From 611e9b1637023eab791c762d13a6361f891b1f86 Mon Sep 17 00:00:00 2001 From: gxt_kt Date: Tue, 6 Aug 2024 18:43:30 +0000 Subject: [PATCH] update --- letterbox_input.jpg | Bin 0 -> 96816 bytes output.jpg | Bin 0 -> 233516 bytes src/guangpo_yolo/CMakeLists.txt | 8 + src/guangpo_yolo/include/dianti.h | 1 + src/guangpo_yolo/include/postprocess.h | 5 +- src/guangpo_yolo/include/yolov5.hpp | 219 ++++-------- src/guangpo_yolo/launch/run.launch | 4 + src/guangpo_yolo/rviz/guangpo.rviz | 208 +++++++++++ src/guangpo_yolo/src/main.cpp | 337 ++---------------- .../src/{main_guangpo.cc => main_rk3588.cc} | 0 src/guangpo_yolo/src/main_test.cc | 37 ++ src/guangpo_yolo/src/postprocess.cc | 64 ++-- 12 files changed, 387 insertions(+), 496 deletions(-) create mode 100644 letterbox_input.jpg create mode 100644 output.jpg create mode 100644 src/guangpo_yolo/launch/run.launch create mode 100644 src/guangpo_yolo/rviz/guangpo.rviz rename src/guangpo_yolo/src/{main_guangpo.cc => main_rk3588.cc} (100%) create 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"" : 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]); } - 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); + 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); } -static double __get_us(struct timeval t) { - return (t.tv_sec * 1000000 + t.tv_usec); -} +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; @@ -98,16 +101,28 @@ static int saveFloat(const char *file_name, float *output, int element_size) { } class Yolov5 { - public: - /*------------------------------------------- - Functions - -------------------------------------------*/ - - std::string model_path_; +public: + rknn_context ctx; + rknn_input inputs[1]; + int img_width = 0; + int img_height = 0; + 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 labels; Yolov5() {} - void Init(const std::string &model_path) {} ~Yolov5() { - deinitPostProcess(); + // deinitPostProcess(); // release rknn_destroy(ctx); @@ -116,39 +131,19 @@ class Yolov5 { free(model_data); } } - - rknn_context ctx; - unsigned char *model_data; - int ret; - - 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]; - } + void SetLabels(const std::vector &labelss) { labels = labelss; } + void LoadModel(const std::string &model_name_str) { + int ret; + const char *model_name = model_name_str.c_str(); + // std::string out_path = "./out.jpg"; // init rga context - rga_buffer_t src; - rga_buffer_t dst; 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); + 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"); @@ -157,7 +152,7 @@ class Yolov5 { ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); - return -1; + std::terminate(); } rknn_sdk_version version; @@ -165,16 +160,15 @@ class Yolov5 { sizeof(rknn_sdk_version)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); - return -1; + std::terminate(); } 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; + std::terminate(); } printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output); @@ -187,13 +181,14 @@ class Yolov5 { sizeof(rknn_tensor_attr)); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); - return -1; + std::terminate(); } dump_tensor_attr(&(input_attrs[i])); } - rknn_tensor_attr output_attrs[io_num.n_output]; - memset(output_attrs, 0, sizeof(output_attrs)); + // 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]), @@ -201,9 +196,6 @@ class Yolov5 { 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]; @@ -218,23 +210,18 @@ class Yolov5 { printf("model input height=%d, width=%d, channel=%d\n", height, width, channel); - } - void Infer() { - 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; - - // 读取图片 - printf("Read %s ...\n", input_path); - cv::Mat orig_img = cv::imread(input_path, 1); - if (!orig_img.data) { - printf("cv::imread %s fail!\n", input_path); - return -1; - } + } + detect_result_group_t Infer(const cv::Mat &orig_img, + const std::string &out_path = "output.jpg", + bool debug = false) { + int ret; cv::Mat img; cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB); img_width = img.cols; @@ -257,10 +244,11 @@ class Yolov5 { ret = resize_rga(src, dst, img, resized_img, target_size); if (ret != 0) { fprintf(stderr, "resize with rga error\n"); - return -1; + std::terminate(); } // 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") { printf("resize image with letterbox\n"); float min_scale = std::min(scale_w, scale_h); @@ -268,11 +256,12 @@ class Yolov5 { scale_h = min_scale; letterbox(img, resized_img, pads, min_scale, target_size); // 保存预处理图片 - cv::imwrite("letterbox_input.jpg", resized_img); + if (debug) + cv::imwrite("letterbox_input.jpg", resized_img); } else { fprintf(stderr, "Invalid resize option. Use 'resize' or 'letterbox'.\n"); - return -1; + std::terminate(); } inputs[0].buf = resized_img.data; } else { @@ -292,9 +281,6 @@ class Yolov5 { // 执行推理 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); // 后处理 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, (int8_t *)outputs[2].buf, height, width, box_conf_threshold, 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 char text[256]; @@ -326,72 +316,11 @@ class Yolov5 { 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 - // 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 rens; - std::vector 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)); + if (!out_path.empty()) { + printf("save detect result to %s\n", out_path.c_str()); + imwrite(out_path, orig_img); } - DealImage(estimator, orig_img, rens, diantis); - // gxt: add my process here ==============end - - 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); + rknn_outputs_release(ctx, io_num.n_output, outputs); + return detect_result_group; } }; diff --git a/src/guangpo_yolo/launch/run.launch b/src/guangpo_yolo/launch/run.launch new file mode 100644 index 0000000..ba1208c --- /dev/null +++ b/src/guangpo_yolo/launch/run.launch @@ -0,0 +1,4 @@ + + + + diff --git a/src/guangpo_yolo/rviz/guangpo.rviz b/src/guangpo_yolo/rviz/guangpo.rviz new file mode 100644 index 0000000..63b513d --- /dev/null +++ b/src/guangpo_yolo/rviz/guangpo.rviz @@ -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: + 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: + 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: + 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 diff --git a/src/guangpo_yolo/src/main.cpp b/src/guangpo_yolo/src/main.cpp index b4ef0f5..003a90b 100644 --- a/src/guangpo_yolo/src/main.cpp +++ b/src/guangpo_yolo/src/main.cpp @@ -6,8 +6,6 @@ #include #include -#define DEBUG 0 - std::string model_name = []() -> std::string { std::filesystem::path current_dir = std::filesystem::path(__FILE__).parent_path(); @@ -22,145 +20,28 @@ std::string input_path = []() -> std::string { return img_path; }(); -#include -#include -#include -#include -#include - -#define _BASETSD_H - -#include "RgaUtils.h" - -#include "postprocess.h" - -#include "preprocess.h" -#include "rknn_api.h" +std::vector labels={"Dianti","Ren"}; #include "debugstream.hpp" #include "dianti.h" - -#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; -} +#include "yolov5.hpp" class ImageReceiver { public: - unsigned char *model_data; - std::string option = "letterbox"; - 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; + Yolov5 yolo; + std::shared_ptr estimator; ImageReceiver(ros::NodeHandle &nh) { - sub_topic = yaml_config["sub_image_topic"].as(); - send_topic = yaml_config["pub_image_topic"].as(); + std::string sub_topic = yaml_config["sub_image_topic"].as(); + std::string send_topic = yaml_config["pub_image_topic"].as(); image_sub_ = nh.subscribe(sub_topic, 1, &ImageReceiver::imageCallback, this); image_pub_ = nh.advertise(send_topic, 1); Init(); + yolo.SetLabels(labels); + yolo.LoadModel(model_name); } - ~ImageReceiver() { - deinitPostProcess(); - // release - ret = rknn_destroy(ctx); - - if (model_data) { - free(model_data); - } - } + ~ImageReceiver() {} void SendImage(const cv::Mat &image) { // 将cv::Mat图像转换为sensor_msgs::Image消息 sensor_msgs::ImagePtr msg = @@ -180,7 +61,6 @@ public: cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8); image_ = cv_ptr->image; - TIME_BEGIN_MS(detect_image); DetectImage(image_); } catch (cv_bridge::Exception &e) { ROS_ERROR("cv_bridge exception: %s", e.what()); @@ -189,87 +69,6 @@ public: void Init() { 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; camera_matrix << yaml_config["camera_in_param"]["dx"].as(), 0.0, @@ -277,119 +76,22 @@ public: yaml_config["camera_in_param"]["dy"].as(), yaml_config["camera_in_param"]["v0"].as(), 0.0, 0.0, 1.0; gDebug(camera_matrix); - estimator = new DistanceEstimator( + estimator = std::make_shared( camera_matrix, yaml_config["image"]["width"].as(), yaml_config["image"]["height"].as()); } void DetectImage(cv::Mat &img) { TIME_BEGIN_MS(DetectImage); - // 读取图片 - // printf("Read %s ...\n", input_path); - cv::Mat &orig_img = img; - if (!orig_img.data) { - printf("cv::imread %s fail!\n", input_path); - return; - } - cv::cvtColor(orig_img, img, cv::COLOR_BGR2RGB); + detect_result_group_t detect_result_group; + detect_result_group = yolo.Infer(img, ""); + + TIME_END(DetectImage); int img_width = img.cols; 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 out_scales; - std::vector 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 // 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 rens; std::vector diantis; gDebug(detect_result_group.count); @@ -411,15 +113,16 @@ public: 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 - SendImage(orig_img); + SendImage(img); #if DEBUG printf("save detect result to %s\n", out_path.c_str()); - imwrite(out_path, orig_img); + imwrite(out_path, img); #endif - ret = rknn_outputs_release(ctx, io_num.n_output, outputs); } private: @@ -449,4 +152,4 @@ int main(int argc, char **argv) { ros::spin(); return 0; -} +} \ No newline at end of file diff --git a/src/guangpo_yolo/src/main_guangpo.cc b/src/guangpo_yolo/src/main_rk3588.cc similarity index 100% rename from src/guangpo_yolo/src/main_guangpo.cc rename to src/guangpo_yolo/src/main_rk3588.cc diff --git a/src/guangpo_yolo/src/main_test.cc b/src/guangpo_yolo/src/main_test.cc new file mode 100644 index 0000000..6bde473 --- /dev/null +++ b/src/guangpo_yolo/src/main_test.cc @@ -0,0 +1,37 @@ +#include "yolov5.hpp" +#include +#include +#include +#include +#include + +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; +} diff --git a/src/guangpo_yolo/src/postprocess.cc b/src/guangpo_yolo/src/postprocess.cc index a1da318..7f11bc1 100644 --- a/src/guangpo_yolo/src/postprocess.cc +++ b/src/guangpo_yolo/src/postprocess.cc @@ -28,9 +28,9 @@ // not use // #define LABEL_NALE_TXT_PATH "" -std::vector mylabels ={"Dianti","Ren"}; +// std::vector 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 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, float nms_threshold, BOX_RECT pads, float scale_w, float scale_h, std::vector &qnt_zps, - std::vector &qnt_scales, detect_result_group_t *group) + std::vector &qnt_scales, detect_result_group_t *group, const std::vector& labels) { - static int init = -1; - if (init == -1) - { - int ret = 0; - // ret = loadLabelName(LABEL_NALE_TXT_PATH, labels); - for(int i=0;i 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.bottom = (int)(clamp(y2, 0, model_in_h) / scale_h); 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); // 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; } -void deinitPostProcess() -{ - for (int i = 0; i < OBJ_CLASS_NUM; i++) - { - if (labels[i] != nullptr) - { - free(labels[i]); - labels[i] = nullptr; - } - } -} +// void deinitPostProcess() +// { +// for (int i = 0; i < OBJ_CLASS_NUM; i++) +// { +// if (labels[i] != nullptr) +// { +// free(labels[i]); +// labels[i] = nullptr; +// } +// } +// }