mirror of
https://github.com/deepinsight/insightface.git
synced 2025-12-30 08:02:27 +00:00
Fixed failed db file creation under Python-Native
This commit is contained in:
tunm
@@ -7,13 +7,12 @@
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#include "herror.h"
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#include <thread>
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namespace inspire {
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std::mutex FeatureHub::mutex_;
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std::shared_ptr<FeatureHub> FeatureHub::instance_ = nullptr;
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FeatureHub::FeatureHub(){}
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FeatureHub::FeatureHub() {}
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std::shared_ptr<FeatureHub> FeatureHub::GetInstance() {
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std::lock_guard<std::mutex> lock(mutex_);
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@@ -67,10 +66,14 @@ int32_t FeatureHub::EnableHub(const DatabaseConfiguration &configuration, Matrix
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m_search_mode_ = m_db_configuration_.search_mode;
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if (m_db_configuration_.feature_block_num <= 0) {
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m_db_configuration_.feature_block_num = 10;
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INSPIRE_LOGW("The number of feature blocks cannot be 0, but has been set to the default number of 10, that is, the maximum number of stored faces is supported: 5120");
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INSPIRE_LOGW(
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"The number of feature blocks cannot be 0, but has been set to the default number of 10, that is, the maximum number of stored faces is "
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"supported: 5120");
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} else if (m_db_configuration_.feature_block_num > 25) {
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m_db_configuration_.feature_block_num = 25;
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INSPIRE_LOGW("The number of feature blocks cannot exceed 25, which has been set to the maximum value, that is, the maximum number of stored faces supported: 12800");
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INSPIRE_LOGW(
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"The number of feature blocks cannot exceed 25, which has been set to the maximum value, that is, the maximum number of stored faces "
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"supported: 12800");
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}
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// Allocate memory for the feature matrix
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for (int i = 0; i < m_db_configuration_.feature_block_num; ++i) {
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@@ -80,7 +83,7 @@ int32_t FeatureHub::EnableHub(const DatabaseConfiguration &configuration, Matrix
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}
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if (m_db_configuration_.enable_use_db) {
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m_db_ = std::make_shared<SQLiteFaceManage>();
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if (IsDirectory(m_db_configuration_.db_path)){
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if (IsDirectory(m_db_configuration_.db_path)) {
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std::string dbFile = m_db_configuration_.db_path + "/" + DB_FILE_NAME;
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ret = m_db_->OpenDatabase(dbFile);
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} else {
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@@ -95,7 +98,7 @@ int32_t FeatureHub::EnableHub(const DatabaseConfiguration &configuration, Matrix
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ret = m_db_->GetTotalFeatures(infos);
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if (ret == HSUCCEED) {
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if (!infos.empty()) {
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for (auto const &info: infos) {
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for (auto const &info : infos) {
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ret = InsertFaceFeature(info.feature, info.tag, info.customId);
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if (ret != HSUCCEED) {
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INSPIRE_LOGE("ID: %d, Inserting error: %d", info.customId, ret);
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@@ -117,9 +120,9 @@ int32_t FeatureHub::EnableHub(const DatabaseConfiguration &configuration, Matrix
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return HSUCCEED;
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}
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int32_t FeatureHub::CosineSimilarity(const std::vector<float>& v1, const std::vector<float>& v2, float &res) {
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int32_t FeatureHub::CosineSimilarity(const std::vector<float> &v1, const std::vector<float> &v2, float &res) {
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if (v1.size() != v2.size() || v1.empty()) {
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return HERR_SESS_REC_CONTRAST_FEAT_ERR; // The similarity cannot be calculated if the vector lengths are not equal
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return HERR_SESS_REC_CONTRAST_FEAT_ERR; // The similarity cannot be calculated if the vector lengths are not equal
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}
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// Calculate the cosine similarity
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res = simd_dot(v1.data(), v2.data(), v1.size());
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@@ -127,22 +130,20 @@ int32_t FeatureHub::CosineSimilarity(const std::vector<float>& v1, const std::ve
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return HSUCCEED;
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}
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int32_t FeatureHub::CosineSimilarity(const float *v1, const float *v2, int32_t size, float &res) {
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res = simd_dot(v1, v2, size);
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return HSUCCEED;
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}
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int32_t FeatureHub::RegisterFaceFeature(const std::vector<float>& feature, int featureIndex, const std::string &tag, int32_t customId) {
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int32_t FeatureHub::RegisterFaceFeature(const std::vector<float> &feature, int featureIndex, const std::string &tag, int32_t customId) {
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if (featureIndex < 0 || featureIndex >= m_feature_matrix_list_.size() * NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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}
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// Compute which FeatureBlock and which row the feature vector should be stored in
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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// Call the appropriate FeatureBlock registration function
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int32_t result = m_feature_matrix_list_[blockIndex]->RegisterFeature(rowIndex, feature, tag, customId);
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@@ -150,7 +151,7 @@ int32_t FeatureHub::RegisterFaceFeature(const std::vector<float>& feature, int f
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return result;
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}
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int32_t FeatureHub::InsertFaceFeature(const std::vector<float>& feature, const std::string &tag, int32_t customId) {
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int32_t FeatureHub::InsertFaceFeature(const std::vector<float> &feature, const std::string &tag, int32_t customId) {
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int32_t ret = HSUCCEED;
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for (int i = 0; i < m_feature_matrix_list_.size(); ++i) {
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auto &block = m_feature_matrix_list_[i];
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@@ -163,14 +164,14 @@ int32_t FeatureHub::InsertFaceFeature(const std::vector<float>& feature, const s
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return ret;
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}
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int32_t FeatureHub::SearchFaceFeature(const std::vector<float>& queryFeature, SearchResult &searchResult, float threshold, bool mostSimilar) {
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int32_t FeatureHub::SearchFaceFeature(const std::vector<float> &queryFeature, SearchResult &searchResult, float threshold, bool mostSimilar) {
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if (queryFeature.size() != NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_FEAT_SIZE_ERR; // Query feature size does not match expectations
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return HERR_SESS_REC_FEAT_SIZE_ERR; // Query feature size does not match expectations
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}
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bool found = false; // Whether matching features are found
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float maxScore = -1.0f; // The maximum score is initialized to a negative number
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int maxIndex = -1; // The index corresponding to the maximum score
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bool found = false; // Whether matching features are found
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float maxScore = -1.0f; // The maximum score is initialized to a negative number
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int maxIndex = -1; // The index corresponding to the maximum score
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std::string tag = "None";
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int maxCid = -1;
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@@ -219,12 +220,11 @@ int32_t FeatureHub::SearchFaceFeature(const std::vector<float>& queryFeature, Se
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searchResult.customId = -1;
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}
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return HSUCCEED; // No matching feature found but not an error
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return HSUCCEED; // No matching feature found but not an error
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}
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int32_t FeatureHub::SearchFaceFeatureTopK(const std::vector<float>& queryFeature, std::vector<SearchResult> &searchResultList, size_t maxTopK, float threshold) {
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int32_t FeatureHub::SearchFaceFeatureTopK(const std::vector<float> &queryFeature, std::vector<SearchResult> &searchResultList, size_t maxTopK,
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float threshold) {
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if (queryFeature.size() != NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_FEAT_SIZE_ERR;
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}
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@@ -243,16 +243,14 @@ int32_t FeatureHub::SearchFaceFeatureTopK(const std::vector<float>& queryFeature
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return result;
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}
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for (const SearchResult& result : tempResultList) {
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for (const SearchResult &result : tempResultList) {
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if (result.score >= threshold) {
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searchResultList.push_back(result);
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}
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}
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}
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std::sort(searchResultList.begin(), searchResultList.end(), [](const SearchResult& a, const SearchResult& b) {
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return a.score > b.score;
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});
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std::sort(searchResultList.begin(), searchResultList.end(), [](const SearchResult &a, const SearchResult &b) { return a.score > b.score; });
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if (searchResultList.size() > maxTopK) {
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searchResultList.resize(maxTopK);
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@@ -263,12 +261,12 @@ int32_t FeatureHub::SearchFaceFeatureTopK(const std::vector<float>& queryFeature
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int32_t FeatureHub::DeleteFaceFeature(int featureIndex) {
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if (featureIndex < 0 || featureIndex >= m_feature_matrix_list_.size() * NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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}
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// Calculate which FeatureBlock and which row the feature vector should be removed in
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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// Call the appropriate FeatureBlock delete function
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int32_t result = m_feature_matrix_list_[blockIndex]->DeleteFeature(rowIndex);
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@@ -278,30 +276,29 @@ int32_t FeatureHub::DeleteFaceFeature(int featureIndex) {
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int32_t FeatureHub::GetFaceFeature(int featureIndex, Embedded &feature) {
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if (featureIndex < 0 || featureIndex >= m_feature_matrix_list_.size() * NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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}
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// Calculate which FeatureBlock and which row the feature vector should be removed in
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int32_t result = m_feature_matrix_list_[blockIndex]->GetFeature(rowIndex, feature);
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return result;
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}
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int32_t FeatureHub::GetFaceEntity(int featureIndex, Embedded &feature, std::string& tag, FEATURE_STATE& status) {
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int32_t FeatureHub::GetFaceEntity(int featureIndex, Embedded &feature, std::string &tag, FEATURE_STATE &status) {
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if (featureIndex < 0 || featureIndex >= m_feature_matrix_list_.size() * NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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}
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// Calculate which FeatureBlock and which row the feature vector should be removed in
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int32_t result = m_feature_matrix_list_[blockIndex]->GetFeature(rowIndex, feature);
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tag = m_feature_matrix_list_[blockIndex]->GetTagFromRow(rowIndex);
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status = m_feature_matrix_list_[blockIndex]->GetStateFromRow(rowIndex);
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return result;
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}
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@@ -309,7 +306,7 @@ int32_t FeatureHub::GetFaceFeatureCount() {
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int totalFeatureCount = 0;
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// Iterate over all FeatureBlocks and add up the number of feature vectors used
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for (const auto& block : m_feature_matrix_list_) {
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for (const auto &block : m_feature_matrix_list_) {
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totalFeatureCount += block->GetUsedCount();
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}
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@@ -322,12 +319,12 @@ int32_t FeatureHub::GetFeatureNum() const {
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int32_t FeatureHub::UpdateFaceFeature(const std::vector<float> &feature, int featureIndex, const std::string &tag, int32_t customId) {
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if (featureIndex < 0 || featureIndex >= m_feature_matrix_list_.size() * NUM_OF_FEATURES_IN_BLOCK) {
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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return HERR_SESS_REC_INVALID_INDEX; // Invalid feature index number
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}
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// Calculate which FeatureBlock and which row the feature vector should be removed in
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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int blockIndex = featureIndex / NUM_OF_FEATURES_IN_BLOCK; // The FeatureBlock where the computation is located
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int rowIndex = featureIndex % NUM_OF_FEATURES_IN_BLOCK; // Calculate the line number in the FeatureBlock
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// Call the appropriate FeatureBlock registration function
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int32_t result = m_feature_matrix_list_[blockIndex]->UpdateFeature(rowIndex, feature, tag, customId);
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@@ -339,7 +336,6 @@ void FeatureHub::PrintFeatureMatrixInfo() {
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m_feature_matrix_list_[0]->PrintMatrix();
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}
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int32_t FeatureHub::FindFeatureIndexByCustomId(int32_t customId) {
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// Iterate over all FeatureBlocks
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for (int blockIndex = 0; blockIndex < m_feature_matrix_list_.size(); ++blockIndex) {
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@@ -356,7 +352,6 @@ int32_t FeatureHub::FindFeatureIndexByCustomId(int32_t customId) {
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return -1; // If none of the featureBlocks is found, -1 is returned
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}
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int32_t FeatureHub::SearchFaceFeature(const Embedded &queryFeature, SearchResult &searchResult) {
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std::lock_guard<std::mutex> lock(mutex_);
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if (!m_enable_) {
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@@ -364,9 +359,8 @@ int32_t FeatureHub::SearchFaceFeature(const Embedded &queryFeature, SearchResult
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return HERR_FT_HUB_DISABLE;
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}
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m_search_face_feature_cache_.clear();
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std::memset(m_string_cache_, 0, sizeof(m_string_cache_)); // Initial Zero
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auto ret = SearchFaceFeature(queryFeature, searchResult, m_recognition_threshold_,
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m_search_mode_ == SEARCH_MODE_EXHAUSTIVE);
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std::memset(m_string_cache_, 0, sizeof(m_string_cache_)); // Initial Zero
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auto ret = SearchFaceFeature(queryFeature, searchResult, m_recognition_threshold_, m_search_mode_ == SEARCH_MODE_EXHAUSTIVE);
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if (ret == HSUCCEED) {
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if (searchResult.index != -1) {
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ret = GetFaceFeature(searchResult.index, m_search_face_feature_cache_);
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@@ -383,7 +377,7 @@ int32_t FeatureHub::SearchFaceFeature(const Embedded &queryFeature, SearchResult
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return ret;
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}
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int32_t FeatureHub::SearchFaceFeatureTopK(const Embedded& queryFeature, size_t topK) {
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int32_t FeatureHub::SearchFaceFeatureTopK(const Embedded &queryFeature, size_t topK) {
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std::lock_guard<std::mutex> lock(mutex_);
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if (!m_enable_) {
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INSPIRE_LOGE("FeatureHub is disabled, please enable it before it can be served");
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@@ -403,8 +397,7 @@ int32_t FeatureHub::SearchFaceFeatureTopK(const Embedded& queryFeature, size_t t
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return ret;
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}
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int32_t FeatureHub::FaceFeatureInsertFromCustomId(const std::vector<float> &feature, const std::string &tag,
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int32_t customId) {
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int32_t FeatureHub::FaceFeatureInsertFromCustomId(const std::vector<float> &feature, const std::string &tag, int32_t customId) {
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std::lock_guard<std::mutex> lock(mutex_);
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if (!m_enable_) {
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INSPIRE_LOGE("FeatureHub is disabled, please enable it before it can be served");
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@@ -445,8 +438,7 @@ int32_t FeatureHub::FaceFeatureRemoveFromCustomId(int32_t customId) {
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return ret;
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}
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int32_t FeatureHub::FaceFeatureUpdateFromCustomId(const std::vector<float> &feature, const std::string &tag,
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int32_t customId) {
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int32_t FeatureHub::FaceFeatureUpdateFromCustomId(const std::vector<float> &feature, const std::string &tag, int32_t customId) {
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std::lock_guard<std::mutex> lock(mutex_);
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if (!m_enable_) {
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INSPIRE_LOGE("FeatureHub is disabled, please enable it before it can be served");
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@@ -512,7 +504,7 @@ void FeatureHub::SetRecognitionSearchMode(SearchMode mode) {
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// =========== Getter ===========
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const Embedded& FeatureHub::GetSearchFaceFeatureCache() const {
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const Embedded &FeatureHub::GetSearchFaceFeatureCache() const {
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return m_search_face_feature_cache_;
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}
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@@ -520,7 +512,7 @@ char *FeatureHub::GetStringCache() {
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return m_string_cache_;
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}
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const std::shared_ptr<FaceFeaturePtr>& FeatureHub::GetFaceFeaturePtrCache() const {
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const std::shared_ptr<FaceFeaturePtr> &FeatureHub::GetFaceFeaturePtrCache() const {
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return m_face_feature_ptr_cache_;
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}
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@@ -532,5 +524,4 @@ std::vector<int32_t> &FeatureHub::GetTopKCustomIdsCache() {
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return m_top_k_custom_ids_cache_;
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}
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} // namespace hyper
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} // namespace inspire
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1
cpp-package/inspireface/python/.gitignore
vendored
1
cpp-package/inspireface/python/.gitignore
vendored
@@ -28,3 +28,4 @@ share/python-wheels/
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.idea/
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*.db
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@@ -622,7 +622,7 @@ class FeatureHubConfiguration:
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HFFeatureHubConfiguration: A C-structure for feature hub configuration.
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"""
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return HFFeatureHubConfiguration(
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enableUseDb=int(self.enable_use_db),
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enablePersistence=int(self.enable_use_db),
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dbPath=String(bytes(self.db_path, encoding="utf8")),
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featureBlockNum=self.feature_block_num,
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searchThreshold=self.search_threshold,
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32
cpp-package/inspireface/python/sample_feature_hub.py
Normal file
32
cpp-package/inspireface/python/sample_feature_hub.py
Normal file
@@ -0,0 +1,32 @@
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import os
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import cv2
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import inspireface as ifac
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from inspireface.param import *
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import numpy as np
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import os
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def case_feature_hub():
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db_path = "test.db"
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if os.path.exists(db_path):
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os.remove(db_path)
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# Configure the feature management system.
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feature_hub_config = ifac.FeatureHubConfiguration(
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feature_block_num=10,
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enable_use_db=True,
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db_path=db_path,
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search_threshold=0.48,
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search_mode=HF_SEARCH_MODE_EAGER,
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)
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ret = ifac.feature_hub_enable(feature_hub_config)
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assert ret, "Failed to enable FeatureHub."
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print(ifac.feature_hub_get_face_count())
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for i in range(10):
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feature = ifac.FaceIdentity(np.random.rand(512), i, "test")
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ifac.feature_hub_face_insert(feature)
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print(ifac.feature_hub_get_face_count())
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assert os.path.exists(db_path), "FeatureHub database file not found."
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if __name__ == "__main__":
|
||||
case_feature_hub()
|
||||
Reference in New Issue
Block a user