You just need predicted values and expected values to have your confusion matrix, with sklearn.metrics.confusion_matrix for example.
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

def plot_conf(y_true, y_pred, label : str = "", figsize=(7,4)) :
    cm = confusion_matrix(y_true, y_pred)
    fig, ax = plt.subplots(figsize=figsize)         # Sample figsize in inches
    fig.suptitle("confusion matrix %s"%label)
    f = sns.heatmap(cm, annot=True, linewidths=.5, ax=ax)

So you can for example find a way to store, from matlab, the latter in a file that you will read afterwards in python for your computations : for example for a vector y, a text file containing on each line an element of y, so that the reconstruction in python will be easy.
Sorry, I don’t master matlab, this is just the idea that comes directly to my mind.