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removed cluster_2x2 and cluster3x3 specializations
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This commit is contained in:
Mazzoleni Alice Francesca
2025-04-16 16:40:42 +02:00
parent 14211047ff
commit c49a2fdf8e
7 changed files with 93 additions and 151 deletions
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111
include/aare/Cluster.hpp
View File

@ -16,94 +16,61 @@
namespace aare {
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType = int16_t>
constexpr bool is_valid_cluster =
std::is_arithmetic_v<T> && std::is_integral_v<CoordType> &&
(ClusterSizeX > 0) && (ClusterSizeY > 0);
// requires clause c++20 maybe update
template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
typename CoordType = int16_t,
typename Enable = std::enable_if_t<
is_valid_cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>>
typename CoordType = int16_t>
struct Cluster {
static_assert(std::is_arithmetic_v<T>, "T needs to be an arithmetic type");
static_assert(std::is_integral_v<CoordType>,
"CoordType needs to be an integral type");
static_assert(ClusterSizeX > 0 && ClusterSizeY > 0,
"Cluster sizes must be bigger than zero");
CoordType x;
CoordType y;
T data[ClusterSizeX * ClusterSizeY];
std::array<T, ClusterSizeX * ClusterSizeY> data;
static constexpr uint8_t cluster_size_x = ClusterSizeX;
static constexpr uint8_t cluster_size_y = ClusterSizeY;
using value_type = T;
using coord_type = CoordType;
T sum() const {
return std::accumulate(data, data + ClusterSizeX * ClusterSizeY, 0);
}
T sum() const { return std::accumulate(data.begin(), data.end(), T{}); }
std::pair<T, int> max_sum_2x2() const {
constexpr size_t num_2x2_subclusters =
(ClusterSizeX - 1) * (ClusterSizeY - 1);
if constexpr (cluster_size_x == 3 && cluster_size_y == 3) {
std::array<T, 4> sum_2x2_subclusters;
sum_2x2_subclusters[0] = data[0] + data[1] + data[3] + data[4];
sum_2x2_subclusters[1] = data[1] + data[2] + data[4] + data[5];
sum_2x2_subclusters[2] = data[3] + data[4] + data[6] + data[7];
sum_2x2_subclusters[3] = data[4] + data[5] + data[7] + data[8];
int index = std::max_element(sum_2x2_subclusters.begin(),
sum_2x2_subclusters.end()) -
sum_2x2_subclusters.begin();
return std::make_pair(sum_2x2_subclusters[index], index);
} else if constexpr (cluster_size_x == 2 && cluster_size_y == 2) {
return std::make_pair(data[0] + data[1] + data[2] + data[3], 0);
} else {
constexpr size_t num_2x2_subclusters =
(ClusterSizeX - 1) * (ClusterSizeY - 1);
std::array<T, num_2x2_subclusters> sum_2x2_subcluster;
for (size_t i = 0; i < ClusterSizeY - 1; ++i) {
for (size_t j = 0; j < ClusterSizeX - 1; ++j)
sum_2x2_subcluster[i * (ClusterSizeX - 1) + j] =
data[i * ClusterSizeX + j] +
data[i * ClusterSizeX + j + 1] +
data[(i + 1) * ClusterSizeX + j] +
data[(i + 1) * ClusterSizeX + j + 1];
std::array<T, num_2x2_subclusters> sum_2x2_subcluster;
for (size_t i = 0; i < ClusterSizeY - 1; ++i) {
for (size_t j = 0; j < ClusterSizeX - 1; ++j)
sum_2x2_subcluster[i * (ClusterSizeX - 1) + j] =
data[i * ClusterSizeX + j] +
data[i * ClusterSizeX + j + 1] +
data[(i + 1) * ClusterSizeX + j] +
data[(i + 1) * ClusterSizeX + j + 1];
}
int index = std::max_element(sum_2x2_subcluster.begin(),
sum_2x2_subcluster.end()) -
sum_2x2_subcluster.begin();
return std::make_pair(sum_2x2_subcluster[index], index);
}
int index = std::max_element(sum_2x2_subcluster.begin(),
sum_2x2_subcluster.end()) -
sum_2x2_subcluster.begin();
return std::make_pair(sum_2x2_subcluster[index], index);
}
};
// Specialization for 2x2 clusters (only one sum exists)
template <typename T> struct Cluster<T, 2, 2, int16_t> {
int16_t x;
int16_t y;
T data[4];
static constexpr uint8_t cluster_size_x = 2;
static constexpr uint8_t cluster_size_y = 2;
using value_type = T;
using coord_type = int16_t;
T sum() const { return std::accumulate(data, data + 4, 0); }
std::pair<T, int> max_sum_2x2() const {
return std::make_pair(data[0] + data[1] + data[2] + data[3],
0); // Only one possible 2x2 sum
}
};
// Specialization for 3x3 clusters
template <typename T> struct Cluster<T, 3, 3, int16_t> {
int16_t x;
int16_t y;
T data[9];
static constexpr uint8_t cluster_size_x = 3;
static constexpr uint8_t cluster_size_y = 3;
using value_type = T;
using coord_type = int16_t;
T sum() const { return std::accumulate(data, data + 9, 0); }
std::pair<T, int> max_sum_2x2() const {
std::array<T, 4> sum_2x2_subclusters;
sum_2x2_subclusters[0] = data[0] + data[1] + data[3] + data[4];
sum_2x2_subclusters[1] = data[1] + data[2] + data[4] + data[5];
sum_2x2_subclusters[2] = data[3] + data[4] + data[6] + data[7];
sum_2x2_subclusters[3] = data[4] + data[5] + data[7] + data[8];
int index = std::max_element(sum_2x2_subclusters.begin(),
sum_2x2_subclusters.end()) -
sum_2x2_subclusters.begin();
return std::make_pair(sum_2x2_subclusters[index], index);
}
};

15
include/aare/ClusterFinder.hpp
View File

@ -77,7 +77,6 @@ class ClusterFinder {
int has_center_pixel_y = ClusterSizeY % 2;
m_clusters.set_frame_number(frame_number);
std::vector<CT> cluster_data(ClusterSizeX * ClusterSizeY);
for (int iy = 0; iy < frame.shape(0); iy++) {
for (int ix = 0; ix < frame.shape(1); ix++) {
@ -124,8 +123,9 @@ class ClusterFinder {
// Store cluster
if (value == max) {
// Zero out the cluster data
std::fill(cluster_data.begin(), cluster_data.end(), 0);
ClusterType cluster{};
cluster.x = ix;
cluster.y = iy;
// Fill the cluster data since we have a photon to store
// It's worth redoing the look since most of the time we
@ -139,20 +139,15 @@ class ClusterFinder {
static_cast<CT>(frame(iy + ir, ix + ic)) -
static_cast<CT>(
m_pedestal.mean(iy + ir, ix + ic));
cluster_data[i] =
cluster.data[i] =
tmp; // Watch for out of bounds access
i++;
}
}
}
ClusterType new_cluster{};
new_cluster.x = ix;
new_cluster.y = iy;
std::copy(cluster_data.begin(), cluster_data.end(),
new_cluster.data);
// Add the cluster to the output ClusterVector
m_clusters.push_back(new_cluster);
m_clusters.push_back(cluster);
}
}
}

5
include/aare/GainMap.hpp
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@ -44,9 +44,8 @@ class GainMap {
cl.data[j] = cl.data[j] * static_cast<T>(m_gain_map(y, x));
}
} else {
memset(cl.data, 0,
ClusterSizeX * ClusterSizeY *
sizeof(T)); // clear edge clusters
// clear edge clusters
cl.data.fill(0);
}
}
}