将分割标签数据从JSON格式转换为YOLOv8的TXT格式

AnyLabeling是一款突破性的开源图像标注工具。

一、主要功能与特点

融合传统标注工具优点：AnyLabeling结合了LabelImg和Labelme等传统标注软件的优点，提供了多边形、矩形、圆形、线条和点等多种标注形式，满足不同场景的需求。
强大的AI自动标注：该工具集成了YOLO（如YOLOv8）和Segment Anything等先进的AI模型，实现了高效的自动标注功能。用户只需点击目标物体，AI模型就能快速生成精确的分割掩码，大大减少了手动描绘轮廓的工作量。
文字识别与关键信息提取：AnyLabeling支持OCR文字检测、识别以及KIE（关键信息提取）标注，进一步扩展了标注工具的功能。
多语言支持：界面支持英语、中文等多种语言，方便全球用户使用。
易于安装和使用：提供可执行文件下载，也可通过pip快速安装。安装完成后，只需在命令行输入anylabeling即可启动程序。

二、应用场景

AnyLabeling在计算机视觉的多个领域都有着广泛的应用，包括但不限于：

目标检测：在交通监控、安防等场景中标注车辆、行人等目标。
图像分类：为各类图像数据集添加标签，如动物识别、植物分类等。
实例分割：在医疗影像分析、遥感图像处理等领域进行精细的目标分割。
人体姿态估计：用于动作识别、人机交互等应用的关键点标注。
车道线检测：自动驾驶场景中的车道线标注。
OCR文字识别：在票据识别、文档处理等场景中进行文字区域和内容标注。

三、开源与社区

开源协议：AnyLabeling采用GPL-3.0开源协议，用户可以自由使用和修改。
社区参与：作为一个开源项目，AnyLabeling欢迎社区成员参与开发和贡献。用户可以根据自己的需求扩展标注工具，支持通过插件或脚本自定义标注流程，适应不同的数据格式和标注要求。

四、安装与使用

AnyLabeling的安装非常简单，用户可以选择以下两种方式之一：

下载可执行文件（windows）：从GitHub Releases页面下载最新版本的可执行文件，直接运行即可。
通过pip安装（linux）：在命令行中输入“pip install anylabeling”即可进行安装。安装完成后，只需在命令行输入“anylabeling”即可启动程序。

五、标签转换

我使用的anylabeling标注的数据，所以也使用了里面的转换文件label_converter.py转换数据格式。

运行的命令如下，按照自己的实际情况修改：

python .\tools\label_converter.py --task polygon --src_path json文件所在位置的路径 --img_path 图像文件所在的位置 --dst_path 将生成txt存放的路径 --classes 任意文件夹路径（但是里面要有classes.txt)文件\classes.txt --mode custom2yolo

classes.txt需要自己先生成并存放在某一位置，文件的内容就是标注的标签，比如说你的标注标签是1-5，那么文件内容就是，以此类推：

具体代码：里面有很多转换格式和函数

import argparse
import json
import os
import os.path as osp
import time
import mathfrom PIL import Image, ImageDraw
from tqdm import tqdm
from datetime import dateimport numpy as np
import xml.dom.minidom as minidom
import xml.etree.ElementTree as ETimport sysclass JsonEncoder(json.JSONEncoder):def default(self, obj):if isinstance(obj, (np.integer, np.floating, np.bool_)):return obj.item()elif isinstance(obj, np.ndarray):return obj.tolist()else:return super(JsonEncoder, self).default(obj)class BaseLabelConverter:def __init__(self, classes_file=None):if classes_file:with open(classes_file, "r", encoding="utf-8") as f:self.classes = f.read().splitlines()else:self.classes = []print(f"import classes is: {self.classes}")def reset(self):self.custom_data = dict(version=VERSION,flags={},shapes=[],imagePath="",imageData=None,imageHeight=-1,imageWidth=-1,)def get_image_size(self, image_file):with Image.open(image_file) as img:width, height = img.sizereturn width, heightdef get_minimal_enclosing_rectangle(self, poly):assert len(poly) == 8, "Input rectangle must contain exactly 8 values."x_coords = [poly[i] for i in range(0, 8, 2)]y_coords = [poly[i] for i in range(1, 8, 2)]min_x = min(x_coords)max_x = max(x_coords)min_y = min(y_coords)max_y = max(y_coords)x = min_xy = min_ywidth = max_x - min_xheight = max_y - min_yreturn [x, y, width, height]def get_poly_area(self, poly):# Ensure that poly contains exactly 8 valuesassert len(poly) == 8, "Input polygon must contain exactly 8 values."# Extract x and y coordinatesx_coords = [poly[i] for i in range(0, 8, 2)]y_coords = [poly[i] for i in range(1, 8, 2)]# Calculate the area using the Shoelace formulaarea = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i]for i in range(3))+ x_coords[3] * y_coords[0]- x_coords[0] * y_coords[3])return areadef get_coco_data(self):coco_data = {"info": {"year": 2023,"version": VERSION,"description": "COCO Label Conversion","contributor": "CVHub","url": "https://github.com/CVHub520/X-AnyLabeling","date_created": str(date.today()),},"licenses": [{"id": 1,"url": "https://www.gnu.org/licenses/gpl-3.0.html","name": "GNU GENERAL PUBLIC LICENSE Version 3",}],"categories": [],"images": [],"annotations": [],}return coco_datadef ensure_output_path(self, output_path, ext=None):if osp.isfile(output_path):# Check if the file has the expected extensionif not output_path.lower().endswith(ext.lower()):raise ValueError(f"The specified file '{output_path}' \does not have the expected '{ext}' extension.")else:# Check if the folder exists, and create it if it doesn'tif not osp.exists(output_path):os.makedirs(output_path, exist_ok=True)class RectLabelConverter(BaseLabelConverter):def custom_to_voc2017(self, input_file, output_dir):with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)image_path = data["imagePath"]image_width = data["imageWidth"]image_height = data["imageHeight"]root = ET.Element("annotation")ET.SubElement(root, "folder").text = osp.dirname(output_dir)ET.SubElement(root, "filename").text = osp.basename(image_path)size = ET.SubElement(root, "size")ET.SubElement(size, "width").text = str(image_width)ET.SubElement(size, "height").text = str(image_height)ET.SubElement(size, "depth").text = "3"for shape in data["shapes"]:label = shape["label"]points = shape["points"]difficult = shape.get("difficult", False)xmin = str(points[0][0])ymin = str(points[0][1])xmax = str(points[2][0])ymax = str(points[2][1])object_elem = ET.SubElement(root, "object")ET.SubElement(object_elem, "name").text = labelET.SubElement(object_elem, "pose").text = "Unspecified"ET.SubElement(object_elem, "truncated").text = "0"ET.SubElement(object_elem, "difficult").text = str(int(difficult))bndbox = ET.SubElement(object_elem, "bndbox")ET.SubElement(bndbox, "xmin").text = xminET.SubElement(bndbox, "ymin").text = yminET.SubElement(bndbox, "xmax").text = xmaxET.SubElement(bndbox, "ymax").text = ymaxxml_string = ET.tostring(root, encoding="utf-8")dom = minidom.parseString(xml_string)formatted_xml = dom.toprettyxml(indent="  ")with open(output_dir, "w") as f:f.write(formatted_xml)def voc2017_to_custom(self, input_file, output_file):self.reset()tree = ET.parse(input_file)root = tree.getroot()image_path = root.find("filename").textimage_width = int(root.find("size/width").text)image_height = int(root.find("size/height").text)self.custom_data["imagePath"] = image_pathself.custom_data["imageHeight"] = image_heightself.custom_data["imageWidth"] = image_widthfor obj in root.findall("object"):label = obj.find("name").textdifficult = "0"if obj.find("difficult") is not None:difficult = str(obj.find("difficult").text)xmin = float(obj.find("bndbox/xmin").text)ymin = float(obj.find("bndbox/ymin").text)xmax = float(obj.find("bndbox/xmax").text)ymax = float(obj.find("bndbox/ymax").text)shape = {"label": label,"description": "","points": [[xmin, ymin],[xmax, ymin],[xmax, ymax],[xmin, ymax],],"group_id": None,"difficult": bool(int(difficult)),"shape_type": "rectangle","flags": {},}self.custom_data["shapes"].append(shape)with open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)def custom_to_yolov5(self, input_file, output_file):with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)image_width = data["imageWidth"]image_height = data["imageHeight"]with open(output_file, "w", encoding="utf-8") as f:for shape in data["shapes"]:label = shape["label"]points = shape["points"]class_index = self.classes.index(label)x_center = (points[0][0] + points[2][0]) / (2 * image_width)y_center = (points[0][1] + points[2][1]) / (2 * image_height)width = abs(points[2][0] - points[0][0]) / image_widthheight = abs(points[2][1] - points[0][1]) / image_heightf.write(f"{class_index} {x_center} {y_center} {width} {height}\n")def yolov5_to_custom(self, input_file, output_file, image_file):self.reset()with open(input_file, "r", encoding="utf-8") as f:lines = f.readlines()img_w, img_h = self.get_image_size(image_file)for line in lines:line = line.strip().split(" ")class_index = int(line[0])cx = float(line[1])cy = float(line[2])nw = float(line[3])nh = float(line[4])xmin = int((cx - nw / 2) * img_w)ymin = int((cy - nh / 2) * img_h)xmax = int((cx + nw / 2) * img_w)ymax = int((cy + nh / 2) * img_h)shape_type = "rectangle"label = self.classes[class_index]points = [[xmin, ymin],[xmax, ymin],[xmax, ymax],[xmin, ymax],]shape = {"label": label,"shape_type": shape_type,"flags": {},"points": points,"group_id": None,"description": None,"difficult": False,"attributes": {},}self.custom_data["shapes"].append(shape)self.custom_data["imagePath"] = os.path.basename(image_file)self.custom_data["imageHeight"] = img_hself.custom_data["imageWidth"] = img_wwith open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)def custom_to_coco(self, input_path, output_path):coco_data = self.get_coco_data()for i, class_name in enumerate(self.classes):coco_data["categories"].append({"id": i + 1, "name": class_name, "supercategory": ""})image_id = 0annotation_id = 0file_list = os.listdir(input_path)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):if not file_name.endswith(".json"):continueimage_id += 1input_file = osp.join(input_path, file_name)with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)image_path = data["imagePath"]image_name = osp.splitext(osp.basename(image_path))[0]coco_data["images"].append({"id": image_id,"file_name": image_name,"width": data["imageWidth"],"height": data["imageHeight"],"license": 0,"flickr_url": "","coco_url": "","date_captured": "",})for shape in data["shapes"]:annotation_id += 1label = shape["label"]points = shape["points"]difficult = shape.get("difficult", False)class_id = self.classes.index(label)x_min = min(points[0][0], points[2][0])y_min = min(points[0][1], points[2][1])x_max = max(points[0][0], points[2][0])y_max = max(points[0][1], points[2][1])width = x_max - x_minheight = y_max - y_minannotation = {"id": annotation_id,"image_id": image_id,"category_id": class_id + 1,"bbox": [x_min, y_min, width, height],"area": width * height,"iscrowd": 0,"ignore": int(difficult),"segmentation": [],}coco_data["annotations"].append(annotation)output_file = osp.join(output_path, "instances_default.json")with open(output_file, "w", encoding="utf-8") as f:json.dump(coco_data, f, indent=4, ensure_ascii=False)def coco_to_custom(self, input_file, image_path):img_dic = {}for file in os.listdir(image_path):img_dic[file] = filewith open(input_file, "r", encoding="utf-8") as f:data = json.load(f)if not self.classes:for cat in data["categories"]:self.classes.append(cat["name"])total_info, label_info = {}, {}# map category_id to namefor dic_info in data["categories"]:label_info[dic_info["id"]] = dic_info["name"]# map image_id to infofor dic_info in data["images"]:total_info[dic_info["id"]] = {"imageWidth": dic_info["width"],"imageHeight": dic_info["height"],"imagePath": img_dic[dic_info["file_name"]],"shapes": [],}for dic_info in data["annotations"]:bbox = dic_info["bbox"]xmin = bbox[0]ymin = bbox[1]width = bbox[2]height = bbox[3]xmax = xmin + widthymax = ymin + heightshape_type = "rectangle"difficult = bool(int(str(dic_info.get("ignore", "0"))))label = self.classes[dic_info["category_id"] - 1]points = [[xmin, ymin],[xmax, ymin],[xmax, ymax],[xmin, ymax],]shape = {"label": label,"shape_type": shape_type,"flags": {},"points": points,"group_id": None,"description": None,"difficult": difficult,"attributes": {},}total_info[dic_info["image_id"]]["shapes"].append(shape)for dic_info in tqdm(total_info.values(),desc="Converting files",unit="file",colour="green",):self.reset()self.custom_data["shapes"] = dic_info["shapes"]self.custom_data["imagePath"] = dic_info["imagePath"]self.custom_data["imageHeight"] = dic_info["imageHeight"]self.custom_data["imageWidth"] = dic_info["imageWidth"]output_file = osp.join(image_path, osp.splitext(dic_info["imagePath"])[0] + ".json")with open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)class PolyLabelConvert(BaseLabelConverter):def mask2box(self, mask):index = np.argwhere(mask == 1)rows = index[:, 0]clos = index[:, 1]left_top_r = np.min(rows)  # yleft_top_c = np.min(clos)  # xright_bottom_r = np.max(rows)right_bottom_c = np.max(clos)return (left_top_c,left_top_r,right_bottom_c - left_top_c,right_bottom_r - left_top_r,)def polygons_to_mask(self, img_shape, polygons):mask = np.zeros(img_shape, dtype=np.uint8)mask = Image.fromarray(mask)xy = list(map(tuple, polygons))ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)mask = np.array(mask, dtype=bool)return maskdef custom_to_coco(self, input_path, output_path):coco_data = self.get_coco_data()for i, class_name in enumerate(self.classes):coco_data["categories"].append({"id": i + 1, "name": class_name, "supercategory": ""})image_id = 0annotation_id = 0file_list = os.listdir(input_path)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):if not file_name.endswith(".json"):continueimage_id += 1input_file = osp.join(input_path, file_name)with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)image_path = data["imagePath"]image_name = osp.splitext(osp.basename(image_path))[0] + ".jpg"coco_data["images"].append({"id": image_id,"file_name": image_name,"width": data["imageWidth"],"height": data["imageHeight"],"license": 0,"flickr_url": "","coco_url": "","date_captured": "",})for shape in data["shapes"]:annotation_id += 1label = shape["label"]points = shape["points"]difficult = shape.get("difficult", False)class_id = self.classes.index(label)mask = self.polygons_to_mask([data["imageHeight"], data["imageWidth"]], points)x_min, y_min, width, height = self.mask2box(mask)annotation = {"id": annotation_id,"image_id": image_id,"category_id": class_id + 1,"bbox": [x_min, y_min, width, height],"segmentation": [list(np.asarray(points).flatten())],"area": width * height,"iscrowd": 0,"ignore": int(difficult),}coco_data["annotations"].append(annotation)output_file = osp.join(output_path, "instances_default.json")with open(output_file, "w", encoding="utf-8") as f:json.dump(coco_data, f, indent=4, ensure_ascii=False, cls=JsonEncoder)def custom_to_yolov5(self, input_file, output_file):with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)image_width = data["imageWidth"]image_height = data["imageHeight"]image_size = np.array([[image_width, image_height]])with open(output_file, "w", encoding="utf-8") as f:for shape in data["shapes"]:label = shape["label"]points = np.array(shape["points"])class_index = self.classes.index(label)norm_points = points / image_sizef.write(f"{class_index} "+ " ".join([" ".join([str(cell[0]), str(cell[1])])for cell in norm_points.tolist()])+ "\n")def yolov5_to_custom(self, input_file, output_file, image_file):self.reset()with open(input_file, "r", encoding="utf-8") as f:lines = f.readlines()image_width, image_height = self.get_image_size(image_file)image_size = np.array([image_width, image_height], np.float64)for line in lines:line = line.strip().split(" ")class_index = int(line[0])label = self.classes[class_index]masks = line[1:]shape = {"label": label,"points": [],"group_id": None,"shape_type": "rectangle","flags": {},"attributes": {},"difficult": False,"description": None,}for x, y in zip(masks[0::2], masks[1::2]):point = [np.float64(x), np.float64(y)]point = np.array(point, np.float64) * image_sizeshape["points"].append(point.tolist())self.custom_data["shapes"].append(shape)self.custom_data["imagePath"] = osp.basename(image_file)self.custom_data["imageHeight"] = image_heightself.custom_data["imageWidth"] = image_widthwith open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)def coco_to_custom(self, input_file, image_path):img_dic = {}for file in os.listdir(image_path):img_dic[file] = filewith open(input_file, "r", encoding="utf-8") as f:data = json.load(f)if not self.classes:for cat in data["categories"]:self.classes.append(cat["name"])total_info, label_info = {}, {}# map category_id to namefor dic_info in data["categories"]:label_info[dic_info["id"]] = dic_info["name"]# map image_id to infofor dic_info in data["images"]:total_info[dic_info["id"]] = {"imageWidth": dic_info["width"],"imageHeight": dic_info["height"],"imagePath": img_dic[dic_info["file_name"]],"shapes": [],}for dic_info in data["annotations"]:points = []segmentation = dic_info["segmentation"][0]for i in range(0, len(segmentation), 2):x, y = segmentation[i : i + 2]point = [float(x), float(y)]points.append(point)difficult = str(dic_info.get("ignore", "0"))shape_info = {"label": self.classes[dic_info["category_id"] - 1],"description": None,"points": points,"group_id": None,"difficult": bool(int(difficult)),"shape_type": "polygon","flags": {},}total_info[dic_info["image_id"]]["shapes"].append(shape_info)for dic_info in tqdm(total_info.values(),desc="Converting files",unit="file",colour="green",):self.reset()self.custom_data["shapes"] = dic_info["shapes"]self.custom_data["imagePath"] = dic_info["imagePath"]self.custom_data["imageHeight"] = dic_info["imageHeight"]self.custom_data["imageWidth"] = dic_info["imageWidth"]output_file = osp.join(image_path, osp.splitext(dic_info["imagePath"])[0] + ".json")with open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)class RotateLabelConverter(BaseLabelConverter):def custom_to_dota(self, input_file, output_file):with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)with open(output_file, "w", encoding="utf-8") as f:for shape in data["shapes"]:label = shape["label"]points = shape["points"]difficult = shape.get("difficult", False)x0 = points[0][0]y0 = points[0][1]x1 = points[1][0]y1 = points[1][1]x2 = points[2][0]y2 = points[2][1]x3 = points[3][0]y3 = points[3][1]f.write(f"{x0} {y0} {x1} {y1} {x2} {y2} {x3} {y3} {label} {int(difficult)}\n")def dota_to_custom(self, input_file, output_file, image_file):self.reset()with open(input_file, "r", encoding="utf-8") as f:lines = f.readlines()image_width, image_height = self.get_image_size(image_file)for line in lines:line = line.strip().split(" ")x0, y0, x1, y1, x2, y2, x3, y3 = [float(i) for i in line[:8]]difficult = line[-1]shape = {"label": line[8],"description": None,"points": [[x0, y0], [x1, y1], [x2, y2], [x3, y3]],"group_id": None,"difficult": bool(int(difficult)),"direction": 0,"shape_type": "rotation","flags": {},}self.custom_data["shapes"].append(shape)self.custom_data["imagePath"] = osp.basename(image_file)self.custom_data["imageHeight"] = image_heightself.custom_data["imageWidth"] = image_widthwith open(output_file, "w", encoding="utf-8") as f:json.dump(self.custom_data, f, indent=2, ensure_ascii=False)def dota_to_dcoco(self, input_path, output_path, image_path):self.ensure_output_path(output_path, "json")coco_data = self.get_coco_data()for i, class_name in enumerate(self.classes):coco_data["categories"].append({"id": i + 1, "name": class_name, "supercategory": ""})image_id = 0annotation_id = 0file_list = os.listdir(image_path)for image_file in tqdm(file_list, desc="Converting files", unit="file", colour="green"):label_file = osp.join(input_path, osp.splitext(image_file)[0] + ".txt")image_width, image_height = self.get_image_size(osp.join(image_path, image_file))image_id += 1coco_data["images"].append({"id": image_id,"file_name": image_file,"width": image_width,"height": image_height,"license": 0,"flickr_url": "","coco_url": "","date_captured": "",})with open(label_file, "r", encoding="utf-8") as f:lines = f.readlines()for line in lines:line = line.strip().split(" ")*poly, label, difficult = linepoly = list(map(float, poly))area = self.get_poly_area(poly)rect = self.get_minimal_enclosing_rectangle(poly)annotation_id += 1class_id = self.classes.index(label)annotation = {"id": annotation_id,"image_id": image_id,"category_id": class_id + 1,"bbox": rect,"segmentation": [poly],"area": area,"iscrowd": 0,"ignore": difficult,}coco_data["annotations"].append(annotation)if osp.isdir(output_path):output_path = osp.join(output_path, "x_anylabeling_coco.json")with open(output_path, "w", encoding="utf-8") as f:json.dump(coco_data, f, indent=4, ensure_ascii=False)def dcoco_to_dota(self, input_file, output_path):self.ensure_output_path(output_path)with open(input_file, "r", encoding="utf-8") as f:data = json.load(f)label_info = {}# map category_id to labelfor dic_info in data["categories"]:label_info[dic_info["id"]] = dic_info["name"]name_info = {}# map image_id to file_naamefor dic_info in data["images"]:name_info[dic_info["id"]] = dic_info["file_name"]total_info = {}for dic_info in data["annotations"]:poly = dic_info["segmentation"][0]image_id = dic_info["image_id"]category_id = dic_info["category_id"]label = label_info[category_id]difficult = dic_info.get("ignore", 0)if image_id not in total_info:total_info[image_id] = [[*poly, label, difficult]]else:total_info[image_id].append([*poly, label, difficult])for image_id, label_info in total_info.items():label_file = osp.basename(name_info[image_id]) + ".txt"output_file = osp.join(output_path, label_file)with open(output_file, "w", encoding="utf-8") as f:for info in label_info:x0, y0, x1, y1, x2, y2, x3, y3, label, difficult = infof.write(f"{x0} {y0} {x1} {y1} {x2} {y2} {x3} {y3} {label} {int(difficult)}\n")def dxml_to_dota(self, input_file, output_file):tree = ET.parse(input_file)root = tree.getroot()with open(output_file, "w", encoding="utf-8") as f:for obj in root.findall("object"):obj_type = obj.find("type").textdifficult = 0if obj.find("difficult") is not None:difficult = obj.find("difficult").textlabel = obj.find("name").textif obj_type == "bndbox":hbndbox = obj.find("bndbox")points = self.hbndbox_to_dota(hbndbox)elif obj_type == "robndbox":rbndbox = obj.find("robndbox")points = self.rbndbox_to_dota(rbndbox)p0, p1, p2, p3 = pointsx0, y0, x1, y1, x2, y2, x3, y3 = *p0, *p1, *p2, *p3f.write(f"{x0} {y0} {x1} {y1} {x2} {y2} {x3} {y3} {label} {difficult}\n")@staticmethoddef rotatePoint(xc, yc, xp, yp, theta):xoff = xp - xcyoff = yp - yccosTheta = math.cos(theta)sinTheta = math.sin(theta)pResx = cosTheta * xoff + sinTheta * yoffpResy = -sinTheta * xoff + cosTheta * yoffreturn xc + pResx, yc + pResydef rbndbox_to_dota(self, box):cx = float(box.find("cx").text)cy = float(box.find("cy").text)w = float(box.find("w").text)h = float(box.find("h").text)angle = float(box.find("angle").text)x0, y0 = self.rotatePoint(cx, cy, cx - w / 2, cy - h / 2, -angle)x1, y1 = self.rotatePoint(cx, cy, cx + w / 2, cy - h / 2, -angle)x2, y2 = self.rotatePoint(cx, cy, cx + w / 2, cy + h / 2, -angle)x3, y3 = self.rotatePoint(cx, cy, cx - w / 2, cy + h / 2, -angle)points = [(x0, y0), (x1, y1), (x2, y2), (x3, y3)]return points@staticmethoddef hbndbox_to_dota(box):xmin = int(box.find("xmin").text)ymin = int(box.find("ymin").text)xmax = int(box.find("xmax").text)ymax = int(box.find("ymax").text)points = [(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)]return pointsdef main():parser = argparse.ArgumentParser(description="Label Converter")parser.add_argument("--task",default="rectangle",choices=["rectangle", "polygon", "rotation"],help="Choose the type of task to perform",)parser.add_argument("--src_path", help="Path to input directory")parser.add_argument("--dst_path", help="Path to output directory")parser.add_argument("--img_path", help="Path to image directory")parser.add_argument("--classes",default=None,help="Path to classes.txt file, \where each line represent a specific class",)parser.add_argument("--mode",help="Choose the conversion mode what you need",choices=["custom2voc","voc2custom","custom2yolo","yolo2custom","custom2coco","coco2custom","custom2dota","dota2custom","dota2dcoco","dcoco2dota","dxml2dota",],)args = parser.parse_args()print(f"Starting conversion to {args.mode} format of {args.task}...")start_time = time.time()if args.task == "rectangle":converter = RectLabelConverter(args.classes)valid_modes = ["custom2voc","voc2custom","custom2yolo","yolo2custom","custom2coco","coco2custom",]assert (args.mode in valid_modes), f"Rectangle tasks are only supported in {valid_modes} now!"elif args.task == "polygon":converter = PolyLabelConvert(args.classes)valid_modes = ["custom2yolo","yolo2custom","coco2custom","custom2coco",]assert (args.mode in valid_modes), f"Polygon tasks are only supported in {valid_modes} now!"elif args.task == "rotation":converter = RotateLabelConverter(args.classes)valid_modes = ["custom2dota","dota2custom","dota2dcoco","dcoco2dota","dxml2dota",]assert (args.mode in valid_modes), f"Rotation tasks are only supported in {valid_modes} now!"if args.mode == "custom2voc":file_list = os.listdir(args.src_path)os.makedirs(args.dst_path, exist_ok=True)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):if not file_name.endswith(".json"):continuesrc_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.dst_path, osp.splitext(file_name)[0] + ".xml")converter.custom_to_voc2017(src_file, dst_file)elif args.mode == "voc2custom":file_list = os.listdir(args.src_path)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):src_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.img_path, osp.splitext(file_name)[0] + ".json")converter.voc2017_to_custom(src_file, dst_file)elif args.mode == "custom2yolo":file_list = os.listdir(args.src_path)os.makedirs(args.dst_path, exist_ok=True)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):if not file_name.endswith(".json"):continuesrc_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.dst_path, osp.splitext(file_name)[0] + ".txt")converter.custom_to_yolov5(src_file, dst_file)elif args.mode == "yolo2custom":img_dic = {}for file in os.listdir(args.img_path):prefix = file.rsplit(".", 1)[0]img_dic[prefix] = filefile_list = os.listdir(args.src_path)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):src_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.img_path, osp.splitext(file_name)[0] + ".json")img_file = osp.join(args.img_path, img_dic[osp.splitext(file_name)[0]])converter.yolov5_to_custom(src_file, dst_file, img_file)elif args.mode == "custom2coco":os.makedirs(args.dst_path, exist_ok=True)converter.custom_to_coco(args.src_path, args.dst_path)elif args.mode == "coco2custom":converter.coco_to_custom(args.src_path, args.img_path)elif args.mode == "custom2dota":file_list = os.listdir(args.src_path)os.makedirs(args.dst_path, exist_ok=True)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):if not file_name.endswith(".json"):continuesrc_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.dst_path, osp.splitext(file_name)[0] + ".txt")converter.custom_to_dota(src_file, dst_file)elif args.mode == "dota2custom":img_dic = {}for file in os.listdir(args.img_path):prefix = file.rsplit(".", 1)[0]img_dic[prefix] = filefile_list = os.listdir(args.src_path)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):src_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.img_path, osp.splitext(file_name)[0] + ".json")img_file = osp.join(args.img_path, img_dic[osp.splitext(file_name)[0]])converter.dota_to_custom(src_file, dst_file, img_file)elif args.mode == "dota2dcoco":converter.dota_to_dcoco(args.src_path, args.dst_path, args.img_path)elif args.mode == "dcoco2dota":converter.dcoco_to_dota(args.src_path, args.dst_path)elif args.mode == "dxml2dota":file_list = os.listdir(args.src_path)os.makedirs(args.dst_path, exist_ok=True)for file_name in tqdm(file_list, desc="Converting files", unit="file", colour="green"):src_file = osp.join(args.src_path, file_name)dst_file = osp.join(args.dst_path, osp.splitext(file_name)[0] + ".txt")converter.dxml_to_dota(src_file, dst_file)end_time = time.time()print(f"Conversion completed successfully: {args.dst_path}")print(f"Conversion time: {end_time - start_time:.2f} seconds")if __name__ == "__main__":main()