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#include <algorithm>
#include <iostream>
#include <cmath>
#include "masked_image.h"
#include "nnf.h"
/**
* Nearest-Neighbor Field (see PatchMatch algorithm).
* This algorithme uses a version proposed by Xavier Philippeau.
*
*/
template <typename T>
T clamp(T value, T min_value, T max_value) {
return std::min(std::max(value, min_value), max_value);
}
void NearestNeighborField::_randomize_field(int max_retry, bool reset) {
auto this_size = source_size();
for (int i = 0; i < this_size.height; ++i) {
for (int j = 0; j < this_size.width; ++j) {
if (m_source.is_globally_masked(i, j)) continue;
auto this_ptr = mutable_ptr(i, j);
int distance = reset ? PatchDistanceMetric::kDistanceScale : this_ptr[2];
if (distance < PatchDistanceMetric::kDistanceScale) {
continue;
}
int i_target = 0, j_target = 0;
for (int t = 0; t < max_retry; ++t) {
i_target = rand() % this_size.height;
j_target = rand() % this_size.width;
if (m_target.is_globally_masked(i_target, j_target)) continue;
distance = _distance(i, j, i_target, j_target);
if (distance < PatchDistanceMetric::kDistanceScale)
break;
}
this_ptr[0] = i_target, this_ptr[1] = j_target, this_ptr[2] = distance;
}
}
}
void NearestNeighborField::_initialize_field_from(const NearestNeighborField &other, int max_retry) {
const auto &this_size = source_size();
const auto &other_size = other.source_size();
double fi = static_cast<double>(this_size.height) / other_size.height;
double fj = static_cast<double>(this_size.width) / other_size.width;
for (int i = 0; i < this_size.height; ++i) {
for (int j = 0; j < this_size.width; ++j) {
if (m_source.is_globally_masked(i, j)) continue;
int ilow = static_cast<int>(std::min(i / fi, static_cast<double>(other_size.height - 1)));
int jlow = static_cast<int>(std::min(j / fj, static_cast<double>(other_size.width - 1)));
auto this_value = mutable_ptr(i, j);
auto other_value = other.ptr(ilow, jlow);
this_value[0] = static_cast<int>(other_value[0] * fi);
this_value[1] = static_cast<int>(other_value[1] * fj);
this_value[2] = _distance(i, j, this_value[0], this_value[1]);
}
}
_randomize_field(max_retry, false);
}
void NearestNeighborField::minimize(int nr_pass) {
const auto &this_size = source_size();
while (nr_pass--) {
for (int i = 0; i < this_size.height; ++i)
for (int j = 0; j < this_size.width; ++j) {
if (m_source.is_globally_masked(i, j)) continue;
if (at(i, j, 2) > 0) _minimize_link(i, j, +1);
}
for (int i = this_size.height - 1; i >= 0; --i)
for (int j = this_size.width - 1; j >= 0; --j) {
if (m_source.is_globally_masked(i, j)) continue;
if (at(i, j, 2) > 0) _minimize_link(i, j, -1);
}
}
}
void NearestNeighborField::_minimize_link(int y, int x, int direction) {
const auto &this_size = source_size();
const auto &this_target_size = target_size();
auto this_ptr = mutable_ptr(y, x);
// propagation along the y direction.
if (y - direction >= 0 && y - direction < this_size.height && !m_source.is_globally_masked(y - direction, x)) {
int yp = at(y - direction, x, 0) + direction;
int xp = at(y - direction, x, 1);
int dp = _distance(y, x, yp, xp);
if (dp < at(y, x, 2)) {
this_ptr[0] = yp, this_ptr[1] = xp, this_ptr[2] = dp;
}
}
// propagation along the x direction.
if (x - direction >= 0 && x - direction < this_size.width && !m_source.is_globally_masked(y, x - direction)) {
int yp = at(y, x - direction, 0);
int xp = at(y, x - direction, 1) + direction;
int dp = _distance(y, x, yp, xp);
if (dp < at(y, x, 2)) {
this_ptr[0] = yp, this_ptr[1] = xp, this_ptr[2] = dp;
}
}
// random search with a progressive step size.
int random_scale = (std::min(this_target_size.height, this_target_size.width) - 1) / 2;
while (random_scale > 0) {
int yp = this_ptr[0] + (rand() % (2 * random_scale + 1) - random_scale);
int xp = this_ptr[1] + (rand() % (2 * random_scale + 1) - random_scale);
yp = clamp(yp, 0, target_size().height - 1);
xp = clamp(xp, 0, target_size().width - 1);
if (m_target.is_globally_masked(yp, xp)) {
random_scale /= 2;
}
int dp = _distance(y, x, yp, xp);
if (dp < at(y, x, 2)) {
this_ptr[0] = yp, this_ptr[1] = xp, this_ptr[2] = dp;
}
random_scale /= 2;
}
}
const int PatchDistanceMetric::kDistanceScale = 65535;
const int PatchSSDDistanceMetric::kSSDScale = 9 * 255 * 255;
namespace {
inline int pow2(int i) {
return i * i;
}
int distance_masked_images(
const MaskedImage &source, int ys, int xs,
const MaskedImage &target, int yt, int xt,
int patch_size
) {
long double distance = 0;
long double wsum = 0;
source.compute_image_gradients();
target.compute_image_gradients();
auto source_size = source.size();
auto target_size = target.size();
for (int dy = -patch_size; dy <= patch_size; ++dy) {
const int yys = ys + dy, yyt = yt + dy;
if (yys <= 0 || yys >= source_size.height - 1 || yyt <= 0 || yyt >= target_size.height - 1) {
distance += (long double)(PatchSSDDistanceMetric::kSSDScale) * (2 * patch_size + 1);
wsum += 2 * patch_size + 1;
continue;
}
const auto *p_si = source.image().ptr<unsigned char>(yys, 0);
const auto *p_ti = target.image().ptr<unsigned char>(yyt, 0);
const auto *p_sm = source.mask().ptr<unsigned char>(yys, 0);
const auto *p_tm = target.mask().ptr<unsigned char>(yyt, 0);
const unsigned char *p_sgm = nullptr;
const unsigned char *p_tgm = nullptr;
if (!source.global_mask().empty()) {
p_sgm = source.global_mask().ptr<unsigned char>(yys, 0);
p_tgm = target.global_mask().ptr<unsigned char>(yyt, 0);
}
const auto *p_sgy = source.grady().ptr<unsigned char>(yys, 0);
const auto *p_tgy = target.grady().ptr<unsigned char>(yyt, 0);
const auto *p_sgx = source.gradx().ptr<unsigned char>(yys, 0);
const auto *p_tgx = target.gradx().ptr<unsigned char>(yyt, 0);
for (int dx = -patch_size; dx <= patch_size; ++dx) {
int xxs = xs + dx, xxt = xt + dx;
wsum += 1;
if (xxs <= 0 || xxs >= source_size.width - 1 || xxt <= 0 || xxt >= source_size.width - 1) {
distance += PatchSSDDistanceMetric::kSSDScale;
continue;
}
if (p_sm[xxs] || p_tm[xxt] || (p_sgm && p_sgm[xxs]) || (p_tgm && p_tgm[xxt]) ) {
distance += PatchSSDDistanceMetric::kSSDScale;
continue;
}
int ssd = 0;
for (int c = 0; c < 3; ++c) {
int s_value = p_si[xxs * 3 + c];
int t_value = p_ti[xxt * 3 + c];
int s_gy = p_sgy[xxs * 3 + c];
int t_gy = p_tgy[xxt * 3 + c];
int s_gx = p_sgx[xxs * 3 + c];
int t_gx = p_tgx[xxt * 3 + c];
ssd += pow2(static_cast<int>(s_value) - t_value);
ssd += pow2(static_cast<int>(s_gx) - t_gx);
ssd += pow2(static_cast<int>(s_gy) - t_gy);
}
distance += ssd;
}
}
distance /= (long double)(PatchSSDDistanceMetric::kSSDScale);
int res = int(PatchDistanceMetric::kDistanceScale * distance / wsum);
if (res < 0 || res > PatchDistanceMetric::kDistanceScale) return PatchDistanceMetric::kDistanceScale;
return res;
}
}
int PatchSSDDistanceMetric::operator ()(const MaskedImage &source, int source_y, int source_x, const MaskedImage &target, int target_y, int target_x) const {
return distance_masked_images(source, source_y, source_x, target, target_y, target_x, m_patch_size);
}
int DebugPatchSSDDistanceMetric::operator ()(const MaskedImage &source, int source_y, int source_x, const MaskedImage &target, int target_y, int target_x) const {
fprintf(stderr, "DebugPatchSSDDistanceMetric: %d %d %d %d\n", source.size().width, source.size().height, m_width, m_height);
return distance_masked_images(source, source_y, source_x, target, target_y, target_x, m_patch_size);
}
int RegularityGuidedPatchDistanceMetricV1::operator ()(const MaskedImage &source, int source_y, int source_x, const MaskedImage &target, int target_y, int target_x) const {
double dx = remainder(double(source_x - target_x) / source.size().width, m_dx1);
double dy = remainder(double(source_y - target_y) / source.size().height, m_dy2);
double score1 = sqrt(dx * dx + dy *dy) / m_scale;
if (score1 < 0 || score1 > 1) score1 = 1;
score1 *= PatchDistanceMetric::kDistanceScale;
double score2 = distance_masked_images(source, source_y, source_x, target, target_y, target_x, m_patch_size);
double score = score1 * m_weight + score2 / (1 + m_weight);
return static_cast<int>(score / (1 + m_weight));
}
int RegularityGuidedPatchDistanceMetricV2::operator ()(const MaskedImage &source, int source_y, int source_x, const MaskedImage &target, int target_y, int target_x) const {
if (target_y < 0 || target_y >= target.size().height || target_x < 0 || target_x >= target.size().width)
return PatchDistanceMetric::kDistanceScale;
int source_scale = m_ijmap.size().height / source.size().height;
int target_scale = m_ijmap.size().height / target.size().height;
// fprintf(stderr, "RegularityGuidedPatchDistanceMetricV2 %d %d %d %d\n", source_y * source_scale, m_ijmap.size().height, source_x * source_scale, m_ijmap.size().width);
double score1 = PatchDistanceMetric::kDistanceScale;
if (!source.is_globally_masked(source_y, source_x) && !target.is_globally_masked(target_y, target_x)) {
auto source_ij = m_ijmap.ptr<float>(source_y * source_scale, source_x * source_scale);
auto target_ij = m_ijmap.ptr<float>(target_y * target_scale, target_x * target_scale);
float di = fabs(source_ij[0] - target_ij[0]); if (di > 0.5) di = 1 - di;
float dj = fabs(source_ij[1] - target_ij[1]); if (dj > 0.5) dj = 1 - dj;
score1 = sqrt(di * di + dj *dj) / 0.707;
if (score1 < 0 || score1 > 1) score1 = 1;
score1 *= PatchDistanceMetric::kDistanceScale;
}
double score2 = distance_masked_images(source, source_y, source_x, target, target_y, target_x, m_patch_size);
double score = score1 * m_weight + score2;
return int(score / (1 + m_weight));
}
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