饮食健康中的食物体积估算云计算技术研究
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摘要
肥胖症已经成为全世界困扰人类健康的流行病。肥胖症的病因除家族遗传因素以外，主要和人的日常饮食密切相关。通过对用户饮食情况进行监测和分析，能够帮助用户获得更加健康的饮食习惯。首先，使用可穿戴设备获取饮食图片，然后使用椭圆自动检测算法计算出饮食图片中的盘子成像的椭圆方程，以盘子为标定物，将椭圆方程作为输入对拍摄时的相机参数进行标定，获取相机和食物之间的相对位置和旋转角度，标定完成后，使用虚拟现实技术构建虚拟场景模拟饮食的相机环境，在虚拟场景中构建和食物类似的三维物体形状，通过对物体进行操作契合原始图片中的食物，契合完成后即可根据虚拟物体的体积来估算盘子中的食物体积。
这套方法已经基本得到完善，但是仍存在一些缺陷：首先便是可穿戴设备的普及，由于可穿戴设备还处于实验室产品阶段，市面上仍然无法买到，为搜集饮食图片造成了阻碍；其次，随着图片的增多，桌面电脑在存储大量图片时面临很大压力，另外，运行在桌面端的食物体积估算算法，由于计算量较大，计算时间较长，处理图片需要太多的时间；最后，在食物契合的过程中，传统的鼠标键盘输入操作会比较繁琐，无法提供高效准确的契合方法。
针对上述问题，本文将对食物体积估算方法进行改进：第一、使用智能终端如智能手机等代替可穿戴设备获取饮食图片。随着智能手机等终端的普及，智能终端的拍摄功能和数据处理功能得到了快速的发展，当前市面上流行的智能终端已经可以拍摄出清晰的饮食图片，因此完全可以使用智能终端来代替可穿戴设备获取饮食图片；第二、采用多点触摸技术实现三维虚拟食物物体的操作，进行食物物体的契合，智能移动终端的处理能力和多点触摸技术，完美的符合我们整体算法中食物契合部分功能的需要，因此，可以把创建三维场景和物体，并采用多点触摸技术进行契合食物的功能在智能终端改进后得以实现；第三、搭建云计算平台，存储大量用户的饮食数据照片，这些照片将会对未来的饮食健康研究提供最基础最准确的素材；第四、搭建稳定高效的云计算处理平台，将需要消耗大量计算的功能，包括椭圆自动检测、相机标定等功能提交至云计算平台进行执行，从而节省智能终端的计算量。
本文主要采用云加端的结构、云端协同合作的方式对系统进行实现。端的功能主要在智能移动终端进行实现，其主要功能是使用三维建模技术模拟图片中食物体积，使用多点触摸技术实现食物物体的契合，从而准确的获得摄入食物体积。
云的作用主要是采用分布式架构存储用户拍摄的饮食照片，并对海量照片进行处理，计算特定照片中饮食场景的三维场景相机参数，将参数返回给端，允许终端在此基础上建立三维模型，使用三维虚拟现实技术模拟图片中的食物进而估算食物体积。
本文的主要创新点体现在：
1)创新性地将三维虚拟现实技术应用于食品体积估算中，提出了基于云端协同的虚拟现实估算方法，研究设计了自主智能的三维多点触摸人机交互技术，实现了虚拟现实模型与实际食物的高度契合，有效地解决了食物体积估算问题。
2)针对HDFS文件系统存储小文件影响效率问题，提出了一种新的文件系统二级索引机制。通过采用虚拟大文件方法，较好地解决了海量食物小文件图片的管理问题。
3)针对HDFS副本放置不平均问题，提出了基于存储节点性能的副本放置策略方法，以存储节点性能指标为基础，文件综合性能为依据，改进了副本放置策略，并通过实验验证了该放置策略的合理性。
4)提出了一种新的云计算平台调度算法，该算法在充分利用计算节点的计算资源和任务参数指标的基础上，在调度过程中加入了任务执行的反馈信息，获得了合理的调度结果，优化了食物体积与能量计算云服务平台的资源配置。
Obesity has become an epidemic all over the world. The cause of obesity is diet in additionto hereditary factors. Reasonable energy intake and eating habits can improve the rapidly spreadof obesity. A method has been built to find a solution with information technology aim to thediet health problem. First, a wearable device has been developed to take diet pictures, and thenthe ellipse automatic detection algorithm is used to detect the plate in the diet pictures to get theelliptic equations of the plate. The plate can be used to calibrate the camera to get the relativeposition and rotation angle between the food in the picture and the camera. After the cameracalibration, the virtual reality technology is used to build virtual scenes according to the dietcameras environment. Then build3D object which is similar to the food in the picture. User canmanipulate the object by rotating, zooming or moving to fit the real food in the picture. Then thevolume of the real food can be estimated according to the volume of the virtual object.
This method has basically been finished, but there are still some drawbacks: First, thepopularity of the wearable device is too low. The wearable device is still a laboratory productand not mature enough to enter commercial market. Besides, this product is only for specialgroups such as the elderly or patients with obesity which means it cannot be used by more users;Second, virtual reality and other functions are running on the desktop. It will take a very longtime to process the pictures due to the large amount of calculation. Finally, the traditional userinput methods like mouse and keyboard are not suitable to manipulate the3D objects in thevirtual reality environment.
To solve these problems, this food volume estimation method need to be improved. First,the smart devices like smartphones can be used to take diet pictures instead of wearable devices.With the popularity of smartphones and other smart devices, the functions of picture taking anddata processing have been developed rapidly. These smart devices can take pictures which areclear enough for research. Second, the processing capabilities of the smart mobile devices andthe multi-touch technology on them, makes them very suitable to finish the relative functions inthis method. Third, a cloud computing platform has been built to store the large number dietpictures taken by users. These pictures will provide basic and accurate material for diet healthresearch. Fourth, a stable and efficient cloud processing platform has been built to process thepictures stored on cloud platform. The functions of ellipse automatically detection and cameracalibration is deployed on the cloud computing platform.
The structure of Cloud plus Client is used to implement this system, in which the Cloud and the Client will cooperate with each other. The functions of Client mainly developed on themobile devices, including drawing or adjusting the ellipse when the plate cannot be detectedautomatically or the detection is not very accurate. At the same time, users can create objectsand fit them to the food to calculate the volume of the food on the Client to get more accuratefood volume.
The Cloud is used to store pictures taken by users with a distributed architecture, andprocess the mass pictures stored on the Cloud by detecting the plate in the pictures andcalibrating the camera. The calculation result can be returned to the Client to allow the Client tocreate3D scenes according to the result.
The innovations include:
1) This system uses virtual reality technology to calculate food volume. This workproposed a Cloud and Client cooperate method, researched and implementedautonomous and intelligent3D multi-touch interactive technology to achieve a virtualreality model to fit real food.
2) To fix the HDFS file system storing small files problem, this work proposed a new filesystem with two level indexing mechanism. By using Virtual Big File method, solvedthe massive food small file picture management issues.
3) To fix HDFS copy strategy problem, this work proposed a new copy strategy basedon the performance of DataNode. This strategy is based on the performance ofDataNode and the comprehensive performance of files. This strategy is verified byexperiments.
4) This work proposed a new cloud computing platform scheduling algorithm, whichtakes full advantage of the computing resources and task parameters. This algorithmadded task execution feedback, and obtained reasonable scheduling result.
这套方法已经基本得到完善，但是仍存在一些缺陷：首先便是可穿戴设备的普及，由于可穿戴设备还处于实验室产品阶段，市面上仍然无法买到，为搜集饮食图片造成了阻碍；其次，随着图片的增多，桌面电脑在存储大量图片时面临很大压力，另外，运行在桌面端的食物体积估算算法，由于计算量较大，计算时间较长，处理图片需要太多的时间；最后，在食物契合的过程中，传统的鼠标键盘输入操作会比较繁琐，无法提供高效准确的契合方法。
针对上述问题，本文将对食物体积估算方法进行改进：第一、使用智能终端如智能手机等代替可穿戴设备获取饮食图片。随着智能手机等终端的普及，智能终端的拍摄功能和数据处理功能得到了快速的发展，当前市面上流行的智能终端已经可以拍摄出清晰的饮食图片，因此完全可以使用智能终端来代替可穿戴设备获取饮食图片；第二、采用多点触摸技术实现三维虚拟食物物体的操作，进行食物物体的契合，智能移动终端的处理能力和多点触摸技术，完美的符合我们整体算法中食物契合部分功能的需要，因此，可以把创建三维场景和物体，并采用多点触摸技术进行契合食物的功能在智能终端改进后得以实现；第三、搭建云计算平台，存储大量用户的饮食数据照片，这些照片将会对未来的饮食健康研究提供最基础最准确的素材；第四、搭建稳定高效的云计算处理平台，将需要消耗大量计算的功能，包括椭圆自动检测、相机标定等功能提交至云计算平台进行执行，从而节省智能终端的计算量。
本文主要采用云加端的结构、云端协同合作的方式对系统进行实现。端的功能主要在智能移动终端进行实现，其主要功能是使用三维建模技术模拟图片中食物体积，使用多点触摸技术实现食物物体的契合，从而准确的获得摄入食物体积。
云的作用主要是采用分布式架构存储用户拍摄的饮食照片，并对海量照片进行处理，计算特定照片中饮食场景的三维场景相机参数，将参数返回给端，允许终端在此基础上建立三维模型，使用三维虚拟现实技术模拟图片中的食物进而估算食物体积。
本文的主要创新点体现在：
1)创新性地将三维虚拟现实技术应用于食品体积估算中，提出了基于云端协同的虚拟现实估算方法，研究设计了自主智能的三维多点触摸人机交互技术，实现了虚拟现实模型与实际食物的高度契合，有效地解决了食物体积估算问题。
2)针对HDFS文件系统存储小文件影响效率问题，提出了一种新的文件系统二级索引机制。通过采用虚拟大文件方法，较好地解决了海量食物小文件图片的管理问题。
3)针对HDFS副本放置不平均问题，提出了基于存储节点性能的副本放置策略方法，以存储节点性能指标为基础，文件综合性能为依据，改进了副本放置策略，并通过实验验证了该放置策略的合理性。
4)提出了一种新的云计算平台调度算法，该算法在充分利用计算节点的计算资源和任务参数指标的基础上，在调度过程中加入了任务执行的反馈信息，获得了合理的调度结果，优化了食物体积与能量计算云服务平台的资源配置。
Obesity has become an epidemic all over the world. The cause of obesity is diet in additionto hereditary factors. Reasonable energy intake and eating habits can improve the rapidly spreadof obesity. A method has been built to find a solution with information technology aim to thediet health problem. First, a wearable device has been developed to take diet pictures, and thenthe ellipse automatic detection algorithm is used to detect the plate in the diet pictures to get theelliptic equations of the plate. The plate can be used to calibrate the camera to get the relativeposition and rotation angle between the food in the picture and the camera. After the cameracalibration, the virtual reality technology is used to build virtual scenes according to the dietcameras environment. Then build3D object which is similar to the food in the picture. User canmanipulate the object by rotating, zooming or moving to fit the real food in the picture. Then thevolume of the real food can be estimated according to the volume of the virtual object.
This method has basically been finished, but there are still some drawbacks: First, thepopularity of the wearable device is too low. The wearable device is still a laboratory productand not mature enough to enter commercial market. Besides, this product is only for specialgroups such as the elderly or patients with obesity which means it cannot be used by more users;Second, virtual reality and other functions are running on the desktop. It will take a very longtime to process the pictures due to the large amount of calculation. Finally, the traditional userinput methods like mouse and keyboard are not suitable to manipulate the3D objects in thevirtual reality environment.
To solve these problems, this food volume estimation method need to be improved. First,the smart devices like smartphones can be used to take diet pictures instead of wearable devices.With the popularity of smartphones and other smart devices, the functions of picture taking anddata processing have been developed rapidly. These smart devices can take pictures which areclear enough for research. Second, the processing capabilities of the smart mobile devices andthe multi-touch technology on them, makes them very suitable to finish the relative functions inthis method. Third, a cloud computing platform has been built to store the large number dietpictures taken by users. These pictures will provide basic and accurate material for diet healthresearch. Fourth, a stable and efficient cloud processing platform has been built to process thepictures stored on cloud platform. The functions of ellipse automatically detection and cameracalibration is deployed on the cloud computing platform.
The structure of Cloud plus Client is used to implement this system, in which the Cloud and the Client will cooperate with each other. The functions of Client mainly developed on themobile devices, including drawing or adjusting the ellipse when the plate cannot be detectedautomatically or the detection is not very accurate. At the same time, users can create objectsand fit them to the food to calculate the volume of the food on the Client to get more accuratefood volume.
The Cloud is used to store pictures taken by users with a distributed architecture, andprocess the mass pictures stored on the Cloud by detecting the plate in the pictures andcalibrating the camera. The calculation result can be returned to the Client to allow the Client tocreate3D scenes according to the result.
The innovations include:
1) This system uses virtual reality technology to calculate food volume. This workproposed a Cloud and Client cooperate method, researched and implementedautonomous and intelligent3D multi-touch interactive technology to achieve a virtualreality model to fit real food.
2) To fix the HDFS file system storing small files problem, this work proposed a new filesystem with two level indexing mechanism. By using Virtual Big File method, solvedthe massive food small file picture management issues.
3) To fix HDFS copy strategy problem, this work proposed a new copy strategy basedon the performance of DataNode. This strategy is based on the performance ofDataNode and the comprehensive performance of files. This strategy is verified byexperiments.
4) This work proposed a new cloud computing platform scheduling algorithm, whichtakes full advantage of the computing resources and task parameters. This algorithmadded task execution feedback, and obtained reasonable scheduling result.
引文
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