湖州公司网站建设,毕业设计心理评测网站开发,重庆建设工程信息网官网查询入口,一站式网站建设比较好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 前言相关介绍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#xff0c;以test.json为例格式转换代码#xff0c;内容如下 前言 由于本人水平有限#xff0c;难免出现错漏#xf… 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 前言相关介绍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为例格式转换代码内容如下 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YOLOv5yolov5s.yaml配置文件解读、增加小目标检测层YOLOv5IoU、GIoU、DIoU、CIoU、EIoUYOLOv7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 NumPy2、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
{annotations: [{id: 2094,iscrowd: 0,image_id: 173,category_id: 1,segmentation: [[1113,777,1115,785,1118,784,1120,786,1120,792,1118,796,1117,801,1117,868,1118,875,1120,880,1124,882,1138,882,1146,884,1153,899,1157,901,1166,901,1176,899,1178,897,1185,888,1204,887,1217,884,1291,886,1299,885,1302,883,1312,883,1323,890,1325,899,1332,905,1353,905,1360,895,1362,885,1364,863,1364,833,1359,797,1351,774,1326,735,1313,726,1297,722,1198,720,1191,716,1186,718,1177,718,1155,732,1150,736,1145,745,1145,747,1140,755,1135,769,1124,776,1121,776,1119,774]],area: 38102,bbox: [1113,716,251,189]},{id: 577,iscrowd: 0,image_id: 43,category_id: 3,segmentation: [[950,795,954,803,960,803,961,802,963,801,959,796,957,794,952,794]],area: 76.5,bbox: [950,794,13,9]},{id: 606,iscrowd: 0,image_id: 43,category_id: 3,segmentation: [[632,782,628,780,619,780,607,785,601,785,597,786,596,787,594,787,591,790,588,791,581,791,569,797,558,799,552,803,541,804,523,809,515,812,510,815,501,816,495,820,485,821,476,825,470,827,459,829,456,832,447,833,435,840,427,840,420,842,418,844,403,847,398,850,390,851,373,857,368,857,356,862,345,864,327,869,315,874,307,875,297,881,300,883,310,883,318,881,321,879,336,876,341,873,381,860,388,859,410,852,421,847,427,847,434,843,451,838,453,836,460,834,467,834,472,831,492,826,500,822,514,818,524,817,536,811,544,810,550,808,556,804,579,797,583,797,594,792,601,792,609,788,622,786]],area: 1939,bbox: [297,780,335,103]}],images: [{id: 762,width: 1920,height: 1080,file_name: 0762.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 179,width: 1920,height: 1080,file_name: 0179.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 13,width: 1920,height: 1080,file_name: 0013.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1871,width: 1920,height: 1080,file_name: 1871.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 396,width: 1920,height: 1080,file_name: 0396.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1762,width: 1920,height: 1080,file_name: 1762.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 89,width: 1920,height: 1080,file_name: 0089.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 2198,width: 1920,height: 1080,file_name: 2198.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 262,width: 1920,height: 1080,file_name: 0262.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1132,width: 1920,height: 1080,file_name: 1099.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 929,width: 1920,height: 1080,file_name: 0929.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1396,width: 1920,height: 1080,file_name: 1318.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 2208,width: 1920,height: 1080,file_name: 2208.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1248,width: 1920,height: 1080,file_name: 1248.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1909,width: 1920,height: 1080,file_name: 1909.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1487,width: 1920,height: 1080,file_name: 1492.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 341,width: 1920,height: 1080,file_name: 0341.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1851,width: 1920,height: 1080,file_name: 1851.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1742,width: 1920,height: 1080,file_name: 1742.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1957,width: 1920,height: 1080,file_name: 1957.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1418,width: 1920,height: 1080,file_name: 1418.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1592,width: 1920,height: 1080,file_name: 1592.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 19,width: 1920,height: 1080,file_name: 0019.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 579,width: 1920,height: 1080,file_name: 0579.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1407,width: 1920,height: 1080,file_name: 1372.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1975,width: 1920,height: 1080,file_name: 1975.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 931,width: 1920,height: 1080,file_name: 0931.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1977,width: 1920,height: 1080,file_name: 1977.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 40,width: 1920,height: 1080,file_name: 0040.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1366,width: 1920,height: 1080,file_name: 1403.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 889,width: 1920,height: 1080,file_name: 0889.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1050,width: 1920,height: 1080,file_name: 1050.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 669,width: 1920,height: 1080,file_name: 0669.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 2216,width: 1920,height: 1080,file_name: 2216.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 659,width: 1920,height: 1080,file_name: 0659.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 294,width: 1920,height: 1080,file_name: 0294.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1895,width: 1920,height: 1080,file_name: 1895.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 706,width: 1920,height: 1080,file_name: 0710.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1273,width: 1920,height: 1080,file_name: 1273.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 461,width: 1920,height: 1080,file_name: 0461.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1023,width: 1920,height: 1080,file_name: 1023.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1594,width: 1920,height: 1080,file_name: 1594.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1747,width: 1920,height: 1080,file_name: 1747.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1854,width: 1920,height: 1080,file_name: 1854.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 750,width: 1920,height: 1080,file_name: 0750.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1580,width: 1920,height: 1080,file_name: 1580.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1735,width: 1920,height: 1080,file_name: 1735.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1504,width: 1920,height: 1080,file_name: 1509.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1630,width: 1920,height: 1080,file_name: 1630.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 629,width: 1920,height: 1080,file_name: 0629.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 367,width: 1920,height: 1080,file_name: 0367.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1833,width: 1920,height: 1080,file_name: 1833.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 583,width: 1920,height: 1080,file_name: 0583.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 178,width: 1920,height: 1080,file_name: 0178.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 199,width: 1920,height: 1080,file_name: 0199.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1836,width: 1920,height: 1080,file_name: 1836.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1636,width: 1920,height: 1080,file_name: 1636.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 382,width: 1920,height: 1080,file_name: 0382.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 421,width: 1920,height: 1080,file_name: 0421.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1296,width: 1920,height: 1080,file_name: 1328.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 2054,width: 1920,height: 1080,file_name: 2054.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1072,width: 1920,height: 1080,file_name: 1095.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 601,width: 1920,height: 1080,file_name: 0601.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1313,width: 1920,height: 1080,file_name: 1345.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 726,width: 1920,height: 1080,file_name: 0704.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 924,width: 1920,height: 1080,file_name: 0924.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 139,width: 1920,height: 1080,file_name: 0139.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 445,width: 1920,height: 1080,file_name: 0445.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1436,width: 1920,height: 1080,file_name: 1441.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 1835,width: 1920,height: 1080,file_name: 1835.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 311,width: 1920,height: 1080,file_name: 0311.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 173,width: 1920,height: 1080,file_name: 0173.jpg,license: ,flickr_url: ,coco_url: ,date_captured: },{id: 43,width: 1920,height: 1080,file_name: 0043.jpg,license: ,flickr_url: ,coco_url: ,date_captured: }],categories: [{id: 1,name: car,color: [52,101,164],supercategory: },{id: 2,name: traffic_sign,color: [245,128,6],supercategory: },{id: 3,name: lane_lines,color: [115,210,22],supercategory: },{id: 4,name: person,color: [239,41,41],supercategory: },{id: 5,name: motorcyclist,color: [145,104,190],supercategory: },{id: 6,name: cyclist,color: [239,41,41],supercategory: }]
}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, encodingutf-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%20 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.jsonread_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YOLOv5yolov5s.yaml配置文件解读、增加小目标检测层YOLOv5IoU、GIoU、DIoU、CIoU、EIoUYOLOv7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
