黔南州建设局门户网站/天津百度推广开户

1、 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yolov5-master，如下图：

2、 打开工程，在根目录新建LabelmeToYolov5.py。写入下面的代码

import osimport numpy as npimport jsonfrom glob import globimport cv2from sklearn.model_selection import train_test_splitfrom os import getcwdclasses = ["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# splittrain_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' % (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]) + '') print(json_filename, xmin, ymin, xmax, ymax, cls_id)ChangeToYolo5(train_files, "2007_train")ChangeToYolo5(val_files, "2007_val")ChangeToYolo5(test_files, "2007_test")file1 = open("tmp/2007_train.txt", "r")file2 = open("tmp/2007_val.txt", "r")file_list1 = file1.readlines() # 将所有变量读入列表file_list1file_list2 = file2.readlines() # 将所有变量读入列表file_list2file3 = open("tmp/train.txt", "w")for line in file_list1: print(line) file3.write(line)for line in file_list2: print(line) file3.write(line)

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。

3、 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 shutilimport osif not os.path.exists('../VOC/images/train'):    os.makedirs('../VOC/images/train')if not os.path.exists('../VOC/images/val'):    os.makedirs('../VOC/images/val')if not os.path.exists('../VOC/labels/train'):    os.makedirs('../VOC/labels/train')if not os.path.exists('../VOC/labels/val'):    os.makedirs('../VOC/labels/val')print(os.path.exists('../tmp/train.txt'))f = open('../tmp/train.txt', 'r')lines = f.readlines()for line in lines:    print(line)    line = "/".join(line.split('/')[-5:]).strip()    shutil.copy(line,"../VOC/images/train")    line = line.replace('jpg', 'txt')    shutil.copy(line, "../VOC/labels/train/")print(os.path.exists('../tmp/2007_test.txt'))f = open('../tmp/2007_test.txt', 'r')lines = f.readlines()for line in lines:    line = "/".join(line.split('/')[-5:]).strip()    print(line)    shutil.copy(line, "../VOC/images/val")    line = line.replace('JPEGImages', 'labels')    line = line.replace('jpg', 'txt')    shutil.copy(line, "../VOC/labels/val")

运行结果如下：