Array-frame conversion

araviq6.array2qvideoframe provides functions to convert numpy array to QVideoFrame.

To convert QVideoFrame to numpy array, convert the frame to QImage by QVideoFrame.toImage() and use qimage2ndarray package.

Note

This module imitates https://github.com/hmeine/qimage2ndarray.

araviq6.array2qvideoframe.array2qvideoframe(array: ndarray, normalize: bool | int | Tuple[int, int] = False) → araviq6.qt_compat.QtMultimedia.QVideoFrame

Convert a 2D or 3D numpy array into 32-bit QVideoFrame.

The dimensions of a 3D array are (width, height, channels), and the channels can be 1, 2, 3 or 4. 2D array with (width, height) dimension is converted to (width, height, 1).

Number of the channels is interpreted as follows:

#channels	interpretation
1	scalar/gray
2	scalar/gray + alpha
3	RGB
4	RGB + alpha

Note that the scalar data will be converted into gray RGB triples.

The parameter normalize can be used to normalize an frame’s value range to 0-255.

If normalize = (nmin, nmax):

Scale & clip frame values from nmin..nmax to 0..255

If normalize = nmax:

Lets nmin default to zero, i.e. scale & clip the range 0..nmax to 0..255

If normalize = True:

Scale frame values to 0..255, except for boolean arays, where False and True are mapped to 0 and 255. Same as passing (gray.min(), gray.max())

If array contains masked values, the corresponding pixels will be transparent in the result. Thus, the result be of Format_BGRA8888 if the input already contains an alhpa channel (i.e., has shape (H, W, 4)) or if there are masked pixels, and Format_BGRX8888 otherwise.