[深度学习概念]·非极大值抑制解析

非极大值抑制(Non-Maximum Suppression)

1. 什么是非极大值抑制

2. 为什么要用非极大值抑制

3. 如何使用非极大值抑制

4. 参考资料

1. 什么是非极大值抑制

非极大值抑制，简称为NMS算法，英文为Non-Maximum Suppression。其思想是搜素局部最大值，抑制极大值。NMS算法在不同应用中的具体实现不太一样，但思想是一样的。非极大值抑制，在计算机视觉任务中得到了广泛的应用，例如边缘检测、人脸检测、目标检测（DPM，YOLO，SSD，Faster R-CNN）等。

2. 为什么要用非极大值抑制

以目标检测为例：目标检测的过程中在同一目标的位置上会产生大量的候选框，这些候选框相互之间可能会有重叠，此时我们需要利用非极大值抑制找到最佳的目标边界框，消除冗余的边界框。Demo如下图：

Object Detection

左图是人脸检测的候选框结果，每个边界框有一个置信度得分(confidence score)，如果不使用非极大值抑制，就会有多个候选框出现。右图是使用非极大值抑制之后的结果，符合我们人脸检测的预期结果。

3. 如何使用非极大值抑制

前提：目标边界框列表及其对应的置信度得分列表，设定阈值，阈值用来删除重叠较大的边界框。 IoU：intersection-over-union，即两个边界框的交集部分除以它们的并集。

非极大值抑制的流程如下：

根据置信度得分进行排序
选择置信度最高的比边界框添加到最终输出列表中，将其从边界框列表中删除
计算所有边界框的面积
计算置信度最高的边界框与其它候选框的IoU。
删除IoU大于阈值的边界框
重复上述过程，直至边界框列表为空。

Python代码如下：

代码语言：javascript复制

#!/usr/bin/env python
# _*_ coding: utf-8 _*_


import cv2
import numpy as np


"""
    Non-max Suppression Algorithm

    @param list  Object candidate bounding boxes
    @param list  Confidence score of bounding boxes
    @param float IoU threshold

    @return Rest boxes after nms operation
"""
def nms(bounding_boxes, confidence_score, threshold):
    # If no bounding boxes, return empty list
    if len(bounding_boxes) == 0:
        return [], []

    # Bounding boxes
    boxes = np.array(bounding_boxes)

    # coordinates of bounding boxes
    start_x = boxes[:, 0]
    start_y = boxes[:, 1]
    end_x = boxes[:, 2]
    end_y = boxes[:, 3]

    # Confidence scores of bounding boxes
    score = np.array(confidence_score)

    # Picked bounding boxes
    picked_boxes = []
    picked_score = []

    # Compute areas of bounding boxes
    areas = (end_x - start_x   1) * (end_y - start_y   1)

    # Sort by confidence score of bounding boxes
    order = np.argsort(score)

    # Iterate bounding boxes
    while order.size > 0:
        # The index of largest confidence score
        index = order[-1]

        # Pick the bounding box with largest confidence score
        picked_boxes.append(bounding_boxes[index])
        picked_score.append(confidence_score[index])

        # Compute ordinates of intersection-over-union(IOU)
        x1 = np.maximum(start_x[index], start_x[order[:-1]])
        x2 = np.minimum(end_x[index], end_x[order[:-1]])
        y1 = np.maximum(start_y[index], start_y[order[:-1]])
        y2 = np.minimum(end_y[index], end_y[order[:-1]])

        # Compute areas of intersection-over-union
        w = np.maximum(0.0, x2 - x1   1)
        h = np.maximum(0.0, y2 - y1   1)
        intersection = w * h

        # Compute the ratio between intersection and union
        ratio = intersection / (areas[index]   areas[order[:-1]] - intersection)

        left = np.where(ratio < threshold)
        order = order[left]

    return picked_boxes, picked_score


# Image name
image_name = 'nms.jpg'

# Bounding boxes
bounding_boxes = [(187, 82, 337, 317), (150, 67, 305, 282), (246, 121, 368, 304)]
confidence_score = [0.9, 0.75, 0.8]

# Read image
image = cv2.imread(image_name)

# Copy image as original
org = image.copy()

# Draw parameters
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1
thickness = 2

# IoU threshold
threshold = 0.4

# Draw bounding boxes and confidence score
for (start_x, start_y, end_x, end_y), confidence in zip(bounding_boxes, confidence_score):
    (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness)
    cv2.rectangle(org, (start_x, start_y - (2 * baseline   5)), (start_x   w, start_y), (0, 255, 255), -1)
    cv2.rectangle(org, (start_x, start_y), (end_x, end_y), (0, 255, 255), 2)
    cv2.putText(org, str(confidence), (start_x, start_y), font, font_scale, (0, 0, 0), thickness)

# Run non-max suppression algorithm
picked_boxes, picked_score = nms(bounding_boxes, confidence_score, threshold)

# Draw bounding boxes and confidence score after non-maximum supression
for (start_x, start_y, end_x, end_y), confidence in zip(picked_boxes, picked_score):
    (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness)
    cv2.rectangle(image, (start_x, start_y - (2 * baseline   5)), (start_x   w, start_y), (0, 255, 255), -1)
    cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (0, 255, 255), 2)
    cv2.putText(image, str(confidence), (start_x, start_y), font, font_scale, (0, 0, 0), thickness)

# Show image
cv2.imshow('Original', org)
cv2.imshow('NMS', image)
cv2.waitKey(0)

源码下载地址：https://github.com/SnailTyan/deep-learning-tools/blob/master/nms.py 记得给个Star。Demo原图在README.md里。

实验结果：

阈值为0.6

threshold = 0.6

阈值为0.5

threshold = 0.5

阈值为0.4

threshold = 0.4

4. 参考资料

https://www.pyimagesearch.com/2014/11/17/non-maximum-suppression-object-detection-python/
http://cs.brown.edu/~pff/papers/lsvm-pami.pdf
http://blog.csdn.net/shuzfan/article/details/52711706
http://www.cnblogs.com/liekkas0626/p/5219244.html
http://www.tk4479.net/yzhang6_10/article/details/50886747
http://blog.csdn.net/qq_14845119/article/details/52064928

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