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you can try xpath instead of name ;

Following is an example how to create a grayscale image representing classes for a segmentation task or similar.

On some black background, draw some shapes with fill values in the range of 1, ..., #classes. For visualization purposes, this mask is plotted as perceived as a regular grayscale image as well as scaled to the said value range – to emphasize that the mask looks all black in general, but there's actual content in it. This mask is saved as a lossless PNG image, and then opened using Pillow, and converted to mode P. Last step is to set up a proper palette for the desired number of colors, and apply that palette using Image.putpalette.

You need to call your function in group_map. Also the function requires two arguments so pass the_dir_ex as well.

Use this function -

Consider assigning multiple columns which should line up by index since per docs:

When a pandas object is returned, then the index is taken from the count.

The error message is due to passing types instead of instances. You should declare the argument types and return type so ctypes can double-check the values passed are correct.

This needs more information to be accurate, but the minimum you need is:

test.cpp

The occurences of m0 and m1 inside the clause in m0 == m1 are actually binding occurences of the variables for the pattern matching construct (in particular for the return clause). Your code is actually the same as

You can do the following:

Normally, destruct t applies when t is an inhabitant of an inductive type I, giving you one goal for each possible constructor for I that could have been used to produce t. Here as you remarked H has type P -> False, which is not an inductive type, but False is. So what happens is this: destruct gives you a first goal corresponding to the P hypothesis of H. Applying H to that goal leads to a term of type False, which is an inductive type, on which destruct works as it should, giving you zero goals since False has no constructors. Many tactics for inductive types work like this on hypothesis of the form P1 -> … -> Pn -> I where I is an inductive type: they give you side-goals for P1 … Pn, and then work on I.

We need to use !! for evaluation with enquo or can modify that to {{}}