###############################
# Complexity - Pixel Symmetry #
###############################
#
# V1.0
# 29/05/2017
#
# Implemented by:
# Thomas Langerak
# (hello@thomaslangerak.nl)
#
# Supervisor:
# Antti Oulasvirta
#
# This work was funded by Technology Industries of Finland in a three-year
# project grant on self-optimizing web services. The principal investigator
# is Antti Oulasvirta of Aalto University (antti.oulasvirta@aalto.fi)
#
###########
# Summary #
###########
#
# One of the Gestalt principles, mirror symmetry - the similarity of an object reflection across a straight axis -
# was claimed to improve interface design. However, quantifying symmetry might be problematic in HCI. Psychologists
# mainly studied mirror symmetry of relatively simple objects, such as dot and line patterns, or human faces.
#
#############
# Technical #
#############
#
# Inputs: PNG image (base64)
# Returns: List of 1 item: Normalized Symmetry (float)
#
##############
# References #
##############
#
# 1. Miniukovich, A. and De Angeli, A. Quantification of interface visual complexity. Proceedings of the 2014
# International Working Conference on Advanced Visual Interfaces - AVI '14, (2014).
#
##############
# Change Log #
##############
#
###############
# Bugs/Issues #
###############
#
# Limiting the number of pixels is not working yet.
#
import cv2
from skimage import util, color
import numpy as np
import base64
from PIL import Image
from io import BytesIO
def get_pixels_in_radius(x, y, width, height, radius):
# Get x border
if x < radius:
rad_x_left = -x
rad_x_right = radius
elif width - x < radius:
rad_x_right = 1 * (width - x)
rad_x_left = -radius
else:
rad_x_left = -radius
rad_x_right = radius
# Get y borders
if y < radius:
rad_y_top = -y
rad_y_bottom = radius
elif height - y < radius:
rad_y_bottom = 1 * (height - y)
rad_y_top = -radius
else:
rad_y_top = -radius
rad_y_bottom = radius
pixels = []
for m in range(rad_x_left, rad_x_right):
for n in range(rad_y_top, rad_y_bottom):
if m != 0 or n != 0:
pixel = [x + m, y + n]
pixels.append(pixel)
return pixels
def execute(b64):
b64 = base64.b64decode(b64)
b64 = BytesIO(b64)
img = Image.open(b64)
img = np.array(img)
img_la = color.rgb2gray(img)
img_la = util.img_as_ubyte(img_la)
# See sigma here: https://dsp.stackexchange.com/questions/4716/differences-between-opencv-canny-and-matlab-canny
img_la = cv2.GaussianBlur(img_la, (7, 7), 2)
edges = cv2.Canny(img_la, 0.11, 0.27)
height, width = edges.shape
# Set all pixels in radius of an edge pixel to 0
# This is not going good yet
radius = 3
all_key = 0
for y in range(height):
for x in range(width):
if edges[y][x] != 0:
all_key += 1
pixels_in_radius = get_pixels_in_radius(x, y, width, height, radius)
for pixel in pixels_in_radius:
edges[pixel[1], pixel[0]] = 0
img = Image.fromarray(edges, 'L')
# img.show()
symmetry_radius = 4
# Check vertical symmetry
sym_key = 0
for y in range(height):
for x in range(width / 2):
if edges[y][x] != 0:
vertical_pixels = get_pixels_in_radius(width - x, y, width, height, symmetry_radius)
horizontal_pixels = get_pixels_in_radius(x, height - y, width, height, symmetry_radius)
for pixel in vertical_pixels:
if edges[int(pixel[1]), int(pixel[0])] != 0:
sym_key += 1
break
for pixel in horizontal_pixels:
if edges[int(pixel[1]), int(pixel[0])] != 0:
sym_key += 1
break
try:
sym_normalized = (float(sym_key) / float(all_key)) * ((float((all_key - 1) * symmetry_radius) / float(width * height)) ** -1)
except ZeroDivisionError:
sym_normalized = 0
return [sym_normalized]
