Vfrdtyky

Gamut I want to plot in CIE1931 space: https://www.google.co.uk/search?biw=1337&bih=1257&tbm=isch&sa=1&ei=9x3kW7rqBo3ygQb-8aWYBw&q=viewpixx+gamut&oq=viewpixx+gamut&gs_l=img.3...2319.2828.0.3036.5.5.0.0.0.0.76.270.5.5.0....0...1c.1.64.img..0.0.0....0.KT8w80tcZik#imgrc=77Ufw31S6UVlYM

I want to create a triangle plot of the ciexyY colours within the these coordinates: (.119,.113),(.162,.723),(.695,.304) as in the image - with a set luminance Y at 30.0.

I have created a 3d array of xy values between 0-1.
I then created a matrix with 1s inside the triangle and 0s outside the triangle.
I multiplied the triangle matrix by the xyY ndarray.
Then I looped through the xyY ndarray and converted xyY values to rgb, and displayed them.

The result is somewhat close but not correct. I think the error is in the last section when I convert to rgb, but I'm not sure why. This is the current image: https://imgur.com/a/7cWY0FI. Any recommendations would be really appreciated.

from __future__ import division

import numpy as np

from colormath.color_objects import sRGBColor, xyYColor

from colormath.color_conversions import convert_color

import matplotlib.pyplot as plt



def frange(x,y,jump):

    while x < y:

        yield x

        x += jump



def onSameSide(p1,p2, A,B):

    cp1 = np.cross(B-A, p1-A)

    cp2 = np.cross(B-A, p2-A)

    if(np.dot(cp1, cp2) >= 0):

        return True

    else:

        return False



def isPointInTriangle(p,A,B,C):

    if(onSameSide(p,A,B,C) and onSameSide(p,B,A,C) and onSameSide(p,C,A,B)):

        return True

    else:

        return False



xlen = 400

ylen = 400



#CIExyY colour space

#Make an array (1,1,3) with each plane representing how x,y,Y vary in the coordinate space

ciexyY = np.zeros((3,xlen,ylen))

ciexyY[2,:,:]=30.0

for x in frange(0,1,1/xlen):

    ciexyY[0,:,int(xlen*x)]=x

    for y in frange(0,1,1/xlen):

        ciexyY[1,int(ylen*y),:]=y



#coordinates from Viewpixx gamut, scaled up to 100

blue=np.array((.119,.113,30.0))

green=np.array((.162,.723,30.0))

red=np.array((.695,.304,30.0))

#scale up to size of image

blue = np.multiply(blue,xlen)

green = np.multiply(green,xlen)

red = np.multiply(red,xlen)



#make an array of zeros and ones to plot the shape of Viewpixx triangle

triangleZeros = np.zeros((xlen,ylen))

for x in frange(0,xlen,1):

    for y in frange(0,ylen,1):

        if(isPointInTriangle((x,y,0),blue,green,red)):

            triangleZeros[x,y]=1

        else:

            triangleZeros[x,y]=0



#cieTriangle

cieTriangle = np.multiply(ciexyY,triangleZeros)



#convert cieTriangle xyY to rgb

rgbTriangle = np.zeros((3,xlen,ylen))

for x in frange(0,xlen,1):

    for y in range(0,ylen,1):

        xyYcolour = xyYColor(cieTriangle[0,x,y],cieTriangle[1,x,y],cieTriangle[2,x,y])

        rgbColour = convert_color(xyYcolour,sRGBColor)

        rgbTriangle[0,x,y] = rgbColour.rgb_r

        rgbTriangle[1,x,y] = rgbColour.rgb_g

        rgbTriangle[2,x,y] = rgbColour.rgb_b



rgbTriangle = np.transpose(rgbTriangle)

plt.imshow(rgbTriangle)

plt.show()

We have all the common Chromaticity Diagrams in Colour, I would recommend it over python-colormath because Colour is vectorised and thus much faster.

Do you have a render of your current image to share though?