将Labelme标注的数据复制到工程的根目录，并将其命名为LabelmeData。我的工程根目录是
import os import numpy as np import json from glob import glob import cv2 from sklearn.model_selection import train_test_split from os import getcwd classes = ["aircraft", "oiltank"] # 1.标签路径 labelme_path = "LabelmeData/" isUseTest = True # 是否创建test集 # 3.获取待处理文件 files = glob(labelme_path + "*.json") files = [i.replace("\\", "/").split("/")[-1].split(".json")[0] for i in files] print(files) if isUseTest: trainval_files, test_files = train_test_split(files, test_size=0.1, random_state=55) else: trainval_files = files # split train_files, val_files = train_test_split(trainval_files, test_size=0.1, random_state=55) def convert(size, box): dw = 1. / (size[0]) dh = 1. / (size[1]) x = (box[0] + box[1]) / 2.0 - 1 y = (box[2] + box[3]) / 2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) wd = getcwd() print(wd) def ChangeToYolo5(files, txt_Name): if not os.path.exists('tmp/'): os.makedirs('tmp/') list_file = open('tmp/%s.txt' % (txt_Name), 'w') for json_file_ in files: json_filename = labelme_path + json_file_ + ".json" imagePath = labelme_path + json_file_ + ".jpg" list_file.write('%s/%s\n' % (wd, imagePath)) out_file = open('%s/%s.txt' % (labelme_path, json_file_), 'w') json_file = json.load(open(json_filename, "r", encoding="utf-8")) height, width, channels = cv2.imread(labelme_path + json_file_ + ".jpg").shape for multi in json_file["shapes"]: points = np.array(multi["points"]) xmin = min(points[:, 0]) if min(points[:, 0]) > 0 else 0 xmax = max(points[:, 0]) if max(points[:, 0]) > 0 else 0 ymin = min(points[:, 1]) if min(points[:, 1]) > 0 else 0 ymax = max(points[:, 1]) if max(points[:, 1]) > 0 else 0 label = multi["label"] if xmax <= xmin: pass elif ymax <= ymin: pass else: cls_id = classes.index(label) b = (float(xmin), float(xmax), float(ymin), float(ymax)) bb = convert((width, height), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') print(json_filename, xmin, ymin, xmax, ymax, cls_id) ChangeToYolo5(train_files, "train") ChangeToYolo5(val_files, "val") ChangeToYolo5(test_files, "test") ''' file1 = open("tmp/train.txt", "r") file2 = open("tmp/val.txt", "r") file_list1 = file1.readlines() # 将所有变量读入列表file_list1 file_list2 = file2.readlines() # 将所有变量读入列表file_list2 file3 = open("tmp/trainval.txt", "w") for line in file_list1: print(line) file3.write(line) for line in file_list2: print(line) file3.write(line) '''
- 打开工程，在根目录新建LabelmeToYolov5.py。写入下面的代码
这段代码执行完成会在LabelmeData生成每个图片的txt标注数据，同时在tmp文件夹下面生成训练集、验证集和测试集的txt，txt记录的是图片的路径，为下一步生成YoloV5训练和测试用的数据集做准备。
在tmp文件夹新建makedata.py。执行完成后会在工程的根目录生成VOC数据集。
import shutil import os file_List = ["train", "val", "test"] for file in file_List: if not os.path.exists('../VOC/images/%s' % file): os.makedirs('../VOC/images/%s' % file) if not os.path.exists('../VOC/labels/%s' % file): os.makedirs('../VOC/labels/%s' % file) print(os.path.exists('../tmp/%s.txt' % file)) f = open('../tmp/%s.txt' % file, 'r') lines = f.readlines() for line in lines: print(line) line = "/".join(line.split('/')[-5:]).strip() shutil.copy(line, "../VOC/images/%s" % file) line = line.replace('jpg', 'txt') shutil.copy(line, "../VOC/labels/%s/" % file)
