assess.py 文件源码

def pr_object(detect, truth, overlap=10):

    # we assume that both truth and detect volumes are separate objects

    from scipy import stats

    # TODO:  64-bit support

    # manual relabel (could be slow!)
    utruth = np.unique(truth)
    utruth = utruth[utruth > 0]
    udetect = np.unique(detect)
    udetect = udetect[udetect > 0]

    tp = 0.0
    fp = 0.0
    fn = 0.0

    # TODO:  removing only greatest match

    # for each truth object find a detection

    for t in utruth:  # background is ignored
        match = detect[truth == t]
        match = match[match > 0]  # get rid of spurious values
        match = stats.mode(match)

        if match[1] >= overlap:
            tp += 1

            # any detected objects can only be used once, so remove them here.
            # detect = mahotas.labeled.remove_regions(detect, match[0])
            detect[detect == match[0]] = 0
        else:
            fn += 1

    # detect_left, fp = mahotas.labeled.relabel(detect)
    fp = np.unique(detect)
    fp = fp[fp > 0]
    fp = len(fp)

    precision = 0
    recall = 0

    if tp + fp > 0:
        precision = tp/(tp+fp)

    if tp + fn > 0:
        recall = tp/(tp+fn)

    if (precision == 0) or (recall == 0):
        f1 = 0
    else:
        f1 = (2*precision*recall)/(precision+recall)

    print(precision)
    print(recall)
    print(f1)

    return precision, recall, f1