电脑，linux，RTX 3090 cuda 11.2

1.制作coco（json）格式数据集

这里我们使用的标注软件是：labelimg

选择voc格式进行标注，标注之后使用以下代码，把voc格式转换成coco格式，注意最后的路径

这个代码只能一次生成一个train.json文件，需要再打标签，生成一个val.json文件，就可以训练了

：

bash 复制代码

import sys
import os
import json
import xml.etree.ElementTree as ET
import glob

START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = {"cat": 1, "person": 2}
# If necessary, pre-define category and its id
#  PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
#  "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
#  "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
#  "motorbike": 14, "person": 15, "pottedplant": 16,
#  "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}


def get(root, name):
    vars = root.findall(name)
    return vars


def get_and_check(root, name, length):
    vars = root.findall(name)
    if len(vars) == 0:
        raise ValueError("Can not find %s in %s." % (name, root.tag))
    if length > 0 and len(vars) != length:
        raise ValueError(
            "The size of %s is supposed to be %d, but is %d."
            % (name, length, len(vars))
        )
    if length == 1:
        vars = vars[0]
    return vars


def get_filename_as_int(filename):
    try:
        filename = filename.replace("\\", "/")
        filename = os.path.splitext(os.path.basename(filename))[0]
        return int(filename)
    except:
        raise ValueError("Filename %s is supposed to be an integer." % (filename))


def get_categories(xml_files):
    """Generate category name to id mapping from a list of xml files.
    
    Arguments:
        xml_files {list} -- A list of xml file paths.
    
    Returns:
        dict -- category name to id mapping.
    """
    classes_names = []
    for xml_file in xml_files:
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall("object"):
            classes_names.append(member[0].text)
    classes_names = list(set(classes_names))
    classes_names.sort()
    return {name: i for i, name in enumerate(classes_names)}


def convert(xml_files, json_file):
    json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
    if PRE_DEFINE_CATEGORIES is not None:
        categories = PRE_DEFINE_CATEGORIES
    else:
        categories = get_categories(xml_files)
    bnd_id = START_BOUNDING_BOX_ID
    for xml_file in xml_files:
        tree = ET.parse(xml_file)
        root = tree.getroot()
        path = get(root, "path")
        if len(path) == 1:
            filename = os.path.basename(path[0].text)
        elif len(path) == 0:
            filename = get_and_check(root, "filename", 1).text
        else:
            raise ValueError("%d paths found in %s" % (len(path), xml_file))
        ## The filename must be a number
        image_id = get_filename_as_int(filename)
        size = get_and_check(root, "size", 1)
        width = int(get_and_check(size, "width", 1).text)
        height = int(get_and_check(size, "height", 1).text)
        image = {
            "file_name": filename,
            "height": height,
            "width": width,
            "id": image_id,
        }
        json_dict["images"].append(image)
        ## Currently we do not support segmentation.
        #  segmented = get_and_check(root, 'segmented', 1).text
        #  assert segmented == '0'
        for obj in get(root, "object"):
            category = get_and_check(obj, "name", 1).text
            if category not in categories:
                new_id = len(categories)
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, "bndbox", 1)
            xmin = int(get_and_check(bndbox, "xmin", 1).text) - 1
            ymin = int(get_and_check(bndbox, "ymin", 1).text) - 1
            xmax = int(get_and_check(bndbox, "xmax", 1).text)
            ymax = int(get_and_check(bndbox, "ymax", 1).text)
            assert xmax > xmin
            assert ymax > ymin
            o_width = abs(xmax - xmin)
            o_height = abs(ymax - ymin)
            ann = {
                "area": o_width * o_height,
                "iscrowd": 0,
                "image_id": image_id,
                "bbox": [xmin, ymin, o_width, o_height],
                "category_id": category_id,
                "id": bnd_id,
                "ignore": 0,
                "segmentation": [],
            }
            json_dict["annotations"].append(ann)
            bnd_id = bnd_id + 1

    for cate, cid in categories.items():
        cat = {"supercategory": "none", "id": cid, "name": cate}
        json_dict["categories"].append(cat)

    os.makedirs(os.path.dirname(json_file), exist_ok=True)
    json_fp = open(json_file, "w")
    json_str = json.dumps(json_dict)
    json_fp.write(json_str)
    json_fp.close()


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(
        description="Convert Pascal VOC annotation to COCO format."
    )
    parser.add_argument("xml_dir", nargs='?',default=r'G:\bsh\dataset\dingzi\Annotations',help="Directory path to xml files.", type=str)
    parser.add_argument("json_file",nargs='?', default=r'G:\bsh\dataset\dingzi\coco_json\train.json',help="Output COCO format json file.", type=str)
    args = parser.parse_args()
    xml_files = glob.glob(os.path.join(args.xml_dir, "*.xml"))

    # If you want to do train/test split, you can pass a subset of xml files to convert function.
    print("Number of xml files: {}".format(len(xml_files)))
    convert(xml_files, args.json_file)
    print("Success: {}".format(args.json_file))

2.配置mmdet环境

去github mmdet官网下载zip文件，然后解压

https://github.com/open-mmlab/mmdetection

bash 复制代码

conda create -n xcb_mmdet31 python=3.8 -y
conda activate xcb_mmdet31
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip install -U openmim  #最后安装成功 0.3.9
mim install mmengine    #最后安装成功 0.7.1
mim install "mmcv>=2.0.0"   # 2.0.0rc4
cd mmdetection
pip install -v -e .  #最后安装成功 3.1.0

可以使用代码测试环境安装是否成功：

bash 复制代码

mim download mmdet --config rtmdet_tiny_8xb32-300e_coco --dest .

python demo/image_demo.py demo/demo.jpg rtmdet_tiny_8xb32-300e_coco.py --weights rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth --device cpu

然后修改配置文件

此次准备使用的是RTMDet模型

3.修改配置文件

3.1 修改文件1

修改configs/rtmdet/rtmdet_l_8xb32-300e_coco.py文件中的32行

bash 复制代码

        num_classes=1,

3.2 修改文件2

修改configs/base /datasets/coco_detection.py文件中

第3行

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data_root = 'data/dingzi/'

第46行47行

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        ann_file='coco_json/train.json',
        data_prefix=dict(img='images/train/'),

第60行第61行

bash 复制代码

        ann_file='coco_json/val.json',
        data_prefix=dict(img='images/val/'),

第69行

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    ann_file=data_root + 'coco_json/val.json',

3.3 修改文件3

修改mmdet/datasets/coco.py文件

第19行

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        ('dingzi', 'heidong'),

3.4 修改文件4

mmdet/evaluation/functional/class_names.py

第75行

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        'dingzi', 'heidong'

3.5修改完成，重新编译

执行

bash 复制代码

python setup.py install

4.开始训练

bash 复制代码

 python tools/train.py configs/rtmdet/rtmdet_l_8xb32-300e_coco.py --work-dir output

训练结束，显示结果：

bash 复制代码

09/08 17:45:03 - mmengine - INFO - Epoch(val) [300][1/1]  
coco/bbox_mAP: 0.3930  coco/bbox_mAP_50: 0.8810  coco/bbox_mAP_75: 0.2470  
coco/bbox_mAP_s: 0.3030  coco/bbox_mAP_m: 0.4620  coco/bbox_mAP_l: -1.0000  
data_time: 0.0520  time: 0.1187

因为我只使用了3张图片，所以效果不好，接下来进行推理测试

5.推理测试

bash 复制代码

python demo/image_demo.py data/dingzi/images/train/ output/rtmdet_l_8xb32-300e_coco.py --weights output/epoch_300.pth

效果达到预期
接下来，要继续研究如何使用训练后的模型进行预测标签的保存选项，自动标注

先去吃饭

无脑014——linux系统，制作coco（json）格式数据集，使用mmdetection训练自己的数据集

1.制作coco（json）格式数据集

2.配置mmdet环境

3.修改配置文件

3.1 修改文件1

3.2 修改文件2

3.3 修改文件3

3.4 修改文件4

3.5修改完成，重新编译

4.开始训练

5.推理测试