The figure below shows what can be done with this package The user can modify the appearance of objects in photographs. For example, they can make the skin smoother or, conversely, make blemishes more visible. They can also enhance the gloss of fruits or make it look wilted. Based on image processing techniques, the user can automatically perform such photo editing effects by simply calling a single R function.
Details of this package (e.g. image processing algorithm) will be described in the article below:
Tsuda and Kawabata (under review). materialmodifier: An R package of photo editing effects for material perception research.
Mac users need to install XQuartz (https://www.xquartz.org/).
The package is available on CRAN. It can be installed with:
Then, attach the package.
You can install the development version of the materialmodifier package via GitHub, by using the devtools package.
NOTE:
To install a package from GitHub,
- On Windows, Rtools needs to be installed.
- On Mac, in some environments, the installation of XCode may be needed.
After you have installed the required software stated above, install the package as follows:
Then, attach the package.
The materialmodifier package contains an image data, which is useful when you want to quickly try out a material editing effect. The variable name for this image is face.
Internally, this data is a numeric array with the size 500 x 500 x 3 [y-coordinate, x-coordinate, color channel], which means a height of 500 pix, a width of 500 pix, and three color channels (Red, Green, and Blue). Each element of the array represents a pixel value, which can be between 0 and 1.
The image size information can be displayed by typing the variable name.
To plot an image, use the plot() function.
To load an image in your computer, use the im_load() function.
The jpg, png, and bmp formats are supported.
You can load an image from the web (URL). For example,
im = im_load("https://raw.githubusercontent.com/tsuda16k/materialmodifier/master/notes/meat.png")
plot(im)will load an image of roasted meat.
In the following example, we use the face image contained in the package.
For consistency purposes, the face image is labeled as im.
Use the modif() function to apply a material editing effect to an image.
(Note: the function name is modif, not modify)
Below is an example of the shine effect.
# apply the shine effect
im2 = modif(im, effect = "shine", strength = 3.0) # may take some seconds
plot(im2) # see the result
On the left is the input image, and on the right is the result of the shine effect.
The strength parameter is used to control the strength of the image processing effect. A larger value of strength will produce a stronger effect:
The strength parameters, from left to right, are 0.1, 1, 2, 3, and 4.
Note that when the strength parameter is 1, the modif() function just returns the input image. Therefore the second image from the left is the same as the input image.
When the strength parameter is between 0 and 1, the output image becomes less shiny than the original (see the leftmost image above).
Below is an example of the aging effect.
# load the face image
im = im_load("https://raw.githubusercontent.com/tsuda16k/materialmodifier/master/notes/meat.png")
# apply the aging effect
im2 = modif(im, "aging", 0.1)
im3 = modif(im, "aging", 2.5)
# show the results
plot(im2)
plot(im)
plot(im3)
Compared to the original (center), skin blemishes are reduced on the left and boosted on the right.
The figure below summarizes outputs of the shine and aging effects.
Using the aging effect as an example, the effect of the strength parameter is examined in more detail in the figure below. If the value of the strength parameter is greater than 1, a boosting effect that increases the stains/blemishes occurs; if it is less than 1, a reducing effect that decreases the stains/blemishes occurs.
To get a boosting effect, a strength value of 2 to 6 usually yields reasonable results. The strength parameter can be a negative value, but in most cases setting a negative value will produce unrealistic results (e.g., contrast reversal)
This package has several other effects in addition to the shine and aging effects. All available effects are: shine, spots, rough, stain, blemish, shadow, aging. A visual summary of these effects is shown in the figure below.
The first column of the figure shows the name of each effect, and the second column shows the perceptual features controlled by that effect. The third and subsequent columns show the input and output images.
The figure contains a column called BS feature. The BS feature is an important term related to the image processing algorithm (briefly, the image area to be manipulated, extracted from the input image based on a certain criterion). The algorithm achieves image editing effects by decreasing or increasing the weight of the BS feature in the input image.
To better understand the nature of each image editing effect, it is helpful to compare the results of all editing effects on a single image. The figure below summarizes the results of the editing effects on a face and a food image (note that the image in the row with a strength value of 1 is the input image). By comparing the images in the rows with large values of the strength parameter, it is easier to see the characteristics of each effect.
Setting a negative value for the strength parameter often results in an unnatural image (see images in the bottom rows of the figure), but using a large negative value for the strength parameter makes it easier to compare which areas are affected by each effect. For example, the rough effect and the blemish effect produce similar results, but if you compare the images in the row with a strength value of -5, you can clearly see that they are not identical.
Technically, the blemish effect is equivalent to giving both the rough and stain effects at the same time. In order to get a more formal understanding of these properties, we need to know more about the specifics of how image processing algorithms work. See our paper for details.
You can also perform multiple editing effects at the same time. For example, you can apply the shine effect and the aging effect at the same time as follows.
# Applying multiple effects at the same time
im2 = modif(im, effect = c("shine", "aging"), strength = c(0.2, 3))
plot(im2)
The above command simultaneously applies a shine effect of strength = 0.2 and an aging effect of strength = 3, resulting in a less shiny and more blemished image.
Applying each effect in sequence produces almost identical results.
# Applying multiple effects at the same time
im2 = modif(im, effect = c("shine", "aging"), strength = c(0.2, 3))
# Applying effects in sequence
im3 = modif(im, effect = "shine", strength = 0.2)
im3 = modif(im3, effect = "aging", strength = 3)
# pixel-wise mean squared error
mean((im2-im3)^2)
# this value is very small, meaning that both images are almost identicalAlthough you can get the same result by applying each effect in turn (e.g., applying an aging effect to the output of a shine effect), we recommend doing them in a single line, because it saves time needed for image processing.
The modif2() function allows for a precise control over which image feature (i.e., BE feature) to manipulate. For example, boosting the LAA feature (low spatial frequency, all (high and low) amplitudes, and all (positive and negative) signs) can be performed as follows.
# modify the LAA feature
im2 = modif2(im, params = list(freq = "L", amp = "A", sign = "A", strength = 2 ))
im2 = modif2(im, params = list(feature = "LAA", strength = 2 )) # equivalent to the aboveDetails about image manipulation features (BS features) are described in our paper.
Here are additional examples for using the modif2() function.
# shine effect (boost the HHP feature)
shine = list(freq = "H", amp = "H", sign = "P", strength = 2)
plot(modif2(face, params = shine))
# shine effect (equivalent to the above)
shine2 = list(freq = modif_dim(face)$high, amp = "H", sign = "P", strength = 2)
plot(modif2(face, params = shine2))
# you can specify a feature name directly, instead of specifying freq/amp/sign separately
plot( modif2(face, params = list(feature = "HHA", strength = 2)) )
plot( modif2(face, params = list(feature = "1HP", strength = 3)) )
# apply multiple effects at the same time
blemish = list(feature = "HLA", strength = 0.1) # less blemished
smooth = list(feature = "HHN", strength = 0.2) # smoother
plot(modif2(face, params = list(blemish, smooth)))The modif() function has 6 arguments.
Table of arguments of the modif() function:
| Argument | Meaning | Value | Default |
|---|---|---|---|
| im | Input image | an image object | |
| effect | Effect name | Either “gloss”, “shine”, “spots”, “blemish”, “rough”, “stain”, “shadow”, or “aging” | |
| strength | Strength of effect | a float value or a float vector | |
| max_size | Image resolution limit | an integer | 1280 |
| log_epsilon | Offset for log transformation | a float value | 0.0001 |
| filter_epsilon | Epsilon parameter of the Guided filter | a float value | 0.01 |
The im is an image object we can get by using the im_load() function.
The effect and strength parameters have been described above.
The max_size parameter can be used to restrict the image resolution. If the shorter side of the input image is larger than this value (the default is 1280), input image is resized before applying effects. For example, when the input image has 1024 x 2048 px resolution, and if max_size is 512, then the input image is first resized to 512 x 1024 px. Because the modif() function is very slow for large-resolution images, it is useful to limit the image resolution to speed up the image processing.
The log_epsilon and filter_epsilon are parameters that are used for image processing procedures. You need not to change this value in most cases.
The modif2() function has 5 arguments.
Table of arguments of the modif() function:
| Argument | Meaning | Value | Default |
|---|---|---|---|
| im | Input image | an image object | |
| params | A list of parameter values | a list | |
| max_size | Image resolution limit | an integer | 1280 |
| log_epsilon | Offset for log transformation | a float value | 0.0001 |
| filter_epsilon | Epsilon parameter of the Guided filter | a float value | 0.01 |
For the params parameter, set a list of material editing parameters:
# shine effect (boost the HHP feature)
shine = list(freq = "H", amp = "H", sign = "P", strength = 2)
plot(modif2(face, params = shine))
# shine effect (equivalent to the above)
shine2 = list(freq = modif_dim(face)$high, amp = "H", sign = "P", strength = 2)
plot(modif2(face, params = shine2))
# you can specify a feature name directly, instead of specifying freq/amp/sign separately
plot( modif2(face, params = list(feature = "HHA", strength = 2)) )
plot( modif2(face, params = list(feature = "1HP", strength = 3)) )
# apply multiple effects at the same time
blemish = list(feature = "HLA", strength = 0.1) # less blemished
smooth = list(feature = "HHN", strength = 0.2) # smoother
plot(modif2(face, params = list(blemish, smooth)))The other parameters are the same as the modif() function.