处理算法如下，在Start方法中分别调用想要的效果就行了。其中，将你需要处理的 图片 拖拽到 image参数上。注意，如果想要图片保持原来的尺寸不被压缩，需要更改图片的导入设置如下图，主要的Texture Type 和 Non Power of 2 这两个参数。
using UnityEngine;
using System.Collections;
using System.IO;
public class ImageDeal : MonoBehaviour
{
public Texture2D image;
private Texture2D outPut;
int Width;
int Height;
// Use this for initialization
void Start ()
{
Width = image.width;
Height = image.height;
outPut = new Texture2D (Width, Height);
Flur ();
writeImage ();
}
/// <summary>
/// 底片效果.
/// </summary>
private void Dipian ()
{
Color pixel;
for (int x = 1; x < Width; x++) {
for (int y = 1; y < Height; y++) {
float r, g, b;
pixel = image.GetPixel (x, y);
r = 1f - pixel.r;
g = 1f - pixel.g;
b = 1f - pixel.b;
outPut.SetPixel (x, y, new Color (r, g, b));
}
}
}
/// <summary>
/// 浮雕效果
/// </summary>
private void Fudiao ()
{
//以浮雕效果显示图像
Color pixel1, pixel2;
for (int x = 0; x < Width - 1; x++) {
for (int y = 0; y < Height - 1; y++) {
float r = 0, g = 0, b = 0;
pixel1 = image.GetPixel (x, y);
pixel2 = image.GetPixel (x + 1, y + 1);
r = Mathf.Abs (pixel1.r - pixel2.r + 128 / 255);
g = Mathf.Abs (pixel1.g - pixel2.g + 128 / 255);
b = Mathf.Abs (pixel1.b - pixel2.b + 128 / 255);
outPut.SetPixel (x, y, new Color (r, g, b));
}
}
}
/// <summary>
/// 黑白效果
/// </summary>
private void Heibai(){
Color pixel;
for (int x = 0; x < Width; x++)
for (int y = 0; y < Height; y++) {
pixel = image.GetPixel (x, y);
float r, g, b, Result = 0;
r = pixel.r;
g = pixel.g;
b = pixel.b;
//实例程序以加权平均值法产生黑白图像
int iType = 2;
switch (iType) {
case 0://平均值法
Result = ((r + g + b) / 3);
break;
case 1://最大值法
Result = r > g ? r : g;
Result = Result > b ? Result : b;
break;
case 2://加权平均值法
Result = ((0.7f * r) + (0.2f * g) + (0.1f * b));
break;
}
outPut.SetPixel (x, y, new Color (Result, Result, Result));
}
}
/// <summary>
/// 柔化3x3高斯模型
/// </summary>
private void Rouhua3(){
Color pixel;
int[] Gauss ={ 1, 2, 1, 2, 4, 2, 1, 2, 1 };
for (int x = 1; x < Width - 1; x++)
for (int y = 1; y < Height - 1; y++)
{
float r = 0, g = 0, b = 0;
int Index = 0;
for (int col = -1; col <= 1; col++)
for (int row = -1; row <= 1; row++)
{
pixel = image.GetPixel(x + row, y + col);
r += pixel.r * Gauss[Index];
g += pixel.g * Gauss[Index];
b += pixel.b * Gauss[Index];
Index++;
}
r /= 16;
g /= 16;
b /= 16;
outPut.SetPixel(x - 1, y - 1, new Color(r, g, b));
}
}
/// <summary>
/// 柔化5x5高斯模型
/// </summary>
private void Rouhua5(){
Color pixel;
int[] Gauss = { 1, 4, 7, 4, 1, 4, 16, 26, 16, 4, 7, 26, 41, 26, 7, 4, 16, 26, 16, 4, 1, 4, 7, 4, 1 };
for (int x = 1; x < Width - 1; x++)
for (int y = 1; y < Height - 1; y++)
{
float r = 0, g = 0, b = 0;
int Index = 0;
for (int col = -2; col <= 2; col++)
for (int row = -2; row <= 2; row++)
{
pixel = image.GetPixel(x + row, y + col);
r += pixel.r * Gauss[Index];
g += pixel.g * Gauss[Index];
b += pixel.b * Gauss[Index];
Index++;
}
r /= 273;
g /= 273;
b /= 273;
outPut.SetPixel(x - 1, y - 1, new Color(r, g, b));
}
}
/// <summary>
/// 柔化，高斯模糊通用方法
/// </summary>
private void Flur(){
Color pixel;
float[] Gauss = GaussianSmooth (10.0f);
for (int x = 1; x < Width - 1; x++)
for (int y = 1; y < Height - 1; y++)
{
float r = 0, g = 0, b = 0;
int Index = 0;
int length = (int)Mathf.Sqrt (Gauss.Length) / 2;
for (int col = -length; col <= length; col++)
for (int row = -length; row <= length; row++)
{
pixel = image.GetPixel(x + row, y + col);
r += pixel.r * Gauss[Index];
g += pixel.g * Gauss[Index];
b += pixel.b * Gauss[Index];
Index++;
}
outPut.SetPixel(x - 1, y - 1, new Color(r, g, b));
}
}
/// <summary>
/// 锐化效果
/// </summary>
private void Ruihua(){
Color pixel;
//拉普拉斯模板
int[] Laplacian ={ -1, -1, -1, -1, 9, -1, -1, -1, -1 };
for (int x = 1; x < Width - 1; x++)
for (int y = 1; y < Height - 1; y++)
{
float r = 0, g = 0, b = 0;
int Index = 0;
for (int col = -1; col <= 1; col++)
for (int row = -1; row <= 1; row++)
{
pixel = image.GetPixel(x + row, y + col);
r += pixel.r * Laplacian[Index];
g += pixel.g * Laplacian[Index];
b += pixel.b * Laplacian[Index];
Index++;
}
outPut.SetPixel(x - 1, y - 1, new Color(r, g, b));
}
}
/// <summary>
/// 写文件
/// </summary>
private void writeImage ()
{
byte[] bytes = outPut.EncodeToJPG ();
File.WriteAllBytes (Application.dataPath + "/test.jpg", bytes);
}
/// <summary>
/// 动态生成高斯模型
/// </summary>
/// <returns>The smooth.</returns>
/// <param name="sigma">Sigma.</param>
private float[] GaussianSmooth( float sigma)
{
sigma = sigma > 0 ? sigma : -sigma;
//高斯核矩阵的大小为(6*sigma+1)*(6*sigma+1)
//ksize为奇数
int ksize = (int)Mathf.Ceil(sigma * 3) * 2 + 1;
//计算一维高斯核
float[] kernel = new float[ksize];
float scale = -0.5f / (sigma * sigma);
const float PI = 3.141592653f;
float cons = 1/ Mathf.Sqrt(-scale / PI);
float sum = 0;
int kcenter = ksize/2;
int i = 0, j = 0;
for(i = 0; i < ksize; i++)
{
int x = i - kcenter;
kernel[i] = cons * Mathf.Exp(x * x * scale);//一维高斯函数
sum +=kernel[i];
}
//归一化,确保高斯权值在[0,1]之间
for(i = 0; i < ksize; i++)
{
kernel[i] = kernel[i] / sum;
}
return kernel;
}
}
时间： 2024-10-01 04:29:32