291 lines
8.0 KiB
C++
291 lines
8.0 KiB
C++
#ifndef _rknnPool_H
|
|
#define _rknnPool_H
|
|
|
|
#include <queue>
|
|
#include <vector>
|
|
#include <iostream>
|
|
#include "rga.h"
|
|
#include "im2d.h"
|
|
#include "RgaUtils.h"
|
|
#include "rknn_api.h"
|
|
#include "postprocess.h"
|
|
#include "opencv2/core/core.hpp"
|
|
#include "opencv2/imgcodecs.hpp"
|
|
#include "opencv2/imgproc.hpp"
|
|
#include "ThreadPool.hpp"
|
|
using cv::Mat;
|
|
using std::queue;
|
|
using std::vector;
|
|
|
|
static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz);
|
|
static unsigned char *load_model(const char *filename, int *model_size);
|
|
|
|
class rknn_lite
|
|
{
|
|
private:
|
|
rknn_context rkModel;
|
|
unsigned char *model_data;
|
|
rknn_sdk_version version;
|
|
rknn_input_output_num io_num;
|
|
rknn_tensor_attr *input_attrs;
|
|
rknn_tensor_attr *output_attrs;
|
|
rknn_input inputs[1];
|
|
int ret;
|
|
int channel = 3;
|
|
int width = 0;
|
|
int height = 0;
|
|
public:
|
|
Mat ori_img;
|
|
int interf();
|
|
rknn_lite(char *dst, int n);
|
|
~rknn_lite();
|
|
};
|
|
|
|
rknn_lite::rknn_lite(char *model_name, int n)
|
|
{
|
|
/* Create the neural network */
|
|
printf("Loading mode...\n");
|
|
int model_data_size = 0;
|
|
// 读取模型文件数据
|
|
model_data = load_model(model_name, &model_data_size);
|
|
// 通过模型文件初始化rknn类
|
|
ret = rknn_init(&rkModel, model_data, model_data_size, 0, NULL);
|
|
if (ret < 0)
|
|
{
|
|
printf("rknn_init error ret=%d\n", ret);
|
|
exit(-1);
|
|
}
|
|
//
|
|
rknn_core_mask core_mask;
|
|
if (n == 0)
|
|
core_mask = RKNN_NPU_CORE_0;
|
|
else if(n == 1)
|
|
core_mask = RKNN_NPU_CORE_1;
|
|
else
|
|
core_mask = RKNN_NPU_CORE_2;
|
|
int ret = rknn_set_core_mask(rkModel, core_mask);
|
|
if (ret < 0)
|
|
{
|
|
printf("rknn_init core error ret=%d\n", ret);
|
|
exit(-1);
|
|
}
|
|
|
|
// 初始化rknn类的版本
|
|
ret = rknn_query(rkModel, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version));
|
|
if (ret < 0)
|
|
{
|
|
printf("rknn_init error ret=%d\n", ret);
|
|
exit(-1);
|
|
}
|
|
|
|
// 获取模型的输入参数
|
|
ret = rknn_query(rkModel, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
|
|
if (ret < 0)
|
|
{
|
|
printf("rknn_init error ret=%d\n", ret);
|
|
exit(-1);
|
|
}
|
|
|
|
// 设置输入数组
|
|
input_attrs = new rknn_tensor_attr[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(rkModel, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));
|
|
if (ret < 0)
|
|
{
|
|
printf("rknn_init error ret=%d\n", ret);
|
|
exit(-1);
|
|
}
|
|
}
|
|
|
|
// 设置输出数组
|
|
output_attrs = new rknn_tensor_attr[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(rkModel, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
|
|
}
|
|
|
|
// 设置输入参数
|
|
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];
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
rknn_lite::~rknn_lite()
|
|
{
|
|
ret = rknn_destroy(rkModel);
|
|
delete[] input_attrs;
|
|
delete[] output_attrs;
|
|
if (model_data)
|
|
free(model_data);
|
|
}
|
|
|
|
int rknn_lite::interf()
|
|
{
|
|
cv::Mat img;
|
|
// 获取图像宽高
|
|
int img_width = ori_img.cols;
|
|
int img_height = ori_img.rows;
|
|
cv::cvtColor(ori_img, img, cv::COLOR_BGR2RGB);
|
|
|
|
// init rga context
|
|
// rga是rk自家的绘图库,绘图效率高于OpenCV
|
|
rga_buffer_t src;
|
|
rga_buffer_t dst;
|
|
memset(&src, 0, sizeof(src));
|
|
memset(&dst, 0, sizeof(dst));
|
|
im_rect src_rect;
|
|
im_rect dst_rect;
|
|
memset(&src_rect, 0, sizeof(src_rect));
|
|
memset(&dst_rect, 0, sizeof(dst_rect));
|
|
|
|
// You may not need resize when src resulotion equals to dst resulotion
|
|
void *resize_buf = nullptr;
|
|
// 如果输入图像不是指定格式
|
|
if (img_width != width || img_height != height)
|
|
{
|
|
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));
|
|
exit(-1);
|
|
}
|
|
IM_STATUS STATUS = imresize(src, dst);
|
|
|
|
cv::Mat resize_img(cv::Size( width, height), CV_8UC3, resize_buf);
|
|
inputs[0].buf = resize_buf;
|
|
}
|
|
else
|
|
inputs[0].buf = (void *)img.data;
|
|
|
|
// 设置rknn的输入数据
|
|
rknn_inputs_set( rkModel, io_num.n_input, inputs);
|
|
|
|
// 设置输出
|
|
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;
|
|
// 调用npu进行推演
|
|
ret = rknn_run( rkModel, NULL);
|
|
// 获取npu的推演输出结果
|
|
ret = rknn_outputs_get( rkModel, io_num.n_output, outputs, NULL);
|
|
|
|
// 总之就是绘图部分
|
|
// post process
|
|
// width是模型需要的输入宽度, img_width是图片的实际宽度
|
|
const float nms_threshold = NMS_THRESH;
|
|
const float box_conf_threshold = BOX_THRESH;
|
|
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);
|
|
int x1 = det_result->box.left;
|
|
int y1 = det_result->box.top;
|
|
rectangle(ori_img, cv::Point(x1, y1), cv::Point(det_result->box.right, det_result->box.bottom), cv::Scalar(0, 0, 255, 0), 3);
|
|
putText(ori_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255));
|
|
}
|
|
ret = rknn_outputs_release( rkModel, io_num.n_output, outputs);
|
|
if (resize_buf)
|
|
{
|
|
free(resize_buf);
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
#endif |