[英]High level implementation of opencv stitcher class in python
I would like to customize the high-level stitcher class (for example, to add an assumption that the images are ordered). 我想自定义高级订书机类(例如,添加一个图像已排序的假设)。 Nevertheless, the python class is only a binding and thus require me to re-implement the entire class in order to be able to customize it. 尽管如此,python类只是一个绑定,因此需要我重新实现整个类才能对其进行自定义。
Is there a Python implementation of the high-level stitcher class available? 是否有可用的高级拼接器类的Python实现?
You can modify the stitching pipeline using the methods provided by the Stitcher class: https://docs.opencv.org/4.1.0/d2/d8d/classcv_1_1Stitcher.html 您可以使用Stitcher类提供的方法修改拼接管道: https : //docs.opencv.org/4.1.0/d2/d8d/classcv_1_1Stitcher.html
You also might be interested in taking a look at https://github.com/opencv/opencv/blob/master/samples/python/stitching_detailed.py 您可能也有兴趣看看https://github.com/opencv/opencv/blob/master/samples/python/stitching_detailed.py
If you modify the detailed example you can do things like speed up computation given you know the order of images by adding this: 如果您修改了详细的示例,则可以通过添加以下内容来执行诸如加快计算速度之类的操作:
match_mask = np.zeros((len(features), len(features)), np.uint8)
for i in range(len(features) - 1):
match_mask[i, i + 1] = 1
(source: https://software.intel.com/en-us/articles/fast-panorama-stitching ) (来源: https : //software.intel.com/zh-cn/articles/fast-panorama-stitching )
and then replacing this line in stitching_detailed.py p=matcher.apply2(features)
with this p = matcher.apply2(features, match_mask)
然后将其替换p=matcher.apply2(features)
该行,并将其替换为p = matcher.apply2(features, match_mask)
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