自己制作目标检测数据集

这里介绍2个制作目标检测数据集的工具：labelImg和labelme。用pip list查看自己电脑是否已安装这两个库，没有的话分别用pip install labelImg和 pip install labelme安装。

用labelImg默认生成的标签是.xml格式的，用labelme生成的标签是.json格式。labelImg可以修改标签格式，labelImg点击Save下面的Pascal VOC可以换成YOLO格式。

使用方法

两者使用方法一样，界面都差不多。直接cmd窗口输入labelImg或者labelme即可打开工具。

首先点击Open Dir打开我们存放图片的文件夹，labelImg点击Create RectBox即可在图片上画框，如果为检测困难物体，在右上角difficult那里打勾，画框介绍点Save或者Next Image就会在存放图片的文件夹下生产对应的.xml文件，一般直接用Next Image。labelImg点击Edit->Create Rectangle即可在图上画框，labelme还可以用Create Polygons在图片上画多边形，保存后在存放图片的文件夹下生产对应的.json文件，同样也是一张图片对应一个json文件，如果要将所有json文件合并成一个，可以用下面的代码实现：

import os
import argparse
import json
from labelme import utils
import numpy as np
import glob
import PIL.Image
#from PIL import Image
class labelme2coco(object):def __init__(self, labelme_json, save_json_path="./val.json"):self.labelme_json = labelme_jsonself.save_json_path = save_json_pathself.images = []self.categories = []self.annotations = []self.label = []self.annID = 1self.height = 0self.width = 0self.save_json()def data_transfer(self):for num, json_file in enumerate(self.labelme_json):with open(json_file, "r") as fp:print(json_file)data = json.load(fp)for key in data:print(key)print(data["shapes"])self.images.append(self.image(data, num))        for shapes in data["shapes"]:                   label = shapes["label"].split("_")if label not in self.label:self.label.append(label)points = shapes["points"]              self.annotations.append(self.annotation(points, label, num))self.annID += 1# Sort all text labels so they are in the same order across data splits.self.label.sort()for label in self.label:self.categories.append(self.category(label))for annotation in self.annotations:annotation["category_id"] = self.getcatid(annotation["category_id"])def image(self, data, num):image = {}img = utils.img_b64_to_arr(data["imageData"])height, width = img.shape[:2]img = Noneimage["height"] = heightimage["width"] = widthimage["id"] = numimage["file_name"] = data["imagePath"].split("/")[-1]self.height = heightself.width = widthreturn imagedef category(self, label):category = {}category["supercategory"] = label[0]category["id"] = len(self.categories)category["name"] = label[0]return categorydef annotation(self, points, label, num):annotation = {}contour = np.array(points)x = contour[:, 0]y = contour[:, 1]area = 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))annotation["segmentation"] = [list(np.asarray(points).flatten())]annotation["iscrowd"] = 0annotation["area"] = areaannotation["image_id"] = numannotation["bbox"] = list(map(float, self.getbbox(points)))annotation["category_id"] = label[0]  # self.getcatid(label)annotation["id"] = self.annIDreturn annotationdef getcatid(self, label):for category in self.categories:if label == category["name"]:return category["id"]print("label: {} not in categories: {}.".format(label, self.categories))exit()return -1def getbbox(self, points):polygons = pointsmask = self.polygons_to_mask([self.height, self.width], polygons)return self.mask2box(mask)def mask2box(self, mask):index = np.argwhere(mask == 1)rows = index[:, 0]clos = index[:, 1]left_top_r = np.min(rows)  # yleft_top_c = np.min(clos)  # xright_bottom_r = np.max(rows)right_bottom_c = np.max(clos)return [left_top_c,left_top_r,right_bottom_c - left_top_c,right_bottom_r - left_top_r,]def polygons_to_mask(self, img_shape, polygons):mask = np.zeros(img_shape, dtype=np.uint8)mask = PIL.Image.fromarray(mask)xy = list(map(tuple, polygons))PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)mask = np.array(mask, dtype=bool)return maskdef data2coco(self):data_coco = {}data_coco["images"] = self.imagesdata_coco["categories"] = self.categoriesdata_coco["annotations"] = self.annotationsreturn data_cocodef save_json(self):print("save coco json")self.data_transfer()self.data_coco = self.data2coco()print(self.save_json_path)os.makedirs(os.path.dirname(os.path.abspath(self.save_json_path)), exist_ok=True)json.dump(self.data_coco, open(self.save_json_path, "w"), indent=4)
if __name__ == "__main__":import argparse#parser = argparse.ArgumentParser(#   description="labelme annotation to coco data json file."#)#parser.add_argument(#   "labelme_images",#    help="Directory to labelme images and annotation json files.",#    type=str,#)#parser.add_argument(#   "--output", help="Output json file path.", default="trainval.json"#)#args = parser.parse_args()#labelme_json = glob.glob(os.path.join(args.labelme_images, "*.json"))filename = os.path.join("C:/Users/user/Desktop/images", "*.json")#print(filename, "first")labelme_json = glob.glob(filename)#print("labelme_json",labelme_json)output = "C:/Users/user/Desktop/images/train.json"labelme2coco(labelme_json, output)