单个样本 4 个数据：[color, instanceIds, labelIds, polygons]
查看图片重包含的 种类 id 和 实例 id
import cv2
import os
import numpy as np
ROOT = '/nfs/xs/Cityscapes/gtFine/train/bremen'
color_img = cv2.imread(os.path.join(ROOT, 'bremen_000000_000019_gtFine_color.png'))
instanceId_img = cv2.imread(os.path.join(ROOT, 'bremen_000000_000019_gtFine_instanceIds.png'), cv2.IMREAD_ANYDEPTH)
labelId_img = cv2.imread(os.path.join(ROOT, 'bremen_000000_000019_gtFine_labelIds.png'), cv2.IMREAD_ANYDEPTH)
print(color_img.shape) # (1024, 2048, 3)
# np img -> tuple list
colors = [tuple(color_img[i][j]) for i in range(1024) for j in range(2048)]
print(len(colors)) # 2097152
colors = set(colors) # remove duplicate colors
print(len(colors)) # 11
print(colors)
# {(153, 153, 153), (142, 0, 0), (0, 220, 220), (232, 35, 244), (70, 70, 70),
# (152, 251, 152), (0, 0, 0), (128, 64, 128), (35, 142, 107), (32, 11, 119), (180, 130, 70)}
print(instanceId_img.shape) # (1024, 2048)
print(np.unique(instanceId_img))
# [ 1 3 4 7 8 11 17 20 21 22 23
# 26000 26001 26002 26003 # 26, 4 instances
# 33000 33001 33002] # 33, 3 instances
# x1000 can clearly show on depth_16 image
print(labelId_img.shape) # (1024, 2048)
print(np.unique(labelId_img))
# [ 1 3 4 7 8 11 17 20 21 22 23 26 33]
instanceId 在处理多个实例时，先将 classId ×10，再累计 +1，好处是 ×10 后的实例灰度值更大，在 depth_16 上更明显。
bremen_000000_000019_gtFine_instanceIds.png
Cityscapes 原有 labels
labels = [
# name id trainId category catId hasInstances ignoreInEval color
Label( 'unlabeled' , 0 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'ego vehicle' , 1 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'rectification border' , 2 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'out of roi' , 3 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'static' , 4 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'dynamic' , 5 , 255 , 'void' , 0 , False , True , (111, 74, 0) ),
Label( 'ground' , 6 , 255 , '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 , 255 , 'flat' , 1 , False , True , (250,170,160) ),
Label( 'rail track' , 10 , 255 , '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 , 255 , 'construction' , 2 , False , True , (180,165,180) ),
Label( 'bridge' , 15 , 255 , 'construction' , 2 , False , True , (150,100,100) ),
Label( 'tunnel' , 16 , 255 , 'construction' , 2 , False , True , (150,120, 90) ),
Label( 'pole' , 17 , 5 , 'object' , 3 , False , False , (153,153,153) ),
Label( 'polegroup' , 18 , 255 , '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 , 255 , 'vehicle' , 7 , True , True , ( 0, 0, 90) ),
Label( 'trailer' , 30 , 255 , '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) ),
]
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