【目标检测】旋转目标检测COCO格式标注转DOTAv1格式

DOTAv1数据集格式：

'imagesource':imagesource

'gsd':gsd

x1, y1, x2, y2, x3, y3, x4, y4, category, difficult

x1, y1, x2, y2, x3, y3, x4, y4, category, difficult

...

imagesource: 图片来源  

gsd: 分辨率

x1, y1, x2, y2, x3, y3, x4, y4：四边形的四个顶点的坐标 顶点按顺时针顺序排列，第一个起点为左上第一个点

category:实例类别

difficult：表示该实例是否难以检测（1表示困难，0表示不困难）

COCO转DOTA：

import json
import cv2
import numpy as np
import os
def calculate_rotated_bbox(poly):
    """将多边形坐标转换为旋转边界框"""
    contour = np.array(poly).reshape((-1, 1, 2)).astype(np.float32)
    rect = cv2.minAreaRect(contour)
    box = cv2.boxPoints(rect)
    return np.int0(box)
def coco_to_dota(coco_annotation_path, dota_annotation_folder, imagesource="Unknown", gsd="Unknown"):
    """将COCO格式的标注转换为DOTA格式，包括imagesource和gsd信息"""
    # 类别ID到名称的映射
    category_map = {
        1: 'Class1',
        2: 'Class2',
    }
    # 确保输出目录存在
    if not os.path.exists(dota_annotation_folder):
        os.makedirs(dota_annotation_folder)
    # 读取COCO格式的JSON文件
    with open(coco_annotation_path, 'r') as f:
        coco_data = json.load(f)
    # 遍历每个图像的标注
    for image in coco_data['images']:
        image_id = image['id']
        image_filename = image['file_name']
        dota_filename = os.path.splitext(image_filename)[0] + '.txt'  # 去掉原始扩展名，添加.txt
        dota_filepath = os.path.join(dota_annotation_folder, dota_filename)
        with open(dota_filepath, 'w') as dota_file:
            # 写入imagesource和gsd信息
            # dota_file.write(f"'imagesource':{imagesource}\n'gsd':{gsd}\n")
            # 找到当前图像的所有标注
            for annotation in filter(lambda x: x['image_id'] == image_id, coco_data['annotations']):
                if 'segmentation' in annotation:
                    for seg in annotation['segmentation']:
                        if type(seg[0]) is list:  # 检查是否是多边形格式
                            seg = seg[0]
                        box = calculate_rotated_bbox(seg)
                        # 从映射中获取类别名称
                        category_name = category_map.get(annotation['category_id'], 'Unknown')
                        # 格式化DOTA标注
                        box_str = ' '.join(map(str, box.flatten().tolist()))
                        dota_annotation = f"{box_str} {category_name} 0\n"
                        dota_file.write(dota_annotation)
# 调用函数，转换COCO到DOTA
coco_annotation_path = 'instances.json'
dota_annotation_folder = 'dota'
coco_to_dota(coco_annotation_path, dota_annotation_folder)

标注可视化：

import cv2
import numpy as np
import os
def draw_rotated_box(img, box, label):
    """在图像上绘制旋转的边界框和标签。"""
    points = np.int0(box)
    cv2.drawContours(img, [points], 0, (0, 255, 0), 2)  # 绘制旋转框
    cv2.putText(img, label, tuple(points[0]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)  # 添加文本标签
def visualize_dota_annotations(image_folder, annotation_folder, output_folder):
    """批量处理图像和DOTA标注文件，绘制旋转边界框和标签"""
    # 确保输出文件夹存在
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    # 遍历图像文件
    for img_filename in os.listdir(image_folder):
        img_path = os.path.join(image_folder, img_filename)
        if os.path.isfile(img_path) and img_filename.endswith(('.jpg', '.png')):
            annot_filename = os.path.splitext(img_filename)[0] + '.txt'
            annot_path = os.path.join(annotation_folder, annot_filename)
            output_img_path = os.path.join(output_folder, img_filename)
            # 读取图像
            img = cv2.imread(img_path)
            if img is None:
                continue  # 如果图像文件无法读取，则跳过
            # 处理对应的标注文件
            if os.path.isfile(annot_path):
                with open(annot_path, 'r') as f:
                    lines = f.readlines()
                    # 跳过文件开头的imagesource和gsd信息
                    for line in lines[2:]:  # 开始处理从第三行起的标注信息
                        parts = line.strip().split(' ')
                        if len(parts)