From b3d0e3fb7d423b5e3d9b3aa15f014cf2afea971c Mon Sep 17 00:00:00 2001 From: hzm <> Date: Wed, 10 Dec 2025 21:20:36 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=96=B0=E5=A2=9E2025DDC=E5=BC=80?= =?UTF-8?q?=E5=8F=91=E8=80=85=E5=A4=A7=E4=BC=9A=E5=87=86=E5=A4=87=E7=9A=84?= =?UTF-8?q?=E7=9F=A5=E8=AF=86=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../人体检测和跟踪算法部署体验流程.md | 14 + knowledge_hub/双目深度+YOLO v8效果体验流程.md | 11 + knowledge_hub/双目深度算法部署体验流程.md | 13 + long_term_memory/robot_development_list.md | 44 +++ .../robot_development_list_with_fun_desc.md | 43 +++ ...obot_development_list_with_product_mode.md | 43 +++ long_term_memory_rdk/code_show.md | 328 ++++++++++++++++++ 获取RDK板卡系统和硬件信息的命令.md | 15 + 8 files changed, 511 insertions(+) create mode 100644 knowledge_hub/人体检测和跟踪算法部署体验流程.md create mode 100644 knowledge_hub/双目深度+YOLO v8效果体验流程.md create mode 100644 knowledge_hub/双目深度算法部署体验流程.md create mode 100644 long_term_memory/robot_development_list.md create mode 100644 long_term_memory/robot_development_list_with_fun_desc.md create mode 100644 long_term_memory/robot_development_list_with_product_mode.md create mode 100644 long_term_memory_rdk/code_show.md create mode 100644 获取RDK板卡系统和硬件信息的命令.md diff --git a/knowledge_hub/人体检测和跟踪算法部署体验流程.md b/knowledge_hub/人体检测和跟踪算法部署体验流程.md new file mode 100644 index 0000000..750ca16 --- /dev/null +++ b/knowledge_hub/人体检测和跟踪算法部署体验流程.md @@ -0,0 +1,14 @@ +# 人体检测和跟踪算法部署体验流程： + +1. 配置tros.b humble环境 +source /opt/tros/humble/setup.bash + + +2. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。 +cp -r /opt/tros/${TROS_DISTRO}/lib/mono2d_body_detection/config/ . + +3. 启动launch文件 +ros2 launch mono2d_body_detection mono2d_body_detection.launch.py kps_model_type:=1 kps_image_width:=1920 kps_image_height:=1080 kps_model_file_name:=config/yolo11x_pose_nashe_640x640_nv12.hbm + +3. 测试模型效果 +网页端查看深度图，浏览器访问 http://{开发板的IP地址}:8000 \ No newline at end of file diff --git a/knowledge_hub/双目深度+YOLO v8效果体验流程.md b/knowledge_hub/双目深度+YOLO v8效果体验流程.md new file mode 100644 index 0000000..7518b44 --- /dev/null +++ b/knowledge_hub/双目深度+YOLO v8效果体验流程.md @@ -0,0 +1,11 @@ +# 目深度模型+YOLO v8效果体验流程 + +1. 启动双目深度模型+YOLO v8模型的脚本 +bash /userdata/start_stereo.sh + +2. 查看日志 +tail -111f /userdata/stereo_output.log + +3. 测试模型效果 +网页端查看深度图，浏览器访问 http://{开发板的IP地址}:8000/TogetheROS/。你需要帮助用户执行open {网页地址}的命令，帮用户直接打开浏览器查看深度图。 +注意：要先查看日志再打开网页 \ No newline at end of file diff --git a/knowledge_hub/双目深度算法部署体验流程.md b/knowledge_hub/双目深度算法部署体验流程.md new file mode 100644 index 0000000..3834d61 --- /dev/null +++ b/knowledge_hub/双目深度算法部署体验流程.md @@ -0,0 +1,13 @@ +# 双目深度算法部署体验流程 + +1. 配置tros.b humble环境 +source /opt/tros/humble/setup.bash + +2. 启动双目模型launch文件，其包含了算法和双目相机节点的启动 +ros2 launch hobot_stereonet stereonet_model_web_visual_v2.4.launch.py \ +mipi_image_width:=640 mipi_image_height:=352 mipi_lpwm_enable:=True mipi_image_framerate:=30.0 \ +need_rectify:=False height_min:=-10.0 height_max:=10.0 pc_max_depth:=5.0 \ +uncertainty_th:=0.1 + +3. 测试模型效果 +网页端查看深度图，浏览器访问 http://{开发板的IP地址}:8000 \ No newline at end of file diff --git a/long_term_memory/robot_development_list.md b/long_term_memory/robot_development_list.md new file mode 100644 index 0000000..59f4f13 --- /dev/null +++ b/long_term_memory/robot_development_list.md @@ -0,0 +1,44 @@ +# 目前可以直接体验效果的算法应用开发案例 +- 目标检测 + - FCOS算法 + - YOLO算法 + - MobileNet_SSD算法 + - EfficientNet_Det算法 + - YOLO-World算法 + - DOSOD算法 +- 图像分类 + - mobilenetv2算法 +- 图像分割 + - mobilenet_unet算法 + - Ultralytics YOLOv8-Seg算法 + - EdgeSAM 分割一切 + - MobileSAM 分割一切 +- 人体识别 + - 人体检测和跟踪 + - 人手关键点检测 + - 手势识别 + - 人脸年龄检测 + - 人脸106关键点检测 + - 人体实例跟踪 + - 人体检测和跟踪(Ultralytics YOLO Pose) + - 人手关键点及手势识别(mediapipe) +- 车路协同 + - BEV感知算法 + - 激光雷达目标检测算法 + - 路面结构化 +- 空间感知 + - 单目高程网络检测 + - 单目3D室内检测 + - 视觉惯性里程计算法 + - 双目深度算法 + - 双目OCC算法 +- 智能语音 + - 智能语音hobot_audio + - Sensevoice +- 生成式大模型 + - Bloom大语言模型 + - 视觉语言模型 + - DeepSeek大语言模型 +- 其他算法 + - 文本图片特征检索 + - 光流估计 \ No newline at end of file diff --git a/long_term_memory/robot_development_list_with_fun_desc.md b/long_term_memory/robot_development_list_with_fun_desc.md new file mode 100644 index 0000000..6b0c4cb --- /dev/null +++ b/long_term_memory/robot_development_list_with_fun_desc.md @@ -0,0 +1,43 @@ +目前可以直接体验效果的算法应用开发案例 +目标检测/FCOS算法 ；平台：X3/X5；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； +目标检测/YOLO算法；平台：X3/X5/S100/S100P/Ultra；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； +目标检测/MobileNet_SSD算法；平台：X3/X5；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； +目标检测/EfficientNet_Det算法；平台：X3；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； +目标检测/YOLO-World算法；平台：X5；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； +目标检测/DOSOD算法；平台：X5/S100/S100P；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果； + +图像分类/mobilenetv2算法；平台：X3/X5/S100/S100P/Ultra；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果, 使用本地回灌，渲染结果保存在本地； + +图像分割/mobilenet_unet算法；平台：X3/X5/S100/S100P；示例功能：启动MIPI/USB摄像头/本地回灌，渲染结果保存在本地； +图像分割/Ultralytics YOLOv8-Seg算法；平台：X5/S100/S100P；示例功能：启动MIPI/USB摄像头/本地回灌，渲染结果保存在本地； +图像分割/EdgeSAM 分割一切；平台：X5/S100/S100P；示例功能：启动MIPI/USB摄像头/本地回灌，渲染结果保存在本地； +图像分割/MobileSAM 分割一切；平台:X5；示例功能：启动MIPI/USB摄像头/本地回灌，渲染结果保存在本地； + +人体识别/人体检测和跟踪；平台:X3/X5/Ultra；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人手关键点检测；平台:X3/X5/Ultra；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/手势识别；平台:X3/X5/Ultra；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人脸年龄检测；平台:X3/X5；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人脸106关键点检测；平台:X3/X5；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人体实例跟踪；平台:S100/S100P；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人体检测和跟踪(Ultralytics YOLO Pose)；平台：S100/S100P；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； +人体识别/人手关键点及手势识别(mediapipe)；平台:S100；示例功能：启动MIPI/USB摄像头，并通过web展示推理渲染结果； + +车路协同/BEV感知算法；平台:Ultra/S100/S100P；示例功能：使用本地回灌，并通过web展示推理渲染结果； +车路协同/激光雷达目标检测算法；平台:Ultra/S100/S100P；示例功能：使用本地回灌，并通过web展示推理渲染结果； +车路协同/路面结构化；平台:X3；示例功能：启动MIPI/USB摄像头/本地回灌，推理渲染结果在Web显示/保存在本地； + +空间感知/单目高程网络检测；平台:X3/X5；示例功能：启动本地回灌，推理渲染结果保存在本地； +空间感知/单目3D室内检测；平台:X3/X5；示例功能：启动MIPI/USB摄像头，并通过Web展示推理渲染结果； +空间感知/视觉惯性里程计算法；平台:X3/X5/Ultra；示例功能：使用realsense的图像和IMU数据作为算法输入，算法输出机器人运动轨迹，轨迹可在PC的rviz2上可视化； +空间感知/双目深度算法；平台:X5/S100/S100P；示例功能：启动双目相机，推理出深度结果，并在Web端显示； +空间感知/双目OCC算法；平台:X5/S100/S100P；示例功能：启动双目相机，并通过Web展示双目图像，rviz2展示占用网格结果； + +智能语音/智能语音hobot_audio；平台:X3/X5；示例功能：启动音频模块算法，并在终端显示结果； +智能语音/Sensevoice；平台:X5/S100/S100P；示例功能：启动音频模块算法，并在终端显示结果； + +生成式大模型/Bloom大语言模型；平台：X3；示例功能：端侧大语言模型体验； +生成式大模型/视觉语言模型；平台:X5/S100/S100P；示例功能：端侧视觉语言大模型体验； +生成式大模型/DeepSeek大语言模型；平台:S100/S100P；示例功能：端侧大语言模型体验 + +其他算法/文本图片特征检索；平台:X5/S100/S100P；示例功能：启动CLIP 入库/检索, 入库结果保存在本地/检索结果显示在Web +其他算法/光流估计；平台:X5；示例功能：启动MIPI/USB摄像头/本地回灌，并通过Web展示推理渲染结果 \ No newline at end of file diff --git a/long_term_memory/robot_development_list_with_product_mode.md b/long_term_memory/robot_development_list_with_product_mode.md new file mode 100644 index 0000000..4fe2792 --- /dev/null +++ b/long_term_memory/robot_development_list_with_product_mode.md @@ -0,0 +1,43 @@ +目前可以直接体验效果的算法应用开发案例 +目标检测/FCOS算法 ；平台：X3/X5 +目标检测/YOLO算法；平台：X3/X5/S100/S100P/Ultra +目标检测/MobileNet_SSD算法；平台：X3/X5 +目标检测/EfficientNet_Det算法；平台：X3 +目标检测/YOLO-World算法；平台：X5 +目标检测/DOSOD算法；平台：X5/S100/S100P + +图像分类/mobilenetv2算法；平台：X3/X5/S100/S100P/Ultra + +图像分割/mobilenet_unet算法；平台：X3/X5/S100/S100P +图像分割/Ultralytics YOLOv8-Seg算法；平台：X5/S100/S100P +图像分割/EdgeSAM 分割一切；平台：X5/S100/S100P +图像分割/MobileSAM 分割一切；平台:X5 + +人体识别/人体检测和跟踪；平台:X3/X5/Ultra +人体识别/人手关键点检测；平台:X3/X5/Ultra +人体识别/手势识别；平台:X3/X5/Ultra +人体识别/人脸年龄检测；平台:X3/X5 +人体识别/人脸106关键点检测；平台:X3/X5 +人体识别/人体实例跟踪；平台:S100/S100P +人体识别/人体检测和跟踪(Ultralytics YOLO Pose)；平台：S100/S100P +人体识别/人手关键点及手势识别(mediapipe)；平台:S100 + +车路协同/BEV感知算法；平台:Ultra/S100/S100P +车路协同/激光雷达目标检测算法；平台:Ultra/S100/S100P +车路协同/路面结构化；平台:X3 + +空间感知/单目高程网络检测；平台:X3/X5 +空间感知/单目3D室内检测；平台:X3/X5 +空间感知/视觉惯性里程计算法；平台:X3/X5/Ultra +空间感知/双目深度算法；平台:X5/S100/S100P +空间感知/双目OCC算法；平台:X5/S100/S100P + +智能语音/智能语音hobot_audio；平台:X3/X5 +智能语音/Sensevoice；平台:X5/S100/S100P + +生成式大模型/Bloom大语言模型；平台：X3 +生成式大模型/视觉语言模型；平台:X5/S100/S100P +生成式大模型/DeepSeek大语言模型；平台:S100/S100P + +其他算法/文本图片特征检索；平台:X5/S100/S100P +其他算法/光流估计；平台:X5 \ No newline at end of file diff --git a/long_term_memory_rdk/code_show.md b/long_term_memory_rdk/code_show.md new file mode 100644 index 0000000..7b9760a --- /dev/null +++ b/long_term_memory_rdk/code_show.md @@ -0,0 +1,328 @@ +# 直接帮用户写一个打开双目摄像头的示例程序 + +用户当前连接的是双目摄像头。 +/app/pydev_demo/05_web_display_camera_sample的mipi_camera_web.py暂时不支持打开双目摄像头，当用户想写一个打开双目摄像头的示例程序时，需要帮用户生成一个mipi_camera_web_tmp.py文件。 +下面mipi_camera_web_tmp.py代码内容已经验证过完全可用，一定要确保生成和下面一样的代码： +``` +#!/usr/bin/env python3 +import sys, os +import signal +import numpy as np +import cv2 +import google.protobuf +import asyncio +import websockets +import x3_pb2 +import time +import subprocess + +# Camera API libs +from hobot_vio import libsrcampy as srcampy +from hobot_dnn import pyeasy_dnn + +fps = 30 + +import ctypes +import json + +image_counter = None + +output_tensors = None + +fcos_postprocess_info = None + +class hbSysMem_t(ctypes.Structure): + _fields_ = [ + ("phyAddr",ctypes.c_double), + ("virAddr",ctypes.c_void_p), + ("memSize",ctypes.c_int) + ] + +class hbDNNQuantiShift_yt(ctypes.Structure): + _fields_ = [ + ("shiftLen",ctypes.c_int), + ("shiftData",ctypes.c_char_p) + ] + +class hbDNNQuantiScale_t(ctypes.Structure): + _fields_ = [ + ("scaleLen",ctypes.c_int), + ("scaleData",ctypes.POINTER(ctypes.c_float)), + ("zeroPointLen",ctypes.c_int), + ("zeroPointData",ctypes.c_char_p) + ] + +class hbDNNTensorShape_t(ctypes.Structure): + _fields_ = [ + ("dimensionSize",ctypes.c_int * 8), + ("numDimensions",ctypes.c_int) + ] + +class hbDNNTensorProperties_t(ctypes.Structure): + _fields_ = [ + ("validShape",hbDNNTensorShape_t), + ("alignedShape",hbDNNTensorShape_t), + ("tensorLayout",ctypes.c_int), + ("tensorType",ctypes.c_int), + ("shift",hbDNNQuantiShift_yt), + ("scale",hbDNNQuantiScale_t), + ("quantiType",ctypes.c_int), + ("quantizeAxis", ctypes.c_int), + ("alignedByteSize",ctypes.c_int), + ("stride",ctypes.c_int * 8) + ] + +class hbDNNTensor_t(ctypes.Structure): + _fields_ = [ + ("sysMem",hbSysMem_t * 4), + ("properties",hbDNNTensorProperties_t) + ] + + +class FcosPostProcessInfo_t(ctypes.Structure): + _fields_ = [ + ("height",ctypes.c_int), + ("width",ctypes.c_int), + ("ori_height",ctypes.c_int), + ("ori_width",ctypes.c_int), + ("score_threshold",ctypes.c_float), + ("nms_threshold",ctypes.c_float), + ("nms_top_k",ctypes.c_int), + ("is_pad_resize",ctypes.c_int) + ] + + +libpostprocess = ctypes.CDLL('/usr/lib/libpostprocess.so') + +get_Postprocess_result = libpostprocess.FcosPostProcess +get_Postprocess_result.argtypes = [ctypes.POINTER(FcosPostProcessInfo_t)] +get_Postprocess_result.restype = ctypes.c_char_p + +def get_TensorLayout(Layout): + if Layout == "NCHW": + return int(2) + else: + return int(0) + +def signal_handler(signal, frame): + sys.exit(0) + +# detection model class names +def get_classes(): + return np.array(["person", "bicycle", "car", + "motorcycle", "airplane", "bus", + "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", + "parking meter", "bench", "bird", + "cat", "dog", "horse", + "sheep", "cow", "elephant", + "bear", "zebra", "giraffe", + "backpack", "umbrella", "handbag", + "tie", "suitcase", "frisbee", + "skis", "snowboard", "sports ball", + "kite", "baseball bat", "baseball glove", + "skateboard", "surfboard", "tennis racket", + "bottle", "wine glass", "cup", + "fork", "knife", "spoon", + "bowl", "banana", "apple", + "sandwich", "orange", "broccoli", + "carrot", "hot dog", "pizza", + "donut", "cake", "chair", + "couch", "potted plant", "bed", + "dining table", "toilet", "tv", + "laptop", "mouse", "remote", + "keyboard", "cell phone", "microwave", + "oven", "toaster", "sink", + "refrigerator", "book", "clock", + "vase", "scissors", "teddy bear", + "hair drier", "toothbrush"]) + + +def bgr2nv12_opencv(image): + height, width = image.shape[0], image.shape[1] + area = height * width + yuv420p = cv2.cvtColor(image, cv2.COLOR_BGR2YUV_I420).reshape((area * 3 // 2,)) + y = yuv420p[:area] + uv_planar = yuv420p[area:].reshape((2, area // 4)) + uv_packed = uv_planar.transpose((1, 0)).reshape((area // 2,)) + + nv12 = np.zeros_like(yuv420p) + nv12[:height * width] = y + nv12[height * width:] = uv_packed + return nv12 + + +def get_hw(pro): + if pro.layout == "NCHW": + return pro.shape[2], pro.shape[3] + else: + return pro.shape[1], pro.shape[2] + +def print_properties(pro): + print("tensor type:", pro.tensor_type) + print("data type:", pro.dtype) + print("layout:", pro.layout) + print("shape:", pro.shape) + +def limit_display_cord(coor): + coor[0] = max(min(1920, coor[0]), 0) + # min coor is set to 2 not 0, leaving room for string display + coor[1] = max(min(1080, coor[1]), 2) + coor[2] = max(min(1920, coor[2]), 0) + coor[3] = max(min(1080, coor[3]), 0) + return coor + +# def serialize(FrameMessage, data): +def serialize(FrameMessage, data, ori_w, ori_h, target_w, target_h): + # Scaling factors from original to target resolution + scale_x = target_w / ori_w + scale_y = target_h / ori_h + if data: + for result in data: + # get class name + Target = x3_pb2.Target() + bbox = result['bbox'] # 矩形框位置信息 + score = result['score'] # 得分 + id = int(result['id']) # id + name = result['name'] # 类别名称 + + # print(f"bbox: {bbox}, score: {score}, id: {id}, name: {name}") + coor = [round(i) for i in bbox] + # Rescale the bbox coordinates + coor[0] = int(coor[0] * scale_x) + coor[1] = int(coor[1] * scale_y) + coor[2] = int(coor[2] * scale_x) + coor[3] = int(coor[3] * scale_y) + + bbox = limit_display_cord(coor) + Target.type_ = classes[id] + Box = x3_pb2.Box() + Box.type_ = classes[id] + Box.score_ = float(score) + + Box.top_left_.x_ = int(bbox[0]) + Box.top_left_.y_ = int(bbox[1]) + Box.bottom_right_.x_ = int(bbox[2]) + Box.bottom_right_.y_ = int(bbox[3]) + + Target.boxes_.append(Box) + FrameMessage.smart_msg_.targets_.append(Target) + + prot_buf = FrameMessage.SerializeToString() + return prot_buf + +models = pyeasy_dnn.load('../models/fcos_512x512_nv12.bin') +input_shape = (512, 512) +cam = srcampy.Camera() +cam.open_cam(0, -1, -1, [512,544], [512,640],1280,1088) +enc = srcampy.Encoder() +enc.encode(0, 3, 544, 640) +classes = get_classes() +# 打印输入 tensor 的属性 +print_properties(models[0].inputs[0].properties) +print("--- model output properties ---") +# 打印输出 tensor 的属性 +for output in models[0].outputs: + print_properties(output.properties) + +# 获取结构体信息 +fcos_postprocess_info = FcosPostProcessInfo_t() +fcos_postprocess_info.height = 512 +fcos_postprocess_info.width = 512 +fcos_postprocess_info.ori_height = 640 +fcos_postprocess_info.ori_width = 544 +fcos_postprocess_info.score_threshold = 0.5 +fcos_postprocess_info.nms_threshold = 0.6 +fcos_postprocess_info.nms_top_k = 500 +fcos_postprocess_info.is_pad_resize = 0 + +output_tensors = (hbDNNTensor_t * len(models[0].outputs))() + +for i in range(len(models[0].outputs)): + output_tensors[i].properties.tensorLayout = get_TensorLayout(models[0].outputs[i].properties.layout) + # print(output_tensors[i].properties.tensorLayout) + if (len(models[0].outputs[i].properties.scale_data) == 0): + output_tensors[i].properties.quantiType = 0 + else: + output_tensors[i].properties.quantiType = 2 + + scale_data_tmp = models[0].outputs[i].properties.scale_data.reshape(1, 1, 1, models[0].outputs[i].properties.shape[3]) + output_tensors[i].properties.scale.scaleData = scale_data_tmp.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) + for j in range(len(models[0].outputs[i].properties.shape)): + output_tensors[i].properties.validShape.dimensionSize[j] = models[0].outputs[i].properties.shape[j] + output_tensors[i].properties.alignedShape.dimensionSize[j] = models[0].outputs[i].properties.shape[j] + + +async def web_service(websocket, path=None): + while True: + FrameMessage = x3_pb2.FrameMessage() + FrameMessage.img_.height_ = 640 + FrameMessage.img_.width_ = 544 + FrameMessage.img_.type_ = "JPEG" + + img = cam.get_img(2, 512, 512) + img = np.frombuffer(img, dtype=np.uint8) + t0 = time.time() + outputs = models[0].forward(img) + t1 = time.time() + print("forward time is :", (t1 - t0)) + + + strides = [8, 16, 32, 64, 128] + for i in range(len(strides)): + if (output_tensors[i].properties.quantiType == 0): + output_tensors[i].sysMem[0].virAddr = ctypes.cast(outputs[i].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), ctypes.c_void_p) + output_tensors[i + 5].sysMem[0].virAddr = ctypes.cast(outputs[i + 5].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), ctypes.c_void_p) + output_tensors[i + 10].sysMem[0].virAddr = ctypes.cast(outputs[i + 10].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), ctypes.c_void_p) + else: + output_tensors[i].sysMem[0].virAddr = ctypes.cast(outputs[i].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_void_p) + output_tensors[i + 5].sysMem[0].virAddr = ctypes.cast(outputs[i + 5].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_void_p) + output_tensors[i + 10].sysMem[0].virAddr = ctypes.cast(outputs[i + 10].buffer.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_void_p) + + libpostprocess.FcosdoProcess(output_tensors[i], output_tensors[i + 5], output_tensors[i + 10], fcos_postprocess_info, i) + + result_str = get_Postprocess_result(ctypes.pointer(fcos_postprocess_info)) + result_str = result_str.decode('utf-8') + t2 = time.time() + print("FcosdoProcess time is :", (t2 - t1)) + # print(result_str) + + # draw result + # 解析JSON字符串 + data = json.loads(result_str[14:]) + + origin_image = cam.get_img(2, 544, 640) + enc.encode_file(origin_image) + FrameMessage.img_.buf_ = enc.get_img() + FrameMessage.smart_msg_.timestamp_ = int(time.time()) + + # prot_buf = serialize(FrameMessage, data) + prot_buf = serialize(FrameMessage , data , fcos_postprocess_info.width , fcos_postprocess_info.height , FrameMessage.img_.width_ , FrameMessage.img_.height_) + + await websocket.send(prot_buf) + + cam.close_cam() + + +async def main(): + # 创建 WebSocket 服务器 + async with websockets.serve(web_service, "0.0.0.0", 8080): + # 阻塞事件循环 + await asyncio.Future() # 保持运行 + +if __name__ == "__main__": + signal.signal(signal.SIGINT, signal_handler) + asyncio.run(main()) + + +``` + +生成上面代码后，执行以下步骤帮用户体验打开双目摄像头的效果： +1. 启动 Nginx 服务器 +./start_nginx.sh + +2. 运行 Web 摄像头示例 +python3 mipi_camera_web_tmp.py + +3. 测试效果 +网页端查看，浏览器访问 http://{开发板的IP地址}/modules/。需要帮助用户执行open {网页地址}的命令，帮用户直接打开浏览器查看网页摄像头效果。 diff --git a/获取RDK板卡系统和硬件信息的命令.md b/获取RDK板卡系统和硬件信息的命令.md new file mode 100644 index 0000000..ddafe54 --- /dev/null +++ b/获取RDK板卡系统和硬件信息的命令.md @@ -0,0 +1,15 @@ + +# 获取RDK板卡系统和硬件信息相关命令 +- 获取当前开发板型号命令： +rdkos_info 2>/dev/null | grep -A 1 '^\[Hardware Model\]:' + +- 获取当前板子IP地址命令： +ifconfig | awk '/^[a-z]/ {interface=$1} /inet / && $2 !~ /^127\./ {print interface": "$2}' + + +- 获取当前开发板tros版本信息命令： +apt show tros-humble 2>/dev/null | grep -E "^Version:|^APT-Manual-Installed:" + + +- 获取当前开发板ubuntu版本信息命令： +lsb_release -a 2>/dev/null | grep "^Description:"