detectron2中无标注图像数据集的推理

Question

动机

• 我有一个detectron2面具R基线模型，它足够好地预测一些物体的边界。
• 我想把这些预测的边界转换成COCO多边形来注释下一个数据集(监督标记)。
• 为此，我需要在没有注释的图像数据集上运行推断。
• detectron2方法register_coco_instances和load_coco_json需要带有注释的图像来正确标记预测的对象。

问题

• 我可以在没有注释文件的情况下注册测试数据集吗？
• 如果不是，使用没有注释的基本图像信息生成可可或拉伯尔梅 JSON文件的最简单方法是什么？

代码

dataset_name = "test_data"
image_dir = "data/test"
coco_file = "data/test_annotations.json"

# Register dataset
# A COCO file is needed with image info, which I don't have
register_coco_instances(dataset_name , {}, coco_file, image_dir)
test_dict = load_coco_json(coco_file, image_dir, dataset_name=dataset_name )
metadata = MetadataCatalog.get(dataset_name)

# config details omitted for brevity
cfg = get_cfg()
predictor = DefaultPredictor(cfg)

# Make predictions for all images
for sample in test_dict:
    image_filename = sample["file_name"]
    img = cv2.imread(image_filename)
    outputs = predictor(img)
    # Display or save image with predictions to file

Answer 1

下面是一种从图像目录生成图像详细信息的方法，并将其写入现有的COCO文件中：

from PIL import Image

def add_images_to_coco(image_dir, coco_filename):
    image_filenames = list(Path(image_dir).glob('*.jpg'))
    images = []
    for i, image_filename in enumerate(image_filenames):
        im = Image.open(image_filename)
        width, height = im.size
        image_details = {
            "id": id + 1,
            "height": height,
            "width": width,
            "file_name": str(image_filename.resolve()),
        }
        images.append(image_details)

    # This will overwrite the image tags in the COCO JSON file
    with open(coco_filename) as f:
        data = json.load(f)

    coco['images'] = images

    with open(coco_filename, 'w') as coco_file:
        json.dump(data, coco_file, indent = 4)

如果您还没有创建基准COCO文件，那么您就需要用您的类别创建一个基线COCO文件。它应该是这样的：

{
    "images": [ ],
    "annotations": [ ],
    "categories": [
        {
            "id": 1,
            "name": "Car",
            "supercategory": "Car"
        },
        {
            "id": 2,
            "name": "Person",
            "supercategory": "Person"
        }
    ]
}