X-Git-Url: https://git.sesse.net/?p=movit;a=blobdiff_plain;f=deconvolution_sharpen_effect_test.cpp;h=f08225b15a6575f741b9469d38a4ae6a50ee8170;hp=bf6dfb81b8f105dbbf7d5d0a96f9d8b5df5f9c86;hb=0490917aca6b8e6057c04c8becc820a8e849a801;hpb=2e3a9b8deae0d4635a9a97fec04f60f66e8a3682

diff --git a/deconvolution_sharpen_effect_test.cpp b/deconvolution_sharpen_effect_test.cpp
index bf6dfb8..f08225b 100644
--- a/deconvolution_sharpen_effect_test.cpp
+++ b/deconvolution_sharpen_effect_test.cpp
@@ -4,7 +4,30 @@
 #include "gtest/gtest.h"
 #include "deconvolution_sharpen_effect.h"
 
-TEST(DeconvolutionSharpenEffect, DeconvolvesCircularBlur) {
+TEST(DeconvolutionSharpenEffectTest, IdentityTransformDoesNothing) {
+	const int size = 4;
+
+	float data[size * size] = {
+		0.0, 1.0, 0.0, 1.0,
+		0.0, 1.0, 1.0, 0.0,
+		0.0, 0.5, 1.0, 0.5,
+		0.0, 0.0, 0.0, 0.0,
+	};
+	float out_data[size * size];
+
+	EffectChainTester tester(data, size, size, FORMAT_GRAYSCALE, COLORSPACE_sRGB, GAMMA_LINEAR);
+	Effect *deconvolution_effect = tester.get_chain()->add_effect(new DeconvolutionSharpenEffect());
+	ASSERT_TRUE(deconvolution_effect->set_int("matrix_size", 5));
+	ASSERT_TRUE(deconvolution_effect->set_float("circle_radius", 0.0f));
+	ASSERT_TRUE(deconvolution_effect->set_float("gaussian_radius", 0.0f));
+	ASSERT_TRUE(deconvolution_effect->set_float("correlation", 0.0001f));
+	ASSERT_TRUE(deconvolution_effect->set_float("noise", 0.0f));
+	tester.run(out_data, GL_RED, COLORSPACE_sRGB, GAMMA_LINEAR);
+
+	expect_equal(data, out_data, size, size);
+}
+
+TEST(DeconvolutionSharpenEffectTest, DeconvolvesCircularBlur) {
 	const int size = 13;
 
 	// Matches exactly a circular blur kernel with radius 2.0.
@@ -53,7 +76,7 @@ TEST(DeconvolutionSharpenEffect, DeconvolvesCircularBlur) {
 	expect_equal(expected_data, out_data, size, size, 0.15f, 0.005f);
 }
 
-TEST(DeconvolutionSharpenEffect, DeconvolvesGaussianBlur) {
+TEST(DeconvolutionSharpenEffectTest, DeconvolvesGaussianBlur) {
 	const int size = 13;
 	const float sigma = 0.5f;
 
@@ -102,5 +125,72 @@ TEST(DeconvolutionSharpenEffect, DeconvolvesGaussianBlur) {
 	expect_equal(expected_data, out_data, size, size);
 }
 
-// TODO: Test no-op (both radii equal to zero).
-// TODO: Test correlation and noise parameters.
+TEST(DeconvolutionSharpenEffectTest, NoiseAndCorrelationControlsReduceNoiseBoosting) {
+	const int size = 13;
+	const float sigma = 0.5f;
+
+	float data[size * size], out_data[size * size];
+	float expected_data[size * size] = {
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+		0.0, 0.0, 0.0, 0.0, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0, 0.0, 0.0, 0.0, 
+	};
+
+	// Gaussian kernel.
+	float sum = 0.0f;	
+	for (int y = 0; y < size; ++y) {
+		for (int x = 0; x < size; ++x) {
+			float z = hypot(x - 6, y - 6);
+			data[y * size + x] = exp(-z*z / (2.0 * sigma * sigma)) / (2.0 * M_PI * sigma * sigma);
+			sum += data[y * size + x];
+		}
+	}
+	for (int y = 0; y < size; ++y) {
+		for (int x = 0; x < size; ++x) {
+			data[y * size + x] /= sum;
+		}
+	}
+
+	// Corrupt with some uniform noise.
+	srand(1234);
+	for (int i = 0; i < size * size; ++i) {
+		data[i] += 0.1 * ((float)rand() / RAND_MAX - 0.5);
+	}
+
+	EffectChainTester tester(data, size, size, FORMAT_GRAYSCALE, COLORSPACE_sRGB, GAMMA_LINEAR);
+	Effect *deconvolution_effect = tester.get_chain()->add_effect(new DeconvolutionSharpenEffect());
+	ASSERT_TRUE(deconvolution_effect->set_int("matrix_size", 5));
+	ASSERT_TRUE(deconvolution_effect->set_float("circle_radius", 0.0f));
+	ASSERT_TRUE(deconvolution_effect->set_float("gaussian_radius", 0.5f));
+	ASSERT_TRUE(deconvolution_effect->set_float("correlation", 0.5f));
+	ASSERT_TRUE(deconvolution_effect->set_float("noise", 0.1f));
+	tester.run(out_data, GL_RED, COLORSPACE_sRGB, GAMMA_LINEAR);
+
+	float sumsq_in = 0.0f, sumsq_out = 0.0f;
+	for (int i = 0; i < size * size; ++i) {
+		sumsq_in += data[i] * data[i];
+		sumsq_out += out_data[i] * out_data[i];
+	}
+
+	// The limits have to be quite lax; deconvolution is not an exact operation.
+	// We special-case the center sample since it's the one with the largest error
+	// almost no matter what we do, so we don't want that to be the dominating
+	// factor in the outlier tests.
+	int center = size / 2;
+	EXPECT_GT(out_data[center * size + center], 0.5f);
+	out_data[center * size + center] = 1.0f;
+	expect_equal(expected_data, out_data, size, size, 0.20f, 0.005f);
+
+	// Check that we didn't boost total energy (which in this case means the noise) more than 10%.
+	EXPECT_LT(sumsq_out, sumsq_in * 1.1f);
+}