Image manipulation with pliman

Image manipulation

Importing images

library(pliman)
#> |======================================================|
#> | Welcome to the pliman package (version 3.0.0)!       |
#> | Developed collaboratively by NEPEM - nepemufsc.com   |
#> | Group lead: Prof. Tiago Olivoto                      |
#> | For citation: type `citation('pliman')`              |
#> | We welcome your feedback and suggestions!            |
#> |======================================================|
soy <- image_pliman("soybean_touch.jpg")

To import a list of images, the argument pattern of the function image_import() is used. All images that match the pattern name are imported into a list.

soy_list <- 
  image_import(pattern = "sev_",
               path = image_pliman()) # choose path directory
names(soy_list)
#> [1] "sev_back.jpg"    "sev_healthy.jpg" "sev_leaf.jpg"    "sev_leaf_nb.jpg"
#> [5] "sev_sympt.jpg"

Displaying images

Single images are displayed with plot(). For combining images, the function image_combine() is used. Users can inform either a comma-separated list of objects or a list of objects of class Image.

# Single images
plot(soy)

# Combine images
image_combine(soy, soy)


# Combine images
image_combine(soy_list, ncol = 5)

Manipulating images

pliman provides a set of image_*() functions to perform image manipulation and transformation of unique images or a list of images based on the EBImage package.

Resize an image

Sometimes resizing of high-resolution images is needed to reduce the processing time. The function image_resize() is used to resize an image. The argument rel_size can be used to resize the image by relative size. For example, by setting rel_size = 50 to an image of width 1280 x 720, the new image will have a size of 640 x 360. This is useful to speed up the time of analysis such as those computed with analyze_objects() and measure_disease().

image_dimension(soy)
#> 
#> ----------------------
#> Image dimension
#> ----------------------
#> Width :  825 
#> Height:  648
soy_resized <- image_resize(soy, rel_size = 50)
image_dimension(soy_resized)
#> 
#> ----------------------
#> Image dimension
#> ----------------------
#> Width :  412 
#> Height:  324

Crop an image

Cropping images is useful to remove noises from the image edge, as well as to reduce the size of images before processing. To crop an image, the function image_crop() is used. Users need to inform a numeric vector indicating the pixel range (width and height) that will be maintained in the cropped image.

crop1 <-
  image_crop(soy,
             width = 55:750,
             height = 20:623,
             plot = TRUE)

If only width or height are informed, the image will be cropped vertically or horizontally.

crop2 <-
  image_crop(soy,
             width = 55:750,
             plot = TRUE)

If both width and height are missing, an iterative process of image cropping is performed.

# only run in an iterative section
image_crop(soy)

Additionally, an automated cropping process can be performed. In this case, the image will be automatically cropped to the area of objects with an edge of five pixels by default.

auto_crop <- image_autocrop(soy, plot = TRUE)

The function image_trim() is used to trim pixels from image edges.

# trim 50 pixels from all edges
soy_trim <- image_trim(soy, edge = 50, plot = TRUE)


# The same is achieved with
soy_trim2 <-
  image_trim(soy,
             top = 50,
             bottom = 50,
             left = 50,
             right = 50,
             plot = TRUE)

# trim 100 pixels from top and bottom
soy_trim3 <-
  image_trim(soy,
             top = 100,
             bottom = 100,
             plot = TRUE)

# trim to 5 pixels around objects' area

Image resolution (DPI)

The function dpi() runs an interactive function to compute the image resolution given a known distance informed by the user. To compute the image resolution (dpi) the user must use the left button mouse to create a line of known distance. This can be done, for example, using a template with known distance in the image (e.g., leaves.JPG).

# only run in an interactive section
leaves <- image_import("./data/leaf_area/leaves.JPG")
dpi(leaves)

Rotate an image

image_rotate() is used to rotates the image clockwise by the given angle.

soy_rotated <- image_rotate(soy, angle = 45, plot = TRUE)

Horizontal and vertical reflection

image_hreflect() and image_vreflect() performs vertical and horizontal reflection of images, respectively.

soy_hrefl <- image_hreflect(soy)
soy_vrefl <- image_vreflect(soy)
image_combine(soy, soy_hrefl, soy_vrefl, ncol = 3)

Horizontal and vertical conversion

image_horizontal() and image_vertical() converts (if needed) an image to a horizontal or vertical image, respectively.

soy_h <- image_horizontal(soy)
soy_v <- image_vertical(soy)
image_combine(soy, soy_h, soy_v, ncol = 3)

Filter, blur, contrast, dilatation, and erosion

soy_filter <- image_filter(soy)
soy_blur <- image_blur(soy)
soy_contrast <- image_contrast(soy)
soy_dilatation <- image_dilate(soy)
soy_erosion <- image_erode(soy)
image_combine(soy, soy_filter, soy_blur, soy_contrast, soy_dilatation, soy_erosion)

Exporting images

To export images to the current directory, use the function image_export(). If a list of images is exported, the images will be saved considering the name and extension present in the list. If no extension is present, the images will be saved as *.jpg files.

image_export(soy, "exported.jpg")

A little bit more!

At this link you will find more examples on how to use {pliman} to analyze plant images. Source code and images can be downloaded here. You can also find a talk (Portuguese language) about {pliman} here. Lights, camera, {pliman}!

Tiago Olivoto

2024-11-04