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-rw-r--r--src/image.cpp136
1 files changed, 67 insertions, 69 deletions
diff --git a/src/image.cpp b/src/image.cpp
index 636ec78..a111d40 100644
--- a/src/image.cpp
+++ b/src/image.cpp
@@ -6,6 +6,7 @@
#include <libcamera/formats.h>
+#include "image.h"
#include "macro.h"
#include "pixels.h"
@@ -20,15 +21,13 @@ uint64_t rot_elapsed_ns = 0;
uint64_t pix_elapsed_ns = 0;
uint64_t dropped_count = 0;
+float process_column(const Image::column_t &column);
+float process_column_center_of_mass(const Image::column_t &column);
+
// float process_column(const uint8_t (&column)[])
// float process_column(const Image::row_t &column)
float process_column(const Image::column_t &column)
{
- // Image::column_t c = column;
- // start_timer(process_column);
- // QElapsedTimer t;
- // t.start();
-
float result = std::numeric_limits<float>::quiet_NaN();
// constexpr uint32_t signalThreshold = 900; // = SKO * sqrt(patternSize)
@@ -47,40 +46,6 @@ float process_column(const Image::column_t &column)
static_assert((img_height % patternSize) == 0, "img_height % patternSize should be 0");
// std::array<uint16_t, img_height / patternSize> sums;
- // uint16_t maxLALASum{0};
- // size_t maxLALAIdx{0};
- // uint16_t sum{0};
-
- // for (size_t i = 0; i < img_height; i += patternSize) {
- // // const auto sum = std::accumulate(column.cbegin() + i,
- // // column.cbegin() + i + patternSize,
- // // 0,
- // // std::plus<uint16_t>());
- // uint16_t sum{0};
-
- // for (size_t j = i; j < i + patternSize; ++j)
- // sum += column[j];
- // // if ((i % patternSize) == 0)
- // // sum = 0;
- // // sum += column[i];
-
- // // if (sum > 0xff)
- // // std::cout << sum << ' ';
-
- // if (sum > maxLALASum) {
- // maxLALASum = sum;
- // maxLALAIdx = i;
- // }
- // }
-
- // maxLALAIdx = patternSize * 32 - 14; // crash
- // maxLALAIdx = patternSize * 32 - 14; // no crash
-
- // maxLALAIdx = std::clamp(uint32_t(maxLALAIdx),
- // uint32_t(patternSize),
- // uint32_t(img_height - patternSize * 2 - 14));
-
- // std::cout << maxLALAIdx << ' ';
// memset(correlation, 0, img_height * sizeof(correlation[0]));
// memset(correlation, 0, patternSize);
// memset(correlation + correlationSize - 1, 0, patternSize);
@@ -93,29 +58,17 @@ float process_column(const Image::column_t &column)
integralSum[i] = column[i] + integralSum[i - 1];
}
- // sum_elapsed_ns += t.nsecsElapsed();
- // t.restart();
-
// pixel * <sum of neighbours>
for (uint32_t i = 0; i < correlationSize; ++i)
// for (uint32_t i = maxLALAIdx; i < maxLALAIdx + patternSize * 2; ++i)
correlation[i + patternSize / 2] = column[i + patternSize / 2]
* (integralSum[i + patternOffset] - integralSum[i]);
- // * (integralSum[i + patternSize] - integralSum[i]);
-
- // corr_elapsed_ns += t.nsecsElapsed();
- // t.restart();
uint32_t cPPP = correlation[0];
uint32_t cPP = correlation[1];
uint32_t cP = correlation[2];
- // uint32_t c = correlation[3];
- // uint32_t cN = correlation[4];
- // uint32_t cNN = correlation[5];
-
for (uint32_t i = 3; i < img_height - 2; ++i) {
- // for (uint32_t i = maxLALAIdx + 3; i < maxLALAIdx + patternSize * 3 - 2; ++i) {
// p - pixel, n - neighbour
// P - pixel used in sum, N - neighbour used in sum
// [N P N]
@@ -124,16 +77,6 @@ float process_column(const Image::column_t &column)
const uint32_t cNN = correlation[i + 2];
const auto sum = cP + c + cN;
- // const int32_t rioux0 = int32_t(cPPP + cPP) - int32_t(c + cN);
- // const int32_t rioux1 = int32_t(cPP + cP) - int32_t(cN + cNN);
-
- // if (sum > maxTripleSum && rioux0 < 0 && rioux1 >= 0) {
- // x1 = i - 1;
- // y1 = rioux0;
- // y2 = rioux1;
- // maxTripleSum = sum;
- // }
-
if (sum > maxTripleSum) {
// [N N n p] - [P N]
const int32_t rioux0 = int32_t(cPPP + cPP) - int32_t(c + cN);
@@ -154,20 +97,71 @@ float process_column(const Image::column_t &column)
cPPP = cPP;
cPP = cP;
cP = c;
- // c = cN;
- // cN = cNN;
- // cNN = correlation[i + 1];
}
- // value_elapsed_ns += t.nsecsElapsed();
- // t.restart();
-
result = (y2 != y1) ? (float(x1) - (float(y1) / (y2 - y1)))
: std::numeric_limits<float>::quiet_NaN();
return result;
}
+float process_column_center_of_mass(const Image::column_t &column)
+{
+ static_assert((img_height % patternSize) == 0, "img_height % patternSize should be 0");
+
+ // const auto &c = column;
+ Image::column_t c = column;
+ constexpr size_t win_size = 2;
+ constexpr size_t wins_count{img_height / win_size};
+ using pixel_sum_t = uint16_t;
+ // using img_pixel_t = decltype(Image::row_t)::value_type;
+ using img_pixel_t = uint8_t;
+ static_assert(std::numeric_limits<pixel_sum_t>::max()
+ > std::numeric_limits<img_pixel_t>::max() * win_size,
+ "choose bigger type for pixels sum");
+ // std::array<pixel_sum_t, wins_count> sums;
+ pixel_sum_t maxSum{0};
+ size_t maxWinI{0};
+ constexpr img_pixel_t blackLevel{5};
+
+ // std::for_each(c.begin(), c.end(), [](auto &v) {
+ // if (v > 20) {
+ // v = 0;
+ // }
+ // });
+
+ for (size_t w{0}; w < wins_count; ++w) {
+ // option 0: manually sum
+ const auto i = w * win_size;
+ // sums[w] = c[i + 0] * 1 + c[i + 1] * 2 + c[i + 2] * 3 + c[i + 3] * 4 + c[i + 4] * 5
+ // + c[i + 5] * 6 + c[i + 6] * 7 + c[i + 7] * 8;
+ // const auto sum{c[i + 0] + c[i + 1] + c[i + 2] + c[i + 3] + c[i + 4] + c[i + 5] + c[i + 6]
+ // + c[i + 7]};
+ const auto sum{c[i + 0] + c[i + 1] + c[i + 2] + c[i + 3]};
+
+ if (sum > maxSum) {
+ maxSum = sum;
+ maxWinI = w;
+ }
+ }
+
+ const auto start = (maxWinI - 1) * win_size;
+ const auto com_size = win_size;
+ float com_sum{0};
+ float com_sum_mass{0};
+ constexpr uint8_t black_level{27};
+ // std::array<uint8_t, win_size * 3>
+
+ for (size_t i{start}; i < start + (win_size * 3); ++i) {
+ const auto value = c[i] > black_level ? c[i] : 0;
+ com_sum += value;
+ com_sum_mass += value * (i);
+ }
+
+ // return com_sum / (win_size * 3);
+ return com_sum_mass / com_sum;
+}
+
// uint8_t &Image::dataAt(size_t row, size_t col)
// {
// const auto index = img_width * row + col;
@@ -206,12 +200,16 @@ std::shared_ptr<Pixels> Image::sharedPixels()
result->counters = counters;
std::transform(rotated_cw.cbegin(), rotated_cw.cend(), result->pixels.begin(), process_column);
+ // std::transform(rotated_cw.cbegin(),
+ // rotated_cw.cend(),
+ // result->pixels.begin(),
+ // process_column_center_of_mass);
static bool found{false};
for (const auto &p : result->pixels) {
- if (p > 400) {
- std::cout << "AAAAAAAAAAAAAAAAAAAAA too big pixel values: " << p << std::endl;
+ if (p > img_height) {
+ // std::cout << "AAAAAAAAAAAAAAAAAAAAA too big pixel values: " << p << std::endl;
found = true;
}
}
generated by cgit v1.3.1 (git 2.54.0) at 2026-08-04 23:47:29 +0000