How can i implement NMS(non-maximum suppression) on Yolov4

Question

I'm training my own datasets using Yolov4 from Alexeyab but i got a multiple bounding boxes like this image below.

I googled and searched about NMS(non-maximum suppression) but all i can find is how to write a code in pytorch or tf.... i'm new to object detection so i have no idea how to implement this. All i wanted to do is just making only one bounding box for one class.

Please help me. Thank you.

Answer 1

I think NMS is ez to code, you could see explain in here. This codes below I see in fast-rcnn for every class.

import numpy as np

def nms(dets, thresh):
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        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:]])

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

        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]

    return keep