Question

我正在使用GD库动态创建图像但是当我使用imagerotate（）函数旋转图像时它工作正常，但它给图像带来了非常刺激性的粗糙边缘旋转。
如图所示。
那么如何使旋转图像的这些边/边平滑？
enter image description here

Answer 1

避免在旋转图像时获取Jaggies effect的一种方法是使用另一种方式对像素进行采样，而不仅仅是调整像素，例如使用Nearest-neighbor interpolation使边缘更平滑。你可以看到matlab代码示例：

im1 = imread('lena.jpg');imshow(im1);  
[m,n,p]=size(im1);
thet = rand(1);
mm = m*sqrt(2);
nn = n*sqrt(2);
for t=1:mm
   for s=1:nn
      i = uint16((t-mm/2)*cos(thet)+(s-nn/2)*sin(thet)+m/2);
      j = uint16(-(t-mm/2)*sin(thet)+(s-nn/2)*cos(thet)+n/2);
      if i>0 && j>0 && i<=m && j<=n           
         im2(t,s,:)=im1(i,j,:);
      end
   end
end
figure;
imshow(im2);

取自（here）。基本上，这意味着在对原始图片中的像素进行采样时，我们对近像素进行采样并对其进行插值以获得目标像素值。这条路你可以通过安装任何附加包来实现你想要的。

修改

我发现了一些我曾用Java编写的旧代码，其中包含几个采样算法的实现。这是代码：

最近邻采样器：

/** * @pre (this!=null) && (this.pixels!=null) * @post returns the sampled pixel of (x,y) by nearest neighbor sampling */ private Pixel sampleNearestNeighbor(double x, double y) { int X = (int) Math.round(x); int Y = (int) Math.round(y); if (X >= 0 && Y >= 0 && X < this.pixels.length && Y < this.pixels[0].length) // (X,Y) is within this.pixels' borders return new Pixel(pixels[X][Y].getRGB()); else return new Pixel(255, 255, 255); // sample color will be default white }

双线性采样器：

/** * @pre (this!=null) && (this.pixels!=null) * @post returns the sampled pixel of (x,y) by bilinear interpolation */ private Pixel sampleBilinear(double x, double y) { int x1, y1, x2, y2; x1 = (int) Math.floor(x); y1 = (int) Math.floor(y); double weightX = x - x1; double weightY = y - y1; if (x1 >= 0 && y1 >= 0 && x1 + 1 < this.pixels.length && y1 + 1 < this.pixels[0].length) { x2 = x1 + 1; y2 = y1 + 1; double redAX = (weightX * this.pixels[x2][y1].getRed()) + (1 - weightX) * this.pixels[x1][y1].getRed(); double greenAX = (weightX * this.pixels[x2][y1].getGreen()) + (1 - weightX) * this.pixels[x1][y1].getGreen(); double blueAX = (weightX * this.pixels[x2][y1].getBlue()) + (1 - weightX) * this.pixels[x1][y1].getBlue(); // bilinear interpolation of A point double redBX = (weightX * this.pixels[x2][y2].getRed()) + (1 - weightX) * this.pixels[x1][y2].getRed(); double greenBX = (weightX * this.pixels[x2][y2].getGreen()) + (1 - weightX) * this.pixels[x1][y2].getGreen(); double blueBX = (weightX * this.pixels[x2][y2].getBlue()) + (1 - weightX) * this.pixels[x1][y2].getBlue(); // bilinear interpolation of B point int red = (int) (weightY * redBX + (1 - weightY) * redAX); int green = (int) (weightY * greenBX + (1 - weightY) * greenAX); int blue = (int) (weightY * blueBX + (1 - weightY) * blueAX); // bilinear interpolation of A and B return new Pixel(red, green, blue); } else if (x1 >= 0 && y1 >= 0 // last row or column && (x1 == this.pixels.length - 1 || y1 == this.pixels[0].length - 1)) { return new Pixel(this.pixels[x1][y1].getRed(), this.pixels[x1][y1] .getGreen(), this.pixels[x1][y1].getBlue()); } else return new Pixel(255, 255, 255); // sample color will be default white }

高斯采样器：

/** * @pre (this!=null) && (this.pixels!=null) * @post returns the sampled pixel of (x,y) by gaussian function */ private Pixel sampleGaussian(double u, double v) { double w = 3; // sampling distance double sqrSigma = Math.pow(w / 3.0, 2); // sigma^2 double normal = 0; double red = 0, green = 0, blue = 0; double minIX = Math.round(u - w); double maxIX = Math.round(u + w); double minIY = Math.round(v - w); double maxIY = Math.round(v + w); for (int ix = (int) minIX; ix <= maxIX; ix++) { for (int iy = (int) minIY; iy <= maxIY; iy++) { double sqrD = Math.pow(ix - u, 2) + Math.pow(iy - v, 2); // squared distance between (ix,iy) and (u,v) if (sqrD < Math.pow(w, 2) && ix >= 0 && iy >= 0 && ix < pixels.length && iy < pixels[0].length) { // gaussian function double gaussianWeight = Math.pow(2, -1 * (sqrD / sqrSigma)); normal += gaussianWeight; red += gaussianWeight * pixels[ix][iy].getRed(); green += gaussianWeight * pixels[ix][iy].getGreen(); blue += gaussianWeight * pixels[ix][iy].getBlue(); } } } red /= normal; green /= normal; blue /= normal; return new Pixel(red, green, blue); }

实际轮播：

/** * @pre (this!=null) && (this.pixels!=null) && (1 <= samplingMethod <= 3) * @post creates a new rotated-by-degrees Image and returns it */ public myImage rotate(double degrees, int samplingMethod) { myImage outputImg = null; int t = 0; for (; degrees < 0 || degrees >= 180; degrees += (degrees < 0) ? 180 : -180) t++; int w = this.pixels.length; int h = this.pixels[0].length; double cosinus = Math.cos(Math.toRadians(degrees)); double sinus = Math.sin(Math.toRadians(degrees)); int width = Math.round((float) (w * Math.abs(cosinus) + h * sinus)); int height = Math.round((float) (h * Math.abs(cosinus) + w * sinus)); w--; h--; // move from (1,..,k) to (0,..,1-k) Pixel[][] pixelsArray = new Pixel[width][height]; double x = 0; // x coordinate in the source image double y = 0; // y coordinate in the source image if (degrees >= 90) { // // 270 or 90 degrees turn double temp = cosinus; cosinus = sinus; sinus = -temp; } for (int i = 0; i < width; i++) { for (int j = 0; j < height; j++) { double x0 = i; double y0 = j; if (degrees >= 90) { if ((t % 2 == 1)) { // 270 degrees turn x0 = j; y0 = width - i - 1; } else { // 90 degrees turn x0 = height - j - 1; y0 = i; } } else if (t % 2 == 1) { // 180 degrees turn x0 = width - x0 - 1; y0 = height - y0 - 1; } // calculate new x/y coordinates and // adjust their locations to the middle of the picture x = x0 * cosinus - (y0 - sinus * w) * sinus; y = x0 * sinus + (y0 - sinus * w) * cosinus; if (x < -0.5 || x > w + 0.5 || y < -0.5 || y > h + 0.5) // the pixels that does not have a source will be painted in // default white pixelsArray[i][j] = new Pixel(255, 255, 255); else { if (samplingMethod == 1) pixelsArray[i][j] = sampleNearestNeighbor(x, y); else if (samplingMethod == 2) pixelsArray[i][j] = sampleBilinear(x, y); else if (samplingMethod == 3) pixelsArray[i][j] = sampleGaussian(x, y); } } outputImg = new myImage(pixelsArray); } return outputImg; }

Answer 2

这可能听起来相当黑客，但这是最简单的方法，甚至大型企业解决方案都使用它。

诀窍是首先创建所需大小的2X图像，然后进行所有绘图调用，然后将其调整为所需的原始大小。

它不仅非常容易实现，而且它的速度也很快，并且可以产生非常好的效果。当我需要对边缘应用模糊时，我会在所有情况下使用此技巧。

另一个优点是它不会在图像的其余部分包含模糊，并且保持清晰 - 只有旋转图像的边框才能变得平滑。

Answer 3

您可以尝试的一件事是使用imageantialias()来平滑边缘。

如果这不符合您的需求，GD本身可能就不够了。

GD使用非常快速的方法来实现所有它的实际平滑或任何相关的东西。如果你想要一些正确的图像编辑，你可以查看ImageMagick（这需要服务器上的额外软件）或者根据GD编写你自己的函数。

请记住，PHP的数据量非常大，因此编写自己的函数可能会令人失望。（根据我的经验，PHP比编译代码慢大约40倍。）

我建议将ImageMagick用于结果质量很重要的任何图像处理。

去除/平滑gd库中的粗糙边缘

3 个答案: