xref: /OK3568_Linux_fs/external/rknpu2/examples/rknn_yolov5_demo/src/main.cc (revision 4882a59341e53eb6f0b4789bf948001014eff981)

// 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 "RgaUtils.h"
#include "im2d.h"
#include "opencv2/core/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.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)
{
  std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]);
  for (int i = 1; i < attr->n_dims; ++i) {
    shape_str += ", " + std::to_string(attr->dims[i]);
  }

  printf("  index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, "
         "type=%s, qnt_type=%s, "
         "zp=%d, scale=%f\n",
         attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride,
         attr->size_with_stride, 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
-------------------------------------------*/
int main(int argc, char** argv)
{
  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));

  if (argc != 3) {
    printf("Usage: %s <rknn model> <jpg> \n", argv[0]);
    return -1;
  }

  printf("post process config: box_conf_threshold = %.2f, nms_threshold = %.2f\n", box_conf_threshold, nms_threshold);

  model_name       = (char*)argv[1];
  char* image_name = argv[2];

  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);
  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);

  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 (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);
  }
  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 = 10;
  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();

  // release
  ret = rknn_destroy(ctx);

  if (model_data) {
    free(model_data);
  }

  if (resize_buf) {
    free(resize_buf);
  }

  return 0;
}