Image filter algorithm code implemented in Ruby

Filters are now widely used in our lives, and we will also touch them in development. This article shares with you several image filter algorithms implemented using ruby, including grayscale, binary, and negative. , relief. It is very simple and practical, friends in need can refer to it.

Original image

1. Grayscale algorithm

The color of each pixel in a color photo The value is composed of a mixture of red, green, and blue values. There are many values ​​​​of red, green, and blue, so the color value of the pixel can also have many color values. This is the principle of color pictures, while grayscale photos There are only 256 colors. The general processing method is to set the three RGB channel values ​​of the image color value to be the same, so that the display effect of the image will be gray.

There are generally three algorithms for grayscale processing:

1. Maximum method: that is, the new color value R=G=B=Max(R, G, B), The image processed by this method appears to have a high brightness value.

2. Average method: that is, the new color value R＝G＝B＝(R＋G＋B)/3, the picture processed in this way is very soft

3. Weighted average method: That is, the new color value R=G=B=(R*Wr+G*Wg+B*Wb). Generally, because the human eye has different sensitivities to different colors, the weights of the three color values ​​are different. Generally speaking, green is the highest, red is second, and blue is the lowest. The most reasonable values ​​are Wr = 30%, Wg = 59%, Wb = 11%

The following is the weighted average Ruby implementation of value method:

##

 #灰度化图片
 #取RGB三色平均值
 def self.grey(bmp)
  for i in 0 .. bmp.height - 1
   for j in 0 .. bmp.width - 1
    rgb = bmp.getRGB(i, j)
    grey = rgb.r.to_f * 0.3+rgb.g.to_f *0.59 +rgb.b.to_f * 0.11.to_i
    bmp.setRGB(i, j, RGB.new(grey, grey, grey))
   end
  end
 end

Grayscale effect:

2. Binarization

Image binarization is to set the gray value of the pixels on the image to 0 or 255, which means that the entire image presents an obvious black and white effect. All pixels whose grayscale is greater than or equal to the threshold are judged to belong to a specific object, and their grayscale value is 255, otherwise these pixels are excluded from the object area, and their grayscale value is 0, which indicates the background or exceptional object area.

Image binarization is often used in image recognition applications such as cracking verification codes

#二值化图片
 #小于一定阈值设为0 0 0，大于设置为255 255 255
 def self.binarization(bmp)
  imageGreyLevel = bmp.getGreyLevel
  for i in 0 .. bmp.height - 1
   for j in 0 .. bmp.width - 1
    rgb = bmp.getRGB(i, j)
    if rgb.getGreyLevel<imageGreyLevel
     bmp.setRGB(i, j, RGB.new(0, 0, 0))
    else
     bmp.setRGB(i, j, RGB.new(255, 255, 255))
    end
   end

  end
 end

Binarization effect

3. Negative film

The realization of the negative film effect is very simple, that is, inverting the value of each RGB channel, that is, using 255 to subtract

#底片化图片
 #RGB取反色255-
 def self.contraryColor(bmp)
  for i in 0 .. bmp.height - 1
   for j in 0 .. bmp.width - 1
    rgb = bmp.getRGB(i, j)
    bmp.setRGB(i, j, rgb.getContrary)
   end
  end
 end

Negative effect

4. Relief effect

The algorithm of relief is relatively complicated. Subtract the RGB value of the adjacent point from the RGB value of the current point and add 128 as the new RGB value. Since the color values ​​of adjacent points in the picture are relatively close, after such algorithm processing, only the edge areas of the color, that is, the results of the parts with large differences in adjacent colors will be more obvious, while other smooth areas will not have the same color values. They are all close to around 128, which is gray, so

has a relief effect.

In the actual effect, after this processing, some areas may still have some "color" dots or strips, so it is best to perform grayscale processing on the new RGB values.

#浮雕效果
 #浮雕的算法相对复杂一些，用当前点的RGB值减去相邻点的RGB值并加上128作为新的RGB值。由于图片中相邻点的颜色值是比较接近的，
 #因此这样的算法 处理之后，只有颜色的边沿区域，也就是相邻颜色差异较大的部分的结果才会比较明显，而其他平滑区域则值都接近128左右，
 #也就是灰色，这样就具有了浮雕效果。
 #在实际的效果中，这样处理后，有些区域可能还是会有”彩色”的一些点或者条状痕迹，所以最好再对新的RGB值做一个灰度处理。
 def self.emboss(bmp)

  preRGB=RGB.new(128, 128, 128)

  for i in 0 .. bmp.height - 1
   for j in 0 .. bmp.width - 1
    currentRGB=bmp.getRGB(i, j)
    r=(currentRGB.r-preRGB.r)*1+128
    g=(currentRGB.g - preRGB.g)*1+128
    b=(currentRGB.b-preRGB.b)*1+128

    bmp.setRGB(i, j, RGB.new(r,g,b).getGreyRGB)
    preRGB = currentRGB
   end
  end

 end

Relief effect

The above content is about the image filter algorithm implemented in Ruby Code, hope it helps everyone.

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