对faster rcnn的一些修改

1. 在network.py中修改anchor_scales,第262行:

    def create_architecture(self, sess, mode, num_classes, tag=None, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):

修改之前，前三轮训练结果：

改为：

    def create_architecture(self, sess, mode, num_classes, tag=None, anchor_scales=(3.125 , 12.5 , 21.875 , 31.25), anchor_ratios=(0.5, 1, 2)):

修改之后，前三轮训练结果：

1. 将nms改为soft-nms
   记得在所有文件中引入模块函数
   首先，将proposal_layer.py中的第48行改为：

    # keep = nms(np.hstack((proposals, scores)), nms_thresh)
    keep = soft_nms(proposals , scores)

然后，在nms_wrapper.py中，加入：

# soft_nms
def soft_nms(dets, sc, sigma=0.5, Nt=0.3, threshold=0.001, method=2):
    keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
                        np.ascontiguousarray(sc, dtype=np.float32),
                        np.float32(sigma), 
                        np.float32(Nt),
                        np.float32(threshold),
                        np.uint8(method))
    return keep

最后，在py_cpu_nms.py中，加入：

# soft_nms
def cpu_soft_nms(dets , sc, sigma=0.5, Nt=0.3, thresh=0.001, method=2):
    """
    py_cpu_softnms
    :param dets:   boexs 坐标矩阵 format [y1, x1, y2, x2]
    :param sc:     每个 boxes 对应的分数
    :param Nt:     iou 交叠门限
    :param sigma:  使用 gaussian 函数的方差
    :param thresh: 最后的分数门限
    :param method: 使用的方法
    :return:       留下的 boxes 的 index
    """

    # indexes concatenate boxes with the last column
    N = dets.shape[0]
    indexes = np.array([np.arange(N)])
    dets = np.concatenate((dets, indexes.T), axis=1)

    # the order of boxes coordinate is [y1,x1,y2,x2]
    y1 = dets[:, 0]
    x1 = dets[:, 1]
    y2 = dets[:, 2]
    x2 = dets[:, 3]
    scores = sc
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)

    for i in range(N):
        # intermediate parameters for later parameters exchange
        tBD = dets[i, :].copy()
        tscore = scores[i].copy()
        tarea = areas[i].copy()
        pos = i + 1

        #
        if i != N-1:
            maxscore = np.max(scores[pos:], axis=0)
            maxpos = np.argmax(scores[pos:], axis=0)
        else:
            maxscore = scores[-1]
            maxpos = 0
        if tscore < maxscore:
            dets[i, :] = dets[maxpos + i + 1, :]
            dets[maxpos + i + 1, :] = tBD
            tBD = dets[i, :]

            scores[i] = scores[maxpos + i + 1]
            scores[maxpos + i + 1] = tscore
            tscore = scores[i]

            areas[i] = areas[maxpos + i + 1]
            areas[maxpos + i + 1] = tarea
            tarea = areas[i]

        # IoU calculate
        xx1 = np.maximum(dets[i, 1], dets[pos:, 1])
        yy1 = np.maximum(dets[i, 0], dets[pos:, 0])
        xx2 = np.minimum(dets[i, 3], dets[pos:, 3])
        yy2 = np.minimum(dets[i, 2], dets[pos:, 2])

        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[pos:] - inter)

        # Three methods: 1.linear 2.gaussian 3.original NMS
        if method == 1:  # linear
            weight = np.ones(ovr.shape)
            weight[ovr > Nt] = weight[ovr > Nt] - ovr[ovr > Nt]
        elif method == 2:  # gaussian
            weight = np.exp(-(ovr * ovr) / sigma)
        else:  # original NMS
            weight = np.ones(ovr.shape)
            weight[ovr > Nt] = 0
        # print(scores.shape)
        # print(np.array([weight]).T.shape)
        scores[pos:] = scores[pos:] * np.array([weight]).T

    # select the boxes and keep the corresponding indexes
    inds = dets[:, 4][scores.flatten() > thresh]
    keep = inds.astype(int)

    return keep