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Mmclassification annotation file generation
2022-07-04 09:40:00 【Coding leaves】
1 Data set composition
mmclassification The dataset mainly contains four folders meta、train、val、test, among meta Stored annotation information , contain train.txt、val.txt and test.txt Three files .train、val、test Images of different categories are stored . Its composition is shown in the figure below :
train.txt、val.txt and test.txt The file stores the image path and category id, As shown in the figure below :
train、val、test Images of different categories are stored , Images of the same category are located in the same subfolder , The name of the subfolder is the corresponding category name , As shown in the figure below :
The data in the above example image comes from minist Handwritten font visualization dataset , According to train、test Folder for storage , Download at :minist Handwritten digital visualization data set - Deep learning document resources -CSDN download .
2 Annotation file generation
Annotation file generation refers to the generation without meta In the case of folders , according to train、val、test The folder is automatically generated meta Folder , And its folder train.txt、val.txt and test.txt.
3 Reference procedure
# -*- coding: utf-8 -*-
"""
The official account of Lele perception school
@author: https://blog.csdn.net/suiyingy
"""
import os
from glob import glob
from pathlib import Path
def generate_mmcls_ann(data_dir, img_type='.png'):
data_dir = str(Path(data_dir)) + '/'
classes = ['0', '1', '2', '3', '4','5', '6', '7', '8', '9']
class2id = dict(zip(classes, range(len(classes))))
data_dir = str(Path(data_dir)) + '/'
dir_types = ['train', 'val', 'test']
sub_dirs = os.listdir(data_dir)
ann_dir = data_dir + 'meta/'
if not os.path.exists(ann_dir):
os.makedirs(ann_dir)
for sd in sub_dirs:
if sd not in dir_types:
continue
annotations = []
target_dir = data_dir + sd + '/'
for d in os.listdir(target_dir):
class_id = str(class2id[d])
images = glob(target_dir+d+'/*'+img_type)
for img in images:
img = d + '/' + os.path.basename(img)
annotations.append(img+' '+ class_id+'\n')
annotations[-1] = annotations[-1].strip()
with open(ann_dir+sd+'.txt', 'w') as f:
f.writelines(annotations)
if __name__ == '__main__':
data_dir = 'data/Minist Handwritten digital visualization data set /'
generate_mmcls_ann(data_dir)
4 Running results
After running the above reference program ,data_dir The folder will automatically generate meta Folder and corresponding txt file ,txt The content of the document is shown in the figure below .
5 【python Three dimensional deep learning 】python 3D point cloud from basic to deep learning _Coding Leaves blog -CSDN Blog _python Three dimensional point cloud
More 3D 、 Please pay attention to two-dimensional perception algorithm and financial quantitative analysis algorithm “ Lele perception school ” WeChat official account , And will continue to update .
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