YOLOv3源码阅读：nms_utils.py

2019-05-22 2019-09-03

目标检测

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一、YOLO简介

YOLO（You Only Look Once）是一个高效的目标检测算法，属于One-Stage大家族，针对于Two-Stage目标检测算法普遍存在的运算速度慢的缺点，YOLO创造性的提出了One-Stage。也就是将物体分类和物体定位在一个步骤中完成。YOLO直接在输出层回归bounding box的位置和bounding box所属类别，从而实现one-stage。

经过两次迭代，YOLO目前的最新版本为YOLOv3，在前两版的基础上，YOLOv3进行了一些比较细节的改动，效果有所提升。

本文正是希望可以将源码加以注释，方便自己学习，同时也愿意分享出来和大家一起学习。由于本人还是一学生，如果有错还请大家不吝指出。

本文参考的源码地址为：https://github.com/wizyoung/YOLOv3_TensorFlow

二、代码和注释

文件目录：YOUR_PATH\YOLOv3_TensorFlow-master_utils.py

这一部分代码主要是非最大值抑制（NMS）的实现，原理都是相同，过程大致如下： - 首先按照目标的置信度从大到小排序 - 取出当前最大的置信度的目标框 - 计算剩下的目标框和取出的目标框的iou - 依次检查iou的大小，如果iou高于一定的阈值，则说明对应的目标框被取出的目标框抑制了，因此只留下iou小于一定阈值的框。 - 重复2~4步骤，直至处理完所有的目标框 - 返回所有取出的目标框，就是NMS的结果

需要注意的是，NMS只针对于一类类别的数据，如果有多个类别，则需要分别处理。

# coding: utf-8

from __future__ import division, print_function

import numpy as np
import tensorflow as tf


def gpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, nms_thresh=0.5):
    """
    Perform NMS on GPU using TensorFlow.

    params:
        boxes: tensor of shape [1, 10647, 4] # 10647=(13*13+26*26+52*52)*3, for input 416*416 image
        scores: tensor of shape [1, 10647, num_classes], score=conf*prob
        num_classes: total number of classes
        max_boxes: integer, maximum number of predicted boxes you'd like, default is 50
        score_thresh: if [ highest class probability score < score_threshold]
                        then get rid of the corresponding box
        nms_thresh: real value, "intersection over union" threshold used for NMS filtering
    """

    boxes_list, label_list, score_list = [], [], []
    max_boxes = tf.constant(max_boxes, dtype='int32')

    # since we do nms for single image, then reshape it
    boxes = tf.reshape(boxes, [-1, 4])  # '-1' means we don't konw the exact number of boxes
    score = tf.reshape(scores, [-1, num_classes])

    # Step 1: Create a filtering mask based on "box_class_scores" by using "threshold".
    mask = tf.greater_equal(score, tf.constant(score_thresh))
    # Step 2: Do non_max_suppression for each class
    for i in range(num_classes):
        # Step 3: Apply the mask to scores, boxes and pick them out
        filter_boxes = tf.boolean_mask(boxes, mask[:, i])
        filter_score = tf.boolean_mask(score[:, i], mask[:, i])
        nms_indices = tf.image.non_max_suppression(boxes=filter_boxes,
                                                   scores=filter_score,
                                                   max_output_size=max_boxes,
                                                   iou_threshold=nms_thresh, name='nms_indices')
        label_list.append(tf.ones_like(tf.gather(filter_score, nms_indices), 'int32') * i)
        boxes_list.append(tf.gather(filter_boxes, nms_indices))
        score_list.append(tf.gather(filter_score, nms_indices))

    boxes = tf.concat(boxes_list, axis=0)
    score = tf.concat(score_list, axis=0)
    label = tf.concat(label_list, axis=0)

    return boxes, score, label


def py_nms(boxes, scores, max_boxes=50, iou_thresh=0.5):
    """
    Pure Python NMS baseline.

    Arguments: boxes: shape of [-1, 4], the value of '-1' means that dont know the
                      exact number of boxes
               scores: shape of [-1,]
               max_boxes: representing the maximum of boxes to be selected by non_max_suppression
               iou_thresh: representing iou_threshold for deciding to keep boxes
    """
    assert boxes.shape[1] == 4 and len(scores.shape) == 1

    # 下面几行的代码主要是用于求解每个box的面积，然后按照每个box的score的大小进行排序
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    areas = (x2 - x1) * (y2 - y1)
    # 按照每个box的score大小进行排序，这里返回的是排序之后的box的index。
    # 本质上order储存的是需要处理的box的索引
    order = scores.argsort()[::-1]

    # keep用于储存保留下来的box的索引index
    keep = []

    # 如果还存在没有被处理的box的索引
    while order.size > 0:
        # 由于之前进行了排序，所以order的第一个肯定是score最高的
        i = order[0]
        # 将这个索引保存起来
        keep.append(i)

        # 下面的代码主要是求解第一个box和剩下的所有的box的IOU，
        # 因为第一个是目标box，所以在order的选取上需要加上[1:]，取遍剩下的所有的box
        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
        # IOU计算
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        # 将和目标box的IOU小于一定阈值的box的索引取出，因为高于这一阈值的box都已经被目标box抑制了
        inds = np.where(ovr <= iou_thresh)[0]
        # 然后更新我们的order，重复下一轮循环。
        order = order[inds + 1]

    # 最后返回给定数目的box的索引
    return keep[:max_boxes]


def cpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, iou_thresh=0.5):
    """
    Perform NMS on CPU.
    Arguments:
        boxes: shape [1, 10647, 4]
        scores: shape [1, 10647, num_classes]
    """

    boxes = boxes.reshape(-1, 4)
    scores = scores.reshape(-1, num_classes)
    # Picked bounding boxes
    picked_boxes, picked_score, picked_label = [], [], []

    for i in range(num_classes):
        indices = np.where(scores[:, i] >= score_thresh)
        filter_boxes = boxes[indices]
        filter_scores = scores[:, i][indices]
        if len(filter_boxes) == 0:
            continue
        # do non_max_suppression on the cpu
        indices = py_nms(filter_boxes, filter_scores,
                         max_boxes=max_boxes, iou_thresh=iou_thresh)
        picked_boxes.append(filter_boxes[indices])
        picked_score.append(filter_scores[indices])
        picked_label.append(np.ones(len(indices), dtype='int32') * i)
    if len(picked_boxes) == 0:
        return None, None, None

    boxes = np.concatenate(picked_boxes, axis=0)
    score = np.concatenate(picked_score, axis=0)
    label = np.concatenate(picked_label, axis=0)

    return boxes, score, label