Factor out the colorchecker test code

2 files changed, 48 insertions, 45 deletions

diff --git a/jakob_hanika.py b/jakob_hanika.py
index 78803a1..3b35670 100644
--- a/jakob_hanika.py
+++ b/jakob_hanika.py
@@ -1,37 +1,9 @@
 import numpy as np, scipy.optimize as optimize
 from colour import *
 from colour.difference import *
-from colour.plotting import *
-from matplotlib import pyplot as plt
-
-
-
-# Makes a comparison plot with SDs and swatches
-def plot_comparison(target_sd, matched_sd, label, error):
-	target_XYZ = sd_to_XYZ(target_sd)
-	target_RGB = np.clip(XYZ_to_sRGB(target_XYZ / 100), 0, 1)
-	target_swatch = ColourSwatch(label, target_RGB)
-	matched_XYZ = sd_to_XYZ(matched_sd)
-	matched_RGB = np.clip(XYZ_to_sRGB(matched_XYZ / 100), 0, 1)
-	matched_swatch = ColourSwatch("Model", matched_RGB)
-      
-	axes = plt.subplot(2, 1, 1)
-	plt.title(label)
-	plot_multi_sds([target_sd, matched_sd], axes=axes, standalone=False)
-      
-	axes = plt.subplot(2, 1, 2)
-	plt.title("ΔE = %g" % error)
-	plot_multi_colour_swatches([target_swatch, matched_swatch], axes=axes)
-
-
-
-# The same illuminant is used throughout
-il = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
 
 # The same wavelength grid is used throughout
-wvl = np.arange(360, 830, 10)
-
-
+wvl = np.arange(360, 830, 5)
 
 # This is the model of spectral reflectivity described in the article.
 def model(wvl, cc):
@@ -45,10 +17,10 @@ def model(wvl, cc):
 
 # The goal is to minimize the color difference between a given distrbution
 # and the one computed from the model above.
-def error_function(cc, target):
+def error_function(cc, target, illuminant):
 	ev = model(wvl, cc)
 	sd = SpectralDistribution(ev, wvl)
-	Lab = XYZ_to_Lab(sd_to_XYZ(sd), il)
+	Lab = XYZ_to_Lab(sd_to_XYZ(sd), illuminant)
 	return delta_E_CIE1976(target, Lab)
 
 
@@ -59,26 +31,20 @@ def error_function(cc, target):
 def cb_basinhopping(x, f, accept):
 	return f < 0.1
 
-# This demo goes through SDs in a color checker
-for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
-	XYZ = sd_to_XYZ(sd)
-	target = XYZ_to_Lab(XYZ, il)
-
-	print("The target is '%s' with L=%g, a=%g, b=%g" % (name, *target))
-
+# Finds coefficients for Jakob and Hanika's function
+def jakob_hanika(target, illuminant):
 	# First, a conventional solver is called. For 'yellow green' this
 	# actually fails: gets stuck at a local minimum that's far away
 	# from the global one.
 	# FIXME: better parameters, a better x0, a better method?
-	# FIXME: stop iterating as soon as delta E is negligible (instead of
-	#        going).
+	# FIXME: stop iterating as soon as delta E is negligible 
 	opt = optimize.minimize(
-		error_function, (0, 0, 0), target, method="L-BFGS-B",
-		options={"disp": True, "ftol": 1e-5}
+		error_function, (0, 0, 0), (target, illuminant),
+		method="L-BFGS-B", options={"disp": True, "ftol": 1e-5}
 	)
 	print(opt)
 
-	error = error_function(opt.x, target)
+	error = error_function(opt.x, target, illuminant)
 	print("Delta E is %g" % error)
 
 	# Basin hopping is far more likely to find the actual minimum we're
@@ -86,7 +52,7 @@ for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
 	if error > 0.1:
 		print("Error too large, trying global optimization")
 		opt = optimize.basinhopping(
-			lambda cc: error_function(cc, target),
+			lambda cc: error_function(cc, target, illuminant),
 			(0, 0, 0), disp=True, callback=cb_basinhopping
 		)
 		print(opt)
@@ -94,4 +60,4 @@ for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
 		print("Global delta E is %g" % error)
 
 	matched_sd = SpectralDistribution(model(wvl, opt.x), wvl, name="Model")
-	plot_comparison(sd, matched_sd, name, error)
+	return opt.x, matched_sd, error
diff --git a/test_colorchecker.py b/test_colorchecker.py
new file mode 100644
index 0000000..907322b
--- /dev/null
+++ b/test_colorchecker.py
@@ -0,0 +1,37 @@
+import numpy as np
+from colour import *
+from colour.plotting import *
+from matplotlib import pyplot as plt
+from jakob_hanika import jakob_hanika
+
+D65 = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
+
+# Makes a comparison plot with SDs and swatches
+def plot_comparison(target_sd, matched_sd, label, error):
+	target_XYZ = sd_to_XYZ(target_sd)
+	target_RGB = np.clip(XYZ_to_sRGB(target_XYZ / 100), 0, 1)
+	target_swatch = ColourSwatch(label, target_RGB)
+	matched_XYZ = sd_to_XYZ(matched_sd)
+	matched_RGB = np.clip(XYZ_to_sRGB(matched_XYZ / 100), 0, 1)
+	matched_swatch = ColourSwatch("Model", matched_RGB)
+      
+	axes = plt.subplot(2, 1, 1)
+	plt.title(label)
+	plot_multi_sds([target_sd, matched_sd], axes=axes, standalone=False)
+      
+	axes = plt.subplot(2, 1, 2)
+	plt.title("ΔE = %g" % error)
+	plot_multi_colour_swatches([target_swatch, matched_swatch], axes=axes)
+
+
+
+if __name__ == "__main__":
+	# This demo goes through SDs in a color checker
+	for name, sd in COLOURCHECKERS_SDS['ColorChecker N Ohta'].items():
+		XYZ = sd_to_XYZ(sd)
+		target = XYZ_to_Lab(XYZ, D65)
+
+		print("The target is '%s' with L=%g, a=%g, b=%g" % (name, *target))
+		_, matched_sd, error = jakob_hanika(target, D65)
+
+		plot_comparison(sd, matched_sd, name, error)