CN113761251A - 一种基于图像找寻商城商品的方法、可读存储介质及计算机设备 - Google Patents

一种基于图像找寻商城商品的方法、可读存储介质及计算机设备 Download PDF

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CN113761251A
CN113761251A CN202111066609.5A CN202111066609A CN113761251A CN 113761251 A CN113761251 A CN 113761251A CN 202111066609 A CN202111066609 A CN 202111066609A CN 113761251 A CN113761251 A CN 113761251A
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孙勇
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Abstract

本发明公开了一种基于图像找寻商城商品的方法、可存储介质及计算机设备，所述方法包括：将商品库中的商品图片生成图片向量，得到原始图片生成向量；将商品库中的所述商品图片去掉背景；再将去掉背景之后的图片生成图片向量，得到去背景图片生成向量；将所述原始图片生成向量和所述去背景图片生成向量保存到向量数据库中；将用户上传的图片生成图片向量，得到上传图片生成向量；将所述用户上传的图片去掉背景，再将去掉背景之后的上传的图片生成图片向量，得到去背景上传图片生成向量；应用所述上传图片生成向量和所述去背景上传图片生成向量到所述向量数据库中查找符合相似度阈值的最相似的图片，将满足条件的图片及其对应的商品返回业务方。

Description

一种基于图像找寻商城商品的方法、可读存储介质及计算机 设备
技术领域
本发明涉及计算机网络技术领域，特别涉及一种基于图像找寻商城商品的方法。
背景技术
运营同事需要把用户上传的商品与公司商品库中已有的商品做关联，但用户上传的商品文字信息描述不清，不确定该商品应该对应到商品库中哪个商品，所以希望用用户上传的商品图片与商品库中商品图片做比较，从而快速找到相似的商品。
现有技术存在如下缺点：
一般将图片使用某个模型抽取特征生成图片向量，然后存到某中数据库中，有新的图片也生成向量到数据库中检索，找出最相似的图片。
因为模型抽取图片特征时图片不同的背景增加了干扰，两个图片相似度就会比较低，导致不确信两个图片是否是同一商品。
发明内容
本发明实施例提供了一种基于图像找寻商城商品的方法。为了对披露的实施例的一些方面有一个基本的理解，下面给出了简单的概括。该概括部分不是泛泛评述，也不是要确定关键/重要组成元素或描绘这些实施例的保护范围。其唯一目的是用简单的形式呈现一些概念，以此作为后面的详细说明的序言。
根据本发明第一方面提供了一种基于图像找寻商城商品的方法，包括：
S1：将商品库中的商品图片生成图片向量，得到原始图片生成向量；
S2：将商品库中的所述商品图片去掉背景；
S3：再将去掉背景之后的图片生成图片向量，得到去背景图片生成向量；
S4：将所述原始图片生成向量和所述去背景图片生成向量保存到向量数据库中；
S5：将用户上传的图片生成图片向量，得到上传图片生成向量；
S6：将所述用户上传的图片去掉背景，再将去掉背景之后的上传的图片生成图片向量，得到去背景上传图片生成向量；
S7：应用所述上传图片生成向量和所述去背景上传图片生成向量到所述向量数据库中查找符合相似度阈值的最相似的图片，将满足条件的图片及其对应的商品返回业务方。
在一些实施例中，所述生成图片向量的方法为：vgg16模型。
在一些实施例中，所述去掉背景的方法为：rembg背景移除工具。
在一些实施例中，所述向量数据库为milvus向量数据库。
在一些实施例中，所述相似度阈值为0-0.5之间的小数。
在一些实施例中，图片的相识度值越接近0越相似，反之越不像。
根据本发明第二方面提供了一种可读存储介质，所述可读存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行，以实现如本申请第一方面所述的基于图像找寻商城商品的方法。
根据本发明第三方面提供了一种计算机设备，包括处理器和存储器，其中，所述存储器，用于存放计算机程序；所述处理器，用于执行存储在所述存储器上的计算机程序时，实现如本申请第一方面所述的基于图像找寻商城商品的方法的步骤。
本发明实施例提供的技术方案可以包括以下有益效果：提高了不同背景同商品图片识别准确性。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本发明。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本发明的实施例，并与说明书一起用于解释本发明的原理。
图1是根据一示例性实施例示出的一种基于图像找寻商城商品的方法的流程图；
图2是根据一示例性实施例示出的商品库中的商品图；
图3是根据一示例性实施例示出的去背景后的商品库中的商品图；
图4是根据一示例性实施例示出的用户上传的图片；
图5是根据一示例性实施例示出的去掉背景之后的上传的图片；
图6是根据一示例性实施例示出的查询匹配结果图1；
图7是根据一示例性实施例示出的查询匹配结果图2；
图8是根据一示例性实施例示出的查询匹配结果图3；
图9是根据一示例性实施例示出的查询匹配结果图4；
图10a-10b是根据一示例性实施例示出的实施例用户上传图片；
图11a-11b是根据一示例性实施例示出的去掉背景之后的上传的图片；
图12是根据一示例性实施例示出的去掉背景之后，实物位置不同对比图；
图13是根据一示例性实施例示出的使实物位置相同过程图一；
图14是根据一示例性实施例示出的使实物位置相同过程图二；
图15是根据一示例性实施例示出的使实物位置相同过程图三；
图16a-16b是根据一示例性实施例示出的使实物位置相同处理后图。
具体实施方式
以下描述和附图充分地示出本发明的具体实施方案，以使本领域的技术人员能够实践它们。实施例仅代表可能的变化。除非明确要求，否则单独的部件和功能是可选的，并且操作的顺序可以变化。一些实施方案的部分和特征可以被包括在或替换其他实施方案的部分和特征。本发明的实施方案的范围包括权利要求书的整个范围，以及权利要求书的所有可获得的等同物。在本文中，各实施方案可以被单独地或总地用术语“发明”来表示，这仅仅是为了方便，并且如果事实上公开了超过一个的发明，不是要自动地限制该应用的范围为任何单个发明或发明构思。本文中，诸如第一和第二等之类的关系术语仅仅用于将一个实体或者操作与另一个实体或操作区分开来，而不要求或者暗示这些实体或操作之间存在任何实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素。本文中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的结构、产品等而言，由于其与实施例公开的部分相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
实施例1：
下面结合附图及实施例对本发明做进一步描述：
如图1所示的一种基于图像找寻商城商品的方法，包括，
S1：应用vgg16模型将商品库中商品图片生成图片向量，得到原始图片生成向量；
S2：应用rembg背景移除工具将商品库中的所述商品图片去掉背景；
S3：再应用vgg16模型将去掉背景之后的图片生成图片向量，得到去背景图片生成向量；
S4：将所述原始图片生成向量和所述去背景图片生成向量保存到milvus向量数据库中；
S5：应用vgg16模型将用户上传的图片生成图片向量，得到上传图片生成向量；
S6：应用rembg背景移除工具将所述用户上传的图片去掉背景，再应用vgg16模型将去掉背景之后的上传的图片生成图片向量，得到去背景上传图片生成向量；
S7：应用所述上传图片生成向量和所述去背景上传图片生成向量到所述向量数据库中查找符合相似度阈值的最相似的图片，将满足条件的图片及其对应的商品返回业务方。
根据上述方案，进一步，所述相似度阈值为0-0.5之间的小数。
根据上述方案，进一步，图片的相识度值越接近0越相似，反之越不像。
实施例1：
S1：应用vgg16模型将商品库中如图2所示的商品图片生成图片向量，得到原始图片生成向量(可以理解为将图片用一串数字表示)；
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S2：应用rembg背景移除工具将商品库中的所述商品图片去掉背景，如图3所示，复杂的背景已经去掉，替换成白底背景；
S3：再应用vgg16模型将去掉背景之后的图片生成图片向量，得到去背景图片生成向量；
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S4：将所述原始图片生成向量和所述去背景图片生成向量保存到milvus向量数据库中；
milvus需要对每个向量都设置一个id，从而可以通过id来标识是哪个图片，给第一步原图向量设置一个id＝10001，给第三步去背景图片向量设置一个id＝10002；然后将两个向量保存到milvus中；
S5：应用如图4所示的用户上传的图片进行测试，看是否可以查到10001或10002图片，并且返回的相似值在阈值范围内即可认为是同一个商品，milvus返回的相似值为0-2之间的小数，越接近0越相似，反之越不像，本例设置一个阈值为0.3，阈值越小结果越准确，只要返回图片向量的相似值小于0.3即可认为是同一个商品。
如图6所示图片的相似值为0.45475172996520996；
如图7所示图片的相似值为0.498405784368515；
如图8所示图片的相似值为0.29607057571411133；
如图9所示图片的相似值为0.5561721920967102。
实施例2：
对物体大小不同匹配相似度优化的处理：
如图10a所示上图长宽600*600，链接：
http://img.alicdn.com/imgextra/i3/2076971977/O1CN01ryFCvB1QTVj2Ct2Kn_！！0-item_pic.jpg_600x600q90.jpg
如图10b所示下图长宽790*1088，链接：
https://img.alicdn.com/imgextra/i2/2076971977/O1CN01rpBrRA1QTViyGF57E_！！2076971977.jpg
如图11a和11b所示，将上图和下图分别做去背景得到下面两个去背景之后的图片，上图长宽600*600，下图实际长宽：1088*1088，去背景后将图片转换成取最大边长正方形，原图像素为790*1088，所以这里会转换成1088*1088的正方形，然后两图比较相似值为：0.12442220002412796；
如图12所示，经过测试案例发现，如果两个图片实物相似，但是白低间距不同或者物体所在白底背景的位置，也会影响它们的相似度。
实现了一个算法为的是让两个实物在图片的相同位置，大小一样，从而相似度得到提高。
如图13所示，第一步将图片去白边，只保留实物部分。
如图14所示，第二步将实物等比放缩为最大一条边为200像素的图像。
如图15所示，第三步创建224*224大小的白底图片，将实物居中显示.
如图16a和16b所示，此时两个图片尺寸，位置、与白底间距、实物尺寸都比较接近了。得到的相似值为0.037911172956228256(越接近0越相似)，比之前更相似了。此时可以给使用者更确信的图片匹配结果。
实施例2：
一种存储单元，所述存储单元存储并执行的指令包括：所述可读存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行，以实现如本申请实施例1所述的XXX基于图像找寻商城商品的方法。
实施例3：
一种计算机设备，包括处理器和存储器，其中，所述存储器，用于存放计算机程序；所述处理器，用于执行存储在所述存储器上的计算机程序时，实现所述的基于图像找寻商城商品的方法的步骤。
本说明书中描述的主题及功能操作的实施例可以在以下中实现：数字电子电路、有形体现的计算机软件或固件、包括本说明书中公开的结构及其结构性等同物的计算机硬件、或者它们中的一个或多个的组合。本说明书中描述的主题的实施例可以实现为一个或多个计算机程序，即编码在有形非暂时性程序载体上以被数据处理装置执行或控制数据处理装置的操作的计算机程序指令中的一个或多个模块。可替代地或附加地，程序指令可以被编码在人工生成的传播信号上，例如机器生成的电、光或电磁信号，该信号被生成以将信息编码并传输到合适的接收机装置以由数据处理装置执行。计算机存储介质可以是机器可读存储设备、机器可读存储基板、随机或串行存取存储器设备、或它们中的一个或多个的组合。
本说明书中描述的处理及逻辑流程可以由执行一个或多个计算机程序的一个或多个可编程计算机执行，以通过根据输入数据进行操作并生成输出来执行相应的功能。所述处理及逻辑流程还可以由专用逻辑电路—例如FPGA(现场可编程门阵列)或ASIC(专用集成电路)来执行，并且装置也可以实现为专用逻辑电路。
适合用于执行计算机程序的计算机包括，例如通用和/或专用微处理器，或任何其他类型的中央处理单元。通常，中央处理单元将从只读存储器和/或随机存取存储器接收指令和数据。计算机的基本组件包括用于实施或执行指令的中央处理单元以及用于存储指令和数据的一个或多个存储器设备。通常，计算机还将包括用于存储数据的一个或多个大容量存储设备，例如磁盘、磁光盘或光盘等，或者计算机将可操作地与此大容量存储设备耦接以从其接收数据或向其传送数据，抑或两种情况兼而有之。然而，计算机不是必须具有这样的设备。此外，计算机可以嵌入在另一设备中，例如移动电话、个人数字助理(PDA)、移动音频或视频播放器、游戏操纵台、全球定位系统(GPS)接收机、或例如通用串行总线(USB)闪存驱动器的便携式存储设备，仅举几例。
适合于存储计算机程序指令和数据的计算机可读介质包括所有形式的非易失性存储器、媒介和存储器设备，例如包括半导体存储器设备(例如EPROM、EEPROM和闪存设备)、磁盘(例如内部硬盘或可移动盘)、磁光盘以及CD ROM和DVD-ROM盘。处理器和存储器可由专用逻辑电路补充或并入专用逻辑电路中。
虽然本说明书包含许多具体实施细节，但是这些不应被解释为限制任何发明的范围或所要求保护的范围，而是主要用于描述特定发明的具体实施例的特征。本说明书内在多个实施例中描述的某些特征也可以在单个实施例中被组合实施。另一方面，在单个实施例中描述的各种特征也可以在多个实施例中分开实施或以任何合适的子组合来实施。此外，虽然特征可以如上所述在某些组合中起作用并且甚至最初如此要求保护，但是来自所要求保护的组合中的一个或多个特征在一些情况下可以从该组合中去除，并且所要求保护的组合可以指向子组合或子组合的变型。
由此，主题的特定实施例已被描述。其他实施例在所附权利要求书的范围以内。在某些情况下，权利要求书中记载的动作可以以不同的顺序执行并且仍实现期望的结果。此外，附图中描绘的处理并非必需所示的特定顺序或顺次顺序，以实现期望的结果。在某些实现中，多任务和并行处理可能是有利的。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明保护的范围之内。
应当理解的是，本发明并不局限于上面已经描述并在附图中示出的流程及结构，并且可以在不脱离其范围进行各种修改和改变。本发明的范围仅由所附的权利要求来限制。

Claims (8)

1.一种基于图像找寻商城商品的方法，其特征在于，包括，
S1：将商品库中的商品图片生成图片向量，得到原始图片生成向量；
S2：将商品库中的所述商品图片去掉背景；
S3：再将去掉背景之后的图片生成图片向量，得到去背景图片生成向量；
S4：将所述原始图片生成向量和所述去背景图片生成向量保存到向量数据库中；
S5：将用户上传的图片生成图片向量，得到上传图片生成向量；
S6：将所述用户上传的图片去掉背景，再将去掉背景之后的上传的图片生成图片向量，得到去背景上传图片生成向量；
S7：应用所述上传图片生成向量和所述去背景上传图片生成向量到所述向量数据库中查找符合相似度阈值的最相似的图片，将满足条件的图片及其对应的商品返回业务方。
2.根据权利要求1所述的基于图像找寻商城商品的方法，其特征在于，所述生成图片向量的方法为：vgg16模型。
3.根据权利要求1所述的基于图像找寻商城商品的方法，其特征在于，所述去掉背景的方法为：rembg背景移除工具。
4.根据权利要求1所述的基于图像找寻商城商品的方法，其特征在于，所述向量数据库为milvus向量数据库。
5.根据权利要求1所述的基于图像找寻商城商品的方法，其特征在于，所述相似度阈值为0-0.5之间的小数。
6.根据权利要求5所述的基于图像找寻商城商品的方法，其特征在于，图片的相识度值越接近0越相似，反之越不像。
7.一种可读存储介质，其特征在于，所述可读存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行，以实现如权利要求1-6任一项所述的基于图像找寻商城商品的方法。
8.一种计算机设备，其特征在于，包括处理器和存储器，其中，所述存储器，用于存放计算机程序；所述处理器，用于执行存储在所述存储器上的计算机程序时，实现如权利要求1-6任一项所述的基于图像找寻商城商品的方法的步骤。
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