mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2025-06-06 21:00:41 +02:00
68dcfca74e
major changes: - add python bindings for all c++ features except network_io - changes to cross compile on windows,linux and macos - fix bugs with cluster_finder - use Dtype in Frame instead of bitdepth - remove boost::program_options and replace with our implementation - add Transforms class that applies a sequence of functions (c++ or python functions) on a Frame. - remove frame reorder and flip from SubFile.cpp. use Transforms instead - Test clusterFinder and Pedestal results in comparison with slsDetectorCalibration --------- Co-authored-by: Bechir <bechir.brahem420@gmail.com> Co-authored-by: Erik Fröjdh <erik.frojdh@gmail.com>
63 lines
2.3 KiB
C++
63 lines
2.3 KiB
C++
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#include "aare.hpp"
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#include "aare/examples/defs.hpp"
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#include <cassert>
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#include <iostream>
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using namespace aare;
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int main() {
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auto PROJECT_ROOT_DIR = std::filesystem::path(getenv("AARE_ROOT_DIR"));
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std::filesystem::path const fpath_frame("/home/l_bechir/tmp/testNewFW20230714/cu_half_speed_master_4.json");
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std::filesystem::path const fpath_cluster("/home/l_bechir/tmp/testNewFW20230714/clust/cu_half_speed_d0_f0_4.clust");
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auto file = RawFile(fpath_frame, "r");
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logger::info("RAW file");
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logger::info("file rows:", file.rows(), "cols:", file.cols());
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logger::info(file.total_frames());
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Frame frame(0, 0, Dtype::NONE);
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for (auto i = 0; i < 10000; i++) {
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if (file.frame_number(i) == 23389) {
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logger::info("frame_number:", file.frame_number(i));
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frame = file.read();
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}
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}
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logger::info("frame", frame.rows(), frame.cols(), frame.bitdepth());
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ClusterFileV2 cf(fpath_cluster, "r");
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auto anna_clusters = cf.read();
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logger::info("Cluster file");
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logger::info("nclusters:", anna_clusters.size());
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logger::info("frame_number", anna_clusters[0].frame_number);
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ClusterFinder clusterFinder(3, 3, 5, 0);
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Pedestal p(file.rows(), file.cols());
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file.seek(0);
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logger::info("Starting Pedestal calculation");
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for (auto i = 0; i < 1000; i++) {
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p.push(file.read().view<uint16_t>());
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}
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logger::info("Pedestal calculation done");
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logger::info("Pedestal mean:", p.mean(0, 0), "std:", p.standard_deviation(0, 0));
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logger::info("Pedestal mean:", p.mean(200, 200), "std:", p.standard_deviation(200, 200));
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FileConfig cfg;
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cfg.dtype = Dtype(typeid(double));
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cfg.rows = p.rows();
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cfg.cols = p.cols();
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NumpyFile np_pedestal("/home/l_bechir/tmp/testNewFW20230714/pedestal.npy", "w", cfg);
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cfg.dtype = Dtype(typeid(uint16_t));
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NumpyFile np_frame("/home/l_bechir/tmp/testNewFW20230714/frame.npy", "w", cfg);
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np_pedestal.write(p.mean());
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np_frame.write(frame.view<uint16_t>());
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auto clusters = clusterFinder.find_clusters_without_threshold(frame.view<uint16_t>(), p);
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logger::info("nclusters:", clusters.size());
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// aare::logger::info("nclusters:", clusters.size());
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// for (auto &cluster : clusters) {
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// aare::logger::info("cluster center:", cluster.to_string<double>());
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// }
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} |