博客
关于我
强烈建议你试试无所不能的chatGPT,快点击我
Qt::WA_StaticContents的作用
阅读量:4160 次
发布时间:2019-05-26

本文共 302 字,大约阅读时间需要 1 分钟。

Qt::WA_StaticContents这个属性告诉Qt,当重新改变窗口部件的大小时,这个窗口部件的内容并没有发生变化,而且它的内容仍旧保留从窗口部件左上角开始的特性。当重新定义窗口部件的大小时,通过使用这个信息,Qt就可以避免对已经显示区域的重新绘制。图5.5图示了这一情形。 

图片

通常情况下,当重新定义一个窗口部件的大小时,Qt会为窗口部件的整个可见区域生成一个绘制事件。但是如果该窗口部件在创建时使用了Qt::WA_StaticContens属性,那么绘制事件的区域就会被严格限定在之前没有被显示的像素部分上。这也就意味着,如果重新把窗口部件改变为比原来还要小的尺寸,那么就根本不会产生任何绘制事件。

转载地址:http://vpdxi.baihongyu.com/

你可能感兴趣的文章
多目标跟踪的简单理解
查看>>
Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor
查看>>
Joint Tracking and Segmentation of Multiple Targets
查看>>
Subgraph Decomposition for Multi-Target Tracking
查看>>
JOTS: Joint Online Tracking and Segmentation
查看>>
CDT: Cooperative Detection and Tracking for Tracing Multiple Objects in Video Sequences
查看>>
Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-ob
查看>>
Virtual Worlds as Proxy for Multi-Object Tracking Analysis
查看>>
Multi-view People Tracking via Hierarchical Trajectory Composition
查看>>
Online Multi-Object Tracking via Structural Constraint Event Aggregation
查看>>
The Solution Path Algotithm for Identity-Aware Multi-Object Tracking
查看>>
Groupwise Tracking of Crowded Similar-Appearance Targets from Low-Continuity Image Sequences
查看>>
CDTS: Collaborative Detection, Tracking, and Segmentation for Online Multiple Object Segmentation
查看>>
Deep Network Flow for Multi-Object Tracking
查看>>
Multiple People Tracking by Lifted Multicut and Person Re-identification
查看>>
Multi-Object Tracking with Quadruplet Convolutional Neural Networks
查看>>
关于多目标跟踪的一点理解
查看>>
Learning by tracking:Siamese CNN for robust target association
查看>>
MUSTer:Multi-Store Tracker:A Cognitive Psychology Inspired Approach to Object Tracking
查看>>
Understanding and Diagnosing Visual Tracking Systems
查看>>