【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 jsonimport cv2import numpy as npimport osdef 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DOTAcoco_annotation_path = 'instances.json'dota_annotation_folder = 'dota'coco_to_dota(coco_annotation_path, dota_annotation_folder)

标注可视化：

import cv2import numpy as npimport osdef 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)