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You need to pass in the correct arguments, if you use a jupyter notebook you can get these by using shift tab, I have copied the help for resize in case you are not using jupyter

Docstring:
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst
.   @brief Resizes an image.
.
.   @param src Source image.
.   @param dst Destination image with the same type as src . The size is dsize (when it is non-zero)
.   or the size is computed from src.size() , fx , and fy .
.   @param dsize Destination image size. If it is zero, it is computed as:
.   \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
.   Either dsize or both fx and fy must be non-zero.
.   @param fx Scale factor along the horizontal axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.width/src.cols}\f]
.   @param fy Scale factor along the vertical axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.height/src.rows}\f]
.   @param interpolation Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are
.   supported for now.
.   @param stream Stream for the asynchronous version.
.
.   @sa resize
Type:      builtin_function_or_method


You should therefore be able to resize with the following

cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),lumGPU,interpolation=cv2.INTER_CUBIC)


if you per-initialize lumGPU, e.g.

lumGPU = cv2.cuda_GpuMat(imgHDY,imgHDX,cv.CV_8UC1)


otherwise you will need lumGPU to be the return value

lumGPU = cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),interpolation=cv2.INTER_CUBIC)


You need to pass in the correct arguments, if you use a jupyter notebook you can get these by using shift tab, I have copied the help for resize in case you are not using jupyter

Docstring:
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst
.   @brief Resizes an image.
.
.   @param src Source image.
.   @param dst Destination image with the same type as src . The size is dsize (when it is non-zero)
.   or the size is computed from src.size() , fx , and fy .
.   @param dsize Destination image size. If it is zero, it is computed as:
.   \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
.   Either dsize or both fx and fy must be non-zero.
.   @param fx Scale factor along the horizontal axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.width/src.cols}\f]
.   @param fy Scale factor along the vertical axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.height/src.rows}\f]
.   @param interpolation Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are
.   supported for now.
.   @param stream Stream for the asynchronous version.
.
.   @sa resize
Type:      builtin_function_or_method


You should therefore be able to resize with the following

cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),lumGPU,interpolation=cv2.INTER_CUBIC)


if you per-initialize lumGPU, e.g.

lumGPU = cv2.cuda_GpuMat(imgHDY,imgHDX,cv.CV_8UC1)
cv2.cuda_GpuMat(imgHDY,imgHDX,lumGPU0.type())


otherwise you will need lumGPU to be the return value

lumGPU = cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),interpolation=cv2.INTER_CUBIC)


You need to pass in the correct arguments, if you use a jupyter notebook you can get these by using shift tab, I have copied the help for resize in case you are not using jupyter

Docstring:
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst
.   @brief Resizes an image.
.
.   @param src Source image.
.   @param dst Destination image with the same type as src . The size is dsize (when it is non-zero)
.   or the size is computed from src.size() , fx , and fy .
.   @param dsize Destination image size. If it is zero, it is computed as:
.   \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
.   Either dsize or both fx and fy must be non-zero.
.   @param fx Scale factor along the horizontal axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.width/src.cols}\f]
.   @param fy Scale factor along the vertical axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.height/src.rows}\f]
.   @param interpolation Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are
.   supported for now.
.   @param stream Stream for the asynchronous version.
.
.   @sa resize
Type:      builtin_function_or_method


You should therefore be able to resize with the following

cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),lumGPU,interpolation=cv2.INTER_CUBIC)


if you per-initialize pre-initialize lumGPU, e.g.

lumGPU = cv2.cuda_GpuMat(imgHDY,imgHDX,lumGPU0.type())


otherwise you will need lumGPU to be the return value

lumGPU = cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),interpolation=cv2.INTER_CUBIC)


You need to pass in the correct arguments, to find these in the python interpreter you can type

help(cv2.cuda.resize)


or if you use are in a jupyter notebook you can get these by using shift tab, shift+tab, I have copied the help included the output from this for resize in case you are not using jupyterfor cv2.cuda.resize function below

Docstring:
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst
.   @brief Resizes an image.
.
.   @param src Source image.
.   @param dst Destination image with the same type as src . The size is dsize (when it is non-zero)
.   or the size is computed from src.size() , fx , and fy .
.   @param dsize Destination image size. If it is zero, it is computed as:
.   \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
.   Either dsize or both fx and fy must be non-zero.
.   @param fx Scale factor along the horizontal axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.width/src.cols}\f]
.   @param fy Scale factor along the vertical axis. If it is zero, it is computed as:
.   \f[\texttt{(double)dsize.height/src.rows}\f]
.   @param interpolation Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are
.   supported for now.
.   @param stream Stream for the asynchronous version.
.
.   @sa resize
Type:      builtin_function_or_method


You should therefore be able to resize with the following

cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),lumGPU,interpolation=cv2.INTER_CUBIC)


if you pre-initialize lumGPU, e.g.

lumGPU = cv2.cuda_GpuMat(imgHDY,imgHDX,lumGPU0.type())


otherwise you will need lumGPU to be the return value

lumGPU = cv2.cuda.resize(lumGPU0,(imgHDX,imgHDY),interpolation=cv2.INTER_CUBIC)