更新到HyperLPR3版本

2023-02-27 15:47:55 +08:00
parent 7ae4d385e1
commit 0864e05f76
912 changed files with 8160 additions and 221461 deletions
@@ -0,0 +1,75 @@
+//
+// Created by tunm on 2023/1/26.
+//
+
+#include <iostream>
+#include "hyper_lpr_sdk.h"
+#include "opencv2/opencv.hpp"
+
+static const std::vector<std::string> TYPES = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+    cv::Mat image = cv::imread(image_path);
+    // create ImageData
+    HLPR_ImageData data = {0};
+    data.data = image.ptr<uint8_t>(0);
+    data.width = image.cols;
+    data.height = image.rows;
+    data.format = STREAM_BGR;
+    data.rotation = CAMERA_ROTATION_0;
+    // create DataBuffer
+    P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
+
+    // create context
+    HLPR_ContextConfiguration configuration = {0};
+    configuration.models_path = model_path;
+    configuration.max_num = 5;
+    configuration.det_level = DETECT_LEVEL_LOW;
+    configuration.use_half = false;
+    configuration.nms_threshold = 0.5f;
+    configuration.rec_confidence_threshold = 0.5f;
+    configuration.box_conf_threshold = 0.30f;
+    configuration.threads = 1;
+    P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
+    HREESULT ret = HLPR_ContextQueryStatus(ctx);
+    if (ret != HResultCode::Ok) {
+        printf("create error.\n");
+        return -1;
+    }
+    // exec plate recognition
+    HLPR_PlateResultList results = {0};
+    double time;
+    time = (double)cv::getTickCount();
+    HLPR_ContextUpdateStream(ctx, buffer, &results);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+    printf("cost: %f\n", time);
+
+
+    for (int i = 0; i < results.plate_size; ++i) {
+        std::string type;
+        if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
+            type = "未知";
+        } else {
+            type = TYPES[results.plates[i].type];
+        }
+
+        cv::rectangle(image, cv::Point2f(results.plates[i].x1, results.plates[i].y1), cv::Point2f(results.plates[i].x2, results.plates[i].y2),
+                      cv::Scalar(100, 100, 200), 3);
+
+        printf("<%d> %s, %s, %f\n", i + 1, type.c_str(),
+                  results.plates[i].code, results.plates[i].text_confidence);
+    }
+
+//    cv::imwrite("out.jpg", image);
+    cv::imshow("out", image);
+    cv::waitKey(0);
+
+    // release buffer
+    HLPR_ReleaseDataBuffer(buffer);
+    // release context
+    HLPR_ReleaseContext(ctx);
+
+    return 0;
+}
@@ -0,0 +1,47 @@
+//
+// Created by tunm on 2023/1/22.
+//
+#include <iostream>
+#include "opencv2/opencv.hpp"
+#include "context_module/all.h"
+#include "buffer_module/all.h"
+
+using namespace hyper;
+
+static const std::vector<std::string> TYPES = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+    cv::Mat image = cv::imread(image_path);
+
+    HyperLPRContext context;
+    auto ret = context.Initialize(model_path, 5, DetectLevel::DETECT_LEVEL_LOW);
+    if (ret != hRetOk) {
+        LOGE("Load error.");
+        return -1;
+    }
+    CameraBuffer buffer;
+    buffer.SetDataBuffer(image.data, image.rows, image.cols);
+    buffer.SetDataFormat(BGR);
+    buffer.SetRotationMode(ROTATION_0);
+    double time;
+    time = (double)cv::getTickCount();
+    context(buffer);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+    LOGD("pipeline cost: %f", time);
+    auto &objs = context.getMObjectResults();
+    for (auto &obj: objs) {
+        cv::rectangle(image,
+                      cv::Point2f(obj.x1, obj.y1),
+                      cv::Point2f(obj.x2, obj.y2),
+                      cv::Scalar(0, 0, 200),
+                      2);
+        LOGD("[%s]%s", TYPES[obj.type].c_str(), obj.code);
+        LOGD("文本均值置信度: %f", obj.text_confidence);
+    }
+
+    cv::imshow("w", image);
+    cv::waitKey(0);
+    return 0;
+}
@@ -0,0 +1,113 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+
+#include <iostream>
+#include "opencv2/opencv.hpp"
+//#include "loader_module/all.h"
+#include "nn_implementation_module/all.h"
+#include "configuration.h"
+
+using namespace hyper;
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+
+    int input_size = 320;
+    std::string backbone_path = std::string(model_path) + "/" + hyper::DETECT_LOW_BACKBONE_FILENAME;
+    std::string head_path = std::string(model_path) + "/" + hyper::DETECT_LOW_HEAD_FILENAME;
+
+//    std::string backbone_path = std::string(model_path) + "/" + hyper::DETECT_HIGH_BACKBONE_FILENAME;
+//    std::string head_path = std::string(model_path) + "/" + hyper::DETECT_HIGH_HEAD_FILENAME;
+//    int input_size = 640;
+
+    cv::Mat image = cv::imread(image_path);
+
+//    DetBackbone backbone;
+//    backbone.Initialize(backbone_model);
+//
+//    DetHeader header;
+//    header.Initialize(header_model);
+//
+//
+//    backbone.Inference(image);
+//
+//    header.Inference(backbone.getMOutputTensorInfoList()[0].GetDataAsFloat(),
+//                     backbone.getMOutputTensorInfoList()[1].GetDataAsFloat(),
+//                     backbone.getMOutputTensorInfoList()[1].GetDataAsFloat());
+
+
+//    for (int i = 0; i < 20; ++i) {
+//        std::cout << header.getMOutputTensorInfoList()[0].GetDataAsFloat()[i] << std::endl;
+//    }
+
+
+//    float *h = header.getMOutputTensorInfoList()[0].GetDataAsFloat();
+//    for (int i = 0; i < 6300 * 15; ++i) {
+////        std::cout << backbone.m_output_feature_map_40p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("head.txt", "a");
+//        fprintf(fp, "%f\n", h[i]);
+//        fclose(fp);
+//    }
+//
+
+
+//    float *p40 = backbone.getMOutputTensorInfoList()[0].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 40 *40; ++i) {
+////        std::cout << backbone.m_output_feature_map_40p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("40.txt", "a");
+//        fprintf(fp, "%f\n", p40[i]);
+//        fclose(fp);
+//    }
+//
+//    std::cout << std::endl;
+//    float *p20 = backbone.getMOutputTensorInfoList()[1].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 20 * 20; ++i) {
+////        std::cout << backbone.m_output_feature_map_20p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("20.txt", "a");
+//        fprintf(fp, "%f\n", p20[i]);
+//        fclose(fp);
+//    }
+//    std::cout << std::endl;
+//    float *p10 = backbone.getMOutputTensorInfoList()[2].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 10 * 10; ++i) {
+////        std::cout << backbone.m_output_feature_map_10p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("10.txt", "a");
+//        fprintf(fp, "%f\n", p10[i]);
+//        fclose(fp);
+//    }
+
+    DetArch arch;
+    arch.Initialize(backbone_path, head_path, input_size);
+    double time;
+    time = (double)cv::getTickCount();
+    arch.Detection(image, true);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+
+    auto &results = arch.m_results_;
+
+    for (auto &plate : results) {
+        std::cout << plate.x1 << ", " << plate.y1 << std::endl;
+        cv::rectangle(image, cv::Point2f(plate.x1, plate.y1), cv::Point2f(plate.x2, plate.y2),
+                      cv::Scalar(100, 100, 200), 1);
+        for (int i = 0; i < 4; ++i) {
+            cv::line(image, cv::Point2f(plate.kps[i * 2 + 0], plate.kps[i * 2 + 1]),
+                     cv::Point2f(plate.kps[i * 2 + 0], plate.kps[i * 2 + 1]), cv::Scalar(100, 220, 20), 1);
+        }
+
+    }
+#ifdef BUILD_LINUX_ARM7
+    cv::imwrite("out.jpg", image);
+#else
+    cv::imshow("w", image);
+    cv::waitKey(0);
+#endif
+
+    return 0;
+
+}