深度学习的训练数据集理论上当然是越大越好，但是有的时候我们为了快速验证和测试，并不需要超大量的训练数据（尤其是未标注的数据）那么从中均匀分布挑选数据就是一个实际的需求。
然后我就造了一个轮子来解决这个问题(带进度条的哟)。当然这个所谓的均匀挑选是基于文件名排序的（例如使用我的这篇博文的代码来从视频提取出的图片，排序后按照一定间隔挑选就是基本均匀的，也就是每个视频的图片都能照顾得到。）
使用案例:
#将"~/Movies/cat_video_frames"文件夹中的图像每9个文件挑选一个出来，默认保存目标文件夹是“源文件夹_picked" python3 pick_files.py --path="~/Movies/cat_video_frames" --n=9
效果：
下面是代码:
#!/usr/bin/env python
# -*- coding:utf-8 -*-
###
# File: /Users/simonliu/Documents/python/pick_files/pick_files.py
# Project: /Users/simonliu/Documents/python/pick_files
# Created Date: 2022-05-20 20:21:14
# Author: Simon Liu
# -----
# Last Modified: 2022-05-21 19:03:44
# Modified By: Simon Liu
# -----
# Copyright (c) 2022 SimonLiu Inc.
#
# May the force be with you.
# -----
# HISTORY:
# Date By Comments
# ---------- --- ----------------------------------------------------------
###
import argparse
from pathlib import Path
import shutil
import time
from pkg_resources import to_filename
def get_total_file_count(dir):
total_file_count = 0
for f in dir.glob('***"), key = lambda f : f.stem)
start = time.perf_counter()
for f in src_file_list:
if f.is_file():
if src_index%n == 0 and picked_count <= dst_count:
dst = dst_path/f.name
shutil.copy(f, dst)
finish = "▓" * int(picked_count/ratio)
rest = "-" * int((dst_count - picked_count)/ratio)
progress = (picked_count / dst_count) * 100
dur = time.perf_counter() - start
print("r{:>3.0f}% {}{} {:.2f}s".format(progress, finish, rest, dur), end="")
time.sleep(0.002)
picked_count += 1
src_index += 1
if __name__ == "__main__":
main()
