
    r, c = m.shape
    cmap = np.zeros((r, c, 3), dtype=np.uint8)
    cmap[:, :, 0] = (m & 1) << 7 | (m & 8) << 3 | (m & 64) >> 1
    cmap[:, :, 1] = (m & 2) << 6 | (m & 16) << 2 | (m & 128) >> 2
    cmap[:, :, 2] = (m & 4) << 5 | (m & 32) << 1
    return cmap


def one_hot_mask(mask, cls_num):
    if len(mask.size()) == 3:
        mask = mask.unsqueeze(1)
    indices = torch.arange(0, cls_num + 1,
                           device=mask.device).view(1, -1, 1, 1)
    return (mask == indices).float()


def masked_image(image, colored_mask, mask, alpha=0.7):
    mask = np.expand_dims(mask > 0, axis=0)
    mask = np.repeat(mask, 3, axis=0)
    show_img = (image * alpha + colored_mask *
                (1 - alpha)) * mask + image * (1 - mask)
    return show_img


def save_image(image, path):
    im = Image.fromarray(np.uint8(image * 255.).transpose((1, 2, 0)))
    im.save(path)


def _save_mask(mask, path, squeeze_idx=None):
    if squeeze_idx is not None:
        unsqueezed_mask = mask * 0
        for idx in range(1, len(squeeze_idx)):
            obj_id = squeeze_idx[idx]
            mask_i = mask == idx
            unsqueezed_mask += (mask_i * obj_id).astype(np.uint8)
        mask = unsqueezed_mask
    mask = Image.fromarray(mask).convert('P')
    mask.putpalette(_palette)
    mask.save(path)


def save_mask(mask_tensor, path, squeeze_idx=None):
    mask = mask_tensor.cpu().numpy().astype('uint8')
    threading.Thread(target=_save_mask, args=[mask, path, squeeze_idx]).start()


def flip_tensor(tensor, dim=0):
    inv_idx = torch.arange(tensor.size(dim) - 1, -1, -1,
                           device=tensor.device).long()
    tensor = tensor.index_select(dim, inv_idx)
    return tensor


def shuffle_obj_mask(mask):

    bs, obj_num, _, _ = mask.size()
    new_masks = []
    for idx in range(bs):
        now_mask = mask[idx]
        random_matrix = torch.eye(obj_num, device=mask.device)
        fg = random_matrix[1:][torch.randperm(obj_num - 1)]
        random_matrix = torch.cat([random_matrix[0:1], fg], dim=0)
        now_mask = torch.einsum('nm,nhw->mhw', random_matrix, now_mask)
        new_masks.append(now_mask)

    return torch.stack(new_masks, dim=0)