Opencv Mat Performance

It can be used to store real or complex valued vectors and matrices grayscale or color images voxel volumes vector fields point clouds tensors histograms though very high dimensional histograms may be better stored in a sparsemat.

Opencv mat performance.

Did you test your code on different opencv version or different machine. The image data from any camera can be. Without opencv removing the two cv mat lines the opencv library is not linked. Although some developers never heard about umat class and its advantages.

The 4 values rows cols type and data are all that is required to represent an image buffer of any format as an opencv mat. The umat class tells opencv functions to process images with an opencl specific code which uses an opencl enabled gpu if exists in the system automatically switching to cpu otherwise. This feature was leveraged to make the camera image data accessible to opencv. Direct access to v4l2 memory.

But there can be exceptions especially when numpy works with views instead of copies. The class mat represents an n dimensional dense numerical single channel or multi channel array. There are several other magic commands to measure performance profiling line profiling memory measurement and. 23 1s on my computer intel i7 8gb ram with opencv 2 4 1 the time elapsed is the computation loop is approx.

According to khronos group opencl open computing language is. The mat is just a simple container for actual image data. We ran this test program. More ipython magic commands.

In this case the time elapsed is the computation loop is approx. I even find that opencv can get better performance on data you gave us. N dimensional dense array class. Normally opencv functions are faster than numpy functions.

With opencv 4 1 1 the time elapsed is the computation loop is approx.