RK3588 RK3568 RK3566 Linux QT Opencv NPU YoloV5 摄像头实时检测

// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. /*------------------------------------------- Includes -------------------------------------------*/ #include <dlfcn.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <sys/time.h> #define _BASETSD_H #include "rga/RgaUtils.h" #include "rga/im2d.h" //#include "opencv2/core/core.hpp" //#include "opencv2/imgcodecs.hpp" //#include "opencv2/imgproc.hpp" #include "postprocess.h" #include "rga/rga.h" #include "rknn/rknn_api.h" #define PERF_WITH_POST 1 /*------------------------------------------- Functions -------------------------------------------*/ static void dump_tensor_attr(rknn_tensor_attr* attr) { printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, " "zp=%d, scale=%f\n", attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3], attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale); } double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); } static unsigned char* load_data(FILE* fp, size_t ofst, size_t sz) { unsigned char* data; int ret; data = NULL; if (NULL == fp) { return NULL; } ret = fseek(fp, ofst, SEEK_SET); if (ret != 0) { printf("blob seek failure.\n"); return NULL; } data = (unsigned char*)malloc(sz); if (data == NULL) { printf("buffer malloc failure.\n"); return NULL; } ret = fread(data, 1, sz, fp); return data; } static unsigned char* load_model(const char* filename, int* model_size) { FILE* fp; unsigned char* data; fp = fopen(filename, "rb"); if (NULL == fp) { printf("Open file %s failed.\n", filename); return NULL; } fseek(fp, 0, SEEK_END); int size = ftell(fp); data = load_data(fp, 0, size); fclose(fp); *model_size = size; return data; } /* static int saveFloat(const char* file_name, float* output, int element_size) { FILE* fp; fp = fopen(file_name, "w"); for (int i = 0; i < element_size; i++) { fprintf(fp, "%.6f\n", output[i]); } fclose(fp); return 0; } */ /*------------------------------------------- Main Functions -------------------------------------------*/ static rknn_context ctx; static unsigned char* model_data; static std::vector<cv::Scalar> colors; int yolov5_init(const char *model_path, const char *label_path) { int ret; // 加载模型其初始化rknn printf("Loading model...\n"); const char *model_name = model_path; const char *label_name = label_path; int model_data_size = 0; model_data = load_model(model_name, &model_data_size); ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } rknn_sdk_version version; ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } printf("sdk version: %s driver version: %s\n", version.api_version, version.drv_version); // 加载label文件 ret = loadLabelName(label_name); if (ret < 0) { printf("loadLabelName error ret=%d\n", ret); return -1; } // 随机生成80种颜色 for(int i = 0; i < OBJ_CLASS_NUM; i++){ colors.push_back(cv::Scalar(rand()&250, rand()&250, rand()&250, rand()&250)); } rknn_input_output_num io_num; ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); rknn_tensor_attr input_attrs[io_num.n_input]; memset(input_attrs, 0, sizeof(input_attrs)); for (uint32_t i = 0; i < io_num.n_input; i++) { input_attrs[i].index = i; ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } dump_tensor_attr(&(input_attrs[i])); } rknn_tensor_attr output_attrs[io_num.n_output]; memset(output_attrs, 0, sizeof(output_attrs)); for (uint32_t i = 0; i < io_num.n_output; i++) { output_attrs[i].index = i; ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr)); dump_tensor_attr(&(output_attrs[i])); } return 0; } int yolov5_detect(cv::Mat &src, cv::Mat &res) { // int status = 0; // size_t actual_size = 0; int img_width = 0; int img_height = 0; // int img_channel = 0; const float nms_threshold = NMS_THRESH; const float box_conf_threshold = BOX_THRESH; struct timeval start_time, stop_time; int ret; // init rga context rga_buffer_t srct; rga_buffer_t dst; im_rect src_rect; im_rect dst_rect; memset(&src_rect, 0, sizeof(src_rect)); memset(&dst_rect, 0, sizeof(dst_rect)); memset(&srct, 0, sizeof(srct)); memset(&dst, 0, sizeof(dst)); cv::Mat orig_img = src; if (!orig_img.data) { printf("cv::imread fail!\n"); return -1; } cv::Mat img; cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB); img_width = img.cols; img_height = img.rows; // printf("img width = %d, img height = %d\n", img_width, img_height); /* Create the neural network */ /* static int init_model = -1; if(init_model == -1){ printf("Loading mode...\n"); const char *model_name = "./install/rknn_yolov5_demo_Linux/model/RK356X/yolov5s-640-640.rknn"; int model_data_size = 0; unsigned char* model_data = load_model(model_name, &model_data_size); ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } rknn_sdk_version version; ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } printf("sdk version: %s driver version: %s\n", version.api_version, version.drv_version); init_model ++; } */ rknn_input_output_num io_num; ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } // printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); rknn_tensor_attr input_attrs[io_num.n_input]; memset(input_attrs, 0, sizeof(input_attrs)); for (uint32_t i = 0; i < io_num.n_input; i++) { input_attrs[i].index = i; ret = rknn_query(ctx, RKNN_QUER