1 files changed, 102 insertions, 0 deletions
diff --git a/test_coverage.py b/test_coverage.py
new file mode 100644
index 0000000..07ceb08
--- /dev/null
+++ b/test_coverage.py
@@ -0,0 +1,102 @@
+import numpy as np, os, multiprocessing
+from colour import *
+from colour.models import eotf_inverse_sRGB
+from jakob_hanika import jakob_hanika, model_sd
+
+
+
+illuminant = "D65"
+ill_xy = ILLUMINANTS["CIE 1931 2 Degree Standard Observer"][illuminant]
+ill_sd = SpectralDistribution(ILLUMINANTS_SDS[illuminant]) # Note: changed in dev (to ILLUMINANTS_SD)
+
+# Resolution of the discretization cubes
+# Both should be 64, but this code is a bit too slow at the moment
+CHROMA_STEPS = 8
+LIGHTNESS_STEPS = 8
+
+
+
+# Solve for a specific RGB color
+def optimize_RGB(linear_RGB, ccp0):
+	RGB = eotf_inverse_sRGB(linear_RGB)
+	target = sRGB_to_XYZ(RGB, ill_xy)
+	ccp, error = jakob_hanika(target, ill_sd, ill_xy, ccp0, verbose=False)
+	if error > 0.1:
+		print("WARNING: convergence failed with L=%s, starting from %s" \
+		      % (linear_RGB, ccp0))
+
+	return ccp, error
+
+# Solve for all lightness values of a fully saturated RGB color
+def optimize_chromaticity(argv):
+	linear_RGB, dirname = argv
+
+	# alpha's aren't spaced equally (see the article)
+	def smoothstep(x):
+		return x**2 * (3 - 2 * x)
+
+	ii = np.arange(0, LIGHTNESS_STEPS)
+	aa = smoothstep(smoothstep(ii / LIGHTNESS_STEPS))
+	ccps = np.empty((LIGHTNESS_STEPS, 3)) # FIXME: dtype?
+
+	def f(alpha, ccp0):
+		path = "%s/%.3f" % (dirname, alpha)
+		print("%s..." % path)
+
+		ccp, error = optimize_RGB(linear_RGB * alpha, ccp0)
+
+		try:
+			os.makedirs(os.path.dirname(path))
+		except:
+			pass
+		with open(path, "w") as fd:
+			fd.write("%s, %s" % (ccp, error))
+			if error > 0.1:
+				fd.write(" FAILED")
+			fd.write("\n")
+
+		return ccp, error
+
+
+	i_mid = LIGHTNESS_STEPS // 2
+	ccps[i_mid, :], _ = f(aa[i_mid], (0, 0, 0))
+
+	ccp0 = ccps[i_mid, :]
+	for i in range(i_mid + 1, LIGHTNESS_STEPS):
+		ccps[i, :], _ = f(aa[i], ccp0)
+
+	ccp0 = ccps[i_mid, :]
+	for i in reversed(range(0, i_mid)):
+		ccps[i, :], _ = f(aa[i], ccp0)
+
+
+
+# This demo discretizes the sRGB space to three 8x8x8 cubes and tries to find
+# the model parameters for all the colors. Coefficients are saved to a file
+# somewhere in the 'out' directory (each color gets its own file).
+# FIXME: This dumpster fire of an output format is to be replaced ASAP.
+# FIXME: This takes a good while (on the order of an hour).
+if __name__ == "__main__":
+	args = []
+
+	done = 0
+	total = 0
+	for A in np.linspace(0, 1, CHROMA_STEPS):
+		for B in np.linspace(0, 1, CHROMA_STEPS):
+			for RGB in [np.array([1, A, B]),
+			            np.array([A, 1, B]),
+			            np.array([A, B, 1])]:
+
+				dirname = "out/R%.3fG%.3fB%.3f" % (*RGB,)
+				total += 1
+
+				if os.path.exists(dirname) and len(os.listdir(dirname)) == LIGHTNESS_STEPS:
+					done += 1
+					continue
+
+				args.append((RGB, dirname))
+
+	print("%d/%d already done" % (done, total))
+
+	pool = multiprocessing.Pool()
+	pool.map(optimize_chromaticity, args)