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DeeJanuzandClaude Opus 5.5
067a03ce38
fh-camd (camd/) borrows XRService's camera buffers and publishes the tracking cameras' frames to a shared ring. fh-tracker (trackd/) finds hands in them with MediaPipe's palm and landmark models on ncnn, triangulates them in 3D, and publishes them for ft-screens. fh-replay replays recordings offline. tracker/ is the earlier Python version; tools/ and probes/ hold the checks and experiments. As of this commit: crop contrast defaults to CLAHE for the palm search and plain crops for the landmarks, --swap-sides works around fh-camd naming the side cameras backwards after some XRService restarts (tools/check_sides.py detects it), and --record-only, --with-dark, --cpus and --keep-presence support the bright-light and CPU-placement tests. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
76 lines
3.4 KiB
C++
76 lines
3.4 KiB
C++
// Check the C++ model code against tracker/models.py on a recorded frame:
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// nettest MODELS_DIR FRAME.pgm cx cy size rotation [--int8]
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// Runs the palm detector on that crop, then the landmark model on each palm's ROI, and
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// prints what they found; tools/nettest_compare.py runs the Python side on the same input.
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#include "nets.h"
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#include <algorithm>
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#include <chrono>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <string>
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#include <vector>
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static bool read_pgm(const char *path, std::vector<uint8_t> &px, int &w, int &h) {
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std::ifstream f(path, std::ios::binary);
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std::string magic;
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int maxv;
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if (!(f >> magic >> w >> h >> maxv) || magic != "P5") return false;
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f.get();
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px.resize(size_t(w) * h);
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return bool(f.read(reinterpret_cast<char *>(px.data()), px.size()));
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}
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int main(int argc, char **argv) {
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if (argc < 7) return std::fprintf(stderr, "usage: nettest MODELS FRAME.pgm cx cy size rotation [--int8] [--bench N]\n"), 1;
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bool int8 = false;
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int bench = 0;
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for (int i = 7; i < argc; ++i) {
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if (!std::strcmp(argv[i], "--int8")) int8 = true;
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else if (!std::strcmp(argv[i], "--bench") && i + 1 < argc) bench = std::atoi(argv[++i]);
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}
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Nets nets;
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std::string err;
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if (!nets.load(argv[1], int8, err)) return std::fprintf(stderr, "%s\n", err.c_str()), 1;
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std::vector<uint8_t> px;
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int w, h;
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if (!read_pgm(argv[2], px, w, h)) return std::fprintf(stderr, "can't read %s\n", argv[2]), 1;
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const Image img{px.data(), w, h, w};
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const V2 c{std::atof(argv[3]), std::atof(argv[4])};
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if (bench > 0) { // steady-state timing: warm up, then the median of N calls each
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const Roi roi{c, std::atof(argv[5]), std::atof(argv[6])};
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auto time = [&](auto fn) {
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std::vector<double> t;
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for (int i = 0; i < bench + 5; ++i) {
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const auto t0 = std::chrono::steady_clock::now();
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fn();
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if (i >= 5) t.push_back(std::chrono::duration<double, std::milli>(std::chrono::steady_clock::now() - t0).count());
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}
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std::sort(t.begin(), t.end());
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return t[t.size() / 2];
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};
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const double pm = time([&] { nets.palms(img, c, roi.size, roi.rotation); });
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const double hm = time([&] { nets.landmarks(img, roi); });
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std::printf("bench%s: palm %.2f ms, hand %.2f ms (median of %d, one thread)\n", int8 ? " int8" : "", pm, hm, bench);
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return 0;
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}
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auto t0 = std::chrono::steady_clock::now();
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const auto palms = nets.palms(img, c, std::atof(argv[5]), std::atof(argv[6]));
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const double palm_ms = std::chrono::duration<double, std::milli>(std::chrono::steady_clock::now() - t0).count();
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std::printf("palm_ms %.2f\n", palm_ms);
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for (const Palm &p : palms) {
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const Roi r = p.roi();
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std::printf("palm %.3f center %.1f %.1f roi %.1f %.1f %.1f %.4f\n", p.score, p.center[0], p.center[1],
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r.center[0], r.center[1], r.size, r.rotation);
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t0 = std::chrono::steady_clock::now();
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const Landmarks lm = nets.landmarks(img, r);
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const double ms = std::chrono::duration<double, std::milli>(std::chrono::steady_clock::now() - t0).count();
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std::printf("hand_ms %.2f presence %.3f right %.3f\n", ms, lm.presence, lm.right);
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std::printf("pts");
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for (const V2 &q : lm.pts) std::printf(" %.1f %.1f", q[0], q[1]);
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std::printf("\n");
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}
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return 0;
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}
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