Use std::scientific when outputting floats, so we do not get issues with 0.0 being...

[movit] / resample_effect.cpp

diff --git a/resample_effect.cpp b/resample_effect.cpp
index f4808c4560437288a327156f10a2f0e794e1f991..54eec864fe7a6c272fa68e3476dfcdf52b31c8a7 100644 (file)
--- a/resample_effect.cpp
+++ b/resample_effect.cpp
@@ -7,6 +7,9 @@
 #include <math.h>
 #include <stdio.h>
 #include <algorithm>
+#include <Eigen/Sparse>
+#include <Eigen/SparseQR>
+#include <Eigen/OrderingMethods>
 
 #include "effect_chain.h"
 #include "effect_util.h"
@@ -15,6 +18,7 @@
 #include "resample_effect.h"
 #include "util.h"
 
+using namespace Eigen;
 using namespace std;
 
 namespace movit {
@@ -59,7 +63,22 @@ unsigned gcd(unsigned a, unsigned b)
 template<class DestFloat>
 unsigned combine_samples(const Tap<float> *src, Tap<DestFloat> *dst, unsigned src_size, unsigned num_src_samples, unsigned max_samples_saved)
 {
+       // Cut off near-zero values at both sides.
        unsigned num_samples_saved = 0;
+       while (num_samples_saved < max_samples_saved &&
+              num_src_samples > 0 &&
+              fabs(src[0].weight) < 1e-6) {
+               ++src;
+               --num_src_samples;
+               ++num_samples_saved;
+       }
+       while (num_samples_saved < max_samples_saved &&
+              num_src_samples > 0 &&
+              fabs(src[num_src_samples - 1].weight) < 1e-6) {
+               --num_src_samples;
+               ++num_samples_saved;
+       }
+
        for (unsigned i = 0, j = 0; i < num_src_samples; ++i, ++j) {
                // Copy the sample directly; it will be overwritten later if we can combine.
                if (dst != NULL) {
@@ -113,6 +132,22 @@ unsigned combine_samples(const Tap<float> *src, Tap<DestFloat> *dst, unsigned sr
        return num_samples_saved;
 }
 
+// Normalize so that the sum becomes one. Note that we do it twice;
+// this sometimes helps a tiny little bit when we have many samples.
+template<class T>
+void normalize_sum(Tap<T>* vals, unsigned num)
+{
+       for (int normalize_pass = 0; normalize_pass < 2; ++normalize_pass) {
+               double sum = 0.0;
+               for (unsigned i = 0; i < num; ++i) {
+                       sum += to_fp64(vals[i].weight);
+               }
+               for (unsigned i = 0; i < num; ++i) {
+                       vals[i].weight = from_fp64<T>(to_fp64(vals[i].weight) / sum);
+               }
+       }
+}
+
 // Make use of the bilinear filtering in the GPU to reduce the number of samples
 // we need to make. This is a bit more complex than BlurEffect since we cannot combine
 // two neighboring samples if their weights have differing signs, so we first need to
@@ -141,19 +176,7 @@ unsigned combine_many_samples(const Tap<float> *weights, unsigned src_size, unsi
                        src_samples,
                        src_samples - src_bilinear_samples);
                assert(int(src_samples) - int(num_samples_saved) == src_bilinear_samples);
-
-               // Normalize so that the sum becomes one. Note that we do it twice;
-               // this sometimes helps a tiny little bit when we have many samples.
-               for (int normalize_pass = 0; normalize_pass < 2; ++normalize_pass) {
-                       double sum = 0.0;
-                       for (int i = 0; i < src_bilinear_samples; ++i) {
-                               sum += to_fp64(bilinear_weights_ptr[i].weight);
-                       }
-                       for (int i = 0; i < src_bilinear_samples; ++i) {
-                               bilinear_weights_ptr[i].weight = from_fp64<DestFloat>(
-                                       to_fp64(bilinear_weights_ptr[i].weight) / sum);
-                       }
-               }
+               normalize_sum(bilinear_weights_ptr, src_bilinear_samples);
        }
        return src_bilinear_samples;
 }
@@ -175,7 +198,7 @@ double compute_sum_sq_error(const Tap<float>* weights, unsigned num_weights,
        int lower_pos = int(floor(to_fp64(bilinear_weights[0].pos) * size - 0.5));
        int upper_pos = int(ceil(to_fp64(bilinear_weights[num_bilinear_weights - 1].pos) * size - 0.5)) + 2;
        lower_pos = min<int>(lower_pos, lrintf(weights[0].pos * size - 0.5));
-       upper_pos = max<int>(upper_pos, lrintf(weights[num_weights - 1].pos * size - 0.5));
+       upper_pos = max<int>(upper_pos, lrintf(weights[num_weights - 1].pos * size - 0.5) + 1);
 
        float* effective_weights = new float[upper_pos - lower_pos];
        for (int i = 0; i < upper_pos - lower_pos; ++i) {
@@ -502,6 +525,9 @@ void SingleResamplePassEffect::update_texture(GLuint glsl_program_num, const str
 
        // Now make use of the bilinear filtering in the GPU to reduce the number of samples
        // we need to make. Try fp16 first; if it's not accurate enough, we go to fp32.
+       // Our tolerance level for total error is a bit higher than the one for invididual
+       // samples, since one would assume overall errors in the shape don't matter as much.
+       const float max_error = 2.0f / (255.0f * 255.0f);
        Tap<fp16_int_t> *bilinear_weights_fp16;
        src_bilinear_samples = combine_many_samples(weights, src_size, src_samples, dst_samples, &bilinear_weights_fp16);
        Tap<float> *bilinear_weights_fp32 = NULL;
@@ -513,11 +539,12 @@ void SingleResamplePassEffect::update_texture(GLuint glsl_program_num, const str
                        bilinear_weights_fp16 + y * src_bilinear_samples, src_bilinear_samples,
                        src_size);
                max_sum_sq_error_fp16 = std::max(max_sum_sq_error_fp16, sum_sq_error_fp16);
+               if (max_sum_sq_error_fp16 > max_error) {
+                       break;
+               }
        }
 
-       // Our tolerance level for total error is a bit higher than the one for invididual
-       // samples, since one would assume overall errors in the shape don't matter as much.
-       if (max_sum_sq_error_fp16 > 2.0f / (255.0f * 255.0f)) {
+       if (max_sum_sq_error_fp16 > max_error) {
                fallback_to_fp32 = true;
                src_bilinear_samples = combine_many_samples(weights, src_size, src_samples, dst_samples, &bilinear_weights_fp32);
        }