优化代码 xml_path = base_xmlpath + filename[:-4] + '.xml'
时间: 2024-05-02 09:20:02 浏览: 11
这是一个关于优化代码的问题。你可以尝试使用 os 模块中的 join() 函数来更简洁地拼接文件路径:
```python
import os
xml_path = os.path.join(base_xmlpath, filename[:-4] + '.xml')
```
这个函数会根据你的操作系统自动选择合适的路径分隔符。
相关问题
在以下代码中<>处填写缺失的参数,实现训练数据集生成: 在<1>处设置所生成的训练数据集地址 本项目统一将生成的训练数据集清单存放于./data/train.txt处,也可使用BASE_PATH+'/data/train.txt'的方式生成动态地址 在<2>处划分训练数据部分 mid变量已经确定了数据集划分的界限,使用切片法即可划分数据集,使用切片[mid:]选取80%的数据为训练集 在<3>处补全xml文件夹路径 使用动态地址生成xml文件夹路径,与<3>后方的xml文件名组成完整的xml文件地址。 执行完成后将在data文件夹下生成train.txt文件。 ftrain = open(<1>,'w') for img_path in paths[<2>]: #print(img_path) filename = img_path.split('/')[-1] pd = filename.split('.')[-1] if pd not in ("jpg", "png", "jpeg"): continue path = <3>+filename.split('.')[0]+'.xml' ftrain.write(extract_xml(path,img_path)+'\n') ftrain.close() ftrain = open(<1>,'w') for img_path in paths[<2>]: #print(img_path) filename = img_path.split('/')[-1] pd = filename.split('.')[-1] if pd not in ("jpg", "png", "jpeg"): continue path = <3>+filename.split('.')[0]+'.xml' ftrain.write(extract_xml(path,img_path)+'\n') ftrain.close()
```
ftrain = open(BASE_PATH+'/data/train.txt','w')
mid = int(len(paths)*0.8)
for img_path in paths[mid:]:
filename = img_path.split('/')[-1]
pd = filename.split('.')[-1]
if pd not in ("jpg", "png", "jpeg"):
continue
path = BASE_PATH+'/data/xml/'+filename.split('.')[0]+'.xml'
ftrain.write(extract_xml(path,img_path)+'\n')
ftrain.close()
```
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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