cv | My CV built using RMarkdown and the pagedown package | GCP library

according to wikipedia it's [[1, 0, -1],[2, 0, -2],[1, 0, 1]] but according to other sources it's [[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]]

Both are used for detecting vertical edges. Difference here is how these kernels mark "left" and "right" edges.

For simplicity sake lets consider 1D example, and let array be

[0, 0, 255, 255, 255]

then if we calculate using padding then

• kernel [2, 0, -2] gives [0, -510, -510, 0, 0]
• kernel [-2, 0, 2] gives [0, 510, 510, 0, 0]

As you can see abrupt increase in value was marked with negative values by first kernel and positive values by second. Note that is is relevant only if you need to discriminate left vs right edges, when you want just to find vertical edges, you might use any of these 2 aboves and then get absolute value.

The issue is due to the intruduction of namespace, indeed you can get a similar issue with this code:

As pointed out in the comments, for the code you have shown, you are getting a warning and this warning can be safely ignored.

For usage in CUDA device code:

For a C++ class to be usable in CUDA device code, any relevant member functions that will be used explicitly or implicitly in CUDA device code, must be marked with the __device__ decorator. (There are a few exceptions e.g. for defaulted constructors which don't apply here.)

The OpenCV class you are attempting to use (cv::KeyPoint), doesn't meet these requirements for use in device code. It won't be usable as-is.

There may be a few options:

1. Recast your work using cv::KeyPoint to use some class that provides similar functionality, that you write yourself, in such a way as to be properly designed and decorated.

2. Perhaps see if OpenCV built with CUDA has an alternate version here (properly designed/decorated) (my guess would be it probably doesn't)

3. Rewrite OpenCV itself, taking into account all necessary design changes to allow the cv::KeyPoint class to be usable in device code.

4. As a variant of suggestion 1, copy the relevant data .response to a separate set of classes or just a bare array, and do your selection work based on that. The selection work done there can be used to "filter" the original array.

there is a typo in your code.

I've splitted my dument like this :

• root[stickChain _id]
• stick[leavesChain[leaf]]

Thanks to Andrey Popov for his explications

From cross_val_predict you already have the predictions. It's a matter of subsetting your data frame where the predictions are not the same as your true label, for example:

Pipeline is used to assemble several steps such as preprocessing, transformations, and modeling. StratifiedKFold is used to split your dataset to assess the performance of your model. It is not meant to be used as a part of the Pipeline as you do not want to perform it on new data.

Therefore it is normal to perform it out of the pipeline's structure.

You have to use callback in child, and then some function in parent to get those data. But remember that you have to trigger the callback function first.

You want to perform a grid search over a Pipeline object. When defining the parameters for the different steps of the pipeline, you have to use the __ syntax: