cityscapes数据集_全景分割 UPSNet 源码分析 (1) - 数据格式
本系列文章针对的数据集是Cityscapes，后续会在Mapillary vista街道数据集上尝试训练出一版模型。
1. 数据集和标注文件
Cityscapes文件夹下
├── annotations 【三个标注文件】
├── disparity 【额外下载的深度数据集】
├── gtFine 【语义分割和实例分割标注图片】
├── images 【作者脚本生成的文件夹，内含5000张图像】
├── labels 【作者脚本生成的文件夹，内含2975+500张标注图像】
├── leftImg8bit 【原始图像】
└── panoptic 【全景分割标注图像】1.1 annotations文件夹
├── cityscapes_fine_val.json
├── instancesonly_gtFine_train.json
└── instancesonly_gtFine_val.jsoninstancesonly_gtFine_train.json是训练集数据的实例标注, 格式如下：
{
"images": list, 存放图像的信息
"categories": list, 存放类别信息
"annotations": list， 存放实例的标注信息
}i）image中的数据示例：
[
{"id": 500,
"width": 2048,
"height": 1024,
"file_name": "aachen_000000_000019_leftImg8bit.png",
"seg_file_name": "aachen_000000_000019_gtFine_instanceIds.png"}
{}
...
]ii）categories：
[
{"id": 1,
"name": 'person'},
{"id": 2,
"name": 'rider'},
{},
...
]iii) annotations
[
{'id': 10118,
'image_id': 500,
'segmentation': [[903, 446, 902, 447, 901, 448, 900, 449, 900, 450, 899
'category_id': 1,
'iscrowd': 0,
'area': 665,
'bbox': [892.0, 446.0, 21.0, 52.0]},
{},
...
]- 同一张图像，可有多个标注，对应不同的实例物体。
- segmentation的标注方式为polygon，每两个数字对应一个坐标，例如坐标（903， 446）是该实例mask边缘的第一个点。同样，同一个实例的mask可能由多个polygon组成。
1.2 images
示例：
1.3 labels
255表示空类，其他非空类的数值是各自的id，由于灰度值很小，所以看起来像全黑的....
示例：
1.4 panoptic
示例
2. 模型输入数据格式
2.1 初始化
首先作者利用coco数据集的处理脚本，在Cityscapes类中初始化生成了roidb，该list存放了每张训练图像的标注信息，具体如下：
[
{'id': 500,
'width': 2048,
'height': 1024,
'file_name': 'aachen_000000_000019_leftImg8bit.png',
'seg_file_name': 'aachen_000000_000019_gtFine_instanceIds.png',
'dataset': ,
'image': '/home/lin/Documents/dataset/Cityscapes/images/aachen_000000_000019_leftImg8bit.png',
'flipped': False,
'has_visible_keypoints': False,
'boxes': array([[ 892., 446., 912., 497.],
[ 901., 444., 934., 497.],
[1831., 440., 1887., 547.],
[1966., 488., 2042., 521.],
[1512., 446., 1660., 498.],
[1479., 456., 1634., 513.],
[1876., 482., 1921., 486.],
[1876., 486., 1955., 512.],
[1721., 450., 1850., 508.],
[ 145., 429., 304., 501.],
[ 609., 420., 807., 531.],
[ 792., 446., 828., 489.],
[1839., 468., 1877., 546.]], dtype=float32),
'segms': [[[903, 446, 902, 447, 901, 448, 900, 449, 900, 450, 899, 451, 899, 452, 899, 453, 899, 454, 899, 455, 898, 456, 898, 457, 897, 458, 896, 458, 895, 459, 894, 459, 894, 460, 894, 461, 894, 462, 894, 463, 894, 464, 894, 465, 894, 466, 893, 467, 893, 468, 893, 469, 892, 470, 892, 471, 892, 472, 892, 473, 892, 474, 892, 475, 892, 476, 892, 477, 892, 478, 892, 479, 892, 480, 892, 481, 892, 482, 892, 483, 892, 484, 892, 485, 892, 486, 892, 487, 892, 488, 892, 489, 892, 490, 892, 491, 893, 492, 893, 493, 893, 494, 893, 495, 893, 496, 894, 496, 895, 496, 896, 496, 897, 496, 898, 496, 899, 496, 900, 497, 901, 497, 902, 497, 903, 497, 904, 497, 903, 496, 902, 495, 902, 494, 901, 493, 901, 492, 901, 491, 900, 490, 900, 489, 900, 488, 900, 487, 900, 486, 901, 485, 901, 484, 901, 483, 902, 482, 903, 481, 904, 480, 905, 479, 906, 479, 907, 478, 908, 477, 909, 476, 910, 475, 911, 475, 911, 474, 911, 473, 911, 472, 911, 471, 911, 470, 911, 469, 911, 468, 911, 467, 911, 466, 911, 465, 912, 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5248., 8739., 17424., 830., 1479.], dtype=float32),
'gt_overlaps': <13x9 sparse matrix of type ''
with 13 stored elements in Compressed Sparse Row format>,
'is_crowd': array([False, False, False, False, False, False, False, False, False,
False, False, False, False]),
'box_to_gt_ind_map': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=int32),
'max_classes': array([1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8]),
'max_overlaps': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32),
'bbox_targets': array([[1., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[2., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[3., 0., 0., 0., 0.],
[8., 0., 0., 0., 0.],
[8., 0., 0., 0., 0.]], dtype=float32)},
{},
...
] 重要字段解释：
- boxes：该张图片中所有实例的bbox
- segms：该张图片中每个实例的mask
- bbox_targets: 作为计算边框回归和分类损失时的label，第一位数字是该box下物体的类别，后四位数字是边框位置微调的变换参数。因为网络不是直接对边框的坐标进行回归，而是回归anchor微调至gt的变换参数（具体如何变换，后续文章会讲到）。由于本身就是gt，所以变换参数都为0。
2.2 __getitem__ 对每个roidb执行的操作
（1）首先，为图像随机选择一个预设的尺寸，缩放图像，翻转图像（视roidb中flip参数而定），归一化图像，将图片格式从HWC转换成CHW;
（2）其次，制作RPN的标签， 核心函数add_rpn_blobs.
① 在不同的特征图上生成anchors。不同的特征图对应不同的anchor_base_size。
生成的all anchor如下表所示。尽管只使用了一种scale，但配合FPN使用了不同大小的anchor_base_size,同样得到了不同大小的anchor，以检测不同大小的物体。
foa即Field of Anchor， 数据格式及含义如下:
{
"field of anchors": 二维数组，图片上anchor的坐标,
"number_cell_anchors": 3
"stride": 即rpn_feat_stride, 同样是特征图相比于原图的缩小倍数，也即特征图上移动一个pixel对应原图上移动的pixel数
"octave": None
"aspect": None
}② 确定正负样本
- 计算每一个anchor和gt box的重叠面积。
- 对于每一个gt box， 都有与它重叠面积最大的anchor，将其label设置为1；
- 对于与gt box 重叠面积超过阈值（0.7）的，将其label设置为1；
- 根据设置的正负样本总和（N）和正样本比例系数（
）这两个超参数进行正负样本的筛选；
- 正样本筛选：正样本最多个数为总和乘以正样本比例（
），若当前正样本个数（N1）超过规定的最多个数，则进行随机筛选。
- 负样本筛选：从诸多负样本中筛选出
个， 将label设置为0。其余非正负样本的label设置为-1
③ 计算正样本的边框变换系数和inside，outside权重
- bbox_inside_weights指用于计算边框回归损失时的权重，由于只计算正样本的损失，所以只将正样本的bbox_inside_weights设置为[1, 1, 1,1],其余设置成[0,0,0,0]
- bbox_outside_weights是为了平衡分类损失和回归损失而设置的参数，其大小为正负样本个数分之一，即
.
（3）最后制作分割的标签
分割的标签包含多个部分，一一列举如下。
- ① seg gt：语义分割的gt图像，每个像素的值为对应的trainId
- ② gt classes： 每一个box的类别
- ③ mask gt ：每一个box内物体的mask， 值为0， 1
- ④ seg roi gt： seg gt 在box内的部分
3. 数据输出
整个Cityscapes类最终输出包含data、label、index
data：
{
"data": ndarray, shape(3, H, W), # 预处理后的图像
"img_info": [[H, W, scale_factor]]
}label:
{'roidb':
{'boxes': array, shape=（17，4）
'segms': list， len=17， list里每个元素是object的polygon标注，同一个object可能包含多个polygon
'seg_areas': ndarray 面积
'gt_classes': array([1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], dtype=int32),
'gt_overlaps':
'is_crowd':
'box_to_gt_ind_map': }
'rpn_labels_fpn4': ndarray， shape=（1， 3， 512， 512） 每一个anchor的前后景标注
'rpn_bbox_targets_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一个anchor边框的回归目标，非前景为0
'rpn_bbox_inside_weights_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一个anchor边框的回归目标，非前景为[0, 0, 0, 0], 前景为[1, 1, 1,1]
'rpn_bbox_outside_weights_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一个anchor边框的回归目标，非前景为[0, 0, 0, 0], 前景为[1, 1, 1,1]/正负样本总和
'rpn_labels_fpn8': 与fpn4相似，宽高变成256
'rpn_bbox_targets_fpn8':
'rpn_bbox_inside_weights_fpn8':
'rpn_bbox_outside_weights_fpn8':
'rpn_labels_fpn16':128
'rpn_bbox_targets_fpn16':
'rpn_bbox_inside_weights_fpn16':
'rpn_bbox_outside_weights_fpn16':
'rpn_labels_fpn32':64
'rpn_bbox_targets_fpn32':
'rpn_bbox_inside_weights_fpn32':
'rpn_bbox_outside_weights_fpn32':
'rpn_labels_fpn64':32
'rpn_bbox_targets_fpn64':
'rpn_bbox_inside_weights_fpn64':
'rpn_bbox_outside_weights_fpn64':
'seg_gt': ndarray shape（H， W）
'gt_classes': array([1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], dtype=int32),
'mask_gt': ndarray， shape（17， H， W），object的语义标注， 值为0,1}