Mahotas 简明教程

Mahotas - RGB to Sepia

焦茶色是指一种特殊的着色效果，可以让图片看起来古色古香和温暖。

Sepia refers to a special coloring effect that makes a picture look old−fashioned and warm.

当你看到一张焦茶色照片时，它看起来会带有红褐色调。这就像通过一种怀旧的滤镜观察图像，赋予它一种复古的感觉。

When you see a sepia−toned photo, it appears to have a reddish−brown tint. It’s like looking at an image through a nostalgic filter that gives it a vintage vibe.

要将 RGB 图像转换为焦茶色，你需要转换每个像素的红色、绿色和蓝色通道以达到所需的焦茶色调。

To convert an RGB image to sepia, you need to transform the red, green, and blue color channels of each pixel to achieve the desired sepia tone.

RGB to Sepia Conversion in Mahotas

在 Mahotas 中，我们可以使用 colors.rgb2sepia() 函数将 RGB 图像转换为焦茶色图像。

In Mahotas, we can convert an RGB image to sepia−toned image using the colors.rgb2sepia() function.

为了理解转换，让我们从 RGB 颜色模型开始 −

To understand the conversion, let’s start with the RGB color model −

  1. In RGB, an image is made up of three primary colors− red, green, and blue.

  2. Each pixel in the image has a value for these three colors, which determines its overall color. For example, if a pixel has high red and low green and blue values, it will appear as a shade of red.

  3. Now, to convert an RGB image to Sepia using mahotas, we follow a specific formula. The formula involves calculating new values for the red, green, and blue channels of each pixel.

  4. These new values create the Sepia effect by giving the image a warm, brownish tone.

RGB to Sepia Conversion Steps

以下是转换过程的简单说明：

Here’s a simple explanation of the conversion process −

  1. Start with an RGB image − An RGB image is composed of three color channels− red, green, and blue. Each pixel in the image has intensity values for these three channels, ranging from 0 to 255.

  2. Calculate the intensity of each pixel − To convert to sepia, we first need to calculate the overall intensity of each pixel. This can be done by taking a weighted average of the red, green, and blue color channels. The weights used in the average can be different depending on the desired sepia effect.

  3. Adjust the intensity values − Once we have the intensity values, we can apply some specific transformations to obtain the sepia effect. These transformations involve adjusting the levels of red, green, and blue channels in a way that mimics the sepia tone. This can be done by increasing the red channel’s intensity, decreasing the blue channel’s intensity, and keeping the green channel relatively unchanged.

  4. Clip the intensity values − After the adjustments, some intensity values may go beyond the valid range (0 to 255 for 8−bit images).To ensure the values stay within this range, we need to clip them. Values below 0 are set to 0, and values above 255 are set to 255.

  5. Reconstruct the sepia image − Finally, the adjusted intensity values are used to reconstruct the sepia image. The image now appears with the desired sepia tone, giving it a vintage look.

Using the mahotas.colors.rgb2sepia() Function

mahotas.colors.rgb2sepia() 函数将 RGB 图像作为输入，并返回图像的复古色版本。

The mahotas.colors.rgb2sepia() function takes an RGB image as input and returns the sepia version of the image.

生成的复古色图像保留了原始 RGB 图像的总体结构和内容，但引入了一种温暖的褐色色调。

The resulting sepia image retains the overall structure and content of the original RGB image but introduces a warm, brownish tone.

以下是 mahotas 中 rgb2sepia() 函数的基本语法：

Following is the basic syntax of the rgb2sepia() function in mahotas −

mahotas.colors.rgb2sepia(rgb)

其中，@ {s5} 是 RGB 色彩空间中的输入图像。

where, rgb is the input image in RGB color space.

在以下示例中，我们使用 mh.colors.rgb2sepia() 函数将一张 RGB 图像转换为复古色图像：

In the following example, we are converting an RGB image to a sepia image using the mh.colors.rgb2sepia() function −

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the image
image = mh.imread('nature.jpeg')
# Converting it to Sepia
sepia_image = mh.colors.rgb2sepia(image)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 2)
# Displaying the original RGB image
axes[0].imshow(image)
axes[0].set_title('RGB Image')
axes[0].set_axis_off()
# Displaying the sepia image
axes[1].imshow(sepia_image)
axes[1].set_title('Sepia Image')
axes[1].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

以下是上面代码的输出： -

Following is the output of the above code −

rgb sepia color image

Using Transformation Factor

我们可以用于将 RGB 转换为复古色的另一种方法是使用预定系数调整每个颜色通道的强度，这些系数基于每个通道对最终复古色图像的贡献。

Another approach we can use to convert an RGB to sepia is by adjusting the intensities of each color channel using predetermined coefficients, which are based on the contribution of each channel to the final sepia image.

各通道对复古色的贡献计算如下：

The contribution of each channel to sepia is calculated as follows −

TR = 0.393 * R + 0.769 * G + 0.189 * B
TG = 0.349 * R + 0.686 * G + 0.168 * B
TB = 0.272 * R + 0.534 * G + 0.131 * B

其中，@ {s6} 分别是红色、绿色和蓝色的转换因子。

where TR, TG and TB are the transformation factors of red, green, and blue respectively.

将这些变换应用到 RGB 图像中的每个像素上，便会得到完整的复古色调图像。

Applying these transformations to each pixel in the RGB image results in the entire sepiatoned image.

Example

以下示例显示了如何使用 RGB 通道的转换因子将 RGB 图像转换为复古色：

The following example shows conversion of an RGB image to sepia using transformation factors of the RGB channels −

import mahotas as mh
import numpy as np
import matplotlib.pyplot as plt
# Loading the image
image = mh.imread('sun.png')
# Getting the dimensions of the image
height, width, _ = image.shape
# Converting it to Sepia
# Creating an empty array for the sepia image
sepia_image = np.empty_like(image)
for y in range(height):
   for x in range(width):
      # Geting the RGB values of the pixel
      r, g, b = image[y, x]
      # Calculating tr, tg, tb
      tr = int(0.393 * r + 0.769 * g + 0.189 * b)
      tg = int(0.349 * r + 0.686 * g + 0.168 * b)
      tb = int(0.272 * r + 0.534 * g + 0.131 * b)
      # Normalizing the values if necessary
      if tr > 255:
         tr = 255
      if tg > 255:
         tg = 255
      if tb > 255:
         tb = 255
      # Setting the new RGB values in the sepia image array
      sepia_image[y, x] = [tr, tg, tb]
# Creating a figure and axes for subplots
fig, axes = plt.subplots(1, 2)
# Displaying the original RGB image
axes[0].imshow(image)
axes[0].set_title('RGB Image')
axes[0].set_axis_off()
# Displaying the sepia image
axes[1].imshow(sepia_image)
axes[1].set_title('Sepia Image')
axes[1].set_axis_off()
# Adjusting spacing between subplots
plt.tight_layout()
# Showing the figures
plt.show()

上述代码的输出如下：

Output of the above code is as follows −

rgb sepia color image1