#!/usr/bin/env python

# Copyright (c) 2010, Jonas Tullus
# All rights reserved.

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# Color vision deficiency simulator
#
# based on http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html
# Written by Jonas Tullus, 2010-11-28

from gimpfu import *
from array import array


# color vision deficiency simulation matrices
# source: http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html
# dimensions:
# 	severity [0.0, 0.1 .. 1.0]
#	protanomaly, deuteranomaly, tritanomaly
#	3x3 RGB transformation matrix
matrices = [[ 
	# severity 0.0
		[[1.000000, 0.000000, -0.000000]
		,[0.000000, 1.000000, 0.000000]
		,[-0.000000, -0.000000, 1.000000]
		]
		,

		[[1.000000, 0.000000, -0.000000]
		,[0.000000, 1.000000, 0.000000]
		,[-0.000000, -0.000000, 1.000000]
		]
		,

		[[1.000000, 0.000000, -0.000000]
		,[0.000000, 1.000000, 0.000000]
		,[-0.000000, -0.000000, 1.000000]
		]
	],[ # severity 0.1
		[[0.856167, 0.182038, -0.038205]
		,[0.029342, 0.955115, 0.015544]
		,[-0.002880, -0.001563, 1.004443]
		]
		,

		[[0.866435, 0.177704, -0.044139]
		,[0.049567, 0.939063, 0.011370]
		,[-0.003453, 0.007233, 0.996220]
		]
		,

		[[0.926670, 0.092514, -0.019184]
		,[0.021191, 0.964503, 0.014306]
		,[0.008437, 0.054813, 0.936750]
		]
	],[ # severity 0.2
		[[0.734766, 0.334872, -0.069637]
		,[0.051840, 0.919198, 0.028963]
		,[-0.004928, -0.004209, 1.009137]
		]
		,

		[[0.760729, 0.319078, -0.079807]
		,[0.090568, 0.889315, 0.020117]
		,[-0.006027, 0.013325, 0.992702]
		]
		,

		[[0.895720, 0.133330, -0.029050]
		,[0.029997, 0.945400, 0.024603]
		,[0.013027, 0.104707, 0.882266]
		]
	],[ # severity 0.3
		[[0.630323, 0.465641, -0.095964]
		,[0.069181, 0.890046, 0.040773]
		,[-0.006308, -0.007724, 1.014032]
		]
		,

		[[0.675425, 0.433850, -0.109275]
		,[0.125303, 0.847755, 0.026942]
		,[-0.007950, 0.018572, 0.989378]
		]
		,

		[[0.905871, 0.127791, -0.033662]
		,[0.026856, 0.941251, 0.031893]
		,[0.013410, 0.148296, 0.838294]
		]
	],[ # severity 0.4
		[[0.539009, 0.579343, -0.118352]
		,[0.082546, 0.866121, 0.051332]
		,[-0.007136, -0.011959, 1.019095]
		]
		,

		[[0.605511, 0.528560, -0.134071]
		,[0.155318, 0.812366, 0.032316]
		,[-0.009376, 0.023176, 0.986200]
		]
		,

		[[0.948035, 0.089490, -0.037526]
		,[0.014364, 0.946792, 0.038844]
		,[0.010853, 0.193991, 0.795156]
		]
	],[ # severity 0.5
		[[0.458064, 0.679578, -0.137642]
		,[0.092785, 0.846313, 0.060902]
		,[-0.007494, -0.016807, 1.024301]
		]
		,

		[[0.547494, 0.607765, -0.155259]
		,[0.181692, 0.781742, 0.036566]
		,[-0.010410, 0.027275, 0.983136]
		]
		,

		[[1.017277, 0.027029, -0.044306]
		,[-0.006113, 0.958479, 0.047634]
		,[0.006379, 0.248708, 0.744913]
		]
	],[ # severity 0.6
		[[0.385450, 0.769005, -0.154455]
		,[0.100526, 0.829802, 0.069673]
		,[-0.007442, -0.022190, 1.029632]
		]
		,

		[[0.498864, 0.674741, -0.173604]
		,[0.205199, 0.754872, 0.039929]
		,[-0.011131, 0.030969, 0.980162]
		]
		,

		[[1.104996, -0.046633, -0.058363]
		,[-0.032137, 0.971635, 0.060503]
		,[0.001336, 0.317922, 0.680742]
		]
	],[ # severity 0.7
		[[0.319627, 0.849633, -0.169261]
		,[0.106241, 0.815969, 0.077790]
		,[-0.007025, -0.028051, 1.035076]
		]
		,

		[[0.457771, 0.731899, -0.189670]
		,[0.226409, 0.731012, 0.042579]
		,[-0.011595, 0.034333, 0.977261]
		]
		,

		[[1.193214, -0.109812, -0.083402]
		,[-0.058496, 0.979410, 0.079086]
		,[-0.002346, 0.403492, 0.598854]
		]
	],[ # severity 0.8
		[[0.259411, 0.923008, -0.182420]
		,[0.110296, 0.804340, 0.085364]
		,[-0.006276, -0.034346, 1.040622]
		]
		,

		[[0.422823, 0.781057, -0.203881]
		,[0.245752, 0.709602, 0.044646]
		,[-0.011843, 0.037423, 0.974421]
		]
		,

		[[1.257728, -0.139648, -0.118081]
		,[-0.078003, 0.975409, 0.102594]
		,[-0.003316, 0.501214, 0.502102]
		]
	],[ # severity 0.9
		[[0.203876, 0.990338, -0.194214]
		,[0.112975, 0.794542, 0.092483]
		,[-0.005222, -0.041043, 1.046265]
		]
		,

		[[0.392952, 0.823610, -0.216562]
		,[0.263559, 0.690210, 0.046232]
		,[-0.011910, 0.040281, 0.971630]
		]
		,

		[[1.278864, -0.125333, -0.153531]
		,[-0.084748, 0.957674, 0.127074]
		,[-0.000989, 0.601151, 0.399838]
		]
	],[ # severity 1.0
		[[0.152286, 1.052583, -0.204868]
		,[0.114503, 0.786281, 0.099216]
		,[-0.003882, -0.048116, 1.051998]
		]
		,

		[[0.367322, 0.860646, -0.227968]
		,[0.280085, 0.672501, 0.047413]
		,[-0.011820, 0.042940, 0.968881]
		]
		,

		[[1.255528, -0.076749, -0.178779]
		,[-0.078411, 0.930809, 0.147602]
		,[0.004733, 0.691367, 0.303900]
		]
	]]



def cvd(img, layer, anomaly, severity):
	# choose correct transformation matrix for anomaly and severity
	matrix = matrices[int(round(severity*10))][anomaly]

	pdb.gimp_image_undo_group_start(img)

	bpp = layer.bpp
	height = layer.height
	width = layer.width
	pr = layer.get_pixel_rgn(0, 0, width, height, dirty=True, shadow=False)

	# complete layer data read as array of unsigned char's
	data = array("B", pr[0:width, 0:height])

	for y in range(height):
		for x in range(width):
			offset = bpp*(y*width + x)
			vector = data[offset:offset+3]
			for row in range(3):
				mr = matrix[row]
				data[offset+row] = min(255, max(0, int(mr[0] * vector[0] + 
										mr[1] * vector[1] + mr[2] * vector[2])))
		gimp.progress_update(float(y) / float(height))
	
	anomaly_name = ["Protanomaly", "Deuteranomaly", "Tritanomaly"][anomaly]
	name = "%s - %0.1f" % (anomaly_name, severity)
	new_layer = gimp.Layer(img, name, width, height
						  ,layer.type, layer.opacity, layer.mode)
	img.add_layer(new_layer, 0)
	out = new_layer.get_pixel_rgn(0, 0, width, height, dirty=True, shadow=False)
	out[0:width, 0:height] = data.tostring()
		
	new_layer.flush()
	new_layer.update(0,0,width,height)

	pdb.gimp_image_undo_group_end(img)



# Register with The Gimp
register(
	"python_fu_cvd",
	"Color Vision Deficiency Simulator",
	"Simulates different types of color vision deficiency with differing levels of severity.\nAt a level of 1.0, it simulates dichromacy, i.e. the complete absence of the relevant color receptor.\n Based on http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html",
	"Jonas Tullus",
	"(c) 2010, Jonas Tullus",
	"2010",
	"<Image>/Python-Fu/Effects/Color Vision Deficiency",
	"RGB*",
	[
		(PF_OPTION, "Anomaly", "Type of color vision anomaly", 0, ["Protanomaly (red weak)", "Deuteranomaly (green weak)", "Tritanomaly (blue weak)"]),
		(PF_SLIDER, "Severity", "Severity of the color vision anomaly", 0.5, (0,1,0.1))
	],
	[],
	cvd)

main()