nathanzabriskie / graphcut Goto Github PK

Hello, thanks of your contribution about such good work.
I am using graph neural network to parcel brain region, however the result is not good, I want to use graph cut as post process.
The inputs of my current model are: adjacency matrix (10242, 10242), feature matrix (10242, 6), label matrix (10242). The output is the probability y that each node belongs to a label, and its dimension is (10242, 36)
I want to use graph cut to update y for better performance.

I have a few questions about your code:
First, most of graph cut is only for two categories. Can you do multi label tasks?
Second, if I want to do post-processing, what should my input be?

    @staticmethod
    def get_qimage(cvimage):
        print(cvimage.shape)
        height, width, bytes_per_pix = cvimage.shape
        bytes_per_line = width * bytes_per_pix;
        cv2.cvtColor(cvimage, cv2.COLOR_BGR2RGB, cvimage)
        return QImage(cvimage.data, width, height, bytes_per_line, QImage.Format_RGB888)

Otherwise the image will display abnormally.

I can not install PyQt4 on my system, could you give some suggestion, thanks!