将灰度图像转换为 RGB，反之亦然

Question

我写了一个代码将地面实况灰度蒙版转换为 RGB，反之亦然，但 RGB2Grayscales 没有按预期工作？
标签示例和转换后的 RGB

from __future__ import print_function, absolute_import, division
from collections import namedtuple
import numpy as np
import cv2

Label = namedtuple('Label',
                   ['name', 'id', 'trainId', 'category', 'categoryId', 'hasInstances', 'ignoreInEval', 'color', ])

labels = [
    #       name                     id    trainId   category            catId     hasInstances   ignoreInEval   color
    Label('unlabeled', 0, 19, 'void', 0, False, True, (0, 0, 0)),
    Label('ego vehicle', 1, 19, 'void', 0, False, True, (0, 0, 0)),
    Label('rectification border', 2, 19, 'void', 0, False, True, (0, 0, 0)),
    Label('out of roi', 3, 19, 'void', 0, False, True, (0, 0, 0)),
    Label('static', 4, 19, 'void', 0, False, True, (0, 0, 0)),
    Label('dynamic', 5, 19, 'void', 0, False, True, (111, 74, 0)),
    Label('ground', 6, 19, 'void', 0, False, True, (81, 0, 81)),
    Label('road', 7, 0, 'flat', 1, False, False, (128, 64, 128)),
    Label('sidewalk', 8, 1, 'flat', 1, False, False, (244, 35, 232)),
    Label('parking', 9, 19, 'flat', 1, False, True, (250, 170, 160)),
    Label('rail track', 10, 19, 'flat', 1, False, True, (230, 150, 140)),
    Label('building', 11, 2, 'construction', 2, False, False, (70, 70, 70)),
    Label('wall', 12, 3, 'construction', 2, False, False, (102, 102, 156)),
    Label('fence', 13, 4, 'construction', 2, False, False, (190, 153, 153)),
    Label('guard rail', 14, 19, 'construction', 2, False, True, (180, 165, 180)),
    Label('bridge', 15, 19, 'construction', 2, False, True, (150, 100, 100)),
    Label('tunnel', 16, 19, 'construction', 2, False, True, (150, 120, 90)),
    Label('pole', 17, 5, 'object', 3, False, False, (153, 153, 153)),
    Label('polegroup', 18, 19, 'object', 3, False, True, (153, 153, 153)),
    Label('traffic light', 19, 6, 'object', 3, False, False, (250, 170, 30)),
    Label('traffic sign', 20, 7, 'object', 3, False, False, (220, 220, 0)),
    Label('vegetation', 21, 8, 'nature', 4, False, False, (107, 142, 35)),
    Label('terrain', 22, 9, 'nature', 4, False, False, (152, 251, 152)),
    Label('sky', 23, 10, 'sky', 5, False, False, (70, 130, 180)),
    Label('person', 24, 11, 'human', 6, True, False, (220, 20, 60)),
    Label('rider', 25, 12, 'human', 6, True, False, (255, 0, 0)),
    Label('car', 26, 13, 'vehicle', 7, True, False, (0, 0, 142)),
    Label('truck', 27, 14, 'vehicle', 7, True, False, (0, 0, 70)),
    Label('bus', 28, 15, 'vehicle', 7, True, False, (0, 60, 100)),
    Label('caravan', 29, 19, 'vehicle', 7, True, True, (0, 0, 90)),
    Label('trailer', 30, 19, 'vehicle', 7, True, True, (0, 0, 110)),
    Label('train', 31, 16, 'vehicle', 7, True, False, (0, 80, 100)),
    Label('motorcycle', 32, 17, 'vehicle', 7, True, False, (0, 0, 230)),
    Label('bicycle', 33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
    Label('license plate', -1, -1, 'vehicle', 7, False, True, (0, 0, 142)),
]


def trainIdToColor(trainId: int):
    for l in labels:
        if l.trainId == trainId:
            color = l.color
            break
    return color


def colortoTrainId(rgbColor):
    trainId = 0
    for l in labels:
        if l.color == rgbColor:
            trainId = l.trainId
            break
    return trainId


def gray2color(grayImage:np.ndarray,num_class:int):
    rgbImage=np.zeros((grayImage.shape[0],grayImage.shape[1],3),dtype='uint8')
    for cls in range(num_class):
        row,col=np.where(grayImage==cls)
        if (len(row)==0):
            continue
        color=trainIdToColor(cls)
        rgbImage[row,col]=color
    return rgbImage


def color2gray(colorImage:np.ndarray, bgr_color_space:bool):
    if bgr_color_space:
        colorImage = cv2.cvtColor(colorImage, cv2.COLOR_BGR2RGB)


    unique_color=np.unique(colorImage.reshape(-1, colorImage.shape[2]), axis=0)
    gray=np.zeros((colorImage.shape[0],colorImage.shape[1]),dtype=np.float32)
    for uc in unique_color:

        where_cond1= np.logical_and(colorImage[:,:,0]==uc[0],
                                    colorImage[:,:,1]==uc[1],
                                    colorImage[:,:,2]==uc[2])
        row,col=np.where(where_cond1)
        gray[row,col]=colortoTrainId(tuple(uc))

    return gray

当我使用gray2color 时一切正常。 但是当我尝试通过color2gray转换 RGB 图像时，它会转换但结果与原始灰度图像不同。 （把19s改成13，其他班没问题） 。 我多次检查代码，但我不知道为什么我得到了不好的结果。
为了澄清
正如您在返回的灰度中看到的那样，没有 19 值，全部加起来为 13 秒。

original grayscale:
unique: 0   1   2   4   5   7   8   10  11  13  19
count:  624649  168701  819940  2802    24885   12192   42082   37098   6791    115270  242742

returned grayscale:
unique: 0   1   2   4   5   7   8   10  11  13
count:  624649  168701  819940  2802    24885   12192   42082   37098   6791    358012

还有color2gray函数很慢很费时间！

Answer 1

您的 trainId 值太低，这就是您的灰度图像非常暗的原因。 增加 Labels 列表中 trainId 的值，并使您的灰度图像数据类型为 uint8

def color2gray(colorImage:np.ndarray, bgr_color_space:bool):
    if bgr_color_space:
        colorImage = cv2.cvtColor(colorImage, cv2.COLOR_BGR2RGB)


    gray=np.zeros((colorImage.shape[0],colorImage.shape[1]),dtype=np.uint8)

    for row in range(colorImage.shape[0]):
        for col in range(colorImage.shape[1]):
            # either multiply the result of colortoTrainId(tuple(uc)) with some number
            # or change the value of trainId in the Labels list
            gray[row,col]=colortoTrainId(tuple(colorImage[row,col]))*10

    return gray

Answer 2

我自己找到了 1 个问题的答案

np.logical_and()只需要两个数组

所以它必须像这样嵌套使用

where_cond1=np.logical_and(np.logical_and(colorImage[:,:,0]==uc[0],colorImage[:,:,1]==uc[1]), colorImage[:,:,2]==uc[2])

但速度问题仍然存在，知道如何矢量化分配吗？