Merge pull request 'add TROS Humble setup (complement Foxy version)' (#3) from fix/tros-humble-env-config into main
Reviewed-on: #3
This commit is contained in:
commit
6d461468e9
@ -7,8 +7,8 @@ wget http://archive.d-robotics.cc/TogetheROS/data/hobot_bev_data.tar.gz
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mkdir -p hobot_bev_data
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tar -zxvf hobot_bev_data.tar.gz -C hobot_bev_data
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3. 配置tros.b环境
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source /opt/tros/setup.bash
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3. 配置tros.b humble环境
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source /opt/tros/humble/setup.bash
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4. 启动websocket服务
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ros2 launch websocket websocket_service.launch.py
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@ -31,8 +31,8 @@ wget http://archive.d-robotics.cc/TogetheROS/data/nuscenes_bev_val/nuscenes_bev_
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mkdir -p ~/hobot_bev_data
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tar -zxvf ~/nuscenes_bev_val.tar.gz -C ~/hobot_bev_data
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3. 配置tros.b humble环境
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source /opt/tros/humble/setup.bash
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3. 配置tros.b
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source /opt/tros/setup.bash
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if [ -L qat ]; then rm qat; fi
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ln -s `ros2 pkg prefix hobot_bev`/lib/hobot_bev/qat/ qat
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ln -s ~/hobot_bev_data/nuscenes_bev_val nuscenes_bev_val
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@ -3,8 +3,9 @@
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1. 安装transformers
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pip3 install transformers -i https://pypi.tuna.tsinghua.edu.cn/simple
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2. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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2. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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3. 下载模型文件
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wget http://archive.d-robotics.cc/llm-model/llm_model.tar.gz
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@ -18,8 +19,9 @@ sudo tar -xf llm_model.tar.gz -C /opt/tros/${TROS_DISTRO}/lib/hobot_llm/
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sudo bash -c 'echo 1 > /sys/devices/system/cpu/cpufreq/boost'
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sudo bash -c 'echo performance > /sys/devices/system/cpu/cpufreq/policy0/scaling_governor'
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6. 重新配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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6. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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7. 启动launch文件
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ros2 run hobot_llm hobot_llm_chat
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@ -45,16 +47,22 @@ sudo bash -c 'echo 1 > /sys/devices/system/cpu/cpufreq/boost'
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sudo bash -c 'echo performance > /sys/devices/system/cpu/cpufreq/policy0/scaling_governor'
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6. 启动 hobot_llm
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重新配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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ros2 run hobot_llm hobot_llm
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7. 新开一个终端订阅输出结果topic
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重新配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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重新配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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ros2 topic echo /text_result
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8. 新开一个终端发布消息
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重新配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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重新配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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ros2 topic pub --once /text_query std_msgs/msg/String "{data: ""中国的首都是哪里""}"
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@ -1,7 +1,8 @@
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# EfficientNet_Det目标检测算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -15,8 +16,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# EfficientNet_Det目标检测算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -29,8 +31,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# EfficientNet_Det目标检测算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/efficient_det_workconfig.json dnn_example_image:=config/target.jpg
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@ -1,7 +1,8 @@
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# FCOS目标检测算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -14,8 +15,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# FCOS目标检测算法部署流程：用USB摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -28,8 +30,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# FCOS目标检测算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/fcosworkconfig.json dnn_example_image:=config/target.jpg
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@ -1,7 +1,8 @@
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# MobileNet_SSD目标检测算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -15,8 +16,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# MobileNet_SSD目标检测算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -29,8 +31,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# MobileNet_SSD目标检测算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/mobilenet_ssd_workconfig.json dnn_example_image:=config/target.jpg
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@ -1,7 +1,8 @@
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# Ultralytics YOLOv8-Seg图像分割算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -11,8 +12,10 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_dump_render_
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# Ultralytics YOLOv8-Seg图像分割算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -22,8 +25,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_dump_render_
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# Ultralytics YOLOv8-Seg图像分割算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/yolov8segworkconfig.json dnn_example_image:=config/test.jpg
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@ -1,7 +1,9 @@
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# YOLO目标检测算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -15,8 +17,10 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# YOLO目标检测算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -29,8 +33,10 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# YOLO目标检测算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/yolov2workconfig.json dnn_example_image:=config/target.jpg
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@ -1,7 +1,8 @@
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# mobilenet_unet图像分割算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -11,8 +12,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_dump_render_
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# mobilenet_unet图像分割算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -23,8 +25,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_dump_render_
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# mobilenet_unet图像分割算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/mobilenet_unet_workconfig.json dnn_example_image:=config/raw_unet.jpg
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@ -1,7 +1,8 @@
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# mobilenetv2图片分类算法部署流程：用MIPI摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置MIPI摄像头
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export CAM_TYPE=mipi
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@ -15,8 +16,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# mobilenetv2图片分类算法部署流程：用usb摄像头发布为照片
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 配置usb摄像头
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export CAM_TYPE=usb
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@ -29,8 +31,9 @@ ros2 launch dnn_node_example dnn_node_example.launch.py dnn_example_config_file:
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# mobilenetv2图片分类算法部署流程：使用本地照片回灌
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1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
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source /opt/tros/setup.bash
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1. 配置tros.b环境
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foxy版本:source /opt/tros/setup.bash
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humble版本:source /opt/tros/humble/setup.bash
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2. 启动launch文件
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ros2 launch dnn_node_example dnn_node_example_feedback.launch.py dnn_example_config_file:=config/mobilenetv2workconfig.json dnn_example_image:=config/target_class.jpg
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@ -1,7 +1,8 @@
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# 人体检测和跟踪算法部署流程：用MIPI摄像头发布为照片
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|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/ .
|
||||
@ -18,8 +19,9 @@ ros2 launch mono2d_body_detection mono2d_body_detection.launch.py
|
||||
|
||||
# 人体检测和跟踪算法部署流程：用usb摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
@ -36,8 +38,9 @@ ros2 launch mono2d_body_detection mono2d_body_detection.launch.py
|
||||
|
||||
# 人体检测和跟踪算法部署流程：使用本地照片回灌
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
2. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# 人手关键点检测算法部署流程：用MIPI摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
@ -17,8 +18,9 @@ ros2 launch hand_lmk_detection hand_lmk_detection.launch.py
|
||||
|
||||
# 人手关键点检测算法部署流程：用usb摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
2. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
|
||||
@ -1,50 +1,53 @@
|
||||
# 光流估计算法部署流程：用MIPI摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/parking_perception/config/ .
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono_pwcnet/config/ .
|
||||
|
||||
3. 配置MIPI摄像头
|
||||
export CAM_TYPE=mipi
|
||||
|
||||
4. 启动launch文件
|
||||
ros2 launch parking_perception parking_perception.launch.py
|
||||
ros2 launch mono_pwcnet pwcnet.launch.py
|
||||
|
||||
4. 测试效果
|
||||
运行成功后在pc端游览器输入：[http://IP:8000](http://ip:8000/)，即可查看图像和算法渲染效果（IP为RDK的IP地址）
|
||||
|
||||
# 光流估计算法部署流程：用usb摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/parking_perception/config/ .
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono_pwcnet/config/ .
|
||||
|
||||
3. 配置sub摄像头
|
||||
export CAM_TYPE=sub
|
||||
3. 配置usb摄像头
|
||||
export CAM_TYPE=usb
|
||||
|
||||
4. 启动launch文件
|
||||
ros2 launch parking_perception parking_perception.launch.py
|
||||
ros2 launch mono_pwcnet pwcnet.launch.py
|
||||
|
||||
4. 测试效果
|
||||
运行成功后在pc端游览器输入：[http://IP:8000](http://ip:8000/)，即可查看图像和算法渲染效果（IP为RDK的IP地址）
|
||||
|
||||
# 光流估计算法部署流程：使用本地照片回灌
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/parking_perception/config/ .
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono_pwcnet/config/ .
|
||||
|
||||
3. 配置MIPI摄像头
|
||||
3. 配置本地回灌图片
|
||||
export CAM_TYPE=fb
|
||||
|
||||
4. 启动launch文件
|
||||
ros2 launch parking_perception parking_perception.launch.py
|
||||
ros2 launch mono_pwcnet pwcnet.launch.py
|
||||
4. 测试效果
|
||||
运行成功后在pc端游览器输入：[http://IP:8000](http://ip:8000/)，即可查看图像和算法渲染效果（IP为RDK的IP地址）
|
||||
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# 单目3D室内检测算法部署流程：
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/mono3d_indoor_detection/config/ .
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# 单目高程网络检测算法部署流程：
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
2. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/elevation_net/config/
|
||||
|
||||
@ -1,13 +1,28 @@
|
||||
# 双目深度算法部署体验流程
|
||||
# 双目深度算法部署体验流程：RDX X5
|
||||
|
||||
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_v2.1.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
|
||||
|
||||
# 双目深度算法部署体验流程：RDX S100
|
||||
|
||||
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 \
|
||||
mipi_image_width:=640 mipi_image_height:=352 mipi_lpwm_enable:=False 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
|
||||
网页端查看深度图，浏览器访问 http://{开发板的IP地址}:8000
|
||||
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# 手势识别算法部署流程：用MIPI摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本: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/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
@ -19,8 +20,9 @@ ros2 launch hand_gesture_detection hand_gesture_detection.launch.py
|
||||
|
||||
# 手势识别算法部署流程：用usb摄像头发布为照片
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
2. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
@ -39,8 +41,9 @@ ros2 launch hand_gesture_detection hand_gesture_detection.launch.py
|
||||
|
||||
# 手势识别算法部署流程：使用本地照片回灌
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/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/${TROS_DISTRO}/lib/mono2d_body_detection/config/ .
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
# 文本图片特征检索算法部署流程：CLIP 入库
|
||||
|
||||
# 文本图片特征检索算法部署流程：
|
||||
1. 依赖安装
|
||||
pip3 install onnxruntime
|
||||
pip3 install ftfy
|
||||
@ -16,32 +15,22 @@ source /opt/tros/humble/setup.bash
|
||||
4. 从tros.b的安装路径中拷贝出运行示例需要的配置文件。
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/clip_encode_image/config/ .
|
||||
|
||||
5. 启动launch文件，RDK S100为例，RDK X5 为ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=0 clip_db_file:=clip.db clip_storage_folder:=/root/config
|
||||
ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=0 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_storage_folder:=/root/config
|
||||
5. 启动入库launch文件
|
||||
RDK S100：ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=0 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_storage_folder:=/root/config
|
||||
RDK X5：ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=0 clip_db_file:=clip.db clip_storage_folder:=/root/config
|
||||
|
||||
|
||||
# 文本图片特征检索算法部署流程：检索
|
||||
|
||||
1. 依赖安装
|
||||
pip3 install onnxruntime
|
||||
pip3 install ftfy
|
||||
pip3 install wcwidth
|
||||
pip3 install regex
|
||||
|
||||
2. 从Web端下载运行示例需要的模型文件。
|
||||
wget http://archive.d-robotics.cc/models/clip_encode_text/text_encoder.tar.gz
|
||||
sudo tar -xf text_encoder.tar.gz -C config
|
||||
|
||||
3. 配置tros.b环境
|
||||
6. 重开一个终端，配置tros.b环境
|
||||
source /opt/tros/humble/setup.bash
|
||||
|
||||
4. 启动launch文件，RDK S100为例，RDK X5 为ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=1 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_result_folder:=result clip_text:="a diagram"
|
||||
ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=1 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_result_folder:=result clip_text:="a diagram"
|
||||
7. 启动检索launch文件
|
||||
RDK S100：ros2 launch clip_manage hobot_clip_manage.launch.py clip_mode:=1 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_result_folder:=result clip_text:="a diagram"
|
||||
RDK X5：launch clip_manage hobot_clip_manage.launch.py clip_mode:=1 clip_image_model_file_name:=config/full_model_11.hbm clip_db_file:=clip.db clip_result_folder:=result clip_text:="a diagram"
|
||||
|
||||
5. 检索结果可视化
|
||||
8. 检索结果可视化
|
||||
打开另一个终端
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/clip_manage/config/index.html .
|
||||
python -m http.server 8080
|
||||
|
||||
6. 查看检索结果
|
||||
9. 查看检索结果
|
||||
在PC端的浏览器输入http://IP:8080 即可查看图像检索结果（IP为设备IP地址）。
|
||||
@ -1,11 +1,13 @@
|
||||
# 智能语音算法部署流程：
|
||||
|
||||
1. 安装智能语音算法包 若为humble版本则用sudo apt install tros-humble-hobot-audio
|
||||
1. 安装智能语音算法包
|
||||
sudo apt update
|
||||
sudo apt install tros-hobot-audio
|
||||
foxy版本：sudo apt install tros-hobot-audio
|
||||
humble版本：sudo apt install tros-humble-hobot-audio
|
||||
|
||||
2. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
2. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
3. 从tros.b的安装路径中拷贝出运行示例需要的配置文件，若已拷贝则可忽略
|
||||
cp -r /opt/tros/${TROS_DISTRO}/lib/hobot_audio/config/ .
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# 视觉惯性里程计算法部署流程：
|
||||
|
||||
1. 配置tros.b环境 若为humble则用source /opt/tros/humble/setup.bash
|
||||
source /opt/tros/setup.bash
|
||||
1. 配置tros.b环境
|
||||
foxy版本:source /opt/tros/setup.bash
|
||||
humble版本:source /opt/tros/humble/setup.bash
|
||||
|
||||
2. 启动launch文件
|
||||
ros2 launch hobot_vio hobot_vio.launch.py
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user