softnms过程 代码

nms为直接将iou>阈值的框全部移除，而softnms 对iou>阈值的框 降低它的权重，遍历完所有预测框之后，再通过一个阈值选择留下还是移除这个框。

import numpy as np
import cv2


def py_cpu_softnms(dets, sc, Nt=0.3, sigma=0.5, 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:
            #每次把最高score的 往上拿
            dets[i, :] = dets[maxpos + i + 1, :] #行置换，score，area也一样
            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

        scores[pos:] = weight * scores[pos:]
        # print(dets)
        # print(scores)
        plot_img(dets, scores)

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

    return keep


# boxes and scores
boxes = np.array([[200, 200, 400, 400], [220, 220, 420, 420], [200, 240, 400, 440], [240, 200, 440, 400], [1, 1, 2, 2]], dtype=np.float32)
boxscores = np.array([0.9, 0.8, 0.7, 0.6, 0.5], dtype=np.float32)

def plot_img(boxes,score):
    img = np.ones((600, 600))
    for i, bbboxes in enumerate(boxes):
        cv2.rectangle(img, (int(bbboxes[0]), int(bbboxes[1])),(int(bbboxes[2]), int(bbboxes[3])),(0,0,255), 1)
        cv2.putText(img, str(score[i]), (int(bbboxes[0]), int(bbboxes[1])),fontFace=cv2.FONT_HERSHEY_SCRIPT_COMPLEX, fontScale=0.7, color=(0,0,244))
    win = cv2.namedWindow('image', cv2.WINDOW_NORMAL)
    cv2.imshow('image', img)
    cv2.waitKey(0)
plot_img(boxes, boxscores)

index = py_cpu_softnms(boxes, boxscores, method=1)
print(index)

我们来可视化一下，第一个是最初的权重（置信度） ，首先选择0.9这个框，计算所有与它iou>阈值的框，3个都大于，然后对他们降低权重，如下面，这里使用的线性方法

3个权重都被降低一次后

这次选择的是0.254449这个框，第一个0.9的不管，两外两个都与它重叠超过阈值，降低权重，如下图

 

然后选择0.07这个框，最后一个也是大于阈值，降低权重后

最后再来一个阈值来选择 是否留下，这里阈值是0.001 ， 所以全部留下

https://github.com/DocF/Soft-NMS 参考的github代码链接