davidtvs / keras-linknet Goto Github PK

    target = np.argmax(y_true, axis=-1).ravel()
    predicted = np.argmax(y_pred, axis=-1).ravel()

    target = np.argmax(y_true, axis=-1).ravel().reshape(batch_size, -1)
    predicted = np.argmax(y_pred, axis=-1).ravel().reshape(batch_size, -1)

    x = predict + num_classes * target
    bincount_2d = np.array([np.bincount(x[i].astype(np.int32), minlength=num_classes**2) for i in range(batch_size)])
    conf = bincount_2d.reshape((-1, num_classes, num_classes))
    
    true_positive = np.array([np.diag(conf[i]) for i in range(batch_size)])
    false_positive = np.sum(conf, axis=1) - true_positive
    false_negative = np.sum(conf, axis=2) - true_positive