本代码主要解决的问题是标签的映射问题,因为COCO官方数据集的标签是不连续的,只有80类categories却到了90, 直接转的话yolo读取标签会出错。因此,以下代码做了一些修正。可以自动映射过去,生成对应的txt文件。…
labels.txt就是你的类别标签,假设我有两个类(lm,ls),那么对应的labels.txt内容如下: __ignore__ _background_ lm ls 在labelme2coco.py文件的目录下,打开命令行执行: python labelme2coco.py --input_dir images --output_dir coco --labels labels.txt --input_dir:指定images文件夹 --output_dir...
class_train.txt', 'w') for img_name in img_names: if not os.path.exists('coco_class/labels/train2014'): os.makedirs('/coco_class/labels/train2014') list_file.write('/coco_class/images/train2014/%s\n'%img_name) image_id = img_name[:-4] convert_annotation(image_id) list_file....
import os import numpy as np import cv2 # 修改输入图片文件夹 img_folder = "train/images/" img_list = os.listdir(img_folder) img_list.sort() # 修改输入标签文件夹 label_folder = "train/labels/" label_list = os.listdir(label_folder) label_list.sort() # 输出图片文件夹位置 path = os...
80. 81. 82. 83. 84. 85. 86. View Code 注意,写入文件每行末尾需要添加换行符;按照coco128的格式,数据集目录是images和labels; 另外,image.txt和label.txt分别是images和labels中的文件路径; AI检测代码解析 ls ./dataset/images/*.png > image.txt ...
80. 封装的类为YOLACTBenchmarks,其中包含了run和parse_opt两个方法。 这个程序文件是用于运行YOLACT的基准测试的。它支持多种导出格式,并可以测试不同格式的模型的推理性能。 程序文件首先导入了必要的库和模块,并定义了一些全局变量。然后定义了两个函数run()和test(),分别用于运行基准测试和导出测试。这两个函...
Alternatively, you can provide the path to a TXT (newline-separated), JSON, or CSV file containing the list of image IDs to load in either of the first two formats include_id (False): whether to include the COCO ID of each sample in the loaded labels include_license (False): ...
cocostuff-labels.txtA list of the 1+91+91 classes in COCO-Stuff2.3 KB cocostuff-readme.txtThis document6.5 KB Older files cocostuff-10k-v1.0.zipCOCO-Stuff dataset version 1.0, including images and annotations2.6 GB To use the COCO-Stuff dataset, please follow these steps: ...
path.join(originLabelsDir, txtFile), 'r') as fr: labelList = fr.readlines() for label in labelList: label = label.strip().split() x = float(label[1]) y = float(label[2]) w = float(label[3]) h = float(label[4]) # convert x,y,w,h to x1,y1,x2,y2 H...
As you can see, the list of objects for the 2014 and 2017 releases are the same, which are 80 objects from the original 91 object categories in the paper. If you need to have the object list as a text file, you can view and download it from this repository. Moving to the discrepanc...