img_symmetry returns the vertical and horizontal mirror symmetry of an image. Higher values indicate higher image symmetry.

img_symmetry(img, vertical = TRUE, horizontal = TRUE, ...)



An image in form of a matrix or array of numeric values. Use e.g. img_read() to read an image file into R.


logical. Should the vertical symmetry be computed? (default: TRUE)


logical. Should the horizontal symmetry be computed? (default: TRUE)


Further options: shift_range to shift the mirror axis, per_channel to switch between a maximal per channel vs. per image symmetry (see details).


a named vector of numeric values (vertical and horizontal symmetry)


The function returns the vertical and horizontal mirror symmetry of an image img. Symmetry values can range between 0 (not symmetrical) and 1 (fully symmetrical). If vertical or horizontal is set to FALSE then vertical or horizontal symmetry is not computed, respectively.

As the perceptual mirror axis is not necessarily exactly in the middle of a picture, the function estimates in a first step several symmetry values with different positions for the mirror axis. To this end, the mirror axis is automatically shifted up to 5% (default) of the image width to the left and to the right (in the case of vertical symmetry; analogously for horizontal symmetry). In the second step, the overall symmetry score is computed as the maximum of the symmetry scores given the different mirror axes. See Mayer & Landwehr (2018) for details.

Advanced users can change the shift range with the optional parameter shift_range, which takes a numeric decimal as input. The default shift_range = 0.05 (i.e., 5%).

For color images, the default is that first a maximal symmetry score (as explained above) is obtained per color channel (parameter per_channel = TRUE). Subsequently, a weighted average between each color channel's maximal score is computed as the image's overall symmetry. Advanced users can reverse this order by setting per_channel = FALSE. This results in first computing the weighted averages for each position of the mirror axis separately, and afterwards finding the maximal overall symmetry score.


Mayer, S. & Landwehr, J, R. (2018). Quantifying Visual Aesthetics Based on Processing Fluency Theory: Four Algorithmic Measures for Antecedents of Aesthetic Preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399--431. doi:10.1037/aca0000187


# Example image with high vertical symmetry: rails
rails <- img_read(system.file("example_images", "rails.jpg", package = "imagefluency"))
# display image
# get symmetry

# Example image with low vertical symmetry: bridge
bridge <- img_read(system.file("example_images", "bridge.jpg", package = "imagefluency"))
# display image
# get symmetry