Python将COCO格式实例分割数据集转换为YOLO格式实例分割数据集

前言

• 由于本人水平有限，难免出现错漏，敬请批评改正。
• 更多精彩内容，可点击进入YOLO系列专栏或我的个人主页查看
• YOLOv5：添加SE、CBAM、CoordAtt、ECA注意力机制
• YOLOv5：yolov5s.yaml配置文件解读、增加小目标检测层
• YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
• YOLOv7训练自己的数据集（口罩检测）
• YOLOv8训练自己的数据集（足球检测）
• 玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测

相关介绍

• Python是一种跨平台的计算机程序设计语言。是一个高层次的结合了解释性、编译性、互动性和面向对象的脚本语言。最初被设计用于编写自动化脚本(shell)，随着版本的不断更新和语言新功能的添加，越多被用于独立的、大型项目的开发。
• PyTorch 是一个深度学习框架，封装好了很多网络和深度学习相关的工具方便我们调用，而不用我们一个个去单独写了。它分为 CPU 和 GPU 版本，其他框架还有 TensorFlow、Caffe 等。PyTorch 是由 Facebook 人工智能研究院（FAIR）基于 Torch 推出的，它是一个基于 Python 的可续计算包，提供两个高级功能：1、具有强大的 GPU 加速的张量计算（如 NumPy）；2、构建深度神经网络时的自动微分机制。
  

COCO格式实例分割数据集转换为YOLO格式实例分割数据集

coco格式对应的json文件，以test.json为例

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}

格式转换代码，内容如下

import os
import json
import shutildef write_yolo_txt_file(txt_file_path,label_seg_x_y_list):if not os.path.exists(txt_file_path):with open(txt_file_path, "w") as file:for element in label_seg_x_y_list:file.write(str(element) + " ")file.write('\n')else:with open(txt_file_path, "a") as file:for element in label_seg_x_y_list:file.write(str(element) + " ")file.write('\n')def read_json(in_json_path,img_dir,target_dir):with open(in_json_path, "r", encoding='utf-8') as f:# json.load数据到变量json_datajson_data = json.load(f) # print(len(json_data['annotations']))# print(len(json_data['images']))# print(len(json_data['categories']))for annotation in json_data['annotations']: # 遍历标注数据信息# print(annotation)category_id = annotation['category_id']image_id = annotation['image_id']for image in json_data['images']: # 遍历图片相关信息if image['id'] == image_id:width = image['width'] # 图片宽height = image['height'] # 图片高img_file_name = image['file_name'] # 图片名称txt_file_name = image['file_name'].split('.')[0] + '.txt' # 要保存的对应txt文件名break# print(width,height,img_file_name,txt_file_name)segmentation = annotation['segmentation'] # 图像分割点信息[[x1,y1,x2,y2,...,xn,yn]]seg_x_y_list = [i/width if num%2==0 else i/height for num,i in enumerate(segmentation[0])] # 归一化图像分割点信息label_seg_x_y_list = seg_x_y_list[:]label_seg_x_y_list.insert(0,category_id) # 图像类别与分割点信息[label,x1,y1,x2,y2,...,xn,yn]# print(label_seg_x_y_list)# 写txt文件txt_file_path = target_dir + txt_file_name# print(txt_file_path)write_yolo_txt_file(txt_file_path,label_seg_x_y_list)# 选出txt对应img文件img_file_path = img_dir + img_file_name# print(img_file_path)shutil.copy(img_file_path,target_dir)if __name__=="__main__":img_dir = 'JPEGImages/'target_dir = 'testset/'if not os.path.exists(target_dir):os.mkdir(target_dir)in_json_path = './test.json'read_json(in_json_path,img_dir,target_dir)

• 由于本人水平有限，难免出现错漏，敬请批评改正。
• 更多精彩内容，可点击进入YOLO系列专栏或我的个人主页查看
• YOLOv5：添加SE、CBAM、CoordAtt、ECA注意力机制
• YOLOv5：yolov5s.yaml配置文件解读、增加小目标检测层
• YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
• YOLOv7训练自己的数据集（口罩检测）
• YOLOv8训练自己的数据集（足球检测）
• 玩转Jetson Nano（五）：TensorRT加速YOLOv5目标检测