<auto_ignore frequency="10" timeframe="3600">no</auto_ignore>这句代码什么意思
时间: 2023-11-01 21:06:52 浏览: 36
这是一个 XML 标签,通常用于描述数据的结构和内容。在这个标签中,“auto_ignore”是标签名,而“frequency”和“timeframe”是该标签的属性。
在这个标签的语境下,它的意思是:如果用户在一小时内发送的消息数量超过10条,则自动忽略这些消息。这个标签的作用是防止用户恶意刷屏或发送过多的无意义消息。
相关问题
将<component name="InspectionProjectProfileManager"> <profile version="1.0"> <option name="myName" value="Project Default" /> <inspection_tool class="JavaDoc" enabled="true" level="WARNING" enabled_by_default="true"> <option name="TOP_LEVEL_CLASS_OPTIONS"> <value> <option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" /> <option name="REQUIRED_TAGS" value="" /> </value> </option> <option name="INNER_CLASS_OPTIONS"> <value> <option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" /> <option name="REQUIRED_TAGS" value="" /> </value> </option> <option name="METHOD_OPTIONS"> <value> <option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" /> <option name="REQUIRED_TAGS" value="@return@param@throws or @exception" /> </value> </option> <option name="FIELD_OPTIONS"> <value> <option name="ACCESS_JAVADOC_REQUIRED_FOR" value="none" /> <option name="REQUIRED_TAGS" value="" /> </value> </option> <option name="IGNORE_DEPRECATED" value="false" /> <option name="IGNORE_JAVADOC_PERIOD" value="true" /> <option name="IGNORE_DUPLICATED_THROWS" value="false" /> <option name="IGNORE_POINT_TO_ITSELF" value="false" /> <option name="myAdditionalJavadocTags" value="date" /> </inspection_tool> </profile> </component>改为JSON格式的文件
{
"component": {
"name": "InspectionProjectProfileManager",
"profile": {
"version": "1.0",
"option": [
{
"name": "myName",
"value": "Project Default"
},
{
"inspection_tool": {
"class": "JavaDoc",
"enabled": "true",
"level": "WARNING",
"enabled_by_default": "true",
"option": [
{
"name": "TOP_LEVEL_CLASS_OPTIONS",
"value": {
"option": [
{
"name": "ACCESS_JAVADOC_REQUIRED_FOR",
"value": "none"
},
{
"name": "REQUIRED_TAGS",
"value": ""
}
]
}
},
{
"name": "INNER_CLASS_OPTIONS",
"value": {
"option": [
{
"name": "ACCESS_JAVADOC_REQUIRED_FOR",
"value": "none"
},
{
"name": "REQUIRED_TAGS",
"value": ""
}
]
}
},
{
"name": "METHOD_OPTIONS",
"value": {
"option": [
{
"name": "ACCESS_JAVADOC_REQUIRED_FOR",
"value": "none"
},
{
"name": "REQUIRED_TAGS",
"value": "@return@param@throws or @exception"
}
]
}
},
{
"name": "FIELD_OPTIONS",
"value": {
"option": [
{
"name": "ACCESS_JAVADOC_REQUIRED_FOR",
"value": "none"
},
{
"name": "REQUIRED_TAGS",
"value": ""
}
]
}
},
{
"name": "IGNORE_DEPRECATED",
"value": "false"
},
{
"name": "IGNORE_JAVADOC_PERIOD",
"value": "true"
},
{
"name": "IGNORE_DUPLICATED_THROWS",
"value": "false"
},
{
"name": "IGNORE_POINT_TO_ITSELF",
"value": "false"
},
{
"name": "myAdditionalJavadocTags",
"value": "date"
}
]
}
}
]
}
}
}
请将下面的halcon代码转写为python代码:<?xml version="1.0" encoding="UTF-8"?> <hdevelop file_version="1.2" halcon_version="20.11.0.0"> <procedure name="main"> <interface/> <body> <c as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[1,1]" as_ord="1">* Image Acquisition 01: Code generated by Image Acquisition 01</c> <l as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[1,2]" as_ord="1">list_files ('G:/Grasp-Dataset/brick-text', ['files','follow_links'], ImageFiles)</l> <l as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[1,3]" as_ord="1">tuple_regexp_select (ImageFiles, ['\\.(tif|tiff|gif|bmp|jpg|jpeg|jp2|png|pcx|pgm|ppm|pbm|xwd|ima|hobj)$','ignore_case'], ImageFiles)</l> <l as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[2,2]" as_ord="1">for Index := 0 to |ImageFiles| - 1 by 1</l> <l as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[2,3]" as_ord="1"> read_image (Image, ImageFiles[Index])</l> <c as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[2,4]" as_ord="1"> * Image Acquisition 01: Do something</c> <l> ImgPath:=ImageFiles[Index]</l> <c> * 分割文件名</c> <l> parse_filename(ImageFiles[Index], BaseName, Extension, Directory)</l> <l> minvalue:=20</l> <l> maxvalue:=60</l> <l> Amp:=minvalue+rand(1)*(maxvalue-minvalue)</l> <l> add_noise_white (Image, ImageNoise, Amp)</l> <l> write_image (ImageNoise, 'jpeg', 0, Directory+BaseName+'noise')</l> <c> </c> <l as_id="image_acquisition" as_name="Image Acquisition 01" as_grp="[3,1]" as_ord="1">endfor</l> </body> <docu id="main"> <parameters/> </docu> </procedure> </hdevelop>
下面是将Halcon代码转换为Python代码的结果:
```python
import os
import random
image_files = []
image_folder = 'G:/Grasp-Dataset/brick-text'
for root, dirs, files in os.walk(image_folder):
for file in files:
if file.lower().endswith(('.tif', '.tiff', '.gif', '.bmp', '.jpg', '.jpeg', '.jp2', '.png', '.pcx', '.pgm', '.ppm', '.pbm', '.xwd', '.ima', '.hobj')):
image_files.append(os.path.join(root, file))
for image_file in image_files:
image = read_image(image_file)
base_name, extension = os.path.splitext(os.path.basename(image_file))
directory = os.path.dirname(image_file)
min_value = 20
max_value = 60
amp = min_value + random.random() * (max_value - min_value)
image_noise = add_noise_white(image, 'uniform', amp)
write_image(image_noise, 'jpeg', 0, os.path.join(directory, base_name + 'noise'))
```
请注意,上述代码中的`read_image`、`add_noise_white`和`write_image`函数需要根据你使用的图像处理库进行相应的替换。此外,还需要导入适当的库以支持所使用的函数和操作。
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