MsCOCO数据集标注详解

COCO数据集的标注格式

COCO数据集的介绍：全称是Common Objects in COntext，是微软团队提供的一个可以用来进行图像识别的数据集。MS COCO数据集中的图像分为训练、验证和测试集。COCO通过在Flickr上搜索80个对象类别和各种场景类型来收集图像，其使用了亚马逊的Mechanical Turk（AMT）。

COCO数据集的标注类型：object instances（目标实例）, object keypoints（目标上的关键点）, and image captions（看图说话）

COCO数据集存储方式：数据集以json文件存储，每个标注类型有训练和验证两个数据集划分，因此3种标注类型共有6个jason文件。

基本的JSON结构体类型

每个json文件又包括info、image、license、annotation四个部分，其中annotation（图像标注）的格式每种标注类型各不相同。

{
    "info": info,
    "licenses": [license],
    "images": [image],
    "annotations": [annotation],
}
    
info{
    "year": int,
    "version": str,
    "description": str,
    "contributor": str,
    "url": str,
    "date_created": datetime,
}
license{
    "id": int,
    "name": str,
    "url": str,
} 
image{
    "id": int,
    "width": int,
    "height": int,
    "file_name": str,
    "license": int,
    "flickr_url": str,
    "coco_url": str,
    "date_captured": datetime,
}

object instances的标注格式

1. 整体json文件格式：

{
    "info": info,
    "licenses": [license],
    "images": [image],
    "annotations": [annotation],
    "categories": [category]
}

2. annotation格式：
annotations字段是包含多个annotation实例的一个数组，annotation类型本身又包含了一系列的字段，如这个目标的category id和segmentation mask。segmentation格式取决于这个实例是一个单个的对象（即iscrowd=0，将使用polygons格式）还是一组对象（即iscrowd=1，将使用RLE格式）。如下所示：

annotation{
    "id": int,
    "image_id": int,
    "category_id": int,
    "segmentation": RLE or [polygon],
    "area": float,
    "bbox": [x,y,width,height],
    "iscrowd": 0 or 1,
}

单个的对象（iscrowd=0)可能需要多个polygon来表示，比如这个对象在图像中被挡住了。而iscrowd=1时（将标注一组对象，比如一群人）的segmentation使用的就是RLE格式。
另外，每个对象（不管是iscrowd=0还是iscrowd=1）都会有一个矩形框bbox ，矩形框左上角的坐标和矩形框的长宽会以数组的形式提供，数组第一个元素就是左上角的横坐标值。
area是area of encoded masks。
最后，annotation结构中的categories字段存储的是当前对象所属的category的id，以及所属的supercategory的name。
下面是从instances_val2017.json文件中摘出的一个annotation的实例：

{
	"segmentation": [[510.66,423.01,511.72,420.03,510.45,416.0,510.34,413.02,510.77,410.26,\
			510.77,407.5,510.34,405.16,511.51,402.83,511.41,400.49,510.24,398.16,509.39,\
			397.31,504.61,399.22,502.17,399.64,500.89,401.66,500.47,402.08,499.09,401.87,\
			495.79,401.98,490.59,401.77,488.79,401.77,485.39,398.58,483.9,397.31,481.56,\
			396.35,478.48,395.93,476.68,396.03,475.4,396.77,473.92,398.79,473.28,399.96,\
			473.49,401.87,474.56,403.47,473.07,405.59,473.39,407.71,476.68,409.41,479.23,\
			409.73,481.56,410.69,480.4,411.85,481.35,414.93,479.86,418.65,477.32,420.03,\
			476.04,422.58,479.02,422.58,480.29,423.01,483.79,419.93,486.66,416.21,490.06,\
			415.57,492.18,416.85,491.65,420.24,492.82,422.9,493.56,424.39,496.43,424.6,\
			498.02,423.01,498.13,421.31,497.07,420.03,497.07,415.15,496.33,414.51,501.1,\
			411.96,502.06,411.32,503.02,415.04,503.33,418.12,501.1,420.24,498.98,421.63,\
			500.47,424.39,505.03,423.32,506.2,421.31,507.69,419.5,506.31,423.32,510.03,\
			423.01,510.45,423.01]],
	"area": 702.1057499999998,
	"iscrowd": 0,
	"image_id": 289343,
	"bbox": [473.07,395.93,38.65,28.67],
	"category_id": 18,
	"id": 1768
},

3. categories字段
从instances_val2017.json文件中摘出的2个category实例如下所示：

{
	"supercategory": "person",
	"id": 1,
	"name": "person"
},
{
	"supercategory": "vehicle",
	"id": 2,
	"name": "bicycle"
},

Object Keypoint 类型的标注格式

1. annotations字段
包含了Object Instance中annotation结构体的所有字段，再加上2个额外的字段。
新增的keypoints是一个长度为3*k的数组，其中k是category中keypoints的总数量。每一个keypoint是一个长度为3的数组，第一和第二个元素分别是x和y坐标值，第三个元素是个标志位v，v为0时表示这个关键点没有标注（这种情况下x=y=v=0），v为1时表示这个关键点标注了但是不可见（被遮挡了），v为2时表示这个关键点标注了同时也可见。
num_keypoints表示这个目标上被标注的关键点的数量（v>0），比较小的目标上可能就无法标注关键点。

{
	"segmentation": [[125.12,539.69,140.94,522.43,100.67,496.54,84.85,469.21,73.35,450.52,104.99,342.65,168.27,290.88,179.78,288,189.84,286.56,191.28,260.67,202.79,240.54,221.48,237.66,248.81,243.42,257.44,256.36,253.12,262.11,253.12,275.06,299.15,233.35,329.35,207.46,355.24,206.02,363.87,206.02,365.3,210.34,373.93,221.84,363.87,226.16,363.87,237.66,350.92,237.66,332.22,234.79,314.97,249.17,271.82,313.89,253.12,326.83,227.24,352.72,214.29,357.03,212.85,372.85,208.54,395.87,228.67,414.56,245.93,421.75,266.07,424.63,276.13,437.57,266.07,450.52,284.76,464.9,286.2,479.28,291.96,489.35,310.65,512.36,284.76,549.75,244.49,522.43,215.73,546.88,199.91,558.38,204.22,565.57,189.84,568.45,184.09,575.64,172.58,578.52,145.26,567.01,117.93,551.19,133.75,532.49]],
	"num_keypoints": 10,
	"area": 47803.27955,
	"iscrowd": 0,
	"keypoints": [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,142,309,1,177,320,2,191,398,2,237,317,2,233,426,2,306,233,2,92,452,2,123,468,2,0,0,0,251,469,2,0,0,0,162,551,2],
	"image_id": 425226,"bbox": [73.35,206.02,300.58,372.5],"category_id": 1,
	"id": 183126
},

2. categories字段

对于每一个category结构体，相比Object Instance中的category新增了2个额外的字段，keypoints是一个长度为k的数组，包含了每个关键点的名字；skeleton定义了各个关键点之间的连接性（比如人的左手腕和左肘就是连接的，但是左手腕和右手腕就不是）。目前，COCO的keypoints只标注了person category （分类为人）。
从person_keypoints_val2017.json文件中摘出一个category的实例如下：

{
	"supercategory": "person",
	"id": 1,
	"name": "person",
	"keypoints": ["nose","left_eye","right_eye","left_ear","right_ear","left_shoulder","right_shoulder","left_elbow","right_elbow","left_wrist","right_wrist","left_hip","right_hip","left_knee","right_knee","left_ankle","right_ankle"],
	"skeleton": [[16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13],[6,7],[6,8],[7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]]
}

摘自：https://blog.csdn.net/yeyang911/article/details/78675942?utm_source=blogkpcl11

COCO数据集图像和注解下载地址：

Images
2014 Train images [83K/13GB]
2014 Val images [41K/6GB]
2015 Test images [81K/12GB]
2017 Train images [118K/18GB]
2017 Val images [5K/1GB]
2017 Test images [41K/6GB]
2017 Unlabeled images [123K/19GB]
Annotations
2014 Train/Val annotations [241MB]
2014 Testing Image info [1MB]
2015 Testing Image info [2MB]
2017 Train/Val annotations [241MB]
2017 Stuff Train/Val annotations [401MB]
2017 Testing Image info [1MB]
2017 Unlabeled Image info [4MB]

摘自：https://blog.csdn.net/u014734886/article/details/78830713