Introduction: How to Change the RK YOLOv5 Demo to Capture Real-time Camera Feeds

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About: Share some projects about single board computer and systems on chip.

I modified the last demo so that it could capture real-time camera feeds.

Supplies

Things used in this project:

A Blade 3 board

An HDMI cable

A monitor

A Type C USB hub

USB camera (this demonstration uses a Logitech c922 USB camera)

Step 1: Build the YOLOv5 Demo on Blade 3

  1. Install adb on the Debian system. You can find some tutorials on Google about how to install it. To use the terminal, you can also connect a keyboard to the Blade 3.(change to root command sudo su)
  2. adb shell into the Blade 3
  3. Install necessary packages on Blade 3 (You can choose another sources.list if yours is slow. For more information about how to change the Debian sources.list, simply search Google.)
apt update
apt install gcc cmake git build-essential

4. install opencv lib

apt-get install libopencv-dev python3-opencv

5.Install the NPU demo on Blade 3. (download links: GitHub - rockchip-linux/rknpu2 8, use YOLOv5 small model)

cd /data
git clone https://github.com/rockchip-linux/rknpu2.git

6.Enter into YOLOv5 demo directory

cd /data/rknpu2/examples/rknn_yolov5_demo

Because the videoio and highgui libraries of OpenCV are not provided by the official, I have to compile the program and install OpenCV.

7.use vi command edit build-linux_RK3588.sh. change GCC_COMPILER and LD_LIBRARY_PATH.

set -e

TARGET_SOC="rk3588"
GCC_COMPILER=/usr/bin/aarch64-linux-gnu

export LD_LIBRARY_PATH=/usr/lib/aarch64-linux-gnu:$LD_LIBRARY_PATH
export CC=${GCC_COMPILER}-gcc
export CXX=${GCC_COMPILER}-g++

ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd )

# build
BUILD_DIR=${ROOT_PWD}/build/build_linux_aarch64

if [[ ! -d "${BUILD_DIR}" ]]; then
  mkdir -p ${BUILD_DIR}
fi

cd ${BUILD_DIR}
cmake ../.. -DCMAKE_SYSTEM_NAME=Linux -DTARGET_SOC=${TARGET_SOC}
make -j4
make install
cd -

8.Download CMakeList.txt, which uses system opencv lib to the computer.

cmake_minimum_required(VERSION 3.4.1)

project(rknn_yolov5_demo)

set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

# skip 3rd-party lib dependencies
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,--allow-shlib-undefined")

# install target and libraries
set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME})

set(CMAKE_SKIP_INSTALL_RPATH FALSE)
set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE)
set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib")

# rknn api
if(TARGET_SOC STREQUAL "rk356x")
  set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime/RK356X/${CMAKE_SYSTEM_NAME}/librknn_api)
elseif(TARGET_SOC STREQUAL "rk3588")
  set(RKNN_API_PATH ${CMAKE_SOURCE_DIR}/../../runtime/RK3588/${CMAKE_SYSTEM_NAME}/librknn_api)
else()
  message(FATAL_ERROR "TARGET_SOC is not set, ref value: rk356x or rk3588 or rv110x")
endif()

if (CMAKE_SYSTEM_NAME STREQUAL "Android")
  set(RKNN_RT_LIB ${RKNN_API_PATH}/${CMAKE_ANDROID_ARCH_ABI}/librknnrt.so)
else()
  if (CMAKE_C_COMPILER MATCHES "aarch64")
    set(LIB_ARCH aarch64)
  else()
    set(LIB_ARCH armhf)
  endif()
  set(RKNN_RT_LIB ${RKNN_API_PATH}/${LIB_ARCH}/librknnrt.so)
endif()
include_directories(${RKNN_API_PATH}/include)
include_directories(/usr/include)
include_directories(/usr/include/opencv4)
set(OpenCV_DIR "/usr/lib/aarch64-linux-gun")
find_package(OpenCV REQUIRED COMPONENTS core imgproc imgcodecs video videoio highgui)
#rga
if(TARGET_SOC STREQUAL "rk356x")
  set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK356X)
elseif(TARGET_SOC STREQUAL "rk3588")
  set(RGA_PATH ${CMAKE_SOURCE_DIR}/../3rdparty/rga/RK3588)
else()
  message(FATAL_ERROR "TARGET_SOC is not set, ref value: rk356x or rk3588")
endif()
if (CMAKE_SYSTEM_NAME STREQUAL "Android")
  set(RGA_LIB ${RGA_PATH}/lib/Android/${CMAKE_ANDROID_ARCH_ABI}/librga.so)
else()
  if (CMAKE_C_COMPILER MATCHES "aarch64")
    set(LIB_ARCH aarch64)
  else()
    set(LIB_ARCH armhf)
  endif()
  set(RGA_LIB ${RGA_PATH}/lib/Linux//${LIB_ARCH}/librga.so)
endif()
include_directories( ${RGA_PATH}/include)

set(CMAKE_INSTALL_RPATH "lib")

# rknn_yolov5_demo
include_directories( ${CMAKE_SOURCE_DIR}/include)

add_executable(rknn_yolov5_demo
        src/main.cc
        src/postprocess.cc
)

target_link_libraries(rknn_yolov5_demo
  ${RKNN_RT_LIB}
  ${RGA_LIB}
  ${OpenCV_LIBS}
)


# install target and libraries
set(CMAKE_INSTALL_PREFIX ${CMAKE_SOURCE_DIR}/install/rknn_yolov5_demo_${CMAKE_SYSTEM_NAME})
install(TARGETS rknn_yolov5_demo DESTINATION ./)

install(PROGRAMS ${RKNN_RT_LIB} DESTINATION lib)
install(PROGRAMS ${RGA_LIB} DESTINATION lib)
install(DIRECTORY model DESTINATION ./)

9.Copy the CMakeLists.txt 3 file from the computer to the Blade 3 board via scp command. (account: mixtile; password: mixtile )

scp CMakeLists.txt mixtile@192.168.202.74:/home/mixtile/

10.Download the main.cc file 7 to the computer.

// 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>
#include <chrono>
#include <ctime>

#define _BASETSD_H

#include "RgaUtils.h"
#include "im2d.h"
#include "opencv2/core/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/opencv.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include "postprocess.h"
#include "rga.h"
#include "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;
}


int inputKnn(cv::Mat &orig_img, rknn_context &ctx);

int main(int argc, char** argv){
  char*          model_name = NULL;
  if (argc != 5) {
    printf("Usage: %s <rknn model> </dev/viedo number> <vedio width> <vedio height>\n", argv[0]);
    return -1;
  }
  model_name       = (char*)argv[1];
  int num = atoi((char*)argv[2]);
  printf("Read video%d ...\n", num);
  int capWidth = atoi((char*)argv[3]);
  int capHeight = atoi((char*)argv[4]);

  cv::namedWindow("Video Player");//Declaring the video to show the video//
  cv::VideoCapture cap(num);//Declaring an object to capture stream of frames from third camera//
  if (!cap.isOpened()){ //This section prompt an error message if no video stream is found//
    printf("No video stream detected");
    return-1;
  }

   //Set the resolution
  cap.set(cv::CAP_PROP_FRAME_WIDTH, capWidth);
  cap.set(cv::CAP_PROP_FRAME_HEIGHT, capHeight);
  cap.set(cv::CAP_PROP_FOURCC, cv::VideoWriter::fourcc('M','J','P','G'));
  int ret = -1;
  rknn_context   ctx;
  /* Create the neural network */
  printf("Loading mode...\n");
  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;
  }

  int fpsCamera = 70;
  int fpsCapture = 10;
  std::chrono::time_point<std::chrono::high_resolution_clock> prev_frame_time(std::chrono::high_resolution_clock::now());
  std::chrono::time_point<std::chrono::high_resolution_clock> new_frame_time;
  while (true){ //Taking an everlasting loop to show the video//
      cv::Mat orig_img;
      printf("start.\n");
      cap >> orig_img;
      if (orig_img.empty()){ //Breaking the loop if no video frame is detected//
          break;
      }
      inputKnn(orig_img, ctx);
      if (orig_img.empty()){ //Breaking the loop if no video frame is detected//
          break;
      }
      new_frame_time = std::chrono::high_resolution_clock::now();
      std::chrono::duration<double> duration1(new_frame_time - prev_frame_time);
      double fps = 1/duration1.count();
      std::cout << "fps : " << fps << std::endl;
      if(duration1.count() > 1/fpsCapture)
      {
                    prev_frame_time = new_frame_time;

        cv::imshow("Video Player", orig_img);
      }

      char c = (char)cv::waitKey(3);
      if (c == 27)
      {
        break; // (27)escape key
      }
  }
  // release
  ret = rknn_destroy(ctx);

  if (model_data) {
    free(model_data);
  }

  cap.release();//Releasing the buffer memory//
  return 0;
}

int inputKnn(cv::Mat &orig_img, rknn_context &ctx)
{
  int            status     = 0;
 // char*          model_name = NULL;
 // rknn_context   ctx;
  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 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));



  //printf("Read %s ...\n", image_name);
  //cv::Mat orig_img = cv::imread(image_name, 1);
  //if (!orig_img.data) {
  //  printf("cv::imread %s fail!\n", image_name);
  // 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 */
  //printf("Loading mode...\n");
  //int            model_data_size = 0;
  //unsigned char* model_data      = load_model(model_name, &model_data_size);


  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);

  rknn_input_output_num io_num;
  memset(&io_num,0,sizeof(rknn_input_output_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 (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) {
      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 (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]));
  }

  int channel = 3;
  int width   = 0;
  int height  = 0;
  if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) {
    printf("model is NCHW input fmt\n");
    channel = input_attrs[0].dims[1];
    height  = input_attrs[0].dims[2];
    width   = input_attrs[0].dims[3];
  } else {
    printf("model is NHWC input fmt\n");
    height  = input_attrs[0].dims[1];
    width   = input_attrs[0].dims[2];
    channel = input_attrs[0].dims[3];
  }

  printf("model input height=%d, width=%d, channel=%d\n", height, width, channel);

  rknn_input inputs[1];
  memset(inputs, 0, sizeof(inputs));
  inputs[0].index        = 0;
  inputs[0].type         = RKNN_TENSOR_UINT8;
  inputs[0].size         = width * height * channel;
  inputs[0].fmt          = RKNN_TENSOR_NHWC;
  inputs[0].pass_through = 0;

  // You may not need resize when src resulotion equals to dst resulotion
  void* resize_buf = nullptr;

  if (img_width != width || img_height != height) {
    printf("resize with RGA!\n");
    resize_buf = malloc(height * width * channel);
    memset(resize_buf, 0x00, height * width * channel);

    src = wrapbuffer_virtualaddr((void*)img.data, img_width, img_height, RK_FORMAT_RGB_888);
    dst = wrapbuffer_virtualaddr((void*)resize_buf, width, height, RK_FORMAT_RGB_888);
    ret = imcheck(src, dst, src_rect, dst_rect);
    if (IM_STATUS_NOERROR != ret) {
      printf("%d, check error! %s", __LINE__, imStrError((IM_STATUS)ret));
      return -1;
    }
    IM_STATUS STATUS = imresize(src, dst);

    // for debug
   // cv::Mat resize_img(cv::Size(width, height), CV_8UC3, resize_buf);
   // cv::imwrite("resize_input.jpg", resize_img);

    inputs[0].buf = resize_buf;
  } else {
    inputs[0].buf = (void*)img.data;
  }

  gettimeofday(&start_time, NULL);
  rknn_inputs_set(ctx, io_num.n_input, inputs);

  rknn_output outputs[io_num.n_output];
  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);
  gettimeofday(&stop_time, NULL);
  printf("once run use %f ms\n", (__get_us(stop_time) - __get_us(start_time)) / 1000);

  // post process
  float scale_w = (float)width / img_width;
  float scale_h = (float)height / img_height;

  detect_result_group_t detect_result_group;
  std::vector<float>    out_scales;
  std::vector<int32_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);
  }
  printf("before post_process\n");
  post_process((int8_t*)outputs[0].buf, (int8_t*)outputs[1].buf, (int8_t*)outputs[2].buf, height, width,
               box_conf_threshold, nms_threshold, scale_w, scale_h, out_zps, out_scales, &detect_result_group);

  // Draw Objects
  char text[256];
  for (int i = 0; i < detect_result_group.count; i++) {
    detect_result_t* det_result = &(detect_result_group.results[i]);
    sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);
    printf("%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);
    int x1 = det_result->box.left;
    int y1 = det_result->box.top;
    int x2 = det_result->box.right;
    int y2 = det_result->box.bottom;
    rectangle(orig_img, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(255, 0, 0, 255), 3);
    putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
  }

 // imwrite("./out.jpg", orig_img);
  ret = rknn_outputs_release(ctx, io_num.n_output, outputs);

  // loop test
  int test_count = 1;
  gettimeofday(&start_time, NULL);
  for (int i = 0; i < test_count; ++i) {
    rknn_inputs_set(ctx, io_num.n_input, inputs);
    ret = rknn_run(ctx, NULL);
    ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);
#if PERF_WITH_POST
    post_process((int8_t*)outputs[0].buf, (int8_t*)outputs[1].buf, (int8_t*)outputs[2].buf, height, width,
                 box_conf_threshold, nms_threshold, scale_w, scale_h, out_zps, out_scales, &detect_result_group);
#endif
    ret = rknn_outputs_release(ctx, io_num.n_output, outputs);
  }
  gettimeofday(&stop_time, NULL);
  printf("loop count = %d , average run  %f ms\n", test_count,
         (__get_us(stop_time) - __get_us(start_time)) / 1000.0 / test_count);

  //deinitPostProcess();


  if (resize_buf) {
    free(resize_buf);
  }

  return 0;
}

11.Copy the main.cc file to the Blade 3 board via scp command. (account: mixtile; password: mixtile )

scp main.cc mixtile@192.168.202.74:/home/mixtile/

12.Move the main.cc and CMakeLists.txt files to the specified folder, as shown below.

chown root:root /home/mixtile/main.cc 
chown root:root /home/mixtile/CMakeLists.txt 
mv /home/mixtile/main.cc /data/rknpu2/examples/rknn_yolov5_demo/src/main.cc
mv /home/mixtile/CMakeLists.txt /data/rknpu2/examples/rknn_yolov5_demo/CMakeLists.txt

Step 2: Build the Demo

13.build the demo

./build-linux_RK3588.sh

14.run the demo(./rknn_yolov5_demo model videoNum(/dev/video18) width height)

cd install/rknn_yolov5_demo_Linux
./rknn_yolov5_demo ./model/RK3588/yolov5s-640-640.rknn 18 1920 1080

To check if it worked, I started snapping casually around the office.

It recognized my cups and bottles, one mouse is right, but another is actually an earphone case lol

My next-door colleague’s seat, the plant, his chair and display, the bottle on his desk, and so on.

There is a little Easter Egg, one of my anime figure is also identified as a “person”, though only 26.5% possibility as a person.It’s interesting that this character appears in JoJo’s Bizarre Adventure and his famous line is “I’m done with human, JOJO! I will go beyond human being!”