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Articles by Import Glob

Detecting AV1-encoded videos with Python

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By Import Glob
| alexwlchan.net
Skip to main content Tagged with python, video Posted 8 November 2025 In my previous post, I wrote about how I’ve saved some AV1-encoded videos that I can’t play on my iPhone. Eventually, I’ll upgrade to a new iPhone which supports AV1, but in the meantime, I want to convert all of those videos to an older codec. The problem is finding all the affected videos – I don’t want to wait until I want to watch a video before discovering it won’t play.
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【图像增强】论文复现：归一化流助力低光图像增强！LLFlow的Pytorch源码复现，跑通全流程，详细教程，代码逐行注释，理论与源码结合，替换数据集路径即可训练自己的数据集！-CSDN博客

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By Import Glob, rrdbResults .shape
| CSDN
原创 于 2025-08-18 13:23:32 发布 · 503 阅读 · 21 · 4 · CC 4.0 BY-SA版权 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。 文章标签： #pytorch #python #计算机视觉 #深度学习 #图像处理 #图像增强 #low-light 请先看【专栏介绍文章】： 订阅专栏即可免费查看全部文章内容，不会错过更新，进交流群免费答疑，更有红包福利哦！ 本文亮点： LLFlow源码复现，详细教程，跑通全流程，包括数据集，数据封装，模型实现，训练和测试； 几乎全部关键代码逐行注释，让你读源码毫无障碍； LLFlow的理论架构和源码结合，进一步加深理解算法原理、明确训练和测试流程； 更换路径和相关参数即可训练自己的图像数据集； 前言 论文题目：Low-Light Image Enhancement with Normalizing Flow —— 基于归一化流的微光图像增强 论文地址：Low-Light Image Enhancement with Normalizing Flow...
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python替换word文件中的图片-CSDN博客

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By Import Glob
| CSDN
python替换word文件中的图片 import glob import os import time import pyautogui import pyautogui as p from PIL import ImageGrab from pynput.keyboard import Controller directory = './' directory1 = './output' for f in glob.glob(os.path.join(directory, '*.docx')): os.remove(f) for f in glob.glob(os.path.join(directory1, '*.png')): os.remove(f) def find_and_click(image_path, confidence): image_location = pyautogui.locateOnScreen(image_path, grayscale=True, confidence=confidence) if image_location:...
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将labelme标注的人体姿态Json文件转成训练Yolov8-Pose的txt格式_labelme骨骼点json转yolo-CSDN博客

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By Import Os, Import Glob, Val Ratios
| CSDN
最近在训练Yolov8-Pose时遇到一个问题，就是如何将自己使用labelme标注的Json文件转化成可用于Yolov8-Pose训练的txt文件。 具体代码有以下： 1、labelme2coco.py 将自己标注的人体姿态信息json文件格式合并转换成CoCo格式 import os import json import numpy as np import glob import shutil np.random.seed(41) import cv2 #0为背景 classname_to_id = {"person": 1} class Lableme2CoCo: def __init__(self, splitDir=''): self.images = [] self.annotations = [] self.categories = [] self.img_id = 0 self.ann_id = 0 self.splitDir = splitDir def save_coco_json(self, instance, save_path):...
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[N0wayback 2023春节红包题] happyGame python反编译

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By Import Glob
| CSDN
这个反编译的比较深 一，从附件的图标看是python打包的exe文件，先用pyinstxtractor.py 解包 生成的文件在main.exe_extracted目录下，在这里边找到main 二，把main改名为pyc然后加上头 这个头从包里找一个带头的pyc文件（这里用的_boot文件）把E3前边的16字节插进来 三，用uncompyle6反编译，生成mai.py N0WayBack\春节红包题\Re_rabbit_Game>uncompyle6 main.pyc > main.py 四，打开main.py查看流程，这里需要运行114514次magic_s()每次会进行一个LCG处理 if self.score < 114514: self.magic_s() self.score += 1 LCG def magic_s(self): p = 16045690984230472446 a = 114514 b = 1919810 self.magic = (a * self.magic + b) % p 然后会打印解密的flag def print_flag(self):...
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python与深度学习(十五):CNN和宝可梦模型_菜yuan~的博客-CSDN博客

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By Import Glob
| CSDN
本篇文章是CNN的另外一个例子，宝可梦模型，是自制数据集的例子。之前的例子都是python中库自带的，但是这次的例子是自己搜集数据集，如下图所示整理。 之前简单介绍如何自制数据集，在这里继续介绍将自制的数据划分为训练集，测试集和验证集。 首先建立一个pokeman的文件夹，然后利用之前介绍的爬虫下载5种宝可梦的图片，然后运行下面代码。 import glob import os import cv2 import numpy as np import random import tensorflow as tf from tensorflow import keras tf.random.set_seed(520) np.random.seed(520) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' assert tf.__version__.startswith('2.') def Data_Generation(): X_data = []; Y_data = [] path_data = []; path_label = [] files...
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使用Python和GDAL处理遥感影像数据超详细教程_gdal python 教程_倾城一少的博客-CSDN博客

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By Import Glob
| CSDN
提示：文章末尾有强化学习代码资源 : ) 在本教程中，我们将学习使用 Python 和地理空间数据抽象库 GDAL 自动处理栅格数据的基本技术。 栅格文件通常用于存储地形模型和遥感数据及其衍生产品，例如植被指数和其他环境数据集。 栅格文件往往很大（例如，想象一个以 30m x 30m 分辨率覆盖全球的栅格数据集）并且通常以较小的部分（切片）交付和处理。 因此，对此类大型数据集的高效处理和分析需要自动化。 在教程中，您将学习如何读写常见的栅格格式，以及如何使用 Python 中的 GDAL/OGR API 和 GDAL 命令行实用程序对一批文件进行基本的栅格数据处理。 在本文总结部分将一些实用的python剪裁批处理，python影像镶嵌以及一些Arcpy相关代码进行链接下载，供有需求的同学进行学习与使用。 使用 GDAL，可以在 Python 中读取和写入多种不同的栅格格式。 导入 GDAL 模块时，Python 会自动注册所有已知的 GDAL 驱动程序以读取支持的格式。 最常见的文件格式包括 TIFF 和 GeoTIFF、ASCII Grid 和 Erdas Imagine...
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Using RAPIDS CuDF To Leverage GPU In Feature Engineering

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By Import Glob, Data Frame"
| Zephyrnet
The fact that particular methods succeeded in solving a problem may not lead to the same outcome on a different scale. When distances change, shoes need to change too. In machine learning, data, and data processing are crucial in ensuring the model’s success, and feature engineering is part of that process. When the data is small, the classical Pandas library can easily handle any processing task on the CPU. However, Pandas can be too slow in processing big data.
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My (Very) Personal Data Warehouse

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By Simon Aubury, Import Glob
| Towards Data Science
Fitbit activity analysis with DuckDB Enter DuckDB — a lightweight, free yet powerful analytical database designed to streamline data analysis workflows — that runs locally. In this blog post, I want to use DuckDB to explore my Fitbit data achieve and share the approach for analysing a variety of data formats and charting my health and fitness goals with the help of Seaborn data visualisations. Firstly, I needed to get hold of all of my historic fitness data.
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Batch export .QGS to .QGZ

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By Import Glob, Kadir Şahbaz
| stackexchange.com
QGIS .qgz files are merely zip containers which have the .qgs file embedded, together with potentially some sidecar files. I have just tried, and on a simple project just zipping the .qgs file alone into a .qgz file generates a usable .qgz project.
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U-Net网络结构解析和代码解析_u-net代码_norah2的博客-CSDN博客

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By Import Glob
| CSDN
在语义分割领域，基于深度学习的语义分割算法开山之作是FCN（Fully Convolutional Networks for Semantic Segmentation），而U-Net是遵循FCN的原理，并进行了相应的改进，使其适应小样本的简单分割问题。U-Net网络在医疗影像领域的应用十分广泛，成为了大多数医疗影像语义分割任务的baseline，同时基于U-Net网络改进网络也纷纷出现，本篇文章主要介绍U-NET网络。 由于医学图像往往包含噪声且边界模糊，仅靠低层次的图像特征难以进行目标检测。同时，由于缺乏图像的细节信息，仅靠图像语义特征无法得到准确的边界。而U-Net通过跳跃连接，将低分辨率和高分辨率的特征映射结合起来，有效地融合了低层次和高层次的图像特征，从而成为医学图像分割任务的一个理想解决方案。目前，U-Net已经成为了大多数医学图像分割任务的一个基准，并且激发了很多有意义的改进方法，其网络结构下图所示。...
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pyqt 显示图片的若干方法_pyqt显示图片_十橙的博客-CSDN博客

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By Import Sys, Import Glob, Q GridLayout, QAbstract Item
| CSDN
date: 2022-11-30 14:23 status: public title: ‘pyqt 显示图片的若干方法’ 使用lable 显示图片 特点是最简单，但功能也最少。 import sys from PyQt5.QtGui import QPixmap from PyQt5.QtWidgets import QWidget, QApplication, QVBoxLayout, QLabel class ImageLabel(QWidget): def __init__(self, parent=None): super().__init__(parent) self.resize(600, 400) self.setWindowTitle("label image") pix = QPixmap(r'C:\fruits.jpg') label = QLabel(self) label.setPixmap(pix) label.setScaledContents(True) layout = QVBoxLayout() layout.addWidget(label)...
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Logging Cookbook

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By Import Glob, Import Json, Import Argparse, Import Datetime
| python.org
Attach a Formatter instance to your instance, with a format string such as: 'ASCII section\ufeffUnicode section' The Unicode code point U+FEFF, when encoded using UTF-8, will be encoded as a UTF-8 BOM – the byte-string b'\xef\xbb\xbf'. Replace the ASCII section with whatever placeholders you like, but make sure that the data that appears in there after substitution is always ASCII (that way, it will remain unchanged after UTF-8 encoding).
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Guide for Data Visualization With Bokeh

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By Import Glob, Kashish Rastogi
| Analytics Vidhya
Introduction I am sure many of you have read several articles around the world stating the buzz around “Machine Learning, “Data Scientist”, “Data Visualization” and so on. Some have branded data science as the sexiest job of the 21st century. A report stated by Anaconda’s State of Data Science Report 2020  that 21% of the time is given to Data Visualization. It is important to use a tool or library to help us in the flow of storytelling.
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How Data Science Could Make Cybersecurity Troubleshooting Easier: Firewall Logs Analysis

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By Import Glob
| Towards Data Science
Notes from Industry Photo by Pixabay from Pexels Without a doubt we are in the information age, there have never been so many sources of information and in the cybersecurity sector it is no different. When someone starts working in data analysis he constantly hears that most of the time is spent in processing, correcting and standardizing the data before working with it.
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5 Python Data Processing Tips & Code Snippets

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By Import Glob, Dataframe compressed, Matthew Mayo
| KDnuggets Verified
Photo by Hitesh Choudhary on Unsplash This article contains 5 useful Python code snippets that a beginner might find helpful for data processing. Python is a flexible, general purpose programming language, providing for many ways to approach and achieve the same task. These snippets shed light on one such approach for a given situation; you might find them useful, or find that you have come across another approach that makes more sense to you. 1.
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There is more to ‘pandas.read_csv()’ than meets the eye

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By Parul Pandey, Import Glob, 'NIFTY IT.csv', 'NIFTY BANK.csv'
| Towards Data Science
Time vector created by stories — www.freepik.com Pandas is one of the most widely used libraries in the Data Science ecosystem. This versatile library gives us tools to read, explore and manipulate data in Python. The primary tool used for data import in pandas is read_csv().This function accepts the file path of a comma-separated value, a.k.a, CSV file as input, and directly returns a panda’s dataframe.
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