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Open Source Computer Vision Library
16b9514543
`PyObject*` to `std::vector<T>` conversion logic:
- If user passed Numpy Array
- If array is planar and T is a primitive type (doesn't require
constructor call) that matches with the element type of array, then
copy element one by one with the respect of the step between array
elements. If compiler is lucky (or brave enough) copy loop can be
vectorized.
For classes that require constructor calls this path is not
possible, because we can't begin an object lifetime without hacks.
- Otherwise fall-back to general case
- Otherwise - execute the general case:
If PyObject* corresponds to Sequence protocol - iterate over the
sequence elements and invoke the appropriate `pyopencv_to` function.
`std::vector<T>` to `PyObject*` conversion logic:
- If `std::vector<T>` is empty - return empty tuple.
- If `T` has a corresponding `Mat` `DataType` than return
Numpy array instance of the matching `dtype` e.g.
`std::vector<cv::Rect>` is returned as `np.ndarray` of shape `Nx4` and
`dtype=int`.
This branch helps to optimize further evaluations in user code.
- Otherwise - execute the general case:
Construct a tuple of length N = `std::vector::size` and insert
elements one by one.
Unnecessary functions were removed and code was rearranged to allow
compiler select the appropriate conversion function specialization.
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OpenCV: Open Source Computer Vision Library
Resources
- Homepage: http://opencv.org
- Docs: http://docs.opencv.org/3.4/
- Q&A forum: https://forum.opencv.org
- previous forum (read only): http://answers.opencv.org
- Issue tracking: https://github.com/opencv/opencv/issues
Contributing
Please read the contribution guidelines before starting work on a pull request.
Summary of the guidelines:
- One pull request per issue;
- Choose the right base branch;
- Include tests and documentation;
- Clean up "oops" commits before submitting;
- Follow the coding style guide.