非极大值抑制(Non-Maximum Suppression)

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一、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，抑制非极大值。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。

二、为什么要用非极大值抑制

三、 如何使用非极大值抑制

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。

其实本质上的思想内涵是在一个区域当中找到置信度(confidence score)最高的那个边界框，搜索这个区域中的局部最大值，抑制非极大值。

四、代码段

#!/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) 

这是一个链接 非极大值抑制(Non-Maximum Suppression)，很能给人启发。