image processing

with magick

Author

Tony Duan

This document provides a practical guide to image processing in R using the magick package. The magick package is a powerful and versatile tool for image manipulation, providing a wide range of functions for reading, writing, editing, and transforming images. It is built on top of the ImageMagick library, a robust and feature-rich image processing software.

We will cover the basics of reading and writing images, as well as more advanced topics like color manipulation, transformations, and adding text and borders. We will also explore how to integrate magick with ggplot2 to create more visually appealing and informative plots.

Code

# Load the magick library
library(magick)

1. Reading and Writing Images

The first step in any image processing workflow is to read an image into R. The image_read() function can read images from a local file or a URL.

Reading an Image

Code

# Read an image from a local file
raw_logo <- image_read('./images/comb.webp')

Getting Image Information

The image_info() function provides useful information about an image, such as its format, width, height, and colorspace.

Code

image_info(raw_logo)

  format width height colorspace matte filesize density
1   WEBP   450    303       sRGB  TRUE    49650   72x72

Changing Image Format

The image_convert() function allows you to convert an image from one format to another.

Code

new_png <- image_convert(raw_logo, "png")
image_info(new_png)

  format width height colorspace matte filesize density
1    PNG   450    303       sRGB  TRUE        0   72x72

Writing an Image

The image_write() function saves an image to a file. You can specify the path and format of the output file.

Code

image_write(new_png, path = "./images/new_png.png", format = "png")

2. Basic Image Manipulations

Let’s read in another image to demonstrate some basic manipulations.

Code

raw_logo <- image_read('./images/logo1.png')
raw_logo

Filling with Color

The image_fill() function can be used to fill areas of an image with a specified color. Here, we fill the white corners of the logo with green. The fuzz argument controls the tolerance of the color matching.

Code

img_filled <- raw_logo %>% 
    image_fill("green", "+1+1", fuzz = 50, refcolor = "white") %>% 
    image_fill("green", "+140+1", fuzz = 50, refcolor = "white") %>% 
    image_fill("green", "+1+99", fuzz = 50, refcolor = "white") %>% 
    image_fill("green", "+140+99", fuzz = 50, refcolor = "white")

img_filled

Rotating an Image

The image_rotate() function rotates an image by a specified angle.

Code

img_filled %>% image_rotate(45)

Adding a Border

The image_border() function adds a border to an image. You can specify the color and size of the border.

Code

img_filled %>% image_border("#CD5C5C", "20x20")

Making a Background Transparent

The image_transparent() function makes a specified color transparent. This is useful for creating images with transparent backgrounds.

Code

# The fuzz argument (0-100) controls how similar colors need to be to be made transparent.
b = img_filled %>% image_transparent(color = 'green', fuzz = 10)
b

Code

image_write(b, path = "images/b.png", format = "png")

Adjusting Opacity

The image_colorize() function can be used to adjust the opacity of an image.

Code

b = img_filled %>% image_colorize(opacity = 80, color = 'white')
b

Adjusting Brightness

The image_modulate() function can be used to adjust the brightness, saturation, and hue of an image.

Code

b = img_filled %>% image_modulate(brightness = 30)
b

Applying a Blur Effect

The image_blur() function applies a blur effect to an image. You can control the radius and sigma of the blur.

Code

b = img_filled %>%  image_blur(10, 5)
b

Adding Text to an Image

The image_annotate() function adds text to an image. You can specify the text, font, size, color, and gravity (position).

Code

b = img_filled %>% image_annotate("The quick brown fox", font = 'Times', size = 20, gravity = "southwest", color = "red")
b

Resizing an Image

The image_resize() and image_scale() functions can be used to resize an image. image_resize() allows you to specify the new dimensions, while image_scale() scales the image by a factor.

Code

b = img_filled %>% image_resize("200x200")
b

Code

image_info(b)

  format width height colorspace matte filesize density
1    PNG   200    143       sRGB  TRUE        0   72x72

3. Advanced Image Manipulations

Creating a Black and White Logo

By combining several magick functions, we can perform more complex image manipulations. Here, we create a black and white version of the logo with a transparent background.

Code

img_filled2 <- raw_logo %>% 
    image_fill("transparent", "+1+1", fuzz = 50, refcolor = "white") %>% 
    image_fill("transparent", "+140+1", fuzz = 50, refcolor = "white") %>% 
    image_fill("transparent", "+1+99", fuzz = 50, refcolor = "white") %>% 
    image_fill("transparent", "+140+99", fuzz = 50, refcolor = "white")

img_filled2 %>% 
    image_channel("Opacity") %>% 
    image_convert(matte = FALSE)

4. Integrating `magick` with `ggplot2`

One of the most powerful features of magick is its ability to be integrated with ggplot2 to create more visually interesting plots.

Adding a Background Image to a Plot

We can use annotation_custom() and rasterGrob() to add an image as the background of a ggplot2 plot.

Code

library(ggplot2)
library(png)
library(grid)

img <- readPNG("./images/new_png.png")

bees <- data.frame(distance = c(0.5, 1, 1.5, 2, 2.5, 3),
                  number = c(40, 34, 32, 22, 18, 10))

ggplot(data = bees, aes(x = distance, y = number)) +
  annotation_custom(rasterGrob(img, 
                               width = unit(1, "npc"),
                               height = unit(1, "npc")), 
                    -Inf, Inf, -Inf, Inf) +
  geom_point() +
  xlab("Distance (km)") +
  ylab("Number of Bees") +
  ylim(0, 45)

Replacing Scatter Points with Images

The ggimage package provides geom_image(), which allows you to use images as points in a scatter plot.

Code

library(ggimage)

bees$image <- "./images/bee.png"

ggplot(data = bees, aes(x = distance, y = number)) +
  annotation_custom(rasterGrob(img, 
                               width = unit(1, "npc"),
                               height = unit(1, "npc")), 
                    -Inf, Inf, -Inf, Inf) +
  geom_image(aes(image = image), size = 0.15) +
  xlab("Distance (km)") +
  ylab("Number of Bees") +
  ylim(0, 45)

Adding a Logo to a Plot

The cowplot package provides ggdraw() and draw_image() which make it easy to add a logo or other images to your ggplot2 plots.

Code

library(cowplot)

img2 = image_read("./images/logo1.png")

p = ggplot(data = bees, aes(x = distance, y = number)) +
  annotation_custom(rasterGrob(img, 
                               width = unit(1, "npc"),
                               height = unit(1, "npc")), 
                    -Inf, Inf, -Inf, Inf) +
  geom_image(aes(image = image), size = 0.15) +
  xlab("Distance (km)") +
  ylab("Number of Bees") +
  ylim(0, 45)

ggdraw() + 
  draw_plot(p, x = 0, y = 0.15, width = 1, height = 0.85) + 
  draw_image(img2, x = 0.1, y = 0.1, width = 0.1, height = 0.1)

5. Conclusion and Further Resources

This document has provided a comprehensive introduction to the magick package in R. You have learned how to read, write, and manipulate images, as well as how to integrate them with ggplot2 to create more engaging visualizations.

For more information and examples, please refer to the following resources:

--- title: "image processing" subtitle: "with magick" author: "Tony Duan" execute: warning: false error: false format: html: toc: true toc-location: right code-fold: show code-tools: true number-sections: false code-block-bg: true code-block-border-left: "#31BAE9" code-copy: true --- This document provides a practical guide to image processing in R using the `magick` package. The `magick` package is a powerful and versatile tool for image manipulation, providing a wide range of functions for reading, writing, editing, and transforming images. It is built on top of the ImageMagick library, a robust and feature-rich image processing software. We will cover the basics of reading and writing images, as well as more advanced topics like color manipulation, transformations, and adding text and borders. We will also explore how to integrate `magick` with `ggplot2` to create more visually appealing and informative plots. ```{r} # Load the magick library library(magick) ``` # 1. Reading and Writing Images The first step in any image processing workflow is to read an image into R. The `image_read()` function can read images from a local file or a URL. ## Reading an Image ```{r} # Read an image from a local file raw_logo <- image_read('./images/comb.webp') ``` ## Getting Image Information The `image_info()` function provides useful information about an image, such as its format, width, height, and colorspace. ```{r} image_info(raw_logo) ``` ## Changing Image Format The `image_convert()` function allows you to convert an image from one format to another. ```{r} new_png <- image_convert(raw_logo, "png") image_info(new_png) ``` ## Writing an Image The `image_write()` function saves an image to a file. You can specify the path and format of the output file. ```{r} image_write(new_png, path = "./images/new_png.png", format = "png") ``` # 2. Basic Image Manipulations Let's read in another image to demonstrate some basic manipulations. ```{r} raw_logo <- image_read('./images/logo1.png') raw_logo ``` ## Filling with Color The `image_fill()` function can be used to fill areas of an image with a specified color. Here, we fill the white corners of the logo with green. The `fuzz` argument controls the tolerance of the color matching. ```{r} img_filled <- raw_logo %>% image_fill("green", "+1+1", fuzz = 50, refcolor = "white") %>% image_fill("green", "+140+1", fuzz = 50, refcolor = "white") %>% image_fill("green", "+1+99", fuzz = 50, refcolor = "white") %>% image_fill("green", "+140+99", fuzz = 50, refcolor = "white") img_filled ``` ## Rotating an Image The `image_rotate()` function rotates an image by a specified angle. ```{r} img_filled %>% image_rotate(45) ``` ## Adding a Border The `image_border()` function adds a border to an image. You can specify the color and size of the border. ```{r} img_filled %>% image_border("#CD5C5C", "20x20") ``` ## Making a Background Transparent The `image_transparent()` function makes a specified color transparent. This is useful for creating images with transparent backgrounds. ```{r} # The fuzz argument (0-100) controls how similar colors need to be to be made transparent. b = img_filled %>% image_transparent(color = 'green', fuzz = 10) b image_write(b, path = "images/b.png", format = "png") ``` ## Adjusting Opacity The `image_colorize()` function can be used to adjust the opacity of an image. ```{r} b = img_filled %>% image_colorize(opacity = 80, color = 'white') b ``` ## Adjusting Brightness The `image_modulate()` function can be used to adjust the brightness, saturation, and hue of an image. ```{r} b = img_filled %>% image_modulate(brightness = 30) b ``` ## Applying a Blur Effect The `image_blur()` function applies a blur effect to an image. You can control the radius and sigma of the blur. ```{r} b = img_filled %>% image_blur(10, 5) b ``` ## Adding Text to an Image The `image_annotate()` function adds text to an image. You can specify the text, font, size, color, and gravity (position). ```{r} b = img_filled %>% image_annotate("The quick brown fox", font = 'Times', size = 20, gravity = "southwest", color = "red") b ``` ## Resizing an Image The `image_resize()` and `image_scale()` functions can be used to resize an image. `image_resize()` allows you to specify the new dimensions, while `image_scale()` scales the image by a factor. ```{r} b = img_filled %>% image_resize("200x200") b image_info(b) ``` # 3. Advanced Image Manipulations ## Creating a Black and White Logo By combining several `magick` functions, we can perform more complex image manipulations. Here, we create a black and white version of the logo with a transparent background. ```{r} img_filled2 <- raw_logo %>% image_fill("transparent", "+1+1", fuzz = 50, refcolor = "white") %>% image_fill("transparent", "+140+1", fuzz = 50, refcolor = "white") %>% image_fill("transparent", "+1+99", fuzz = 50, refcolor = "white") %>% image_fill("transparent", "+140+99", fuzz = 50, refcolor = "white") img_filled2 %>% image_channel("Opacity") %>% image_convert(matte = FALSE) ``` # 4. Integrating `magick` with `ggplot2` One of the most powerful features of `magick` is its ability to be integrated with `ggplot2` to create more visually interesting plots. ## Adding a Background Image to a Plot We can use `annotation_custom()` and `rasterGrob()` to add an image as the background of a `ggplot2` plot. ```{r} library(ggplot2) library(png) library(grid) img <- readPNG("./images/new_png.png") bees <- data.frame(distance = c(0.5, 1, 1.5, 2, 2.5, 3), number = c(40, 34, 32, 22, 18, 10)) ggplot(data = bees, aes(x = distance, y = number)) + annotation_custom(rasterGrob(img, width = unit(1, "npc"), height = unit(1, "npc")), -Inf, Inf, -Inf, Inf) + geom_point() + xlab("Distance (km)") + ylab("Number of Bees") + ylim(0, 45) ``` ## Replacing Scatter Points with Images The `ggimage` package provides `geom_image()`, which allows you to use images as points in a scatter plot. ```{r} library(ggimage) bees$image <- "./images/bee.png" ggplot(data = bees, aes(x = distance, y = number)) + annotation_custom(rasterGrob(img, width = unit(1, "npc"), height = unit(1, "npc")), -Inf, Inf, -Inf, Inf) + geom_image(aes(image = image), size = 0.15) + xlab("Distance (km)") + ylab("Number of Bees") + ylim(0, 45) ``` ## Adding a Logo to a Plot The `cowplot` package provides `ggdraw()` and `draw_image()` which make it easy to add a logo or other images to your `ggplot2` plots. ```{r} library(cowplot) img2 = image_read("./images/logo1.png") p = ggplot(data = bees, aes(x = distance, y = number)) + annotation_custom(rasterGrob(img, width = unit(1, "npc"), height = unit(1, "npc")), -Inf, Inf, -Inf, Inf) + geom_image(aes(image = image), size = 0.15) + xlab("Distance (km)") + ylab("Number of Bees") + ylim(0, 45) ggdraw() + draw_plot(p, x = 0, y = 0.15, width = 1, height = 0.85) + draw_image(img2, x = 0.1, y = 0.1, width = 0.1, height = 0.1) ``` # 5. Conclusion and Further Resources This document has provided a comprehensive introduction to the `magick` package in R. You have learned how to read, write, and manipulate images, as well as how to integrate them with `ggplot2` to create more engaging visualizations. For more information and examples, please refer to the following resources: - [The `magick` Package Vignette](httpshttps://cran.r-project.org/web/packages/magick/vignettes/intro.html) - [Removing Image Backgrounds with `magick`](https://themockup.blog/posts/2021-01-28-removing-image-backgrounds-with-magick/) - [Fun and Easy R Graphs with Images](https://buzzrbeeline.blog/2018/06/13/fun-and-easy-r-graphs-with-images/)

1. Reading and Writing Images

Reading an Image

Getting Image Information

Changing Image Format

Writing an Image

2. Basic Image Manipulations

Filling with Color

Rotating an Image

Adding a Border

Making a Background Transparent

Adjusting Opacity

Adjusting Brightness

Applying a Blur Effect

Adding Text to an Image

Resizing an Image

3. Advanced Image Manipulations

Creating a Black and White Logo

4. Integrating magick with ggplot2

Adding a Background Image to a Plot

Replacing Scatter Points with Images

Adding a Logo to a Plot

5. Conclusion and Further Resources

4. Integrating `magick` with `ggplot2`