深度学习和目标检测系列教程 8-300：目标检测常见的标注工具LabelImg和将xml文件提取图像信息

Posted 2021-08-03 刘润森！

@Author：Runsen

图像标注主要用于创建数据集进行图片的标注。本篇博客将推荐一款非常实用的图片标注工具LabelImg，重点介绍其安装使用过程。如果想简单点，请直接下载打包版（下载地址见结尾），无需编译，直接打开即可！

感谢原作者对Github的贡献，博主发现软件已经更新，可以关注最新版本。这个工具是一个用 Python 和 Qt 编写的完整的图形界面。最有意思的是，它的标注信息可以直接转换成XML文件，这和PASCAL VOC和ImageNet使用的XML是一样的。

附注。作者在5月份更新了代码，现在最新版本号是1.3.0，博主亲测，源码在Windows 10和Ubuntu 16.04上正常运行。

具体的安装查看Github教程：https://github.com/wkentaro/labelme/#installation

在原作者的github下载源码：https://github.com/tzutalin/labelImg
。解压名为labelImg-master的文件夹，进入当前目录的命令行窗口，输入如下语句依次打开软件。

python labelImg.py

具体使用

• 修改默认的XML文件保存位置，使用快捷键“Ctrl+R”，更改为自定义位置，这里的路径一定不能包含中文，否则不会保存。

• 使用notepad++打开源文件夹中的data/predefined_classes.txt，修改默认分类，如person、car、motorcycle这三个分类。

• “打开目录”打开图片文件夹，选择第一张图片开始标注，用“创建矩形框”或“Ctrl+N”启动框，点击结束框，双击选择类别。完成一张图片点击“保存”保存后，XML文件已经保存到本地了。单击“下一张图片”转到下一张图片。

• 贴标过程可以随时返回修改，保存的文件会覆盖上一个。

• 完成注解后，打开XML文件，发现和PASCAL VOC格式一样。

将xml文件提取图像信息

下面列举如何将xml文件提取图像信息，图片保存到image文件夹，xml保存标注内容。图片和标注的文件名字一样的。

下面是images图片中的一个。

下面是对应的xml文件。

<annotation>
	<folder>train</folder>
	<filename>apple_30.jpg</filename>
	<path>C:\\tensorflow1\\models\\research\\object_detection\\images\\train\\apple_30.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>800</width>
		<height>800</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>apple</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>254</xmin>
			<ymin>163</ymin>
			<xmax>582</xmax>
			<ymax>487</ymax>
		</bndbox>
	</object>
	<object>
		<name>apple</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>217</xmin>
			<ymin>448</ymin>
			<xmax>535</xmax>
			<ymax>713</ymax>
		</bndbox>
	</object>
	<object>
		<name>apple</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>603</xmin>
			<ymin>470</ymin>
			<xmax>800</xmax>
			<ymax>716</ymax>
		</bndbox>
	</object>
	<object>
		<name>apple</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>468</xmin>
			<ymin>179</ymin>
			<xmax>727</xmax>
			<ymax>467</ymax>
		</bndbox>
	</object>
	<object>
		<name>apple</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>1</xmin>
			<ymin>63</ymin>
			<xmax>308</xmax>
			<ymax>414</ymax>
		</bndbox>
	</object>
</annotation>

将xml文件提取图像信息，主要使用xml和opencv，基于torch提取，代码比较凌乱。

import os
import numpy as np
import cv2
import torch
import matplotlib.patches as patches
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from matplotlib import pyplot as plt
from torch.utils.data import Dataset
from xml.etree import ElementTree as et
from torchvision import transforms as torchtrans

# defining the files directory and testing directory
train_image_dir = 'train/train/image'
train_xml_dir = 'train/train/xml'
# test_image_dir = 'test/test/image'
# test_xml_dir = 'test/test/xml'

class FruitImagesDataset(Dataset):

    def __init__(self, image_dir, xml_dir, width, height, transforms=None):
        self.transforms = transforms
        self.image_dir = image_dir
        self.xml_dir = xml_dir
        self.height = height
        self.width = width

        # sorting the images for consistency
        # To get images, the extension of the filename is checked to be jpg
        self.imgs = [image for image in os.listdir(self.image_dir)
                     if image[-4:] == '.jpg']
        self.xmls = [xml for xml in os.listdir(self.xml_dir)
                     if xml[-4:] == '.xml']

        # classes: 0 index is reserved for background
        self.classes = ['apple', 'banana', 'orange']

    def __getitem__(self, idx):

        img_name = self.imgs[idx]
        image_path = os.path.join(self.image_dir, img_name)

        # reading the images and converting them to correct size and color
        img = cv2.imread(image_path)
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)
        img_res = cv2.resize(img_rgb, (self.width, self.height), cv2.INTER_AREA)
        # diving by 255
        img_res /= 255.0

        # annotation file
        annot_filename = img_name[:-4] + '.xml'
        annot_file_path = os.path.join(self.xml_dir, annot_filename)

        boxes = []
        labels = []
        tree = et.parse(annot_file_path)
        root = tree.getroot()

        # cv2 image gives size as height x width
        wt = img.shape[1]
        ht = img.shape[0]

        # box coordinates for xml files are extracted and corrected for image size given
        for member in root.findall('object'):
            labels.append(self.classes.index(member.find('name').text))

            # bounding box
            xmin = int(member.find('bndbox').find('xmin').text)
            xmax = int(member.find('bndbox').find('xmax').text)

            ymin = int(member.find('bndbox').find('ymin').text)
            ymax = int(member.find('bndbox').find('ymax').text)

            xmin_corr = (xmin / wt) * self.width
            xmax_corr = (xmax / wt) * self.width
            ymin_corr = (ymin / ht) * self.height
            ymax_corr = (ymax / ht) * self.height
            boxes.append([xmin_corr, ymin_corr, xmax_corr, ymax_corr])

        # convert boxes into a torch.Tensor
        boxes = torch.as_tensor(boxes, dtype=torch.float32)

        # getting the areas of the boxes
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])

        # suppose all instances are not crowd
        iscrowd = torch.zeros((boxes.shape[0],), dtype=torch.int64)

        labels = torch.as_tensor(labels, dtype=torch.int64)

        target = {}
        target["boxes"] = boxes
        target["labels"] = labels
        target["area"] = area
        target["iscrowd"] = iscrowd
        # image_id
        image_id = torch.tensor([idx])
        target["image_id"] = image_id

        if self.transforms:
            sample = self.transforms(image=img_res,
                                     bboxes=target['boxes'],
                                     labels=labels)

            img_res = sample['image']
            target['boxes'] = torch.Tensor(sample['bboxes'])

        return img_res, target

    def __len__(self):
        return len(self.imgs)

# function to convert a torchtensor back to PIL image
def torch_to_pil(img):
    return torchtrans.ToPILImage()(img).convert('RGB')


def plot_img_bbox(img, target):
    # plot the image and bboxes
    fig, a = plt.subplots(1, 1)
    fig.set_size_inches(5, 5)
    a.imshow(img)
    for box in (target['boxes']):
        x, y, width, height = box[0], box[1], box[2] - box[0], box[3] - box[1]
        rect = patches.Rectangle((x, y),
                                 width, height,
                                 linewidth=2,
                                 edgecolor='r',
                                 facecolor='none')

        # Draw the bounding box on top of the image
        a.add_patch(rect)
    plt.show()


def get_transform(train):
    if train:
        return A.Compose([
            A.HorizontalFlip(0.5),
            # ToTensorV2 converts image to pytorch tensor without div by 255
            ToTensorV2(p=1.0)
        ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
    else:
        return A.Compose([
            ToTensorV2(p=1.0)
        ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})



dataset = FruitImagesDataset(train_image_dir,train_xml_dir, 480, 480, transforms= get_transform(train=True))

print(len(dataset))
# getting the image and target for a test index.  Feel free to change the index.
img, target = dataset[29]
print(img.shape, '\\n', target)
plot_img_bbox(torch_to_pil(img), target)

输出如下：

torch.Size([3, 480, 480]) 
 {'boxes': tensor([[130.8000,  97.8000, 327.6000, 292.2000],
        [159.0000, 268.8000, 349.8000, 427.8000],
        [  0.0000, 282.0000, 118.2000, 429.6000],
        [ 43.8000, 107.4000, 199.2000, 280.2000],
        [295.2000,  37.8000, 479.4000, 248.4000]]), 'labels': tensor([0, 0, 0, 0, 0]), 'area': tensor([38257.9258, 30337.2012, 17446.3223, 26853.1270, 38792.5195]), 'iscrowd': tensor([0, 0, 0, 0, 0]), 'image_id': tensor([29])}

下载地址

链接：https://pan.baidu.com/s/1QZDgeYTHyAlD2xhtJqZ-Yw
提取码：srjn