h5企业网站通用源码,小米公司的企业文化建设,总部在上海的互联网公司,重庆璧山新闻最新消息RK提供了两个模型#xff0c;mobilenet和YOLO5。 mobilenet模型相对小#xff0c;使用起来不是很明显yolo5模型大一些#xff0c;可以对88种目标进行检测#xff0c;提供检测的结果包括类别、包围框坐标、可信度等信息。基于rknn_yolov5_demo进行分析。 rknn_yolov5_demo基…RK提供了两个模型mobilenet和YOLO5。 mobilenet模型相对小使用起来不是很明显yolo5模型大一些可以对88种目标进行检测提供检测的结果包括类别、包围框坐标、可信度等信息。基于rknn_yolov5_demo进行分析。 rknn_yolov5_demo基本信息 代码位置hardware/rockchip/rknpu2/examples/rknn_yolov5_demo编译脚本 build-android_RK3588.sh 使用cmake编译 CMakeLists.txt 编译源文件 92 # rknn_yolov5_demo93 include_directories( ${CMAKE_SOURCE_DIR}/include)94 95 add_executable(rknn_yolov5_demo96 src/main.cc97 src/postprocess.cc98 )99 100 target_link_libraries(rknn_yolov5_demo 101 ${RKNN_RT_LIB} 102 ${RGA_LIB} 103 ${OpenCV_LIBS} 104 )头文件在include目录源代码有两.cc文件依赖库有rknn_rt、rga、opencv 26 # rknn api27 if(TARGET_SOC STREQUAL rk356x)28 set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime/RK356X/${CMAKE_SYSTEM_NAME}/librknn_api)29 elseif(TARGET_SOC STREQUAL rk3588)30 set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime/RK3588/${CMAKE_SYSTEM_NAME}/librknn_api)31 else()32 message(FATAL_ERROR TARGET_SOC is not set, ref value: rk356x or rk3588 or rv110x)33 endif()34 35 if (CMAKE_SYSTEM_NAME STREQUAL Android)36 set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so)37 else()38 set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so)39 endif()40 include_directories(${RKNN_API_PATH}/include)41 include_directories(${CMAKE_SOURCE_DIR}/../3rdparty)42 43 # opencv44 if (CMAKE_SYSTEM_NAME STREQUAL Android)45 set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/OpenCV-android-sdk/sdk/native/jni/abi-${CMAKE_ANDROID_ARCH_ABI})46 else()47 if(LIB_ARCH STREQUAL armhf)48 set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-armhf/share/OpenCV)49 else()50 set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/../3rdparty/opencv/opencv-linux-aarch64/share/OpenCV)51 endif()52 endif()53 find_package(OpenCV REQUIRED)54 55 #rga56 if(TARGET_SOC STREQUAL rk356x)57 set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK356X)58 elseif(TARGET_SOC STREQUAL rk3588)59 set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK3588)60 else()61 message(FATAL_ERROR TARGET_SOC is not set, ref value: rk356x or rk3588)62 endif()63 if (CMAKE_SYSTEM_NAME STREQUAL Android)64 set(RGA_LIB ${RGA_PATH}/lib/Android/${CMAKE_ANDROID_ARCH_ABI}/librga.so)65 else()66 set(RGA_LIB ${RGA_PATH}/lib/Linux//${LIB_ARCH}/librga.so)67 endif()68 include_directories( ${RGA_PATH}/include)rknn_rtrknn的runtime环境有这个库yolo5就能运行起来rga图像缩放和颜色空间转换例如YUV到RGBopencv支持图像压缩算法比如jpg、png等等支持图像文件载入比如bmp等等rga和opencv提供的是对yolo5输入和输出图像的支持并非必须 yolo5应用移植 目标 移植yolo5应用从camera采集图像用yolo5对图像进行检测得到图像中人类别的信息 移植分析 前面分析过只有两个.cc文件头文件在include中寻找根据需要添加 main.cc 需要规整为一个C的类postprocess.cc 作为方法实现文件 移植代码 确定使用到的就是箭头指向的文件 创建class AiAlgoYolo5 class AiAlgoYolo5 : public webrtc::test::VideoFrameSubscriber { public:AiAlgoYolo5(int source_id);virtual ~AiAlgoYolo5();int start(int source_id);void stop();void dump_tensor_attr(rknn_tensor_attr *attr);uint8_t *load_data(FILE *fp, size_t ofst, size_t sz);uint8_t *load_model(const char *filename, int *model_size);int saveFloat(const char *file_name, float *output, int element_size);virtual void OnFrame(const webrtc::VideoFrame ) override;virtual void OnFrame(BOOAT::SharedBufferPtr videoFrame) override;virtual void CleanFrame() override;int DealOneFrame();private:std::shared_ptrstd::thread _th{nullptr};ThreadQueuewebrtc::VideoFrame _qu;ThreadQueueBOOAT::SharedBufferPtr _bufferQ;bool _is_stop{ true };int _source_id;int _width;int _height;unsigned char *model_data;rknn_context ctx;rknn_input_output_num io_num;int yolo_input_channel;int yolo_input_width;int yolo_input_height;rknn_input inputs[YOLO5_INPUT_NUMBER];rknn_output outputs[YOLO5_OUTPUT_NUMBER];rknn_tensor_attr input_attrs[YOLO5_INPUT_NUMBER];rknn_tensor_attr output_attrs[YOLO5_OUTPUT_NUMBER];void *resize_buf;private:uint64_t _frame_num;};- start()准备运行环境- DealOneFrame()采集一张camera图像完成一帧检测工作可以多次检测- stop()退出运行环境start()实现 int AiAlgoYolo5::start(int source_id){int ret 0;LOGD(IN \t[%s] \r\n, __FUNCTION__);LOGD(AiAlgoYolo5::start(): _source_id [ %d ].\r\n, _source_id);/* Create the neural network */LOGD(Loading mode...\n);char model_name[80] /vendor/bin/yolo5/yolov5s-640-640.rknn;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) {LOGE(AiAlgoYolo5 start 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) {LOGE(AiAlgoYolo5 start rknn_init error ret%d\n, ret);return -1;}LOGD(AiAlgoYolo5 start sdk version: %s driver version: %s\n, version.api_version, version.drv_version);ret rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, io_num, sizeof(io_num));if (ret 0) {LOGE(AiAlgoYolo5 start rknn_init error ret%d\n, ret);return -1;}if ((io_num.n_input ! YOLO5_INPUT_NUMBER) || (io_num.n_output ! YOLO5_OUTPUT_NUMBER)) {LOGE(AiAlgoYolo5 start model error input num: %d, output num: %d\n, io_num.n_input, io_num.n_output);} else {LOGD(AiAlgoYolo5 start model input num: %d, output num: %d\n, io_num.n_input, io_num.n_output);}memset(input_attrs, 0, sizeof(input_attrs));for (int 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) {LOGE(AiAlgoYolo5 start rknn_init error ret%d\n, ret);return -1;}dump_tensor_attr((input_attrs[i]));}memset(output_attrs, 0, sizeof(output_attrs));for (int 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]));}if (input_attrs[0].fmt RKNN_TENSOR_NCHW) {LOGD(AiAlgoYolo5 start model is NCHW input fmt\n);yolo_input_channel input_attrs[0].dims[1];yolo_input_height input_attrs[0].dims[2];yolo_input_width input_attrs[0].dims[3];} else {LOGD(AiAlgoYolo5 start model is NHWC input fmt\n);yolo_input_height input_attrs[0].dims[1];yolo_input_width input_attrs[0].dims[2];yolo_input_channel input_attrs[0].dims[3];}LOGD(AiAlgoYolo5 start model input height%d, width%d, channel%d\n, yolo_input_height, yolo_input_width, yolo_input_channel);memset(inputs, 0, sizeof(inputs));inputs[0].index 0;inputs[0].type RKNN_TENSOR_UINT8;inputs[0].size yolo_input_width * yolo_input_height * yolo_input_channel;inputs[0].fmt RKNN_TENSOR_NHWC;inputs[0].pass_through 0;resize_buf malloc(640 * 640 * 3);LOGD(OUT \t[%s] \r\n, __FUNCTION__);return ret;}rknn_init加载模型yolov5s-640-640.rknnrknn_query获取模型中参数信息进行相应的准备工作比如inputoutput。 - stop()实现void AiAlgoYolo5::stop(){LOGD(IN \t[%s] \r\n, __FUNCTION__);deinitPostProcess();// releaserknn_destroy(ctx);if (model_data) {free(model_data);model_data nullptr;}if (resize_buf) {free(resize_buf);resize_buf nullptr;}LOGD(OUT \t[%s] \r\n, __FUNCTION__);}rknn_destroy环境退出资源释放 - DealOneFrame()实现int AiAlgoYolo5::DealOneFrame() { uint64_t start_time, stop_time, scal_start_time, scal_stop_time; char text[256]; bool flag false; int ret 0; const float nms_threshold NMS_THRESH;const float box_conf_threshold BOX_THRESH;rga_buffer_t src;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(src, 0, sizeof(src));memset(dst, 0, sizeof(dst));// 这一段是从camera获取nv12实时图像的代码依赖图像获取class开始_bufferQ.clear();BOOAT::SharedBufferPtr frame _bufferQ.pop(flag, std::chrono::milliseconds(1000));//ms//LOGD(AiAlgoYolo5::DealOneFrame _source_id %d flag %d, _source_id, flag);if (flag) {MP::VideoBufferParam *pBufInfo static_castMP::VideoBufferParam *(frame-getParam());uint8_t *nv12Data pBufInfo-yuvPtr;//LOGD(AiAlgoYolo5::DealOneFrame _source_id %d [%d %d] data %p, _source_id, pBufInfo-width, pBufInfo-height, nv12Data);_width pBufInfo-width;_height pBufInfo-height;// 这一段是从camera获取nv12实时图像的代码依赖图像获取class结束// 这一段是从camera获取到的nv12图像缩放到640x640的RGB图像用到了rga结束scal_start_time AiGetTimeMs();//LOGD(AiAlgoYolo5::DealOneFrame scal_start_time %ld, scal_start_time);if (_width ! yolo_input_width || _height ! yolo_input_height) {//LOGD(AiAlgoYolo5::DealOneFrame resize with RGA!\n);if (resize_buf) {memset(resize_buf, 0x00, yolo_input_height * yolo_input_width * yolo_input_channel);} else {LOGE(AiAlgoYolo5::DealOneFrame %d, resize_buf check error!, __LINE__);return -1;}src wrapbuffer_virtualaddr((void *)nv12Data, _width, _height, RK_FORMAT_YCbCr_420_SP);dst wrapbuffer_virtualaddr((void *)resize_buf, yolo_input_width, yolo_input_height, RK_FORMAT_RGB_888);ret imcheck(src, dst, src_rect, dst_rect);if (IM_STATUS_NOERROR ! ret) {LOGE(AiAlgoYolo5::DealOneFrame %d, check error! %s, __LINE__, imStrError((IM_STATUS)ret));return -1;}IM_STATUS STATUS imresize(src, dst);inputs[0].buf resize_buf;} else {inputs[0].buf (void *)nv12Data;}scal_stop_time AiGetTimeMs();//LOGD(AiAlgoYolo5::DealOneFrame scal_stop_time %ld, scal_stop_time);LOGD(AiAlgoYolo5::DealOneFrame scal once run use %ld ms, (scal_stop_time - scal_start_time));// 这一段是从camera获取到的nv12图像缩放到640x640的RGB图像用到了rga库结束// 这一段是yolo运行检测开始start_time AiGetTimeMs();//LOGD(AiAlgoYolo5::DealOneFrame start_time %ld, start_time);rknn_inputs_set(ctx, io_num.n_input, inputs);memset(outputs, 0, sizeof(outputs));for (int i 0; i io_num.n_output; i) {outputs[i].want_float 0;}ret rknn_run(ctx, NULL);ret rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);stop_time AiGetTimeMs();//LOGD(AiAlgoYolo5::DealOneFrame stop_time %ld, stop_time);LOGD(AiAlgoYolo5::DealOneFrame once run use %ld ms, (stop_time - start_time));// 这一段是yolo运行检测结束// 这一段对yolo检测结果分析目前只关心person类别开始// post processfloat scale_w (float)yolo_input_width / _width;float scale_h (float)yolo_input_height / _height;detect_result_group_t detect_result_group;std::vectorfloat out_scales;std::vectorint32_t out_zps;for (int i 0; i io_num.n_output; i) {out_scales.push_back(output_attrs[i].scale);out_zps.push_back(output_attrs[i].zp);}post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, (int8_t *)outputs[2].buf, yolo_input_height, yolo_input_width,box_conf_threshold, nms_threshold, scale_w, scale_h, out_zps, out_scales, detect_result_group);for (int i 0; i detect_result_group.count; i) {detect_result_t *det_result (detect_result_group.results[i]);if (strcmp(det_result-name, person) 0) {sprintf(text, %.1f%%, det_result-prop * 100);LOGD(AiAlgoYolo5::DealOneFrame %s (%d %d %d %d) %f\n, det_result-name, det_result-box.left, det_result-box.top,det_result-box.right, det_result-box.bottom, det_result-prop);}}}// 这一段对yolo检测结果分析目前只关心person类别结束return 0;} Android.bp实现 cc_library_static {name: libAiAlgo,local_include_dirs: [api/,base/,base/media_base/,base/log/include/,modules/ai_algo/include/,],include_dirs: [hardware/rockchip/librga/include/,hardware/rockchip/librga/im2d_api/,],srcs: [modules/ai_algo/src/ai_algo_yolo5.cpp,modules/ai_algo/src/postprocess.cpp,],shared_libs: [librga,libyuv,librknnrt,],cflags: [-g,-fexceptions,-Wno-unused-variable,-Wno-unused-function,-Wno-unused-parameter,-Wno-format,], }由于camera采集到的就是nv12图像不需要图像的压缩算法没有用到opencv库。图像的缩放用了rga库也可以用yuv库实现。 运行日志 04-03 15:45:50.027 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:50.027 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame _source_id 1 [3840 2160] data 0x7367a42000 04-03 15:45:50.027 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_start_time 1712130350027 04-03 15:45:50.035 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_stop_time 1712130350035 04-03 15:45:50.035 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 8 ms 04-03 15:45:50.035 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame start_time 1712130350035 04-03 15:45:50.044 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:50.061 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame stop_time 1712130350061 04-03 15:45:50.061 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 26 ms 04-03 15:45:50.061 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:50.061 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame person (750 0 1650 1370) 0.356758 04-03 15:45:51.211 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:51.211 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame _source_id 1 [3840 2160] data 0x73708a4000 04-03 15:45:51.211 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_start_time 1712130351211 04-03 15:45:51.219 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_stop_time 1712130351219 04-03 15:45:51.219 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 8 ms 04-03 15:45:51.219 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame start_time 1712130351219 04-03 15:45:51.228 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:51.242 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame stop_time 1712130351242 04-03 15:45:51.242 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 23 ms 04-03 15:45:51.242 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame person (996 3 2136 1329) 0.711339 04-03 15:45:51.244 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:52.244 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame _source_id 1 [3840 2160] data 0x7372848000 04-03 15:45:52.244 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_start_time 1712130352244 04-03 15:45:52.253 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_stop_time 1712130352253 04-03 15:45:52.253 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 9 ms 04-03 15:45:52.253 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame start_time 1712130352253 04-03 15:45:52.261 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:52.277 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame stop_time 1712130352277 04-03 15:45:52.277 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-03 15:45:52.277 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame person (2040 0 3012 1356) 0.641171 04-03 15:45:53.296 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:53.296 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame _source_id 1 [3840 2160] data 0x7363afa000 04-03 15:45:53.296 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_start_time 1712130353296 04-03 15:45:53.306 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal_stop_time 1712130353306 04-03 15:45:53.306 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-03 15:45:53.306 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame start_time 1712130353306 04-03 15:45:53.314 13755 13760 D RKCodecEngine: AiAlgoYolo5::OnFrame bufsid 1 sid 1, [3840 2160] 04-03 15:45:53.330 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame stop_time 1712130353330 04-03 15:45:53.330 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-03 15:45:53.330 13755 13755 D RKCodecEngine: AiAlgoYolo5::DealOneFrame person (1524 13 2610 1373) 0.404886同事配合每次都能检测到他只漏出身体一小部分也能行效只漏出身体一小部分也能行可以的DealOneFrame person (996 3 2136 1329) 0.711339 性能 04-02 19:14:24.590 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:24.613 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 23 ms 04-02 19:14:25.640 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:25.661 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 21 ms 04-02 19:14:26.673 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:26.697 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-02 19:14:27.855 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:27.876 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 21 ms 04-02 19:14:28.888 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:28.909 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 21 ms 04-02 19:14:29.923 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:29.944 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 21 ms 04-02 19:14:30.955 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:30.985 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 30 ms 04-02 19:14:32.005 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:32.028 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 23 ms 04-02 19:14:33.054 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 9 ms 04-02 19:14:33.076 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:34.089 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:34.111 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:35.139 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:35.163 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-02 19:14:36.187 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 9 ms 04-02 19:14:36.210 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:37.238 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:37.262 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 23 ms 04-02 19:14:38.421 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:38.443 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:39.454 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:39.476 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:40.487 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 10 ms 04-02 19:14:40.511 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-02 19:14:41.538 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 12 ms 04-02 19:14:41.562 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 24 ms 04-02 19:14:42.588 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:42.610 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:43.621 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:43.643 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms 04-02 19:14:44.654 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame scal once run use 11 ms 04-02 19:14:44.676 24382 24382 D RKCodecEngine: AiAlgoYolo5::DealOneFrame once run use 22 ms采集图像的缩放从4k到640x640用rga实现耗时11ms yolo5检测22ms 能够做到30fps