Comments (5)
The dataset preparation follows visual geo-localization benchmark. If you want to use it on your own dataset, you'll need to rewrite the image names in this standard format:
@ UTM_east @ UTM_north @ UTM_zone_number @ UTM_zone_letter @ latitude @ longitude @ pano_id @ tile_num @ heading @ pitch @ roll @ height @ timestamp @ note @ extension
where the only indispensable values are UTM coordinates (UTM_east and UTM_north). The character @
is used as the separator. So we can use path.split("@")[1], path.split("@")[2]
to get the UTM coordinates, which are further used to calculate geographical distances.
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If your images are labeled by latitude and longitude, you need to convert it to get UTM coordinates. If they are annotated by frame-level correspondence, you can create pseudo UTM coordinates. For example, you can rewrite the name of the i-th image in the Nordland-test dataset to @i@0@@@@@@@@@@@@@.jpg
, and set the evaluation parameter val_positive_dist_threshold
to 2 (i.e. the threshold is 2 frames).
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假如我的数据,没有任何标识,我就是想从一大堆图片中找出 最相似的图像呢,虽然这样听起来不像地理定位,但是有时候更多的场景是盲定位吧
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那估计还是得按这个命名格式改一下图片名字,你可以把原来的名字写到@note
那个字段,然后开头的UTM坐标的两个值随便给(我不确定不赋值会不会报错,你也可以试一下留成空的),在计算完predictions
之后直接输出结果就行,就不再计算Recall@N指标了
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对,我试过了,不赋值是报错的,对,任何直接计算pre就行,这样就可以用做盲定位了
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