import numpy as np
import math
def ssd_anchor_one_layer(img_shape,feat_shape,sizes,ratios,step,offset=0.5,dtype=np.float32):
"""Computer SSD default anchor boxes for one feature layer.
Determine the relative position grid of the centers, and the relative
width and height.
Arguments:
feat_shape: Feature shape, used for computing relative position grids;
size: Absolute reference sizes;
ratios: Ratios to use on these features;
img_shape: Image shape, used for computing height, width relatively to the
former;
offset: Grid offset.
Return:
y, x, h, w: Relative x and y grids, and height and width.
"""
# Compute the position grid: simple way.
# y, x = np.mgrid[0:feat_shape[0], 0:feat_shape[1]]
# y = (y.astype(dtype) + offset) / feat_shape[0]
# x = (x.astype(dtype) + offset) / feat_shape[1]
# Weird SSD-Caffe computation using steps values...
#对于第一个特征图（block4：38x38）；y=[[0,0,……0],[1,1,……1]，……[37,37,……，37]]；
# 而x=[[0,1,2……，37]，[0,1,2……，37],……[0,1,2……，37]]
y, x = np.mgrid[0:feat_shape[0], 0:feat_shape[1]]
# 可以得到在原图上，相对原图比例大小的每个锚点中心坐标x,y
# 将38个cell对应锚点框的x,y坐标偏移至每个cell中心，然后乘以相对原图缩放的比例，再除以原图
y = (y.astype(dtype) + offset) * step / img_shape[0]
x = (x.astype(dtype) + offset) * step / img_shape[1]
# Expand dims to support easy broadcasting.
# 对于第一个特征图，y的shape=38x38x1；x的shape=38x38x1
y = np.expand_dims(y, axis=-1)
x = np.expand_dims(x, axis=-1)
# Compute relative height and width.
# Tries to follow the original implementation of SSD for the order.
# 该特征图上每个点对应的锚点框数量;如：对于第一个特征图每个点预测4个锚点框（block4：38x38），
#num_anchors :2+2=4
num_anchors = len(sizes) + len(ratios)
# 对于第一个特征图，h的shape=4x；w的shape=4x
h = np.zeros((num_anchors, ), dtype=dtype)
w = np.zeros((num_anchors, ), dtype=dtype)
# Add first anchor boxes with ratio=1.
# 第一个锚点框的高h[0]=起始锚点的高/原图大小的高；例如：h[0]=21/300
h[0] = sizes[0] / img_shape[0]
# 第一个锚点框的宽w[0]=起始锚点的宽/原图大小的宽；例如：w[0]=21/300
w[0] = sizes[0] / img_shape[1]
di = 1 #锚点宽个数偏移
if len(sizes) > 1:
# 第二个锚点框的高h[1]=sqrt（起始锚点的高*起始锚点的宽）/原图大小的高；例如：h[1]=sqrt(21*45)/300
h[1] = math.sqrt(sizes[0] * sizes[1]) / img_shape[0]
# 第二个锚点框的高w[1]=sqrt（起始锚点的高*起始锚点的宽）/原图大小的宽；例如：w[1]=sqrt(21*45)/300
w[1] = math.sqrt(sizes[0] * sizes[1]) / img_shape[1]
di += 1
for i, r in enumerate(ratios):
# 遍历长宽比例，第一个特征图，r只有两个，2和0.5；共四个锚点宽size（h[0]~h[3]）
# 例如：对于第一个特征图，h[0+2]=h[2]=21/300/sqrt(2);w[0+2]=w[2]=45/300*sqrt(2)
# 例如：对于第一个特征图，h[1+2]=h[3]=21/300/sqrt(0.5);w[1+2]=w[3]=45/300*sqrt(0.5)
# 返回没有归一化前的锚点坐标和尺寸
h[i+di] = sizes[0] / img_shape[0] / math.sqrt(r)
w[i+di] = sizes[0] / img_shape[1] * math.sqrt(r)
return y, x, h, w
y, x, h, w = ssd_anchor_one_layer((300,300),(38,38),(21., 45.),[2, .5],8)
print(h)
print(w)
[0.07 0.10246951 0.04949747 0.09899495]
[0.07 0.10246951 0.09899495 0.04949747]
其中第一个38*38的特征图，每个点产生4个anchor,分别为0.07和0.102的正方形两个，宽高分别为0.049、0.098的长方形两个。