因為分割的標簽是一個彩色圖像，而不是理想中的每一個像素就是他的類別，所以我們需要處理一下

調色板

如果你用pillow讀，就會發現他的模式是P,代表著他用調色板把灰度圖映射成彩色圖了，所以首先要獲取調色板

pillow

簡單來說就是讀取任意標簽，然后獲取他的調色板

#!/usr/bin/env python # _*_ coding:utf-8 _*_ import numpy as np from PIL import Imageif __name__ == '__main__':# 任意標簽label_path ='/data/datasets/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png'palette = np.array(Image.open(label_path).getpalette()).reshape((-1, 3))print(palette[:21])

官方matlab

在這個鏈接里可以找到http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar
VOCdevkit_18-May-2011/VOCdevkit/VOCcode/VOClabelcolormap.m

% VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different % colors. Useful for reading and writing index images which contain large indices, % by encoding them as RGB images. % % CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries. function cmap = labelcolormap(N)if nargin==0N=256 end cmap = zeros(N,3); for i=1:Nid = i-1; r=0;g=0;b=0;for j=0:7r = bitor(r, bitshift(bitget(id,1),7 - j));g = bitor(g, bitshift(bitget(id,2),7 - j));b = bitor(b, bitshift(bitget(id,3),7 - j));id = bitshift(id,-3);endcmap(i,1)=r; cmap(i,2)=g; cmap(i,3)=b; end cmap = cmap / 255;

結論

這里除了原本的20類+背景，還有一個邊界

VOC_COLORMAP = [[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], [192, 0, 0], [64, 128, 0], [192, 128, 0],[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],[0, 64, 128], [224, 224, 192]]VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat','bottle', 'bus', 'car', 'cat', 'chair', 'cow','diningtable', 'dog', 'horse', 'motorbike', 'person','potted plant', 'sheep', 'sofa', 'train', 'tv/monitor', 'void']

http://host.robots.ox.ac.uk/pascal/VOC/voc2012/segexamples/index.html
https://zhuanlan.zhihu.com/p/102303256

映射

因為python不能把數組映射成數字，所以要把(r,g,b)編碼成一個數字，可以考慮
$r<<16|g<<8|b=r?256?256+g?256+b$
然后再映射

第一種方法

做一個數組，包含所有的顏色的映射，然后把圖片輸進去就可以了
這里注意圖片一定要轉成int（不然貌似會超出uint8）
我這里把邊界映射成了0（就是注釋掉的21那里）

#!/usr/bin/env python # _*_ coding:utf-8 _*_ import numpy as np from cv2 import cv2VOC_COLORMAP = [[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], [192, 0, 0], [64, 128, 0], [192, 128, 0],[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],[0, 64, 128], [224, 224, 192]]VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat','bottle', 'bus', 'car', 'cat', 'chair', 'cow','diningtable', 'dog', 'horse', 'motorbike', 'person','potted plant', 'sheep', 'sofa', 'train', 'tv/monitor', 'void']if __name__ == '__main__':label_path ='/data/datasets/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png'label_color_map = np.zeros(1 << 24, dtype=np.uint8)for i, (r, g, b) in enumerate(VOC_COLORMAP[:21]):# label_color_map[r << 16 | g << 8 | b] = ilabel_color_map[b << 16 | g << 8 | r] = ir, g, b = VOC_COLORMAP[-1]# label_color_map[r << 16 | g << 8 | b] = 21# label_color_map[r << 16 | g << 8 | b] = 0# label_color_map[b << 16 | g << 8 | r] = 21label_color_map[b << 16 | g << 8 | r] = 0img = cv2.imread(label_path).astype(np.int64)result = label_color_map[img[..., 0] << 16 | img[..., 1] << 8 | img[..., 2]].astype(np.uint8)print(np.unique(result))

第二種方法

其實和第一種差不多，只是用了向量化（其實我也不知道會不會更快）

#!/usr/bin/env python # _*_ coding:utf-8 _*_ import numpy as np from cv2 import cv2VOC_COLORMAP = [[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], [192, 0, 0], [64, 128, 0], [192, 128, 0],[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],[0, 64, 128], [224, 224, 192]]VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat','bottle', 'bus', 'car', 'cat', 'chair', 'cow','diningtable', 'dog', 'horse', 'motorbike', 'person','potted plant', 'sheep', 'sofa', 'train', 'tv/monitor', 'void']if __name__ == '__main__':label_path ='/data/datasets/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png'label_color_map = {}for i, (r, g, b) in enumerate(VOC_COLORMAP[:21]):# label_color_map[r << 16 | g << 8 | b] = ilabel_color_map[b << 16 | g << 8 | r] = ir, g, b = VOC_COLORMAP[-1]# label_color_map[r << 16 | g << 8 | b] = 21# label_color_map[r << 16 | g << 8 | b] = 0# label_color_map[b << 16 | g << 8 | r] = 21label_color_map[b << 16 | g << 8 | r] = 0bgr2label = np.vectorize(label_color_map.get)img = cv2.imread(label_path).astype(np.int64)result = bgr2label(img[..., 0] << 16 | img[..., 1] << 8 | img[..., 2]).astype(np.uint8)print(np.unique(result))

保存圖片

因為最后讓你交的圖片還是要調色板，但是我們模型輸出的一般是0-21的，所以需要調整一下

def get_palette():return [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, 0,192, 0, 0, 64, 128, 0, 192, 128, 0, 64, 0, 128, 192, 0, 128, 64, 128, 128, 192, 128, 128, 0, 64, 0, 128, 64,0, 0, 192, 0, 128, 192, 0, 0, 64, 128, 128, 64, 128, 0, 192, 128, 128, 192, 128, 64, 64, 0, 192, 64, 0, 64,192, 0, 192, 192, 0, 64, 64, 128, 192, 64, 128, 64, 192, 128, 192, 192, 128, 0, 0, 64, 128, 0, 64, 0, 128,64, 128, 128, 64, 0, 0, 192, 128, 0, 192, 0, 128, 192, 128, 128, 192, 64, 0, 64, 192, 0, 64, 64, 128, 64,192, 128, 64, 64, 0, 192, 192, 0, 192, 64, 128, 192, 192, 128, 192, 0, 64, 64, 128, 64, 64, 0, 192, 64, 128,192, 64, 0, 64, 192, 128, 64, 192, 0, 192, 192, 128, 192, 192, 64, 64, 64, 192, 64, 64, 64, 192, 64, 192,192, 64, 64, 64, 192, 192, 64, 192, 64, 192, 192, 192, 192, 192, 32, 0, 0, 160, 0, 0, 32, 128, 0, 160, 128,0, 32, 0, 128, 160, 0, 128, 32, 128, 128, 160, 128, 128, 96, 0, 0, 224, 0, 0, 96, 128, 0, 224, 128, 0, 96,0, 128, 224, 0, 128, 96, 128, 128, 224, 128, 128, 32, 64, 0, 160, 64, 0, 32, 192, 0, 160, 192, 0, 32, 64,128, 160, 64, 128, 32, 192, 128, 160, 192, 128, 96, 64, 0, 224, 64, 0, 96, 192, 0, 224, 192, 0, 96, 64, 128,224, 64, 128, 96, 192, 128, 224, 192, 128, 32, 0, 64, 160, 0, 64, 32, 128, 64, 160, 128, 64, 32, 0, 192,160, 0, 192, 32, 128, 192, 160, 128, 192, 96, 0, 64, 224, 0, 64, 96, 128, 64, 224, 128, 64, 96, 0, 192, 224,0, 192, 96, 128, 192, 224, 128, 192, 32, 64, 64, 160, 64, 64, 32, 192, 64, 160, 192, 64, 32, 64, 192, 160,64, 192, 32, 192, 192, 160, 192, 192, 96, 64, 64, 224, 64, 64, 96, 192, 64, 224, 192, 64, 96, 64, 192, 224,64, 192, 96, 192, 192, 224, 192, 192, 0, 32, 0, 128, 32, 0, 0, 160, 0, 128, 160, 0, 0, 32, 128, 128, 32,128, 0, 160, 128, 128, 160, 128, 64, 32, 0, 192, 32, 0, 64, 160, 0, 192, 160, 0, 64, 32, 128, 192, 32, 128,64, 160, 128, 192, 160, 128, 0, 96, 0, 128, 96, 0, 0, 224, 0, 128, 224, 0, 0, 96, 128, 128, 96, 128, 0, 224,128, 128, 224, 128, 64, 96, 0, 192, 96, 0, 64, 224, 0, 192, 224, 0, 64, 96, 128, 192, 96, 128, 64, 224, 128,192, 224, 128, 0, 32, 64, 128, 32, 64, 0, 160, 64, 128, 160, 64, 0, 32, 192, 128, 32, 192, 0, 160, 192, 128,160, 192, 64, 32, 64, 192, 32, 64, 64, 160, 64, 192, 160, 64, 64, 32, 192, 192, 32, 192, 64, 160, 192, 192,160, 192, 0, 96, 64, 128, 96, 64, 0, 224, 64, 128, 224, 64, 0, 96, 192, 128, 96, 192, 0, 224, 192, 128, 224,192, 64, 96, 64, 192, 96, 64, 64, 224, 64, 192, 224, 64, 64, 96, 192, 192, 96, 192, 64, 224, 192, 192, 224,192, 32, 32, 0, 160, 32, 0, 32, 160, 0, 160, 160, 0, 32, 32, 128, 160, 32, 128, 32, 160, 128, 160, 160, 128,96, 32, 0, 224, 32, 0, 96, 160, 0, 224, 160, 0, 96, 32, 128, 224, 32, 128, 96, 160, 128, 224, 160, 128, 32,96, 0, 160, 96, 0, 32, 224, 0, 160, 224, 0, 32, 96, 128, 160, 96, 128, 32, 224, 128, 160, 224, 128, 96, 96,0, 224, 96, 0, 96, 224, 0, 224, 224, 0, 96, 96, 128, 224, 96, 128, 96, 224, 128, 224, 224, 128, 32, 32, 64,160, 32, 64, 32, 160, 64, 160, 160, 64, 32, 32, 192, 160, 32, 192, 32, 160, 192, 160, 160, 192, 96, 32, 64,224, 32, 64, 96, 160, 64, 224, 160, 64, 96, 32, 192, 224, 32, 192, 96, 160, 192, 224, 160, 192, 32, 96, 64,160, 96, 64, 32, 224, 64, 160, 224, 64, 32, 96, 192, 160, 96, 192, 32, 224, 192, 160, 224, 192, 96, 96, 64,224, 96, 64, 96, 224, 64, 224, 224, 64, 96, 96, 192, 224, 96, 192, 96, 224, 192, 224, 224, 192]im = Image.fromarray(output, mode='P') # output為灰度圖（0-21） im.putpalette(get_palette()) im.save(os.path.join(target_path, filename + '.png')

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