自己制作目标检测数据集

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_json
        self.save_json_path = save_json_path
        self.images = []
        self.categories = []
        self.annotations = []
        self.label = []
        self.annID = 1
        self.height = 0
        self.width = 0
        self.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 = None
        image["height"] = height
        image["width"] = width
        image["id"] = num
        image["file_name"] = data["imagePath"].split("/")[-1]
        self.height = height
        self.width = width
        return image
    def category(self, label):
        category = {}
        category["supercategory"] = label[0]
        category["id"] = len(self.categories)
        category["name"] = label[0]
        return category
    def 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"] = 0
        annotation["area"] = area
        annotation["image_id"] = num
        annotation["bbox"] = list(map(float, self.getbbox(points)))
        annotation["category_id"] = label[0]  # self.getcatid(label)
        annotation["id"] = self.annID
        return annotation
    def 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 -1
    def getbbox(self, points):
        polygons = points
        mask = 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)  # y
        left_top_c = np.min(clos)  # x
        right_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 mask
    def data2coco(self):
        data_coco = {}
        data_coco["images"] = self.images
        data_coco["categories"] = self.categories
        data_coco["annotations"] = self.annotations
        return data_coco
    def 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)