看本文之前,请先了解 faster rcnn 的网络结构及原理;

其中 关于 anchor 的部分比较晦涩,不容易搞清楚,本文重点解释下;

 

先上代码

import numpy as np


# 生成anchors总函数:ratios为一个列表,表示宽高比为:1:2,1:1,2:1
# 2**x表示:2^x,scales:[2^3 2^4 2^5],即:[8 16 32]

def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
                     scales=2 ** np.arange(3, 6)):
    """
    Generate anchor (reference) windows by enumerating aspect ratios X
    scales wrt a reference (0, 0, 15, 15) window.
    """
    base_anchor = np.array([1, 1, base_size, base_size]) - 1  # 新建一个数组:base_anchor:[0 0 15 15]
    ratio_anchors = _ratio_enum(base_anchor, ratios)  # 枚举各种宽高比
    anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)  # 枚举各种尺度,vstack:竖向合并数组
                         for i in range(ratio_anchors.shape[0])])  # shape[0]:读取矩阵第一维长度,其值为3
    return anchors


# 用于返回width,height,(x,y)中心坐标(对于一个anchor窗口)
def _whctrs(anchor):
    """
    Return width, height, x center, and y center for an anchor (window).
    """
    # anchor:存储了窗口左上角,右下角的坐标
    w = anchor[2] - anchor[0] + 1
    h = anchor[3] - anchor[1] + 1
    x_ctr = anchor[0] + 0.5 * (w - 1)  # anchor中心点坐标
    y_ctr = anchor[1] + 0.5 * (h - 1)
    return w, h, x_ctr, y_ctr


# 给定一组宽高向量,输出各个anchor,即预测窗口,**输出anchor的面积相等,只是宽高比不同**
def _mkanchors(ws, hs, x_ctr, y_ctr):
    # ws:[23 16 11],hs:[12 16 22],ws和hs一一对应。
    """
    Given a vector of widths (ws) and heights (hs) around a center
    (x_ctr, y_ctr), output a set of anchors (windows).
    """
    print('ctr', x_ctr, y_ctr)      # ctr 7.5 7.5
    print('ws1', ws)                # 23. 16. 11.]
    ws = ws[:, np.newaxis]          # newaxis:将数组转置
    print('ws2', ws)                # [[23.]
                                     # [16.]
                                     # [11.]]
    hs = hs[:, np.newaxis]
    anchors = np.hstack((x_ctr - 0.5 * (ws - 1),  # hstack、vstack:合并数组
                         y_ctr - 0.5 * (hs - 1),  # anchor:[[-3.5 2 18.5 13]  7.5-0.5*(23-1)=7.5-11=-3.5
                         x_ctr + 0.5 * (ws - 1),  #          [0  0  15  15]
                         y_ctr + 0.5 * (hs - 1)))  #         [2.5 -3 12.5 18]]
    return anchors


# 枚举一个anchor的各种宽高比,anchor[0 0 15 15],ratios[0.5,1,2]
def _ratio_enum(anchor, ratios):
    """   列举关于一个anchor的三种宽高比 1:2,1:1,2:1
    Enumerate a set of anchors for each aspect ratio wrt an anchor.
    """

    w, h, x_ctr, y_ctr = _whctrs(anchor)  # 返回宽高和中心坐标,w:16,h:16,x_ctr:7.5,y_ctr:7.5
    size = w * h  # size:16*16=256
    size_ratios = size / ratios  # 256/ratios[0.5,1,2]=[512,256,128]
    # round()方法返回x的四舍五入的数字,sqrt()方法返回数字x的平方根
    ws = np.round(np.sqrt(size_ratios))  # ws:[23 16 11]
    hs = np.round(ws * ratios)  # hs:[12 16 22],ws和hs一一对应。as:23&12
    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)  # 给定一组宽高向量,输出各个预测窗口
    return anchors


# 枚举一个anchor的各种尺度,以anchor[0 0 15 15]为例,scales[8 16 32]
def _scale_enum(anchor, scales):
    """   列举关于一个anchor的三种尺度 128*128,256*256,512*512
    Enumerate a set of anchors for each scale wrt an anchor.
    """
    w, h, x_ctr, y_ctr = _whctrs(anchor)  # 返回宽高和中心坐标,w:16,h:16,x_ctr:7.5,y_ctr:7.5
    ws = w * scales  # [128 256 512]
    hs = h * scales  # [128 256 512]
    anchors = _mkanchors(ws, hs, x_ctr, y_ctr)  # [[-56 -56 71 71] [-120 -120 135 135] [-248 -248 263 263]]
    return anchors


if __name__ == '__main__':  # 主函数
    import time

    t = time.time()
    a = generate_anchors()  # 生成anchor(窗口)
    print(time.time() - t)  # 显示时间
    print(a)

    from IPython import embed
    embed()

在 经过 backbone 卷积网络 生成 feature map 后,

feature map 每个格子可以对应到原图的一个区域,这个区域的大小 可以自己设定;

然后 以这个区域为 基准,通过 各种长宽比变换、尺度变换,生成多个 anchor;

 

比较抽象,不容易说清楚,下图有详解,自己体会吧

 

在 faster rcnn 中,输入 图像需 resize 到 M=800,N=600 大小,anchor  512 scale 大小为 512x512=262144,352x704=247808,736x384=282624

 

 

参考资料:

https://blog.csdn.net/sinat_33486980/article/details/81099093   faster R-CNN中anchors 的生成过程

https://blog.csdn.net/xzzppp/article/details/52317863  faster rcnn RPN之anchor(generate_anchors)源码解析