mirror of
https://github.com/deepinsight/insightface.git
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Update inspireface to 1.2.0
This commit is contained in:
Jingyu
670
cpp-package/inspireface/cpp/test/unit/api/test_benchmark.cpp
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670
cpp-package/inspireface/cpp/test/unit/api/test_benchmark.cpp
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@@ -0,0 +1,670 @@
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/**
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* Created by Jingyu Yan
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* @date 2025-01-12
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*/
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#include <iostream>
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#include "settings/test_settings.h"
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#include "inspireface/c_api/inspireface.h"
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#include "unit/test_helper/simple_csv_writer.h"
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#include "unit/test_helper/test_help.h"
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#include "unit/test_helper/test_tools.h"
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#include "middleware/costman.h"
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#ifdef ISF_ENABLE_BENCHMARK
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TEST_CASE("test_BenchmarkFaceDetect", "[benchmark]") {
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DRAW_SPLIT_LINE
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TEST_PRINT_OUTPUT(true);
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const int loop = 1000;
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SECTION("Benchmark face detection@160") {
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auto pixLevel = 160;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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HFDetectMode detMode = HF_DETECT_MODE_ALWAYS_DETECT;
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HFSession session;
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ret = HFCreateInspireFaceSession(parameter, detMode, 3, pixLevel, -1, &session);
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REQUIRE(ret == HSUCCEED);
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// Get a face picture
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HFImageStream imgHandle;
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auto image = inspirecv::Image::Create(GET_DATA("data/bulk/kun.jpg"));
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ret = CVImageToImageStream(image, imgHandle);
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Detect@160");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceData.detectedNum == 1);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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SECTION("Benchmark face detection@320") {
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auto pixLevel = 320;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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HFDetectMode detMode = HF_DETECT_MODE_ALWAYS_DETECT;
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HFSession session;
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ret = HFCreateInspireFaceSession(parameter, detMode, 3, pixLevel, -1, &session);
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REQUIRE(ret == HSUCCEED);
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// Get a face picture
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HFImageStream imgHandle;
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auto image = inspirecv::Image::Create(GET_DATA("data/bulk/kun.jpg"));
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ret = CVImageToImageStream(image, imgHandle);
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Detect@320");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceData.detectedNum == 1);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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SECTION("Benchmark face detection@640") {
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auto pixLevel = 640;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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HFDetectMode detMode = HF_DETECT_MODE_ALWAYS_DETECT;
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HFSession session;
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ret = HFCreateInspireFaceSession(parameter, detMode, 3, pixLevel, -1, &session);
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REQUIRE(ret == HSUCCEED);
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// Get a face picture
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HFImageStream imgHandle;
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auto image = inspirecv::Image::Create(GET_DATA("data/bulk/kun.jpg"));
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ret = CVImageToImageStream(image, imgHandle);
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Detect@640");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceData.detectedNum == 1);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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}
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TEST_CASE("test_BenchmarkFaceTrack", "[benchmark]") {
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DRAW_SPLIT_LINE
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TEST_PRINT_OUTPUT(true);
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const int loop = 1000;
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auto pixLevel = 160;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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HFDetectMode detMode = HF_DETECT_MODE_LIGHT_TRACK;
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HFSession session;
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ret = HFCreateInspireFaceSession(parameter, detMode, 3, pixLevel, -1, &session);
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REQUIRE(ret == HSUCCEED);
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// Get a face picture
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HFImageStream imgHandle;
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auto image = inspirecv::Image::Create(GET_DATA("data/bulk/kun.jpg"));
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ret = CVImageToImageStream(image, imgHandle);
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Track");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceData.detectedNum == 1);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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TEST_CASE("test_BenchmarkFaceExtractWithAlign", "[benchmark]") {
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DRAW_SPLIT_LINE
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TEST_PRINT_OUTPUT(true);
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const int loop = 1000;
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auto pixLevel = 160;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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HFDetectMode detMode = HF_DETECT_MODE_LIGHT_TRACK;
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HFSession session;
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ret = HFCreateInspireFaceSessionOptional(HF_ENABLE_FACE_RECOGNITION, detMode, 3, -1, -1, &session);
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REQUIRE(ret == HSUCCEED);
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// Face track
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auto dstImage = inspirecv::Image::Create(GET_DATA("data/search/Teresa_Williams_0001_1k.jpg"));
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// Get a face picture
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HFImageStream imgHandle;
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ret = CVImageToImageStream(dstImage, imgHandle);
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REQUIRE(ret == HSUCCEED);
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Extract With Align");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceData = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceData);
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REQUIRE(ret == HSUCCEED);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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TEST_CASE("test_BenchmarkFaceComparison", "[benchmark]") {
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DRAW_SPLIT_LINE
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TEST_PRINT_OUTPUT(true);
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int loop = 1000;
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HResult ret;
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HFSessionCustomParameter parameter = {0};
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parameter.enable_recognition = 1;
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HFDetectMode detMode = HF_DETECT_MODE_ALWAYS_DETECT;
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HFSession session;
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ret = HFCreateInspireFaceSession(parameter, detMode, 3, -1, -1, &session);
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REQUIRE(ret == HSUCCEED);
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auto image = inspirecv::Image::Create(GET_DATA("data/bulk/woman.png"));
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HFImageStream imgHandle;
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ret = CVImageToImageStream(image, imgHandle);
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REQUIRE(ret == HSUCCEED);
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// Extract basic face information from photos
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HFMultipleFaceData multipleFaceDataZy = {0};
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ret = HFExecuteFaceTrack(session, imgHandle, &multipleFaceDataZy);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceDataZy.detectedNum > 0);
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HInt32 featureNum;
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HFGetFeatureLength(&featureNum);
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// Extract face feature
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HFloat featureCacheZy[featureNum];
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ret = HFFaceFeatureExtractCpy(session, imgHandle, multipleFaceDataZy.tokens[0], featureCacheZy);
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HFFaceFeature featureZy = {0};
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featureZy.size = featureNum;
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featureZy.data = featureCacheZy;
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REQUIRE(ret == HSUCCEED);
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auto imageQuery = inspirecv::Image::Create(GET_DATA("data/bulk/woman_search.jpeg"));
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HFImageStream imgHandleQuery;
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ret = CVImageToImageStream(imageQuery, imgHandleQuery);
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REQUIRE(ret == HSUCCEED);
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HFMultipleFaceData multipleFaceDataQuery = {0};
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ret = HFExecuteFaceTrack(session, imgHandleQuery, &multipleFaceDataQuery);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(multipleFaceDataQuery.detectedNum > 0);
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// Extract face feature
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HFloat featureCacheZyQuery[featureNum];
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ret = HFFaceFeatureExtractCpy(session, imgHandleQuery, multipleFaceDataQuery.tokens[0], featureCacheZyQuery);
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HFFaceFeature featureZyQuery = {0};
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featureZyQuery.data = featureCacheZyQuery;
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featureZyQuery.size = featureNum;
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REQUIRE(ret == HSUCCEED);
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inspirecv::TimeSpend timeSpend("Face Comparison");
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for (int i = 0; i < loop; ++i) {
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timeSpend.Start();
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HFloat compRes;
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ret = HFFaceComparison(featureZy, featureZyQuery, &compRes);
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REQUIRE(ret == HSUCCEED);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFReleaseImageStream(imgHandle);
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REQUIRE(ret == HSUCCEED);
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ret = HFReleaseImageStream(imgHandleQuery);
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REQUIRE(ret == HSUCCEED);
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// Finish
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ret = HFReleaseInspireFaceSession(session);
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REQUIRE(ret == HSUCCEED);
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}
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TEST_CASE("test_BenchmarkFaceHubSearchPersistence", "[benchmark]") {
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DRAW_SPLIT_LINE
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TEST_PRINT_OUTPUT(true);
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SECTION("Benchmark search 1k@Persistence") {
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const int loop = 1000;
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HResult ret;
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HFFeatureHubConfiguration configuration;
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auto dbPath = GET_SAVE_DATA(".test");
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HString dbPathStr = new char[dbPath.size() + 1];
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std::strcpy(dbPathStr, dbPath.c_str());
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configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
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configuration.enablePersistence = 1;
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configuration.persistenceDbPath = dbPathStr;
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configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
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configuration.searchThreshold = 0.48f;
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// Delete the previous data before testing
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if (std::remove(configuration.persistenceDbPath) != 0) {
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spdlog::trace("Error deleting file");
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}
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ret = HFFeatureHubDataEnable(configuration);
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REQUIRE(ret == HSUCCEED);
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std::vector<std::vector<HFloat>> baseFeatures;
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size_t genSizeOfBase = 1000;
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HInt32 featureLength;
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HFGetFeatureLength(&featureLength);
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REQUIRE(featureLength > 0);
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for (int i = 0; i < genSizeOfBase; ++i) {
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auto feat = GenerateRandomFeature(featureLength);
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baseFeatures.push_back(feat);
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// Construct face feature
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HFFaceFeature feature = {0};
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feature.size = feat.size();
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feature.data = feat.data();
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HFFaceFeatureIdentity identity = {0};
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identity.feature = &feature;
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HFaceId allocId;
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ret = HFFeatureHubInsertFeature(identity, &allocId);
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REQUIRE(ret == HSUCCEED);
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}
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HInt32 totalFace;
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ret = HFFeatureHubGetFaceCount(&totalFace);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(totalFace == genSizeOfBase);
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HInt32 targetId = 800;
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auto targetFeature = baseFeatures[targetId - 1];
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auto searchFeat = SimulateSimilarVector(targetFeature);
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HFFaceFeature searchFeature = {0};
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searchFeature.size = searchFeat.size();
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searchFeature.data = searchFeat.data();
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HFloat confidence = 0.0f;
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HFFaceFeatureIdentity mostSimilar = {0};
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inspirecv::TimeSpend timeSpend("Face Search 1k@Persistence");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(mostSimilar.id == targetId);
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REQUIRE(confidence > 0.88f);
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timeSpend.Stop();
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}
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std::cout << timeSpend << std::endl;
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ret = HFFeatureHubDataDisable();
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REQUIRE(ret == HSUCCEED);
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delete[] dbPathStr;
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}
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SECTION("Benchmark search 5k@Persistence") {
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const int loop = 1000;
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HResult ret;
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HFFeatureHubConfiguration configuration;
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auto dbPath = GET_SAVE_DATA(".test");
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HString dbPathStr = new char[dbPath.size() + 1];
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std::strcpy(dbPathStr, dbPath.c_str());
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configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
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configuration.enablePersistence = 1;
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configuration.persistenceDbPath = dbPathStr;
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configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
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configuration.searchThreshold = 0.48f;
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// Delete the previous data before testing
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if (std::remove(configuration.persistenceDbPath) != 0) {
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spdlog::trace("Error deleting file");
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}
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ret = HFFeatureHubDataEnable(configuration);
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REQUIRE(ret == HSUCCEED);
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std::vector<std::vector<HFloat>> baseFeatures;
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size_t genSizeOfBase = 5000;
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HInt32 featureLength;
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HFGetFeatureLength(&featureLength);
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REQUIRE(featureLength > 0);
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for (int i = 0; i < genSizeOfBase; ++i) {
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auto feat = GenerateRandomFeature(featureLength);
|
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baseFeatures.push_back(feat);
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// Construct face feature
|
||||
HFFaceFeature feature = {0};
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feature.size = feat.size();
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feature.data = feat.data();
|
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HFFaceFeatureIdentity identity = {0};
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identity.feature = &feature;
|
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HFaceId allocId;
|
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ret = HFFeatureHubInsertFeature(identity, &allocId);
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REQUIRE(ret == HSUCCEED);
|
||||
}
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HInt32 totalFace;
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ret = HFFeatureHubGetFaceCount(&totalFace);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(totalFace == genSizeOfBase);
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||||
|
||||
HInt32 targetId = 4800;
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auto targetFeature = baseFeatures[targetId - 1];
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||||
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auto searchFeat = SimulateSimilarVector(targetFeature);
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HFFaceFeature searchFeature = {0};
|
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searchFeature.size = searchFeat.size();
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searchFeature.data = searchFeat.data();
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HFloat confidence = 0.0f;
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HFFaceFeatureIdentity mostSimilar = {0};
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inspirecv::TimeSpend timeSpend("Face Search 5k@Persistence");
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for (size_t i = 0; i < loop; i++) {
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timeSpend.Start();
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ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
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REQUIRE(ret == HSUCCEED);
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REQUIRE(mostSimilar.id == targetId);
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REQUIRE(confidence > 0.88f);
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timeSpend.Stop();
|
||||
}
|
||||
std::cout << timeSpend << std::endl;
|
||||
|
||||
ret = HFFeatureHubDataDisable();
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
delete[] dbPathStr;
|
||||
}
|
||||
|
||||
SECTION("Benchmark search 10k@Persistence") {
|
||||
const int loop = 1000;
|
||||
HResult ret;
|
||||
HFFeatureHubConfiguration configuration;
|
||||
auto dbPath = GET_SAVE_DATA(".test");
|
||||
HString dbPathStr = new char[dbPath.size() + 1];
|
||||
std::strcpy(dbPathStr, dbPath.c_str());
|
||||
configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
|
||||
configuration.enablePersistence = 1;
|
||||
configuration.persistenceDbPath = dbPathStr;
|
||||
configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
|
||||
configuration.searchThreshold = 0.48f;
|
||||
// Delete the previous data before testing
|
||||
if (std::remove(configuration.persistenceDbPath) != 0) {
|
||||
spdlog::trace("Error deleting file");
|
||||
}
|
||||
ret = HFFeatureHubDataEnable(configuration);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
std::vector<std::vector<HFloat>> baseFeatures;
|
||||
size_t genSizeOfBase = 10000;
|
||||
HInt32 featureLength;
|
||||
HFGetFeatureLength(&featureLength);
|
||||
REQUIRE(featureLength > 0);
|
||||
for (int i = 0; i < genSizeOfBase; ++i) {
|
||||
auto feat = GenerateRandomFeature(featureLength);
|
||||
baseFeatures.push_back(feat);
|
||||
// Construct face feature
|
||||
HFFaceFeature feature = {0};
|
||||
feature.size = feat.size();
|
||||
feature.data = feat.data();
|
||||
HFFaceFeatureIdentity identity = {0};
|
||||
identity.feature = &feature;
|
||||
HFaceId allocId;
|
||||
ret = HFFeatureHubInsertFeature(identity, &allocId);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
HInt32 totalFace;
|
||||
ret = HFFeatureHubGetFaceCount(&totalFace);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(totalFace == genSizeOfBase);
|
||||
|
||||
HInt32 targetId = 9800;
|
||||
auto targetFeature = baseFeatures[targetId - 1];
|
||||
|
||||
auto searchFeat = SimulateSimilarVector(targetFeature);
|
||||
HFFaceFeature searchFeature = {0};
|
||||
searchFeature.size = searchFeat.size();
|
||||
searchFeature.data = searchFeat.data();
|
||||
HFloat confidence = 0.0f;
|
||||
HFFaceFeatureIdentity mostSimilar = {0};
|
||||
inspirecv::TimeSpend timeSpend("Face Search 10k@Persistence");
|
||||
for (size_t i = 0; i < loop; i++) {
|
||||
timeSpend.Start();
|
||||
ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(mostSimilar.id == targetId);
|
||||
REQUIRE(confidence > 0.88f);
|
||||
timeSpend.Stop();
|
||||
}
|
||||
std::cout << timeSpend << std::endl;
|
||||
|
||||
ret = HFFeatureHubDataDisable();
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
delete[] dbPathStr;
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CASE("test_BenchmarkFaceHubSearchMemory", "[benchmark]") {
|
||||
DRAW_SPLIT_LINE
|
||||
TEST_PRINT_OUTPUT(true);
|
||||
|
||||
SECTION("Benchmark search 1k@Memory") {
|
||||
const int loop = 1000;
|
||||
HResult ret;
|
||||
HFFeatureHubConfiguration configuration;
|
||||
configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
|
||||
configuration.enablePersistence = 0;
|
||||
configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
|
||||
configuration.searchThreshold = 0.48f;
|
||||
ret = HFFeatureHubDataEnable(configuration);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
std::vector<std::vector<HFloat>> baseFeatures;
|
||||
size_t genSizeOfBase = 1000;
|
||||
HInt32 featureLength;
|
||||
HFGetFeatureLength(&featureLength);
|
||||
REQUIRE(featureLength > 0);
|
||||
for (int i = 0; i < genSizeOfBase; ++i) {
|
||||
auto feat = GenerateRandomFeature(featureLength);
|
||||
baseFeatures.push_back(feat);
|
||||
// Construct face feature
|
||||
HFFaceFeature feature = {0};
|
||||
feature.size = feat.size();
|
||||
feature.data = feat.data();
|
||||
HFFaceFeatureIdentity identity = {0};
|
||||
identity.feature = &feature;
|
||||
HFaceId allocId;
|
||||
ret = HFFeatureHubInsertFeature(identity, &allocId);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
HInt32 totalFace;
|
||||
ret = HFFeatureHubGetFaceCount(&totalFace);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(totalFace == genSizeOfBase);
|
||||
|
||||
HInt32 targetId = 800;
|
||||
auto targetFeature = baseFeatures[targetId - 1];
|
||||
|
||||
auto searchFeat = SimulateSimilarVector(targetFeature);
|
||||
HFFaceFeature searchFeature = {0};
|
||||
searchFeature.size = searchFeat.size();
|
||||
searchFeature.data = searchFeat.data();
|
||||
HFloat confidence = 0.0f;
|
||||
HFFaceFeatureIdentity mostSimilar = {0};
|
||||
inspirecv::TimeSpend timeSpend("Face Search 1k@Memory");
|
||||
for (size_t i = 0; i < loop; i++) {
|
||||
timeSpend.Start();
|
||||
ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(mostSimilar.id == targetId);
|
||||
REQUIRE(confidence > 0.88f);
|
||||
timeSpend.Stop();
|
||||
}
|
||||
std::cout << timeSpend << std::endl;
|
||||
|
||||
ret = HFFeatureHubDataDisable();
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
|
||||
SECTION("Benchmark search 5k@Persistence") {
|
||||
const int loop = 1000;
|
||||
HResult ret;
|
||||
HFFeatureHubConfiguration configuration;
|
||||
configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
|
||||
configuration.enablePersistence = 0;
|
||||
configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
|
||||
configuration.searchThreshold = 0.48f;
|
||||
ret = HFFeatureHubDataEnable(configuration);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
std::vector<std::vector<HFloat>> baseFeatures;
|
||||
size_t genSizeOfBase = 5000;
|
||||
HInt32 featureLength;
|
||||
HFGetFeatureLength(&featureLength);
|
||||
REQUIRE(featureLength > 0);
|
||||
for (int i = 0; i < genSizeOfBase; ++i) {
|
||||
auto feat = GenerateRandomFeature(featureLength);
|
||||
baseFeatures.push_back(feat);
|
||||
// Construct face feature
|
||||
HFFaceFeature feature = {0};
|
||||
feature.size = feat.size();
|
||||
feature.data = feat.data();
|
||||
HFFaceFeatureIdentity identity = {0};
|
||||
identity.feature = &feature;
|
||||
HFaceId allocId;
|
||||
ret = HFFeatureHubInsertFeature(identity, &allocId);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
HInt32 totalFace;
|
||||
ret = HFFeatureHubGetFaceCount(&totalFace);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(totalFace == genSizeOfBase);
|
||||
|
||||
HInt32 targetId = 4800;
|
||||
auto targetFeature = baseFeatures[targetId - 1];
|
||||
|
||||
auto searchFeat = SimulateSimilarVector(targetFeature);
|
||||
HFFaceFeature searchFeature = {0};
|
||||
searchFeature.size = searchFeat.size();
|
||||
searchFeature.data = searchFeat.data();
|
||||
HFloat confidence = 0.0f;
|
||||
HFFaceFeatureIdentity mostSimilar = {0};
|
||||
inspirecv::TimeSpend timeSpend("Face Search 5k@Memory");
|
||||
for (size_t i = 0; i < loop; i++) {
|
||||
timeSpend.Start();
|
||||
ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(mostSimilar.id == targetId);
|
||||
REQUIRE(confidence > 0.88f);
|
||||
timeSpend.Stop();
|
||||
}
|
||||
std::cout << timeSpend << std::endl;
|
||||
|
||||
ret = HFFeatureHubDataDisable();
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
|
||||
SECTION("Benchmark search 10k@Persistence") {
|
||||
const int loop = 1000;
|
||||
HResult ret;
|
||||
HFFeatureHubConfiguration configuration;
|
||||
configuration.primaryKeyMode = HF_PK_AUTO_INCREMENT;
|
||||
configuration.enablePersistence = 0;
|
||||
configuration.searchMode = HF_SEARCH_MODE_EXHAUSTIVE;
|
||||
configuration.searchThreshold = 0.48f;
|
||||
|
||||
ret = HFFeatureHubDataEnable(configuration);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
|
||||
std::vector<std::vector<HFloat>> baseFeatures;
|
||||
size_t genSizeOfBase = 10000;
|
||||
HInt32 featureLength;
|
||||
HFGetFeatureLength(&featureLength);
|
||||
REQUIRE(featureLength > 0);
|
||||
for (int i = 0; i < genSizeOfBase; ++i) {
|
||||
auto feat = GenerateRandomFeature(featureLength);
|
||||
baseFeatures.push_back(feat);
|
||||
// Construct face feature
|
||||
HFFaceFeature feature = {0};
|
||||
feature.size = feat.size();
|
||||
feature.data = feat.data();
|
||||
HFFaceFeatureIdentity identity = {0};
|
||||
identity.feature = &feature;
|
||||
HFaceId allocId;
|
||||
ret = HFFeatureHubInsertFeature(identity, &allocId);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
HInt32 totalFace;
|
||||
ret = HFFeatureHubGetFaceCount(&totalFace);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(totalFace == genSizeOfBase);
|
||||
|
||||
HInt32 targetId = 9800;
|
||||
auto targetFeature = baseFeatures[targetId - 1];
|
||||
|
||||
auto searchFeat = SimulateSimilarVector(targetFeature);
|
||||
HFFaceFeature searchFeature = {0};
|
||||
searchFeature.size = searchFeat.size();
|
||||
searchFeature.data = searchFeat.data();
|
||||
HFloat confidence = 0.0f;
|
||||
HFFaceFeatureIdentity mostSimilar = {0};
|
||||
inspirecv::TimeSpend timeSpend("Face Search 10k@Memory");
|
||||
for (size_t i = 0; i < loop; i++) {
|
||||
timeSpend.Start();
|
||||
ret = HFFeatureHubFaceSearch(searchFeature, &confidence, &mostSimilar);
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
REQUIRE(mostSimilar.id == targetId);
|
||||
REQUIRE(confidence > 0.88f);
|
||||
timeSpend.Stop();
|
||||
}
|
||||
std::cout << timeSpend << std::endl;
|
||||
|
||||
ret = HFFeatureHubDataDisable();
|
||||
REQUIRE(ret == HSUCCEED);
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
Reference in New Issue
Block a user