120 lines
4.3 KiB
C++
120 lines
4.3 KiB
C++
//
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// Created by fujiayi on 2020/7/5.
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//
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#include "native.h"
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#include "ocr_ppredictor.h"
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#include <algorithm>
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#include <paddle_api.h>
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#include <string>
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static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
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extern "C" JNIEXPORT jlong JNICALL
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Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
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JNIEnv *env, jobject thiz, jstring j_det_model_path,
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jstring j_rec_model_path, jstring j_cls_model_path, jint j_use_opencl, jint j_thread_num,
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jstring j_cpu_mode) {
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std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
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std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
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std::string cls_model_path = jstring_to_cpp_string(env, j_cls_model_path);
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int thread_num = j_thread_num;
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std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
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ppredictor::OCR_Config conf;
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conf.use_opencl = j_use_opencl;
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conf.thread_num = thread_num;
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conf.mode = str_to_cpu_mode(cpu_mode);
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ppredictor::OCR_PPredictor *orc_predictor =
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new ppredictor::OCR_PPredictor{conf};
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orc_predictor->init_from_file(det_model_path, rec_model_path, cls_model_path);
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return reinterpret_cast<jlong>(orc_predictor);
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}
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/**
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* "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
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* @param cpu_mode
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* @return
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*/
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static paddle::lite_api::PowerMode
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str_to_cpu_mode(const std::string &cpu_mode) {
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static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
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{"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
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{"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
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{"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
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{"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
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{"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
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{"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}};
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std::string upper_key;
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std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
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::toupper);
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auto index = cpu_mode_map.find(upper_key.c_str());
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if (index == cpu_mode_map.end()) {
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LOGE("cpu_mode not found %s", upper_key.c_str());
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return paddle::lite_api::LITE_POWER_HIGH;
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} else {
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return index->second;
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}
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}
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extern "C" JNIEXPORT jfloatArray JNICALL
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Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
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JNIEnv *env, jobject thiz, jlong java_pointer, jobject original_image,jint j_max_size_len, jint j_run_det, jint j_run_cls, jint j_run_rec) {
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LOGI("begin to run native forward");
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if (java_pointer == 0) {
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LOGE("JAVA pointer is NULL");
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return cpp_array_to_jfloatarray(env, nullptr, 0);
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}
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cv::Mat origin = bitmap_to_cv_mat(env, original_image);
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if (origin.size == 0) {
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LOGE("origin bitmap cannot convert to CV Mat");
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return cpp_array_to_jfloatarray(env, nullptr, 0);
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}
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int max_size_len = j_max_size_len;
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int run_det = j_run_det;
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int run_cls = j_run_cls;
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int run_rec = j_run_rec;
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ppredictor::OCR_PPredictor *ppredictor =
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(ppredictor::OCR_PPredictor *)java_pointer;
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std::vector<int64_t> dims_arr;
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std::vector<ppredictor::OCRPredictResult> results =
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ppredictor->infer_ocr(origin, max_size_len, run_det, run_cls, run_rec);
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LOGI("infer_ocr finished with boxes %ld", results.size());
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// 这里将std::vector<ppredictor::OCRPredictResult> 序列化成
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// float数组，传输到java层再反序列化
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std::vector<float> float_arr;
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for (const ppredictor::OCRPredictResult &r : results) {
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float_arr.push_back(r.points.size());
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float_arr.push_back(r.word_index.size());
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float_arr.push_back(r.score);
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// add det point
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for (const std::vector<int> &point : r.points) {
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float_arr.push_back(point.at(0));
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float_arr.push_back(point.at(1));
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}
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// add rec word idx
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for (int index : r.word_index) {
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float_arr.push_back(index);
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}
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// add cls result
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float_arr.push_back(r.cls_label);
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float_arr.push_back(r.cls_score);
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}
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return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
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}
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extern "C" JNIEXPORT void JNICALL
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Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(
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JNIEnv *env, jobject thiz, jlong java_pointer) {
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if (java_pointer == 0) {
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LOGE("JAVA pointer is NULL");
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return;
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}
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ppredictor::OCR_PPredictor *ppredictor =
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(ppredictor::OCR_PPredictor *)java_pointer;
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delete ppredictor;
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}
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