标签: aspect-ratio

如何使表单具有固定的宽高比,并在调整大小时保留？

我知道可以通过覆盖OnSizeChanged和手动修改[new]高度/宽度来完成,但这会导致闪烁,因为它在调用事件之前调整大小一次(大小与宽高比不匹配)然后再次调整大小(到正确的宽高比).有没有更好的办法？

我正在制作一款使用手机相机的Android 1.6应用程序.

为了独立完成这个app分辨率,我需要设置一个兼容的宽高比来预览SurfaceLayout上的摄像头.在1.6 sdk中,无法获得支持的相机预览尺寸.可以使用4:3或3:2的宽高比,并且没有错误吗？

另一方面,我需要一种方法来制作一个xml布局,在每个分辨率中以此(未知)宽高比表示此Surfacelayout.我假设无法在运行时更改SurfaceLayout大小.我可以用"dp"单位吗？另一种方法是以编程方式进行此布局？

有一些应用程序,如Vignette或Android相机应用程序,有一些技巧来制作类似的东西,如黑条(晕影)或固定按钮栏,但我不知道如何在任何类型的分辨率.

有任何想法吗？

谢谢!

如何根据integer:integer给定因子计算纵横比(格式化为)？

例如,宽高比16:9的因子为1.778,因为16/9 = 1.778.但是如何通过该因子找到比率？所以

Dimension getAspectRatio(double factor) {
    ...
}

public static void main(String[] arguments) {
    Dimension d = getAspectRatio(16d / 9d);
    System.out.println(d.width + ":" + d.height);
}

应该回来

16:9

我无法相信我找不到这个公式.我正在使用一个名为SLIR的PHP脚本来调整图像大小.该脚本要求指定裁剪的宽高比.我想根据我允许用户输入这些值的形式指定图像的宽度和高度来获得宽高比.例如,如果用户输入1024x768图像,我会得到宽高比4:3.对于我的生活,我找不到PHP或Javascript中的公式示例我可以用来获得基于知道w,h的宽高比值并将宽高比插入变量.

是否可以检测HTML5视频元素的宽高比？

我知道视频会缩小以适应<video>元素的尺寸,但我想检测源视频的宽高比.

这样你可以删除任何黑条.

对不起,我没有在其他地方看到这个答案.

存在一个方形div,它是需要随窗口缩放的任意百分比宽度(和相同的高量).

它必须保持在视口的范围内(即:不在外面剪裁)并保持其方形 - 基本上复制了background-size: contain;CSS 的特征.

我需要支持iOS Safari v3.2,所以我不能使用vw/vh/vmin/vmax,并且非常喜欢只支持CSS的解决方案.

我有点困惑但是如何使iPhone相机拍摄的图像适合任何给定大小的UIImageView.显然,每个图像视图的比例会有所不同,因此图像不会完全"适合".但是,假设我有一个尺寸为200pt x 400 pt的UIImageView.什么是适合UIImage这个的最佳方式UIImageView？

我想我想做类似以下的事情:

[imageView setContentMode:UIViewContentModeScaleAspectFill];

这种工作,但它似乎增加了我的UIImageView的大小,但我不希望这...

我正在CardView为我的应用程序设计一个富媒体标题.

我尝试做这样的事情:

根据谷歌材料设计规范,图片应该有16:9的宽高比:

所以,我的问题,如何实现这个(代码或XML)？

如果我使用定义的大小,它将不是真正的16:9宽高比,我将不得不处理所有屏幕尺寸和方向的许多资源文件.

否则,我没有成功的代码,因为在设置大小onBindViewHolder(...),getWidth()对我的看法回报0.

任何的想法 ？

我目前正在研究增强现实应用程序.目标设备是光学看到的HMD我需要校准其显示器以实现虚拟对象的正确注册.我使用SPA的SPAAM实现来完成它,结果足够精确到我的目的.

我的问题是,校准应用程序在输出中给出了一个4x4 投影矩阵,我可以直接使用OpenGL作为示例.但是,我使用的增强现实框架只接受光学校准参数,格式为Field of View某些参数+ Aspect Ratio某些参数+ 4x4 View矩阵.

这是我有的:

错误格式校正校准结果:

 6.191399, 0.114267, -0.142429, -0.142144
-0.100027, 11.791289, 0.05604,   0.055928
 0.217304,-0.486923, -0.990243, -0.988265
 0.728104, 0.005347, -0.197072,  0.003122

您可以在此处查看生成此结果的代码.

我所理解的是单点有源对准方法产生3×4矩阵,然后程序将该矩阵乘以正交投影矩阵以得到上面的结果.以下是用于生成正交矩阵的参数:

near : 0.1, far : 100.0, right : 960, left : 0, top :  540, bottom:  0

正确格式的校准结果不正确:

Param 1 : 12.465418
Param 2 : 1.535465

 0.995903,   -0.046072,   0.077501,  0.000000   
 0.050040,    0.994671,  -0.047959,  0.000000
-0.075318,    0.051640,   0.992901,  0.000000
 114.639359, -14.115030, -24.993097, …

我正在尝试创建一个“相似度图”，以快速显示表格中的项目相似度与其他项目。

一个简单的例子：

要使用的“ property_data.csv”文件：

"","Country","Town","Property","Property_value"
"1","UK","London","Road_quality","Bad"
"2","UK","London","Air_quality","Very bad"
"3","UK","London","House_quality","Average"
"4","UK","London","Library_quality","Good"
"5","UK","London","Pool_quality","Average"
"6","UK","London","Park_quality","Bad"
"7","UK","London","River_quality","Very good"
"8","UK","London","Water_quality","Decent"
"9","UK","London","School_quality","Bad"
"10","UK","Liverpool","Road_quality","Bad"
"11","UK","Liverpool","Air_quality","Very bad"
"12","UK","Liverpool","House_quality","Average"
"13","UK","Liverpool","Library_quality","Good"
"14","UK","Liverpool","Pool_quality","Average"
"15","UK","Liverpool","Park_quality","Bad"
"16","UK","Liverpool","River_quality","Very good"
"17","UK","Liverpool","Water_quality","Decent"
"18","UK","Liverpool","School_quality","Bad"
"19","USA","New York","Road_quality","Bad"
"20","USA","New York","Air_quality","Very bad"
"21","USA","New York","House_quality","Average"
"22","USA","New York","Library_quality","Good"
"23","USA","New York","Pool_quality","Average"
"24","USA","New York","Park_quality","Bad"
"25","USA","New York","River_quality","Very good"
"26","USA","New York","Water_quality","Decent"
"27","USA","New York","School_quality","Bad"

码：

prop <- read.csv('property_data.csv')

Property_col_vector <- c("NA" = "#e6194b",
                "Very bad" = "#e6194B",
                "Bad" = "#ffe119",
                "Average" = "#bfef45",
                "Decent" = "#3cb44b",
                "Good" = "#42d4f4",
                "Very good" = "#4363d8")

plot_likeliness <- function(town_property_table){
    g …