将注记从Mask-RCNN数据集格式转换为COCO格式

Question

我想训练一个在图像中检测车辆和道路的模型。为此，我将使用Mask R-CNN和YOLACT++。我用vgg图像注释器为Mask R-CNN标记了一些图像，分割点如下图所示。

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如您所见，这里没有area参数或bbox参数。我可以使用minx miny maxx maxy找到我的实例的bbox，但我找不到如何找到该分割区域的区域。您可以在下图中看到Yolact注释的形成。

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标记所有实例需要大量时间。我花了至少10分钟来标记图像中的所有汽车，我已经有500张图像被标记了。你有什么建议或想法可以帮助我节省我的时间，同时转换第一个注释形成为第二个(掩码r-cnn到coco(yolact))？

Answer 1

类似这样的东西，但这取决于您在vgg中的注释方式。

def vgg_to_coco(vgg_path: str, outfile: str=None, class_keyword: str = "Class"):
    with open(vgg_path) as f:
        vgg = json.load(f)

    images_ids_dict = {v["filename"]: i for i, v in enumerate(vgg.values())}
    # TDOD fix
    images_info = [{"file_name": k, "id": v, "width": 1024, "height": 1024} for k, v in images_ids_dict.items()]

    classes = {class_keyword} | {r["region_attributes"][class_keyword] for v in vgg.values() for r in v["regions"]
                                 if class_keyword in r["region_attributes"]}
    category_ids_dict = {c: i for i, c in enumerate(classes, 1)}
    categories = [{"supercategory": class_keyword, "id": v, "name": k} for k, v in category_ids_dict.items()]
    annotations = []
    suffix_zeros = math.ceil(math.log10(len(vgg)))
    for i, v in enumerate(vgg.values()):
        for j, r in enumerate(v["regions"]):
            if class_keyword in r["region_attributes"]:
                x, y = r["shape_attributes"]["all_points_x"], r["shape_attributes"]["all_points_y"]
                annotations.append({
                    "segmentation": [list(chain.from_iterable(zip(x, y)))],
                    "area": helper.polygon_area(x, y),
                    "bbox": helper.bbox(x, y, out_format="width_height"),
                    "image_id": images_ids_dict[v["filename"]],
                    "category_id": category_ids_dict[r["region_attributes"][class_keyword]],
                    "id": int(f"{i:0>{suffix_zeros}}{j:0>{suffix_zeros}}"),
                    "iscrowd": 0
                })

    coco = {
        "images": images_info,
        "categories": categories,
        "annotations": annotations
    }
    if outfile is None:
        outfile = vgg_path.replace(".json", "_coco.json")
    with open(outfile, "w") as f:
        json.dump(coco, f)

您必须将1024更改为您的图像大小，或者如果您有可变的图像大小，则必须为此创建一个映射。

Answer 2

您必须创建自己的脚本并将其转换，我必须将其从xml注释转换为json maskrcnn。

您可以查看示例：https://github.com/adions025/XMLtoJson_遮罩_RCNN

Answer 3

工作解决方案:扩展自@Zac Tod的答案

图像大小可以随时随地计算出来。

import skimage 
import math
from itertools import chain
import numpy as np

def vgg_to_coco(dataset_dir, vgg_path: str, outfile: str=None, class_keyword: str = "label"):
    with open(vgg_path) as f:
        vgg = json.load(f)

    images_ids_dict = {}
    images_info = []
    for i,v in enumerate(vgg.values()):

        images_ids_dict[v["filename"]] = I
        image_path = os.path.join(dataset_dir, v['filename'])
        image = skimage.io.imread(image_path)
        height, width = image.shape[:2]  
        images_info.append({"file_name": v["filename"], "id": i, "width": width, "height": height})

    classes = {class_keyword} | {r["region_attributes"][class_keyword] for v in vgg.values() for r in v["regions"].values()
                             if class_keyword in r["region_attributes"]}
    category_ids_dict = {c: i for i, c in enumerate(classes, 1)}
    categories = [{"supercategory": class_keyword, "id": v, "name": k} for k, v in category_ids_dict.items()]
    annotations = []
    suffix_zeros = math.ceil(math.log10(len(vgg)))
    for i, v in enumerate(vgg.values()):
        for j, r in enumerate(v["regions"].values()):
            if class_keyword in r["region_attributes"]:
                x, y = r["shape_attributes"]["all_points_x"], r["shape_attributes"]["all_points_y"]
                annotations.append({
                    "segmentation": [list(chain.from_iterable(zip(x, y)))],
                    "area": PolyArea(x, y),
                    "bbox": [min(x), min(y), max(x)-min(x), max(y)-min(y)],
                    "image_id": images_ids_dict[v["filename"]],
                    "category_id": category_ids_dict[r["region_attributes"][class_keyword]],
                    "id": int(f"{i:0>{suffix_zeros}}{j:0>{suffix_zeros}}"),
                    "iscrowd": 0
                    })

    coco = {
        "images": images_info,
        "categories": categories,
        "annotations": annotations
        }
    if outfile is None:
        outfile = vgg_path.replace(".json", "_coco.json")
    with open(outfile, "w") as f:
        json.dump(coco, f)

我的数据被标注为makesense.ai和区域class_keyword="label"属性如下所示在函数调用中。

"region_attributes": {
      "label": "box"
    }

为了计算多边形面积，代码复制自这个答案

def PolyArea(x,y):
    return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))