NMS 和soft NMS代码

# coding:utf-8
import numpy as np
def py_cpu_nms(dets, thresh):
    """Pure Python NMS baseline."""
    # 所有图片的坐标信息
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)  # 计算出所有图片的面积
    order = scores.argsort()[::-1]  # 图片评分按升序排序

    keep = []  # 用来存放最后保留的图片的相应评分
    while order.size > 0:
        i = order[0]  # i 是还未处理的图片中的最大评分
        keep.append(i)  # 保留改图片的值
        # 矩阵操作，下面计算的是图片i分别与其余图片相交的矩形的坐标22         xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        # 计算出各个相交矩形的面积
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        # 计算重叠比例
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        # 只保留比例小于阙值的图片，然后继续处理
        inds = np.where(ovr <= thresh)[0]
        indsd= inds+1
        order = order[inds + 1]

    return keep
boxes = np.array([[100, 100, 150, 168, 0.63],[166, 70, 312, 190, 0.55],[221, 250, 389, 500, 0.79],[12, 190, 300, 399, 0.9],[28, 130, 134, 302, 0.3]])
thresh = 0.1
keep = py_cpu_nms(boxes, thresh)
print(keep)

---

# coding:utf-8
import numpy as np


def soft_nms(boxes, sigma=0.5, Nt=0.1, threshold=0.001, method=1):
    N = boxes.shape[0]
    pos = 0
    maxscore = 0
    maxpos = 0

# boxes = np.array([[100, 100, 150, 168, 0.63], [166, 70, 312, 190, 0.55], [
                #  221, 250, 389, 500, 0.79], [12, 190, 300, 399, 0.9], [28, 130, 134, 302, 0.3]])
    for i in range(N):
        maxscore = boxes[i, 4]
        maxpos = i

        tx1 = boxes[i, 0]
        ty1 = boxes[i, 1]
        tx2 = boxes[i, 2]
        ty2 = boxes[i, 3]
        ts = boxes[i, 4]

        pos = i + 1
    # get max box
        while pos < N:
            if maxscore < boxes[pos, 4]:
                maxscore = boxes[pos, 4]
                maxpos = pos
            pos = pos + 1

    # add max box as a detection
        boxes[i, 0] = boxes[maxpos, 0]
        boxes[i, 1] = boxes[maxpos, 1]
        boxes[i, 2] = boxes[maxpos, 2]
        boxes[i, 3] = boxes[maxpos, 3]
        boxes[i, 4] = boxes[maxpos, 4]

    # swap ith box with position of max box
        boxes[maxpos, 0] = tx1
        boxes[maxpos, 1] = ty1
        boxes[maxpos, 2] = tx2
        boxes[maxpos, 3] = ty2
        boxes[maxpos, 4] = ts

        tx1 = boxes[i, 0]
        ty1 = boxes[i, 1]
        tx2 = boxes[i, 2]
        ty2 = boxes[i, 3]
        ts = boxes[i, 4]

        pos = i + 1
    # NMS iterations, note that N changes if detection boxes fall below threshold
        while pos < N:
            x1 = boxes[pos, 0]
            y1 = boxes[pos, 1]
            x2 = boxes[pos, 2]
            y2 = boxes[pos, 3]
            s = boxes[pos, 4]

            area = (x2 - x1 + 1) * (y2 - y1 + 1)
            iw = (min(tx2, x2) - max(tx1, x1) + 1)
            if iw > 0:
                ih = (min(ty2, y2) - max(ty1, y1) + 1)
                if ih > 0:
                    ua = float((tx2 - tx1 + 1) *
                               (ty2 - ty1 + 1) + area - iw * ih)
                    ov = iw * ih / ua  # iou between max box and detection box

                    if method == 1:  # linear
                        if ov > Nt:
                            weight = 1 - ov
                        else:
                            weight = 1
                    elif method == 2:  # gaussian
                        weight = np.exp(-(ov * ov)/sigma)
                    else:  # original NMS
                        if ov > Nt:
                            weight = 0
                        else:
                            weight = 1

                    boxes[pos, 4] = weight*boxes[pos, 4]
                    print(boxes[:, 4])

            # if box score falls below threshold, discard the box by swapping with last box
            # update N
                    if boxes[pos, 4] < threshold:
                        boxes[pos, 0] = boxes[N-1, 0]
                        boxes[pos, 1] = boxes[N-1, 1]
                        boxes[pos, 2] = boxes[N-1, 2]
                        boxes[pos, 3] = boxes[N-1, 3]
                        boxes[pos, 4] = boxes[N-1, 4]
                        N = N - 1
                        pos = pos - 1

            pos = pos + 1
    keep = [i for i in range(N)]
    return keep


boxes = np.array([[100, 100, 150, 168, 0.63], [166, 70, 312, 190, 0.55], [
                 221, 250, 389, 500, 0.79], [12, 190, 300, 399, 0.9], [28, 130, 134, 302, 0.3]])
keep = soft_nms(boxes)
print(keep)