计算机视觉数据集——扑克识别数据集

扑克数据集

数据集地址：https://download.csdn.net/download/matt45m/89130302

这是一个检测扑克牌种类的数据集，检测种类目前只有6种，分别是
🔥计算机视觉、图像处理、毕业辅导、作业帮助、代码获取，远程协助，代码定制，私聊会回复!
✍🏻作者简介：机器学习，深度学习，卷积神经网络处理，图像处理
🚀B站项目实战：https://space.bilibili.com/364224477
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"queen", "ten", "nine", "king", "jack", "ace"

数据集共含有363张图片，标注的工具是labelimg，数据标签是xml，格式如下：

<annotation>
	<folder>train</folder>
	<filename>cam_image37.jpg</filename>
	<path>C:\tensorflow_cards\train\cam_image37.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>960</width>
		<height>540</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>ace</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>293</xmin>
			<ymin>41</ymin>
			<xmax>448</xmax>
			<ymax>247</ymax>
		</bndbox>
	</object>
	<object>
		<name>ace</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>481</xmin>
			<ymin>276</ymin>
			<xmax>654</xmax>
			<ymax>518</ymax>
		</bndbox>
	</object>
	<object>
		<name>king</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>488</xmin>
			<ymin>32</ymin>
			<xmax>653</xmax>
			<ymax>246</ymax>
		</bndbox>
	</object>
	<object>
		<name>king</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>279</xmin>
			<ymin>280</ymin>
			<xmax>442</xmax>
			<ymax>518</ymax>
		</bndbox>
	</object>
</annotation>

数据可视化：

from xml.dom.minidom import parse
import xml.dom.minidom

def acquire_label_xml(img_path):
    DOMTree = xml.dom.minidom.parse(img_path)
    collection = DOMTree.documentElement
    boundingbox = collection.getElementsByTagName("object")
    img_lable = []
    for i in boundingbox:
        tmp = []
        category = i.getElementsByTagName("name")[0].childNodes[0].data
        tmp.append(float(
            [j.childNodes[0].data for j in i.getElementsByTagName("bndbox")[0].getElementsByTagName("xmin")][
                0]))
        tmp.append(float(
            [j.childNodes[0].data for j in i.getElementsByTagName("bndbox")[0].getElementsByTagName("ymin")][
                0]))
        tmp.append(float(
            [j.childNodes[0].data for j in i.getElementsByTagName("bndbox")[0].getElementsByTagName("xmax")][
                0]))
        tmp.append(float(
            [j.childNodes[0].data for j in i.getElementsByTagName("bndbox")[0].getElementsByTagName("ymax")][
                0]))
        tmp.append(category)
        img_lable.append(tmp)
    return img_lable