# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
# array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
#下面这个函数根据anchor的参数生成anchors，也就是anchor框，基础的大小为base_size=16，也就是16x16
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
ratio_anchors = _ratio_enum(base_anchor, ratios)
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in range(ratio_anchors.shape[0])])
return anchors
def _whctrs(anchor):#计算anchor基本框和中心点
"""
Return width, height, x center, and y center for an anchor (window).
"""
w = anchor[2] - anchor[0] + 1#anchor座标中：x2-x1+1=宽度
h = anchor[3] - anchor[1] + 1#anchor座标中：y2-y1+1=高度
x_ctr = anchor[0] + 0.5 * (w - 1)#计算anchor的中心像素的x座标，x1+一半的宽-1
y_ctr = anchor[1] + 0.5 * (h - 1)#计算anchor的中心像素的y座标，y1+一半的高-1
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
#给定一组计算好的ws和hs以及中心点，输出9个anchor框座标，也就是anchors
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
def _ratio_enum(anchor, ratios):#anchor的ws和hs有两个维度，第一个是大小的变化，叫做scale_enum（）
#另一个就是这个ratio_enum（）针对基本的anchor信息w, h, x_ctr, y_ctr，首先放缩出一些列的hs和ws，然后按比率信息得到一系列的ws和hs，
#在调用的时候是这样的，对于一个基本anchor框16*16，首先按比例参数得到3个不同比例的框，然后这三个框再进行scale_enum得到9个不同比例和不同大小的框。
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):#根据比例生成一个长宽序列。【ws,hs】，对于每一个anchors，ws和hs里面分别是9个长和9个宽，然后组合到anchors数组里面，
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
if __name__ == '__main__':#主函数。
import time
t = time.time()
a = generate_anchors()
print(time.time() - t)
print(a)
from IPython import embed;
embed()
