train_simple.py 文件源码

def precisionAuc(positions, groundTruth, radius, nStep):
    thres = np.linspace(0, radius, nStep)

    errs = np.zeros([nStep], dtype=np.float32)

    distances = np.sqrt(np.power(positions[:, 0]-groundTruth[:, 0], 2)+np.power(positions[:, 1]-groundTruth[:, 1], 2))
    distances[np.where(np.isnan(distances))] = []

    for p in range(0, nStep):
        errs[p] = np.shape(np.where(distances > thres[p]))[-1]

    score = np.trapz(errs)

    return score