灰度标签图显示查看，加颜色显示

example_1

import numpy as np
import cv2
import os
color_segmentation=np.asarray([
    [0,0,0],            #[0]背景
    [180,120,120],
    [6,230,230],
    [80,50,50],
    [4,200,3],
    [120,120,80],
    [140,140,140],
    [204,5,255],
    [230,230,230],
    [4,250,7],
    [40,150,255],
    [235,255,7],
    [150,5,61],
    [120,120,70],
    [8,255,51],
    [255,6,82],
    [143,255,140],
    [204,255,4],
    [255,51,7],
    [204,70,3],
    [0,102,200],
    [61,230,250],
    [255,6,51],
    [11,102,255],
    [255,7,71],
    [255,9,224],
    [9,7,230],
    [220,220,220],
    [255, 9, 92]
],dtype=np.uint8)


def decode_segmap(label_mask,n_classes = 21):
    r = label_mask.copy()
    g = label_mask.copy()
    b = label_mask.copy()
    for ll in range(0, n_classes):
        position = label_mask == ll
        b[label_mask == ll] = color_segmentation[ll, 0]
        g[label_mask == ll] = color_segmentation[ll, 1]
        r[label_mask == ll] = color_segmentation[ll, 2]
    rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))

    rgb[:, :, 0] = b
    rgb[:, :, 1] = g
    rgb[:, :, 2] = r
    rgb = rgb.astype(np.uint8)  ##重要！ 要不然opencv显示有问题
    return rgb


root_dir = "./voc/VOCdevkit/VOC2012/SegmentationClassAug/"
list_gray_img = os.listdir(root_dir)
for img_name in list_gray_img:
    path_gray = root_dir + img_name
    laber_mask = cv2.imread(path_gray,0) ##灰度 单通道读取
    # print(laber_mask.shape)
    color_img = decode_segmap(laber_mask)

    cv2.imshow("laber_mask", laber_mask*20)
    cv2.imshow("color",color_img)
    cv2.waitKey()

example_2

import numpy as np
import cv2

def generate_colors(_color_num):
    """
    生成一定数量的颜色
    Args:
        _color_num: 颜色的数量
    Returns:    所有生成的颜色
    """
    to_return_palette = []
    import colorsys

    # 最多50种颜色
    for i in range(_color_num):
        hue_value = (i % 50) * 0.02
        (r, g, b) = colorsys.hsv_to_rgb(hue_value, 1, 1)
        to_return_palette.append([int(b * 255), int(g * 255), int(r * 255)])
    return to_return_palette



def annotate_segmentation(
        _to_draw_image,
        _segmentation_result,
        _background_index=0,
):
    """
    标注分割区域
    Args:
        _to_draw_image:     需要标注的图像
        _segmentation_result:   分割的结果
        _background_index:  背景部分的下标
    Returns:    标注完成的图
    """
    h, w = _to_draw_image.shape[:2]
    if _to_draw_image.shape[:2] != _segmentation_result.shape[:2]:
        _segmentation_result = cv2.resize(_segmentation_result, (w, h), cv2.INTER_NEAREST)
    distinct_index = np.sort(np.unique(_segmentation_result), axis=None)
    candidate_colors = generate_colors(len(distinct_index))
    mask_result_image = _to_draw_image.copy()
    for m_index, m_candidate_color in zip(distinct_index.tolist(), candidate_colors):
        if m_index == _background_index:
            continue
        m_index_segment_result = _segmentation_result == m_index
        np.putmask(mask_result_image, np.repeat(m_index_segment_result[..., None], 3, 
axis=-1), m_candidate_color)
    add_weighted_result_image = cv2.addWeighted(_to_draw_image, 0.5, mask_result_image, 0.5, 0)
    return add_weighted_result_image


path_img_src = "/src_jj.png"
img_gray_src = cv2.imread(path_img_src)

path_img = "./gray_jj.png"
img_gray = cv2.imread(path_img)
img_gray2 = cv2.cvtColor(img_gray,cv2.COLOR_BGR2GRAY)
h,w = img_gray2.shape
img_color = np.zeros([h, w, 3], np.uint8)
img_color_new = annotate_segmentation(img_gray_src,img_gray2)

cv2.imshow("img_color_new",img_color_new)
cv2.waitKey()

example2是把标注颜色打在原图上面查看的，自动生成颜色表。