屏幕截图或图标可以转换(缩放、旋转、倾斜……)?我有很多方法可以帮助你:

Simple euclidean distance as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold). (Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold. Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two. Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.

其中大部分已经在OpenCV中实现了——例如，参见cvMatchTemplate方法(使用直方图匹配):http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html。突出点/区域检测器也可用-参见OpenCV特征检测。

在双重库中使用哈希函数来检测(接近)相同的图像(免责声明:我也是作者)。这是一种简单而快速的方法来比较两个或多个图像的相似性。它使用预处理图像(灰度化、归一化和缩放)、计算图像哈希以及基于阈值对图像进行分组的多步过程来工作。

截图中是否只有图标?如果是这样，两幅图像的L2距离可能就足够了。如果L2距离不起作用，下一步就是尝试一些简单而成熟的方法，比如:Lucas-Kanade。我确定OpenCV中有。

如果你想比较图像的相似性，我建议你使用OpenCV。在OpenCV中，特征匹配和模板匹配很少。在特征匹配方面，有SURF、SIFT、FAST等检测仪。你可以用它来检测、描述和匹配图像。之后，您可以使用特定的索引来查找两个图像之间的匹配数。

胡不变矩是比较两幅图像非常有力的工具

屏幕截图或图标可以转换(缩放、旋转、倾斜……)?我有很多方法可以帮助你:

Simple euclidean distance as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold). (Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold. Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two. Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.

其中大部分已经在OpenCV中实现了——例如，参见cvMatchTemplate方法(使用直方图匹配):http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html。突出点/区域检测器也可用-参见OpenCV特征检测。