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gxt_kt 2024-08-06 18:43:30 +00:00
parent 4349005f83
commit 611e9b1637
12 changed files with 387 additions and 496 deletions
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letterbox_input.jpg Normal file
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8
src/guangpo_yolo/CMakeLists.txt
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@ -5,6 +5,7 @@ project(guangpo_yolo)
# add_compile_options(-std=c++11) # add_compile_options(-std=c++11)
set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD 17)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_STANDARD_REQUIRED ON)
## Find catkin macros and libraries ## Find catkin macros and libraries
@ -152,6 +153,7 @@ include/rga/include
## With catkin_make all packages are built within a single CMake context ## With catkin_make all packages are built within a single CMake context
## The recommended prefix ensures that target names across packages don't collide ## The recommended prefix ensures that target names across packages don't collide
add_executable(${PROJECT_NAME}_node src/main.cpp src/postprocess.cc src/preprocess.cc yaml_config/yaml_config.cpp) add_executable(${PROJECT_NAME}_node src/main.cpp src/postprocess.cc src/preprocess.cc yaml_config/yaml_config.cpp)
add_executable(${PROJECT_NAME}_test_node src/main_test.cc src/postprocess.cc src/preprocess.cc yaml_config/yaml_config.cpp)
## Rename C++ executable without prefix ## Rename C++ executable without prefix
## The above recommended prefix causes long target names, the following renames the ## The above recommended prefix causes long target names, the following renames the
@ -170,6 +172,12 @@ target_link_libraries(${PROJECT_NAME}_node
${LIB_FILES} ${LIB_FILES}
yaml-cpp yaml-cpp
) )
target_link_libraries(${PROJECT_NAME}_test_node
${catkin_LIBRARIES}
${OpenCV_LIBS}
${LIB_FILES}
yaml-cpp
)
############# #############
## Install ## ## Install ##

1
src/guangpo_yolo/include/dianti.h
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@ -1,3 +1,4 @@
#pragma once
#include <Eigen/Core> #include <Eigen/Core>
#include <cmath> #include <cmath>
#include <iostream> #include <iostream>

5
src/guangpo_yolo/include/postprocess.h
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@ -3,6 +3,7 @@
#include <stdint.h> #include <stdint.h>
#include <vector> #include <vector>
#include <string>
#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NAME_MAX_SIZE 16
#define OBJ_NUMB_MAX_SIZE 64 #define OBJ_NUMB_MAX_SIZE 64
@ -36,7 +37,7 @@ typedef struct _detect_result_group_t
int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w,
float conf_threshold, float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, float conf_threshold, float nms_threshold, BOX_RECT pads, float scale_w, float scale_h,
std::vector<int32_t> &qnt_zps, std::vector<float> &qnt_scales, std::vector<int32_t> &qnt_zps, std::vector<float> &qnt_scales,
detect_result_group_t *group); detect_result_group_t *group, const std::vector<std::string>& labels);
void deinitPostProcess(); // void deinitPostProcess();
#endif //_RKNN_YOLOV5_DEMO_POSTPROCESS_H_ #endif //_RKNN_YOLOV5_DEMO_POSTPROCESS_H_

219
src/guangpo_yolo/include/yolov5.hpp
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@ -4,6 +4,7 @@
Includes Includes
-------------------------------------------*/ -------------------------------------------*/
#include <dlfcn.h> #include <dlfcn.h>
#include <exception>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
@ -12,13 +13,18 @@
#define _BASETSD_H #define _BASETSD_H
#include "RgaUtils.h" #include "RgaUtils.h"
#include "debugstream.hpp"
#include "dianti.h"
#include "postprocess.h" #include "postprocess.h"
#include "preprocess.h" #include "preprocess.h"
#include "rknn_api.h" #include "rknn_api.h"
#include "debugstream.hpp"
#include "dianti.h"
#define PERF_WITH_POST 1 #define PERF_WITH_POST 1
/*-------------------------------------------
Functions
-------------------------------------------*/
static void dump_tensor_attr(rknn_tensor_attr *attr) { static void dump_tensor_attr(rknn_tensor_attr *attr) {
std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]); std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]);
@ -26,20 +32,17 @@ static void dump_tensor_attr(rknn_tensor_attr *attr) {
shape_str += ", " + std::to_string(attr->dims[i]); shape_str += ", " + std::to_string(attr->dims[i]);
} }
printf( printf(" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, "
" index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, " "w_stride = %d, size_with_stride=%d, fmt=%s, "
"w_stride = %d, size_with_stride=%d, fmt=%s, " "type=%s, qnt_type=%s, "
"type=%s, qnt_type=%s, " "zp=%d, scale=%f\n",
"zp=%d, scale=%f\n", attr->index, attr->name, attr->n_dims, shape_str.c_str(),
attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->n_elems, attr->size, attr->w_stride, attr->size_with_stride,
attr->size, attr->w_stride, attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type),
get_format_string(attr->fmt), get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
} }
static double __get_us(struct timeval t) { double __get_us(struct timeval t) { return (t.tv_sec * 1000000 + t.tv_usec); }
return (t.tv_sec * 1000000 + t.tv_usec);
}
static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz) { static unsigned char *load_data(FILE *fp, size_t ofst, size_t sz) {
unsigned char *data; unsigned char *data;
@ -98,16 +101,28 @@ static int saveFloat(const char *file_name, float *output, int element_size) {
} }
class Yolov5 { class Yolov5 {
public: public:
/*------------------------------------------- rknn_context ctx;
Functions rknn_input inputs[1];
-------------------------------------------*/ int img_width = 0;
int img_height = 0;
std::string model_path_; int img_channel = 0;
int channel = 3;
int width = 0;
int height = 0;
rga_buffer_t src;
rga_buffer_t dst;
std::string option = "letterbox";
rknn_input_output_num io_num;
struct timeval start_time, stop_time;
rknn_tensor_attr *output_attrs;
const float nms_threshold = NMS_THRESH; // 默认的NMS阈值
const float box_conf_threshold = BOX_THRESH; // 默认的置信度阈值
unsigned char *model_data;
std::vector<std::string> labels;
Yolov5() {} Yolov5() {}
void Init(const std::string &model_path) {}
~Yolov5() { ~Yolov5() {
deinitPostProcess(); // deinitPostProcess();
// release // release
rknn_destroy(ctx); rknn_destroy(ctx);
@ -116,39 +131,19 @@ class Yolov5 {
free(model_data); free(model_data);
} }
} }
void SetLabels(const std::vector<std::string> &labelss) { labels = labelss; }
rknn_context ctx; void LoadModel(const std::string &model_name_str) {
unsigned char *model_data; int ret;
int ret; const char *model_name = model_name_str.c_str();
// std::string out_path = "./out.jpg";
void LoadModel() {
int img_width = 0;
int img_height = 0;
int img_channel = 0;
const float nms_threshold = NMS_THRESH; // 默认的NMS阈值
const float box_conf_threshold = BOX_THRESH; // 默认的置信度阈值
struct timeval start_time, stop_time;
// char *model_name = (char *)argv[1];
// char *input_path = argv[2];
std::string option = "letterbox";
std::string out_path = "./out.jpg";
if (argc >= 4) {
option = argv[3];
}
if (argc >= 5) {
out_path = argv[4];
}
// init rga context // init rga context
rga_buffer_t src;
rga_buffer_t dst;
memset(&src, 0, sizeof(src)); memset(&src, 0, sizeof(src));
memset(&dst, 0, sizeof(dst)); memset(&dst, 0, sizeof(dst));
printf( printf("post process config: box_conf_threshold = %.2f, nms_threshold = "
"post process config: box_conf_threshold = %.2f, nms_threshold = " "%.2f\n",
"%.2f\n", box_conf_threshold, nms_threshold);
box_conf_threshold, nms_threshold);
/* Create the neural network */ /* Create the neural network */
printf("Loading mode...\n"); printf("Loading mode...\n");
@ -157,7 +152,7 @@ class Yolov5 {
ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL);
if (ret < 0) { if (ret < 0) {
printf("rknn_init error ret=%d\n", ret); printf("rknn_init error ret=%d\n", ret);
return -1; std::terminate();
} }
rknn_sdk_version version; rknn_sdk_version version;
@ -165,16 +160,15 @@ class Yolov5 {
sizeof(rknn_sdk_version)); sizeof(rknn_sdk_version));
if (ret < 0) { if (ret < 0) {
printf("rknn_init error ret=%d\n", ret); printf("rknn_init error ret=%d\n", ret);
return -1; std::terminate();
} }
printf("sdk version: %s driver version: %s\n", version.api_version, printf("sdk version: %s driver version: %s\n", version.api_version,
version.drv_version); version.drv_version);
rknn_input_output_num io_num;
ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
if (ret < 0) { if (ret < 0) {
printf("rknn_init error ret=%d\n", ret); printf("rknn_init error ret=%d\n", ret);
return -1; std::terminate();
} }
printf("model input num: %d, output num: %d\n", io_num.n_input, printf("model input num: %d, output num: %d\n", io_num.n_input,
io_num.n_output); io_num.n_output);
@ -187,13 +181,14 @@ class Yolov5 {
sizeof(rknn_tensor_attr)); sizeof(rknn_tensor_attr));
if (ret < 0) { if (ret < 0) {
printf("rknn_init error ret=%d\n", ret); printf("rknn_init error ret=%d\n", ret);
return -1; std::terminate();
} }
dump_tensor_attr(&(input_attrs[i])); dump_tensor_attr(&(input_attrs[i]));
} }
rknn_tensor_attr output_attrs[io_num.n_output]; // rknn_tensor_attr output_attrs[io_num.n_output];
memset(output_attrs, 0, sizeof(output_attrs)); 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++) { for (int i = 0; i < io_num.n_output; i++) {
output_attrs[i].index = i; output_attrs[i].index = i;
ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]),
@ -201,9 +196,6 @@ class Yolov5 {
dump_tensor_attr(&(output_attrs[i])); dump_tensor_attr(&(output_attrs[i]));
} }
int channel = 3;
int width = 0;
int height = 0;
if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) { if (input_attrs[0].fmt == RKNN_TENSOR_NCHW) {
printf("model is NCHW input fmt\n"); printf("model is NCHW input fmt\n");
channel = input_attrs[0].dims[1]; channel = input_attrs[0].dims[1];
@ -218,23 +210,18 @@ class Yolov5 {
printf("model input height=%d, width=%d, channel=%d\n", height, width, printf("model input height=%d, width=%d, channel=%d\n", height, width,
channel); channel);
}
void Infer() {
rknn_input inputs[1];
memset(inputs, 0, sizeof(inputs)); memset(inputs, 0, sizeof(inputs));
inputs[0].index = 0; inputs[0].index = 0;
inputs[0].type = RKNN_TENSOR_UINT8; inputs[0].type = RKNN_TENSOR_UINT8;
inputs[0].size = width * height * channel; inputs[0].size = width * height * channel;
inputs[0].fmt = RKNN_TENSOR_NHWC; inputs[0].fmt = RKNN_TENSOR_NHWC;
inputs[0].pass_through = 0; inputs[0].pass_through = 0;
}
// 读取图片 detect_result_group_t Infer(const cv::Mat &orig_img,
printf("Read %s ...\n", input_path); const std::string &out_path = "output.jpg",
cv::Mat orig_img = cv::imread(input_path, 1); bool debug = false) {
if (!orig_img.data) { int ret;
printf("cv::imread %s fail!\n", input_path);
return -1;
}
cv::Mat img; cv::Mat img;
cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB); cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB);
img_width = img.cols; img_width = img.cols;
@ -257,10 +244,11 @@ class Yolov5 {
ret = resize_rga(src, dst, img, resized_img, target_size); ret = resize_rga(src, dst, img, resized_img, target_size);
if (ret != 0) { if (ret != 0) {
fprintf(stderr, "resize with rga error\n"); fprintf(stderr, "resize with rga error\n");
return -1; std::terminate();
} }
// 保存预处理图片 // 保存预处理图片
cv::imwrite("resize_input.jpg", resized_img); if (debug)
cv::imwrite("resize_input.jpg", resized_img);
} else if (option == "letterbox") { } else if (option == "letterbox") {
printf("resize image with letterbox\n"); printf("resize image with letterbox\n");
float min_scale = std::min(scale_w, scale_h); float min_scale = std::min(scale_w, scale_h);
@ -268,11 +256,12 @@ class Yolov5 {
scale_h = min_scale; scale_h = min_scale;
letterbox(img, resized_img, pads, min_scale, target_size); letterbox(img, resized_img, pads, min_scale, target_size);
// 保存预处理图片 // 保存预处理图片
cv::imwrite("letterbox_input.jpg", resized_img); if (debug)
cv::imwrite("letterbox_input.jpg", resized_img);
} else { } else {
fprintf(stderr, fprintf(stderr,
"Invalid resize option. Use 'resize' or 'letterbox'.\n"); "Invalid resize option. Use 'resize' or 'letterbox'.\n");
return -1; std::terminate();
} }
inputs[0].buf = resized_img.data; inputs[0].buf = resized_img.data;
} else { } else {
@ -292,9 +281,6 @@ class Yolov5 {
// 执行推理 // 执行推理
ret = rknn_run(ctx, NULL); ret = rknn_run(ctx, NULL);
ret = rknn_outputs_get(ctx, io_num.n_output, outputs, 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);
// 后处理 // 后处理
detect_result_group_t detect_result_group; detect_result_group_t detect_result_group;
@ -307,7 +293,11 @@ class Yolov5 {
post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf, post_process((int8_t *)outputs[0].buf, (int8_t *)outputs[1].buf,
(int8_t *)outputs[2].buf, height, width, box_conf_threshold, (int8_t *)outputs[2].buf, height, width, box_conf_threshold,
nms_threshold, pads, scale_w, scale_h, out_zps, out_scales, nms_threshold, pads, scale_w, scale_h, out_zps, out_scales,
&detect_result_group); &detect_result_group, labels);
gettimeofday(&stop_time, NULL);
printf("once run use %f ms\n",
(__get_us(stop_time) - __get_us(start_time)) / 1000);
// 画框和概率 // 画框和概率
char text[256]; char text[256];
@ -326,72 +316,11 @@ class Yolov5 {
putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX, putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX,
0.4, cv::Scalar(255, 255, 255)); 0.4, cv::Scalar(255, 255, 255));
} }
if (!out_path.empty()) {
// gxt: add my process here ==============begin printf("save detect result to %s\n", out_path.c_str());
// 相机内参 imwrite(out_path, orig_img);
Eigen::Matrix3d camera_matrix;
camera_matrix << 787.22316869, 0.0, 628.91534144, 0.0, 793.45182,
313.46301416, 0.0, 0.0, 1.0;
// int my_width=orig_img.cols;
// int my_height=orig_img.rows;
DistanceEstimator estimator(camera_matrix, img_width, img_height);
std::vector<Box> rens;
std::vector<Box> diantis;
gDebug(detect_result_group.count);
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);
Box box;
box.x = (double)(det_result->box.left + det_result->box.right) / 2.0 /
img_width;
box.y = (double)(det_result->box.top + det_result->box.bottom) / 2.0 /
img_height;
box.w = (double)std::abs(det_result->box.right - det_result->box.left) /
img_width;
box.h = (double)std::abs(det_result->box.bottom - det_result->box.top) /
img_height;
std::string class_name = det_result->name;
if (class_name == "Dianti") {
diantis.push_back(box);
} else if (class_name == "Ren") {
rens.push_back(box);
}
// 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(256, 0, 0, 256), 3); putText(orig_img, text, cv::Point(x1,
// y1 + 12), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255));
} }
DealImage(estimator, orig_img, rens, diantis); rknn_outputs_release(ctx, io_num.n_output, outputs);
// gxt: add my process here ==============end return detect_result_group;
printf("save detect result to %s\n", out_path.c_str());
imwrite(out_path, orig_img);
ret = rknn_outputs_release(ctx, io_num.n_output, outputs);
// 耗时统计
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, pads, 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);
} }
}; };

4
src/guangpo_yolo/launch/run.launch Normal file
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@ -0,0 +1,4 @@
<launch>
<node pkg="rviz" type="rviz" name="rviz" args="-d $(find guangpo_yolo)/rviz/guangpo.rviz"></node>
<node pkg="guangpo_yolo" type="guangpo_yolo_node" name="guangpo_yolo_node" output="screen" ></node>
</launch>

208
src/guangpo_yolo/rviz/guangpo.rviz Normal file
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@ -0,0 +1,208 @@
Panels:
- Class: rviz/Displays
Help Height: 78
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
- /MarkerArray1/Status1
- /PointCloud22
Splitter Ratio: 0.5
Tree Height: 257
- Class: rviz/Selection
Name: Selection
- Class: rviz/Tool Properties
Expanded:
- /2D Pose Estimate1
- /2D Nav Goal1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: rviz/Time
Name: Time
SyncMode: 0
SyncSource: Image
Preferences:
PromptSaveOnExit: true
Toolbars:
toolButtonStyle: 2
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz/Image
Enabled: true
Image Topic: /usb_cam/image_raw
Max Value: 1
Median window: 5
Min Value: 0
Name: Image
Normalize Range: true
Queue Size: 2
Transport Hint: raw
Unreliable: false
Value: true
- Alpha: 1
Class: rviz/Axes
Enabled: true
Length: 1
Name: Axes
Radius: 0.10000000149011612
Reference Frame: <Fixed Frame>
Show Trail: false
Value: true
- Class: rviz/MarkerArray
Enabled: true
Marker Topic: /obstacles_topic
Name: MarkerArray
Namespaces:
"": true
Queue Size: 100
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: PointCloud2
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic: /cloud_ground
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 2.202476739883423
Min Value: 0.08952760696411133
Value: true
Axis: Z
Channel Name: intensity
Class: rviz/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Min Color: 0; 0; 0
Name: PointCloud2
Position Transformer: XYZ
Queue Size: 10
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic: /cloud_noground
Unreliable: false
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Default Light: true
Fixed Frame: world
Frame Rate: 30
Name: root
Tools:
- Class: rviz/Interact
Hide Inactive Objects: true
- Class: rviz/MoveCamera
- Class: rviz/Select
- Class: rviz/FocusCamera
- Class: rviz/Measure
- Class: rviz/SetInitialPose
Theta std deviation: 0.2617993950843811
Topic: /initialpose
X std deviation: 0.5
Y std deviation: 0.5
- Class: rviz/SetGoal
Topic: /move_base_simple/goal
- Class: rviz/PublishPoint
Single click: true
Topic: /clicked_point
Value: true
Views:
Current:
Class: rviz/Orbit
Distance: 19.902732849121094
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Field of View: 0.7853981852531433
Focal Point:
X: 0
Y: 0
Z: 0
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 1.344796061515808
Target Frame: <Fixed Frame>
Yaw: 3.2617619037628174
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 740
Hide Left Dock: false
Hide Right Dock: false
Image:
collapsed: false
QMainWindow State: 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
Selection:
collapsed: false
Time:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 1512
X: 0
Y: 0

337
src/guangpo_yolo/src/main.cpp
View File

@ -6,8 +6,6 @@
#include <ros/ros.h> #include <ros/ros.h>
#include <sensor_msgs/Image.h> #include <sensor_msgs/Image.h>
#define DEBUG 0
std::string model_name = []() -> std::string { std::string model_name = []() -> std::string {
std::filesystem::path current_dir = std::filesystem::path current_dir =
std::filesystem::path(__FILE__).parent_path(); std::filesystem::path(__FILE__).parent_path();
@ -22,145 +20,28 @@ std::string input_path = []() -> std::string {
return img_path; return img_path;
}(); }();
#include <dlfcn.h> std::vector<std::string> labels={"Dianti","Ren"};
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/time.h>
#define _BASETSD_H
#include "RgaUtils.h"
#include "postprocess.h"
#include "preprocess.h"
#include "rknn_api.h"
#include "debugstream.hpp" #include "debugstream.hpp"
#include "dianti.h" #include "dianti.h"
#include "yolov5.hpp"
#define PERF_WITH_POST 1
/*-------------------------------------------
Functions
-------------------------------------------*/
int ret;
rknn_context ctx;
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 std::string &filename, int *model_size) {
FILE *fp;
unsigned char *data;
fp = fopen(filename.c_str(), "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;
}
class ImageReceiver { class ImageReceiver {
public: public:
unsigned char *model_data; Yolov5 yolo;
std::string option = "letterbox"; std::shared_ptr<DistanceEstimator> estimator;
std::string out_path = "./out.jpg";
int img_channel = 0;
int channel = 3;
int width = 0;
int height = 0;
rknn_input_output_num io_num;
rknn_input inputs[1];
rknn_tensor_attr *output_attrs;
// init rga context
rga_buffer_t src;
rga_buffer_t dst;
size_t actual_size = 0;
const float nms_threshold = NMS_THRESH; // 默认的NMS阈值
const float box_conf_threshold = BOX_THRESH; // 默认的置信度阈值
std::string sub_topic;
std::string send_topic;
DistanceEstimator *estimator;
ImageReceiver(ros::NodeHandle &nh) { ImageReceiver(ros::NodeHandle &nh) {
sub_topic = yaml_config["sub_image_topic"].as<std::string>(); std::string sub_topic = yaml_config["sub_image_topic"].as<std::string>();
send_topic = yaml_config["pub_image_topic"].as<std::string>(); std::string send_topic = yaml_config["pub_image_topic"].as<std::string>();
image_sub_ = image_sub_ =
nh.subscribe(sub_topic, 1, &ImageReceiver::imageCallback, this); nh.subscribe(sub_topic, 1, &ImageReceiver::imageCallback, this);
image_pub_ = nh.advertise<sensor_msgs::Image>(send_topic, 1); image_pub_ = nh.advertise<sensor_msgs::Image>(send_topic, 1);
Init(); Init();
yolo.SetLabels(labels);
yolo.LoadModel(model_name);
} }
~ImageReceiver() { ~ImageReceiver() {}
deinitPostProcess();
// release
ret = rknn_destroy(ctx);
if (model_data) {
free(model_data);
}
}
void SendImage(const cv::Mat &image) { void SendImage(const cv::Mat &image) {
// 将cv::Mat图像转换为sensor_msgs::Image消息 // 将cv::Mat图像转换为sensor_msgs::Image消息
sensor_msgs::ImagePtr msg = sensor_msgs::ImagePtr msg =
@ -180,7 +61,6 @@ public:
cv_bridge::CvImagePtr cv_ptr = cv_bridge::CvImagePtr cv_ptr =
cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8); cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
image_ = cv_ptr->image; image_ = cv_ptr->image;
TIME_BEGIN_MS(detect_image);
DetectImage(image_); DetectImage(image_);
} catch (cv_bridge::Exception &e) { } catch (cv_bridge::Exception &e) {
ROS_ERROR("cv_bridge exception: %s", e.what()); ROS_ERROR("cv_bridge exception: %s", e.what());
@ -189,87 +69,6 @@ public:
void Init() { void Init() {
gDebug(model_name); gDebug(model_name);
gDebug(input_path);
memset(&src, 0, sizeof(src));
memset(&dst, 0, sizeof(dst));
printf("post process config: box_conf_threshold = %.2f, nms_threshold = "
"%.2f\n",
box_conf_threshold, nms_threshold);
/* Create the neural network */
printf("Loading mode...\n");
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;
}
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;
}
printf("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) {
printf("rknn_init error ret=%d\n", ret);
return;
}
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;
}
dump_tensor_attr(&(input_attrs[i]));
}
// rknn_tensor_attr output_attrs[io_num.n_output];
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(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) {
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);
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;
Eigen::Matrix3d camera_matrix; Eigen::Matrix3d camera_matrix;
camera_matrix << yaml_config["camera_in_param"]["dx"].as<double>(), 0.0, camera_matrix << yaml_config["camera_in_param"]["dx"].as<double>(), 0.0,
@ -277,119 +76,22 @@ public:
yaml_config["camera_in_param"]["dy"].as<double>(), yaml_config["camera_in_param"]["dy"].as<double>(),
yaml_config["camera_in_param"]["v0"].as<double>(), 0.0, 0.0, 1.0; yaml_config["camera_in_param"]["v0"].as<double>(), 0.0, 0.0, 1.0;
gDebug(camera_matrix); gDebug(camera_matrix);
estimator = new DistanceEstimator( estimator = std::make_shared<DistanceEstimator>(
camera_matrix, yaml_config["image"]["width"].as<double>(), camera_matrix, yaml_config["image"]["width"].as<double>(),
yaml_config["image"]["height"].as<double>()); yaml_config["image"]["height"].as<double>());
} }
void DetectImage(cv::Mat &img) { void DetectImage(cv::Mat &img) {
TIME_BEGIN_MS(DetectImage); TIME_BEGIN_MS(DetectImage);
// 读取图片 detect_result_group_t detect_result_group;
// printf("Read %s ...\n", input_path); detect_result_group = yolo.Infer(img, "");
cv::Mat &orig_img = img;
if (!orig_img.data) { TIME_END(DetectImage);
printf("cv::imread %s fail!\n", input_path);
return;
}
cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB);
int img_width = img.cols; int img_width = img.cols;
int img_height = img.rows; int img_height = img.rows;
printf("img width = %d, img height = %d\n", img_width, img_height);
// 指定目标大小和预处理方式,默认使用LetterBox的预处理
BOX_RECT pads;
memset(&pads, 0, sizeof(BOX_RECT));
cv::Size target_size(width, height);
cv::Mat resized_img(target_size.height, target_size.width, CV_8UC3);
// 计算缩放比例
float scale_w = (float)target_size.width / img.cols;
float scale_h = (float)target_size.height / img.rows;
if (img_width != width || img_height != height) {
// 直接缩放采用RGA加速
if (option == "resize") {
printf("resize image by rga\n");
ret = resize_rga(src, dst, img, resized_img, target_size);
if (ret != 0) {
fprintf(stderr, "resize with rga error\n");
return;
}
// 保存预处理图片
#if DEBUG
cv::imwrite("resize_input.jpg", resized_img);
#endif
} else if (option == "letterbox") {
printf("resize image with letterbox\n");
float min_scale = std::min(scale_w, scale_h);
scale_w = min_scale;
scale_h = min_scale;
letterbox(img, resized_img, pads, min_scale, target_size);
// 保存预处理图片
#if DEBUG
cv::imwrite("letterbox_input.jpg", resized_img);
#endif
} else {
fprintf(stderr,
"Invalid resize option. Use 'resize' or 'letterbox'.\n");
return;
}
inputs[0].buf = resized_img.data;
} else {
inputs[0].buf = img.data;
}
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].index = i;
outputs[i].want_float = 0;
}
// 执行推理
ret = rknn_run(ctx, NULL);
ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);
// 后处理
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, pads, scale_w, scale_h, out_zps, out_scales,
&detect_result_group);
// 画框和概率
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(256, 0, 0, 256), 3);
putText(orig_img, text, cv::Point(x1, y1 + 12), cv::FONT_HERSHEY_SIMPLEX,
0.4, cv::Scalar(255, 255, 255));
}
// gxt: add my process here ==============begin // gxt: add my process here ==============begin
// 相机内参 // 相机内参
// Eigen::Matrix3d camera_matrix;
// camera_matrix << 787.22316869, 0.0, 628.91534144, 0.0, 793.45182,
// 313.46301416, 0.0, 0.0, 1.0;
// // int my_width=orig_img.cols;
// // int my_height=orig_img.rows;
// DistanceEstimator estimator(camera_matrix, img_width, img_height);
std::vector<Box> rens; std::vector<Box> rens;
std::vector<Box> diantis; std::vector<Box> diantis;
gDebug(detect_result_group.count); gDebug(detect_result_group.count);
@ -411,15 +113,16 @@ public:
rens.push_back(box); rens.push_back(box);
} }
} }
DealImage(*estimator, orig_img, rens, diantis); TIME_END(DetectImage);
DealImage(*estimator, img, rens, diantis);
TIME_END(DetectImage);
// gxt: add my process here ==============end // gxt: add my process here ==============end
SendImage(orig_img); SendImage(img);
#if DEBUG #if DEBUG
printf("save detect result to %s\n", out_path.c_str()); printf("save detect result to %s\n", out_path.c_str());
imwrite(out_path, orig_img); imwrite(out_path, img);
#endif #endif
ret = rknn_outputs_release(ctx, io_num.n_output, outputs);
} }
private: private:
@ -449,4 +152,4 @@ int main(int argc, char **argv) {
ros::spin(); ros::spin();
return 0; return 0;
} }

0
src/guangpo_yolo/src/main_guangpo.cc → src/guangpo_yolo/src/main_rk3588.cc
View File

37
src/guangpo_yolo/src/main_test.cc Normal file
View File

@ -0,0 +1,37 @@
#include "yolov5.hpp"
#include <cv_bridge/cv_bridge.h>
#include <filesystem>
#include <opencv2/opencv.hpp>
#include <ros/ros.h>
#include <sensor_msgs/Image.h>
std::string model_name = []() -> std::string {
std::filesystem::path current_dir =
std::filesystem::path(__FILE__).parent_path();
std::filesystem::path model_path =
current_dir / "../model/RK3588/guangpo.rknn";
return model_path;
}();
std::string input_path = []() -> std::string {
std::filesystem::path current_dir =
std::filesystem::path(__FILE__).parent_path();
std::filesystem::path img_path = current_dir / "../model/RK3588/889.jpg";
return img_path;
}();
int main(int argc, char **argv) {
ros::init(argc, argv, "main_test");
ros::NodeHandle nh;
Yolov5 yolo;
std::cout << "yolo begin" << std::endl;
yolo.SetLabels({"Dianti", "Ren"});
yolo.LoadModel(model_name);
cv::Mat image = cv::imread(input_path, 1);
yolo.Infer(image, "output.jpg");
ros::spin();
return 0;
}

64
src/guangpo_yolo/src/postprocess.cc
View File

@ -28,9 +28,9 @@
// not use // not use
// #define LABEL_NALE_TXT_PATH "" // #define LABEL_NALE_TXT_PATH ""
std::vector<std::string> mylabels ={"Dianti","Ren"}; // std::vector<std::string> mylabels ={"Dianti","Ren"};
static char *labels[OBJ_CLASS_NUM]; // static char *labels[OBJ_CLASS_NUM];
const int anchor0[6] = {10, 13, 16, 30, 33, 23}; const int anchor0[6] = {10, 13, 16, 30, 33, 23};
const int anchor1[6] = {30, 61, 62, 45, 59, 119}; const int anchor1[6] = {30, 61, 62, 45, 59, 119};
@ -264,26 +264,26 @@ static int process(int8_t *input, int *anchor, int grid_h, int grid_w, int heigh
int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, float conf_threshold, int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h, int model_in_w, float conf_threshold,
float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, std::vector<int32_t> &qnt_zps, float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, std::vector<int32_t> &qnt_zps,
std::vector<float> &qnt_scales, detect_result_group_t *group) std::vector<float> &qnt_scales, detect_result_group_t *group, const std::vector<std::string>& labels)
{ {
static int init = -1; // static int init = -1;
if (init == -1) // if (init == -1)
{ // {
int ret = 0; // int ret = 0;
// ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); // // ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
for(int i=0;i<mylabels.size();i++) { // for(int i=0;i<mylabels.size();i++) {
labels[i] = new char[mylabels[i].size() + 1]; // labels[i] = new char[mylabels[i].size() + 1];
std::strcpy((labels[i]),mylabels[i].c_str()); // std::strcpy((labels[i]),mylabels[i].c_str());
// gDebug(mylabels); // // gDebug(mylabels);
} // }
ret = 0; // ret = 0;
if (ret < 0) // if (ret < 0)
{ // {
return -1; // return -1;
} // }
init = 0; // init = 0;
} // }
memset(group, 0, sizeof(detect_result_group_t)); memset(group, 0, sizeof(detect_result_group_t));
std::vector<float> filterBoxes; std::vector<float> filterBoxes;
@ -359,7 +359,7 @@ int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h,
group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w); group->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / scale_w);
group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); group->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h);
group->results[last_count].prop = obj_conf; group->results[last_count].prop = obj_conf;
char *label = labels[id]; const char *label = labels.at(id).c_str();
strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE); strncpy(group->results[last_count].name, label, OBJ_NAME_MAX_SIZE);
// printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, // printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left,
@ -372,14 +372,14 @@ int post_process(int8_t *input0, int8_t *input1, int8_t *input2, int model_in_h,
return 0; return 0;
} }
void deinitPostProcess() // void deinitPostProcess()
{ // {
for (int i = 0; i < OBJ_CLASS_NUM; i++) // for (int i = 0; i < OBJ_CLASS_NUM; i++)
{ // {
if (labels[i] != nullptr) // if (labels[i] != nullptr)
{ // {
free(labels[i]); // free(labels[i]);
labels[i] = nullptr; // labels[i] = nullptr;
} // }
} // }
} // }