ROS2 OpenVINO Runtime Framework Study

# ROS2 OpenVINO Runtime Framework Study ## Github https://github.com/intel/ros2_openvino_toolkit ## Architecture ![](https://hackmd.io/_uploads/SJMv4Dn6h.png) ## Logic Flow ![](https://hackmd.io/_uploads/HJVtNv262.png) ## Features of dynamic_vino_sample ### Input Resource |Input Resource|Description| |--------------------|------------------------------------------------------------------| |StandardCamera|Any RGB camera with USB port supporting. Currently only the first USB camera if many are connected.| |RealSenseCamera| Intel RealSense RGB-D Camera, directly calling RealSense Camera via librealsense plugin of openCV.| |ImageTopic| Any ROS topic which is structured in image message.| |Image| Any image file which can be parsed by openCV, such as .png, .jpeg.| |Video| Any video file which can be parsed by openCV.| |IpCamera| Any RTSP server which can push video stream.| ### Inference Feature |Inference|Description|YAML Configuration|Model Used| |-----------------------|------------------------------------------------------------------|----------------------|----------------------| |Face Detection| Object Detection task applied to face recognition using a sequence of neural networks.|[pipeline_image.yaml](./sample/param/pipeline_image.yaml) [pipeline_image_video.yaml](./sample/param/pipeline_image_video.yaml) [pipeline_people.yaml](./sample/param/pipeline_people.yaml) [pipeline_people_ip.yaml](./sample/param/pipeline_people_ip.yaml)|[face-detection-adas-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/face-detection-adas-0001) [age-gender-recognition-retail-0013](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/age-gender-recognition-retail-0013) [emotions-recognition-retail-0003](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/emotions-recognition-retail-0003) [head-pose-estimation-adas-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/head-pose-estimation-adas-0001)| |Emotion Recognition| Emotion recognition based on detected face image.|[pipeline_image.yaml](./sample/param/pipeline_image.yaml) [pipeline_image_video.yaml](./sample/param/pipeline_image_video.yaml) [pipeline_people.yaml](./sample/param/pipeline_people.yaml) [pipeline_people_ip.yaml](./sample/param/pipeline_people_ip.yaml)|[emotions-recognition-retail-0003](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/emotions-recognition-retail-0003)| |Age & Gender Recognition| Age and gender recognition based on detected face image.|[pipeline_image.yaml](./sample/param/pipeline_image.yaml) [pipeline_image_video.yaml](./sample/param/pipeline_image_video.yaml) [pipeline_people.yaml](./sample/param/pipeline_people.yaml) [pipeline_people_ip.yaml](./sample/param/pipeline_people_ip.yaml)|[age-gender-recognition-retail-0013](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/age-gender-recognition-retail-0013)| |Head Pose Estimation| Head pose estimation based on detected face image.|[pipeline_image.yaml](./sample/param/pipeline_image.yaml) [pipeline_image_video.yaml](./sample/param/pipeline_image_video.yaml) [pipeline_people.yaml](./sample/param/pipeline_people.yaml) [pipeline_people_ip.yaml](./sample/param/pipeline_people_ip.yaml)|[head-pose-estimation-adas-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/head-pose-estimation-adas-0001)| |Object Detection| Object detection based on SSD-based trained models.|[pipeline_object.yaml](./sample/param/pipeline_object.yaml) [pipeline_object_topic.yaml](./sample/param/pipeline_object_topic.yaml)|[mobilenet-ssd](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/public/mobilenet-ssd)| |Vehicle and License Detection| Vehicle and license detection based on Intel models.|[pipeline_vehicle_detection.yaml](./sample/param/pipeline_vehicle_detection.yaml)|[vehicle-license-plate-detection-barrier-0106](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/vehicle-license-plate-detection-barrier-0106) [vehicle-attributes-recognition-barrier-0039](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/vehicle-attributes-recognition-barrier-0039) [license-plate-recognition-barrier-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/license-plate-recognition-barrier-0001)| |Object Segmentation| Object segmentation.|[pipeline_segmentation.yaml](./sample/param/pipeline_segmentation.yaml) [pipeline_segmentation_image.yaml](./sample/param/pipeline_segmentation_image.yaml) [pipeline_video.yaml](./sample/param/pipeline_video.yaml)|[semantic-segmentation-adas-0001](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/semantic-segmentation-adas-0001) [deeplabv3](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/public/deeplabv3)| |Person Attributes| Person attributes based on object detection.|[pipeline_person_attributes.yaml](./sample/param/pipeline_person_attributes.yaml)|[person-attributes-recognition-crossroad-0230](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/person-attributes-recognition-crossroad-0230) [person-detection-retail-0013](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/person-detection-retail-0013)| |Person Reidentification|Person reidentification based on object detection.|[pipeline_person_reidentification.yaml](./sample/param/pipeline_reidentification.yaml)|[person-detection-retail-0013](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/person-detection-retail-0013) [person-reidentification-retail-0277](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/intel/person-reidentification-retail-0277)| |Object Segmentation Maskrcnn| Object segmentation and detection based on maskrcnn model.|[pipeline_segmentation_maskrcnn.yaml](./sample/param/pipeline_segmentation_maskrcnn.yaml)|[mask_rcnn_inception_v2_coco_2018_01_28](https://github.com/openvinotoolkit/open_model_zoo/tree/releases/2022/3/models/public/mask_rcnn_inception_resnet_v2_atrous_coco)| ### Published Topic |Inference|Published Topic| |---|---| |People Detection|```/ros2_openvino_toolkit/face_detection```([object_msgs:msg:ObjectsInBoxes](https://github.com/intel/ros2_object_msgs/blob/master/msg/ObjectsInBoxes.msg))| |Emotion Recognition|```/ros2_openvino_toolkit/emotions_recognition```([object_msgs:msg:EmotionsStamped](../../../object_msgs/msg/EmotionsStamped.msg))| |Age and Gender Recognition|```/ros2_openvino_toolkit/age_genders_Recognition```([object_msgs:msg:AgeGenderStamped](../../../object_msgs/msg/AgeGenderStamped.msg))| |Head Pose Estimation|```/ros2_openvino_toolkit/headposes_estimation```([object_msgs:msg:HeadPoseStamped](../../../object_msgs/msg/HeadPoseStamped.msg))| |Object Detection|```/ros2_openvino_toolkit/detected_objects```([object_msgs::msg::ObjectsInBoxes](https://github.com/intel/ros2_object_msgs/blob/master/msg/ObjectsInBoxes.msg))| |Object Segmentation|```/ros2_openvino_toolkit/segmented_obejcts```([object_msgs::msg::ObjectsInMasks](../../../object_msgs/msg/ObjectsInMasks.msg))| |Object Segmentation Maskrcnn|```/ros2_openvino_toolkit/segmented_obejcts```([object_msgs::msg::ObjectsInMasks](../../../object_msgs/msg/ObjectsInMasks.msg))| |Person Reidentification|```/ros2_openvino_toolkit/reidentified_persons```([object_msgs::msg::ReidentificationStamped](../../../object_msgs/msg/ReidentificationStamped.msg))| |Vehicle Detection|```/ros2_openvino_toolkit/detected_vehicles_attribs```([object_msgs::msg::VehicleAttribsStamped](../../../object_msgs/msg/PersonAttributeStamped.msg))| |Vehicle License Detection|```/ros2_openvino_toolkit/detected_license_plates```([object_msgs::msg::LicensePlateStamped](../../../object_msgs/msg/LicensePlateStamped.msg))| ## Service |Inference|Service| |---|---| |Object Detection Service|```/detect_object```([object_msgs::srv::DetectObject](https://github.com/intel/ros2_object_msgs/blob/master/srv/DetectObject.srv))| |Face Detection Service|```/detect_face```([object_msgs::srv::DetectObject](https://github.com/intel/ros2_object_msgs/blob/master/srv/DetectObject.srv))| |Age Gender Detection Service|```/detect_age_gender```([object_msgs::srv::AgeGender](./object_msgs/srv/AgeGenderSrv.srv))| |Headpose Detection Service|```/detect_head_pose```([object_msgs::srv::HeadPose](./object_msgs/srv/HeadPoseSrv.srv))| |Emotion Detection Service|```/detect_emotion```([object_msgs::srv::Emotion](./object_msgs/srv/EmotionSrv.srv))| ## Launch program ``` $ source setup-env-rss.bash $ ros2 launch dynamic_vino_sample pipeline_object.launch.py ``` * **pipeline_object.launch.py** ```python import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription import launch_ros.actions from launch.substitutions import LaunchConfiguration, PythonExpression import launch def generate_launch_description(): return LaunchDescription([ launch.actions.DeclareLaunchArgument(name='yaml_path', default_value = os.path.join(get_package_share_directory('dynamic_vino_sample'), 'param','pipeline_object.yaml')), # Openvino detection launch_ros.actions.Node( package='dynamic_vino_sample', executable='pipeline_with_params', arguments=['-config', LaunchConfiguration('yaml_path')], remappings=[ ('/openvino_toolkit/object/detected_objects', '/ros2_openvino_toolkit/detected_objects'), ('/openvino_toolkit/object/images', '/ros2_openvino_toolkit/image_rviz')], output='screen'), # Rviz #launch_ros.actions.Node( # package='rviz2', node_executable='rviz2', output='screen', # arguments=['--display-config', default_rviz]), ]) ``` * **pipeline_object.yaml** ```yaml Pipelines: - name: object inputs: [StandardCamera] infers: - name: ObjectDetection model: /opt/openvino_toolkit/models/public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.xml engine: CPU label: /opt/openvino_toolkit/models/public/ssd_mobilenet_v2_coco/FP16/ssd_mobilenet_v2_coco.labels batch: 1 confidence_threshold: 0.5 enable_roi_constraint: true # set enable_roi_constraint to false if you don't want to make the inferred ROI (region of interest) constrained into the camera frame outputs: [ImageWindow, RosTopic, RViz] connects: - left: StandardCamera right: [ObjectDetection] - left: ObjectDetection right: [ImageWindow, RosTopic, RViz] OpenvinoCommon: ``` ![圖片](https://hackmd.io/_uploads/B1H-e2eXC.png) **ssd_mobilenet_v2_coco.xml** ```xml <?xml version="1.0" ?> <net name="ssd_mobilenet_v2_coco" version="10"> <layers> <layer id="0" name="image_tensor" type="Parameter" version="opset1"> <data shape="1, 3, 300, 300" element_type="f16"/> <output> <port id="0" precision="FP16"> <dim>1</dim> <dim>3</dim> <dim>300</dim> <dim>300</dim> </port> </output> </layer> <layer id="1" name="Preprocessor/mul/x" type="Const" version="opset1"> <data element_type="f16" shape="1, 1, 1, 1" offset="0" size="2"/> <output> <port id="0" precision="FP16" names="Preprocessor/mul/x:0"> <dim>1</dim> <dim>1</dim> <dim>1</dim> <dim>1</dim> </port> </output> </layer> <layer id="2" name="Preprocessor/mul" type="Multiply" version="opset1"> <data auto_broadcast="numpy"/> <input> <port id="0" precision="FP16"> <dim>1</dim> <dim>1</dim> <dim>1</dim> <dim>1</dim> </port> <port id="1" precision="FP16"> <dim>1</dim> <dim>3</dim> <dim>300</dim> <dim>300</dim> </port> </input> <output> <port id="2" precision="FP16" names="Preprocessor/mul:0"> <dim>1</dim> <dim>3</dim> <dim>300</dim> <dim>300</dim> </port> </output> </layer> . . . ``` **ssd_mobilenet_v2_coco.labels** ``` __background__ person bicycle car motorcycle airplan bus train truck boat trafficlight firehydrant streetsign stopsign parkingmeter bench bird cat dog horse sheep cow elephant bear zebra giraffe hat backpack umbrella shoe eyeglasses handbag tie suitcase frisbee skis snowboard sportsball kite baseballbat baseballglove skateboard surfboard tennisracket bottle plate wineglass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hotdog pizza donut cake chair couch pottedplant bed mirror diningtable window desk toilet door tv laptop mouse remote keyboard cellphone microwave oven toaster sink refrigerator blender book clock vase scissors teddybear hairdrier toothbrush hairbrush ``` Result: ![](https://i.imgur.com/oxyIRaW.png) ## Create pipline /my_ros2_ws/src/ros2_openvino_toolkit/sample/pipeline_with_params.cpp ```cpp void signalHandler(int signum) { slog::warn << "!!!!!!!!!!!Interrupt signal (" << signum << ") received!!!!!!!!!!!!" << slog::endl; // cleanup and close up stuff here // terminate program PipelineManager::getInstance().stopAll(); // exit(signum); } int main(int argc, char * argv[]) { rclcpp::init(argc, argv); rclcpp::executors::SingleThreadedExecutor exec; rclcpp::Node::SharedPtr main_node = rclcpp::Node::make_shared("openvino_pipeline"); rclcpp::Node::SharedPtr service_node = std::make_shared<vino_service::PipelineProcessingServer <pipeline_srv_msgs::srv::PipelineSrv>>("pipeline_service"); // register signal SIGINT and signal handler //signal(SIGINT, signalHandler); try { std::cout << "InferenceEngine: " << InferenceEngine::GetInferenceEngineVersion() << std::endl; // ----- Parsing and validation of input args----------------------- std::string config = getConfigPath(argc, argv); if(config.empty()){ throw std::runtime_error("Config File is not correctly set."); return -1; } slog::info << "Config File Path =" << config << slog::endl; Params::ParamManager::getInstance().parse(config); Params::ParamManager::getInstance().print(); auto pipelines = Params::ParamManager::getInstance().getPipelines(); if (pipelines.size() < 1) { throw std::logic_error("Pipeline parameters should be set!"); } // auto createPipeline = PipelineManager::getInstance().createPipeline; for (auto & p : pipelines) { PipelineManager::getInstance().createPipeline(p, main_node); } PipelineManager::getInstance().runAll(); //rclcpp::spin(main_node); exec.add_node(main_node); exec.add_node(service_node); exec.spin(); PipelineManager::getInstance().stopAll(); rclcpp::shutdown(); } catch (const std::exception & error) { slog::err << error.what() << slog::endl; return -2; } catch (...) { slog::err << "Unknown/internal exception happened." << slog::endl; return -3; } return 0; } ``` **Manage pipline** /my_ros2_ws/src/ros2_openvino_toolkit/dynamic_vino_lib/src/pipeline_manager.cpp ```cpp std::shared_ptr<Pipeline> PipelineManager::createPipeline(const Params::ParamManager::PipelineRawData & params, rclcpp::Node::SharedPtr node) { if (params.name == "") { throw std::logic_error("The name of pipeline won't be empty!"); } std::shared_ptr<Pipeline> pipeline = std::make_shared<Pipeline>(params.name); pipeline->getParameters()->update(params); PipelineData data; data.parent_node = node; data.pipeline = pipeline; data.params = params; data.state = PipelineState_ThreadNotCreated; auto inputs = parseInputDevice(data); if (inputs.size() != 1) { slog::err << "currently one pipeline only supports ONE input." << slog::endl; return nullptr; } for (auto it = inputs.begin(); it != inputs.end(); ++it) { pipeline->add(it->first, it->second); auto node = it->second->getHandler(); if (node != nullptr) { data.spin_nodes.emplace_back(node); } } auto outputs = parseOutput(data); for (auto it = outputs.begin(); it != outputs.end(); ++it) { pipeline->add(it->first, it->second); } auto infers = parseInference(params); for (auto it = infers.begin(); it != infers.end(); ++it) { pipeline->add(it->first, it->second); } slog::info << "Updating connections ..." << slog::endl; for (auto it = params.connects.begin(); it != params.connects.end(); ++it) { pipeline->add(it->first, it->second); } // slog::info << "Updateing filters ..." << slog::endl; // pipeline->addFilters(params.filters); pipelines_.insert({params.name, data}); pipeline->setCallback(); slog::info << "One Pipeline Created!" << slog::endl; pipeline->printPipeline(); return pipeline; } std::map<std::string, std::shared_ptr<Outputs::BaseOutput>> PipelineManager::parseOutput(const PipelineData & pdata) { std::map<std::string, std::shared_ptr<Outputs::BaseOutput>> outputs; for (auto & name : pdata.params.outputs) { slog::info << "Parsing Output: " << name << slog::endl; std::shared_ptr<Outputs::BaseOutput> object = nullptr; if (name == kOutputTpye_RosTopic) { object = std::make_shared<Outputs::RosTopicOutput>(pdata.params.name, pdata.parent_node); } else if (name == kOutputTpye_ImageWindow) { object = std::make_shared<Outputs::ImageWindowOutput>(pdata.params.name); } else if (name == kOutputTpye_RViz) { object = std::make_shared<Outputs::RvizOutput>(pdata.params.name, pdata.parent_node); } else if (name == kOutputTpye_RosService) { object = std::make_shared<Outputs::RosServiceOutput>(pdata.params.name); } else { slog::err << "Invalid output name: " << name << slog::endl; } if (object != nullptr) { outputs.insert({name, object}); slog::info << " ... Adding one Output: " << name << slog::endl; } } return outputs; } std::shared_ptr<dynamic_vino_lib::BaseInference> PipelineManager::createObjectDetection( const Params::ParamManager::InferenceRawData & infer) { std::shared_ptr<Models::ObjectDetectionModel> object_detection_model; std::shared_ptr<dynamic_vino_lib::ObjectDetection> object_inference_ptr; slog::debug << "for test in createObjectDetection()" << slog::endl; if (infer.model_type == kInferTpye_ObjectDetectionTypeSSD) { object_detection_model = std::make_shared<Models::ObjectDetectionSSDModel>(infer.label, infer.model, infer.batch); } if (infer.model_type == kInferTpye_ObjectDetectionTypeYolov2) { object_detection_model = std::make_shared<Models::ObjectDetectionYolov2Model>(infer.label, infer.model, infer.batch); } slog::debug << "for test in createObjectDetection(), Created SSDModel" << slog::endl; object_inference_ptr = std::make_shared<dynamic_vino_lib::ObjectDetection>( infer.enable_roi_constraint, infer.confidence_threshold); // To-do theshold configuration slog::debug << "for test in createObjectDetection(), before modelInit()" << slog::endl; object_detection_model->modelInit(); auto object_detection_engine = engine_manager_.createEngine( infer.engine, object_detection_model); slog::debug << "for test in createObjectDetection(), before loadNetwork" << slog::endl; object_inference_ptr->loadNetwork(object_detection_model); object_inference_ptr->loadEngine(object_detection_engine); slog::debug << "for test in createObjectDetection(), OK" << slog::endl; return object_inference_ptr; } void PipelineManager::runAll() { for (auto it = pipelines_.begin(); it != pipelines_.end(); ++it) { if (it->second.state != PipelineState_ThreadRunning) { it->second.state = PipelineState_ThreadRunning; } if (service_.state != PipelineState_ThreadRunning) { service_.state = PipelineState_ThreadRunning; } if (it->second.thread == nullptr) { it->second.thread = std::make_shared<std::thread>(&PipelineManager::threadPipeline, this, it->second.params.name.c_str()); } } } ``` **Create publisher and handle output** /my_ros2_ws/src/ros2_openvino_toolkit/dynamic_vino_lib/src/outputs/ros_topic_output.cpp ```cpp Outputs::RosTopicOutput::RosTopicOutput(std::string output_name, const rclcpp::Node::SharedPtr node) : BaseOutput(output_name) { ... pub_detected_object_ = node_->create_publisher<object_msgs::msg::ObjectsInBoxes>( "/openvino_toolkit/" + output_name_ + "/detected_objects", 16); ... } void Outputs::RosTopicOutput::accept( const std::vector<dynamic_vino_lib::FaceDetectionResult> & results) { faces_topic_ = std::make_shared<object_msgs::msg::ObjectsInBoxes>(); object_msgs::msg::ObjectInBox face; for (auto r : results) { // slog::info << ">"; auto loc = r.getLocation(); face.roi.x_offset = loc.x; face.roi.y_offset = loc.y; face.roi.width = loc.width; face.roi.height = loc.height; face.object.object_name = r.getLabel(); face.object.probability = r.getConfidence(); faces_topic_->objects_vector.push_back(face); detected_objects_topic_->objects_vector.push_back(face); } } void Outputs::RosTopicOutput::handleOutput() { auto header = getPipeline()->getInputDevice()->getLockedHeader(); ... if (detected_objects_topic_ != nullptr) { // slog::info << "publishing detected objects outputs." << slog::endl; detected_objects_topic_->header = header; pub_detected_object_->publish(*detected_objects_topic_); detected_objects_topic_ = nullptr; } } ... ``` ## Generic OpenVINO Sample # Reference ## Pre-trained Object | Item | Objects | Remark | |------|----------------|------------| | 1 | person | 人 | | 2 | bicycle | 自行車 | | 3 | car | 汽車 | | 4 | motorcycle | 摩托車 | | 5 | airplan | 飛機 | | 6 | bus | 公車 | | 7 | train | 火車 | | 8 | truck | 卡車 | | 9 | boat | 船 | | 10 | traffic light | 交通燈 | | 11 | fire hydrant | 消防栓 | | 12 | street sign | 街道標誌 | | 13 | stop sign | 停車標誌 | | 14 | parking meter | 停車計時器 | | 15 | bench | 長椅 | | 16 | bird | 鳥 | | 17 | cat | 貓 | | 18 | dog | 狗 | | 19 | horse | 馬 | | 20 | sheep | 綿羊 | | 21 | cow | 牛 | | 22 | elephant | 大象 | | 23 | bear | 熊 | | 24 | zebra | 斑馬 | | 25 | giraffe | 長頸鹿 | | 26 | hat | 帽子 | | 27 | backpack | 背包 | | 28 | umbrella | 雨傘 | | 29 | shoe | 鞋子 | | 30 | eye glasses | 眼鏡 | | 31 | handbag | 手提包 | | 32 | tie | 領帶 | | 33 | suitcase | 行李箱 | | 34 | frisbee | 飛盤 | | 35 | skis | 滑雪板 | | 36 | snowboard | 雪地板 | | 37 | sports ball | 運動球 | | 38 | kite | 風箏 | | 39 | baseball bat | 棒球棒 | | 40 | baseball glove | 棒球手套 | | 41 | skateboard | 滑板 | | 42 | surfboard | 衝浪板 | | 43 | tennis racket | 網球拍 | | 44 | bottle | 瓶子 | | 45 | plate | 盤子 | | 46 | wine glass | 紅酒杯 | | 47 | cup | 杯子 | | 48 | fork | 叉子 | | 49 | knife | 刀子 | | 50 | spoon | 湯匙 | | 51 | bowl | 碗 | | 52 | banana | 香蕉 | | 53 | apple | 蘋果 | | 54 | sandwich | 三明治 | | 55 | orange | 橙子 | | 56 | broccoli | 花椰菜 | | 57 | carrot | 胡蘿蔔 | | 58 | hot dog | 熱狗 | | 59 | pizza | 披薩 | | 60 | donut | 甜甜圈 | | 61 | cake | 蛋糕 | | 62 | chair | 椅子 | | 63 | couch | 沙發 | | 64 | potted plant | 盆栽植物 | | 65 | bed | 床 | | 66 | mirror | 鏡子 | | 67 | dining table | 餐桌 | | 68 | window | 窗戶 | | 69 | desk | 書桌 | | 70 | toilet | 馬桶 | | 71 | door | 門 | | 72 | tv | 電視 | | 73 | laptop | 筆記型電腦 | | 74 | mouse | 滑鼠 | | 75 | remote | 遙控器 | | 76 | keyboard | 鍵盤 | | 77 | cell phone | 手機 | | 78 | microwave | 微波爐 | | 79 | oven | 烤箱 | | 80 | toaster | 烤麵包機 | | 81 | sink | 水槽 | | 82 | refrigerator | 冰箱 | | 83 | blender | 搅拌機 | | 84 | book | 書 | | 85 | clock | 鐘 | | 86 | vase | 花瓶 | | 87 | scissors | 剪刀 | | 88 | teddy bear | 泰迪熊 | | 89 | hair drier | 吹風機 | | 90 | toothbrush | 牙刷 | | 91 | hair brush | 髮刷 |