本系列文章針對(duì)的數(shù)據(jù)集是Cityscapes，后續(xù)會(huì)在Mapillary vista街道數(shù)據(jù)集上嘗試訓(xùn)練出一版模型。

1. 數(shù)據(jù)集和標(biāo)注文件

Cityscapes文件夾下

├── annotations 【三個(gè)標(biāo)注文件】 ├── disparity 【額外下載的深度數(shù)據(jù)集】 ├── gtFine 【語(yǔ)義分割和實(shí)例分割標(biāo)注圖片】 ├── images 【作者腳本生成的文件夾，內(nèi)含5000張圖像】 ├── labels 【作者腳本生成的文件夾，內(nèi)含2975+500張標(biāo)注圖像】 ├── leftImg8bit 【原始圖像】 └── panoptic 【全景分割標(biāo)注圖像】

1.1 annotations文件夾

├── cityscapes_fine_val.json ├── instancesonly_gtFine_train.json └── instancesonly_gtFine_val.json

instancesonly_gtFine_train.json是訓(xùn)練集數(shù)據(jù)的實(shí)例標(biāo)注, 格式如下：

{ "images": list, 存放圖像的信息 "categories": list, 存放類(lèi)別信息 "annotations": list， 存放實(shí)例的標(biāo)注信息 }

i）image中的數(shù)據(jù)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]},{},... ]

• 同一張圖像，可有多個(gè)標(biāo)注，對(duì)應(yīng)不同的實(shí)例物體。
• segmentation的標(biāo)注方式為polygon，每?jī)蓚€(gè)數(shù)字對(duì)應(yīng)一個(gè)坐標(biāo)，例如坐標(biāo)（903， 446）是該實(shí)例mask邊緣的第一個(gè)點(diǎn)。同樣，同一個(gè)實(shí)例的mask可能由多個(gè)polygon組成。

1.2 images

示例：

1.3 labels

255表示空類(lèi)，其他非空類(lèi)的數(shù)值是各自的id，由于灰度值很小，所以看起來(lái)像全黑的....

示例：

1.4 panoptic

示例

2. 模型輸入數(shù)據(jù)格式

2.1 初始化

首先作者利用coco數(shù)據(jù)集的處理腳本，在Cityscapes類(lèi)中初始化生成了roidb，該list存放了每張訓(xùn)練圖像的標(biāo)注信息，具體如下：

[{'id': 500, 'width': 2048, 'height': 1024, 'file_name': 'aachen_000000_000019_leftImg8bit.png', 'seg_file_name': 'aachen_000000_000019_gtFine_instanceIds.png', 'dataset': <upsnet.dataset.json_dataset.JsonDataset object at 0x7ffa067a97f0>, '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, 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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)},{},... ]

重要字段解釋：

• boxes：該張圖片中所有實(shí)例的bbox
• segms：該張圖片中每個(gè)實(shí)例的mask
• bbox_targets: 作為計(jì)算邊框回歸和分類(lèi)損失時(shí)的label，第一位數(shù)字是該box下物體的類(lèi)別，后四位數(shù)字是邊框位置微調(diào)的變換參數(shù)。因?yàn)榫W(wǎng)絡(luò)不是直接對(duì)邊框的坐標(biāo)進(jìn)行回歸，而是回歸anchor微調(diào)至gt的變換參數(shù)（具體如何變換，后續(xù)文章會(huì)講到）。由于本身就是gt，所以變換參數(shù)都為0。

2.2 __getitem__ 對(duì)每個(gè)roidb執(zhí)行的操作

（1）首先，為圖像隨機(jī)選擇一個(gè)預(yù)設(shè)的尺寸，縮放圖像，翻轉(zhuǎn)圖像（視r(shí)oidb中flip參數(shù)而定），歸一化圖像，將圖片格式從HWC轉(zhuǎn)換成CHW;

（2）其次，制作RPN的標(biāo)簽， 核心函數(shù)add_rpn_blobs.

① 在不同的特征圖上生成anchors。不同的特征圖對(duì)應(yīng)不同的anchor_base_size。

生成的all anchor如下表所示。盡管只使用了一種scale，但配合FPN使用了不同大小的anchor_base_size,同樣得到了不同大小的anchor，以檢測(cè)不同大小的物體。

foa即Field of Anchor， 數(shù)據(jù)格式及含義如下:

{"field of anchors": 二維數(shù)組，圖片上anchor的坐標(biāo),"number_cell_anchors": 3"stride": 即rpn_feat_stride, 同樣是特征圖相比于原圖的縮小倍數(shù)，也即特征圖上移動(dòng)一個(gè)pixel對(duì)應(yīng)原圖上移動(dòng)的pixel數(shù)"octave": None"aspect": None }

② 確定正負(fù)樣本

• 計(jì)算每一個(gè)anchor和gt box的重疊面積。
• 對(duì)于每一個(gè)gt box， 都有與它重疊面積最大的anchor，將其label設(shè)置為1；
• 對(duì)于與gt box 重疊面積超過(guò)閾值（0.7）的，將其label設(shè)置為1；
• 根據(jù)設(shè)置的正負(fù)樣本總和（N）和正樣本比例系數(shù)（ ）這兩個(gè)超參數(shù)進(jìn)行正負(fù)樣本的篩選；
• 正樣本篩選：正樣本最多個(gè)數(shù)為總和乘以正樣本比例（ ），若當(dāng)前正樣本個(gè)數(shù)（N1）超過(guò)規(guī)定的最多個(gè)數(shù)，則進(jìn)行隨機(jī)篩選。
• 負(fù)樣本篩選：從諸多負(fù)樣本中篩選出 個(gè)， 將label設(shè)置為0。其余非正負(fù)樣本的label設(shè)置為-1

③ 計(jì)算正樣本的邊框變換系數(shù)和inside，outside權(quán)重

• bbox_inside_weights指用于計(jì)算邊框回歸損失時(shí)的權(quán)重，由于只計(jì)算正樣本的損失，所以只將正樣本的bbox_inside_weights設(shè)置為[1, 1, 1,1],其余設(shè)置成[0,0,0,0]
• bbox_outside_weights是為了平衡分類(lèi)損失和回歸損失而設(shè)置的參數(shù)，其大小為正負(fù)樣本個(gè)數(shù)分之一，即 .

（3）最后制作分割的標(biāo)簽

分割的標(biāo)簽包含多個(gè)部分，一一列舉如下。

• ① seg gt：語(yǔ)義分割的gt圖像，每個(gè)像素的值為對(duì)應(yīng)的trainId
• ② gt classes： 每一個(gè)box的類(lèi)別
• ③ mask gt ：每一個(gè)box內(nèi)物體的mask， 值為0， 1
• ④ seg roi gt： seg gt 在box內(nèi)的部分

3. 數(shù)據(jù)輸出

整個(gè)Cityscapes類(lèi)最終輸出包含data、label、index

data：

{"data": ndarray, shape(3, H, W), # 預(yù)處理后的圖像"img_info": [[H, W, scale_factor]] }

label:

{'roidb': {'boxes': array, shape=（17，4）'segms': list， len=17， list里每個(gè)元素是object的polygon標(biāo)注，同一個(gè)object可能包含多個(gè)polygon'seg_areas': ndarray 面積'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） 每一個(gè)anchor的前后景標(biāo)注'rpn_bbox_targets_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一個(gè)anchor邊框的回歸目標(biāo)，非前景為0'rpn_bbox_inside_weights_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一個(gè)anchor邊框的回歸目標(biāo)，非前景為[0, 0, 0, 0], 前景為[1, 1, 1,1]'rpn_bbox_outside_weights_fpn4': ndrray， shape=（1， 12， 512， 512）， 每一個(gè)anchor邊框的回歸目標(biāo)，非前景為[0, 0, 0, 0], 前景為[1, 1, 1,1]/正負(fù)樣本總和'rpn_labels_fpn8': 與fpn4相似，寬高變成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的語(yǔ)義標(biāo)注， 值為0,1}

總結(jié)

以上是生活随笔為你收集整理的cityscapes数据集_全景分割 UPSNet 源码分析 (1) - 数据格式的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

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