def class_measurement(intersect_area, pred_area, label_area):
    """
    Calculate accuracy, calss precision and class recall.

    Args:
        intersect_area (Tensor): The intersection area of prediction and ground truth on all classes.
        pred_area (Tensor): The prediction area on all classes.
        label_area (Tensor): The ground truth area on all classes.

    Returns:
        float: The mean accuracy.
        np.ndarray: The precision of all classes.
        np.ndarray: The recall of all classes.
    """
    intersect_area = intersect_area.numpy()
    pred_area = pred_area.numpy()
    label_area = label_area.numpy()

    mean_acc = np.sum(intersect_area) / np.sum(pred_area)
    class_precision = []
    class_recall = []
    for i in range(len(intersect_area)):
        precision = 0 if pred_area[i] == 0 \
            else intersect_area[i] / pred_area[i]
        recall = 0 if label_area[i] == 0 \
            else intersect_area[i] / label_area[i]
        class_precision.append(float(precision))
        class_recall.append(float(recall))

    return mean_acc, np.array(class_precision), np.array(class_recall)

    Calculate accuracy, calss precision and class recall.

    Args:
        intersect_area (Tensor): The intersection area of prediction and ground truth on all classes.
        pred_area (Tensor): The prediction area on all classes.
        label_area (Tensor): The ground truth area on all classes.

    Returns:
        float: The mean accuracy.
        np.ndarray: The precision of all classes.
        np.ndarray: The recall of all classes.
    
paddleseg/utils/metrics.py