def test_anchor(h=40, w=40):
x = nd.random_uniform(shape=(1, 3, h, w))
y = MultiBoxPrior(x, sizes=[0.5, 0.25, 0.1], ratios=[1, 2, 0.5])
boxes = y.reshape((h, w, -1, 4))
print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :])
return boxes
import mxnet as mx
from mxnet import nd
from mxnet.contrib.ndarray import MultiBoxPrior
import matplotlib.pyplot as plt
n = 40
# shape: batch x channel x height x weight
x = nd.random_uniform(shape=(1, 3, n, n))
y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5])
# the first anchor box generated for pixel at (20,20)
# its format is (x_min, y_min, x_max, y_max)
boxes = y.reshape((n, n, -1, 4))
print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :])
from mxnet.gluon import nn
def class_predictor(num_anchors, num_classes):
"""return a layer to predict classes"""
return nn.Conv2D(num_anchors * (num_classes + 1), 3, padding=1)
cls_pred = class_predictor(5, 10)
cls_pred.initialize()
x = nd.zeros((2, 3, 20, 20))
print('Class prediction', cls_pred(x).shape)
def box_predictor(num_anchors):
"""return a layer to predict delta locations"""
return nn.Conv2D(num_anchors * 4, 3, padding=1)
box_pred = box_predictor(10)
#!/usr/bin/env python
#-*- coding:utf-8 -*-
import mxnet as mx
from mxnet import nd
from mxnet.contrib.ndarray import MultiBoxPrior##MultiBoxPrior产生预设框
n = 40
# 输入形状: batch × channel × height × weight
x = nd.random_uniform(shape=(1, 3, n, n))
## 图像 n 个预设尺寸 m 个预设的长宽比 输出为 n+m-1 个方框
y = MultiBoxPrior(x, sizes=[.5, .25, .1], ratios=[1, 2, .5])
## 取位于 (20,20) 像素点的第一个预设框
# box的格式为 (x_min, y_min, x_max, y_max) 且为比例
boxes = y.reshape((n, n, -1, 4))
print('The first anchor box at row 21, column 21:', boxes[20, 20, 0, :])
import matplotlib.pyplot as plt
#"""convert an anchor box to a matplotlib rectangle"""
def box_to_rect(box, color, linewidth=3):
box = box.asnumpy()
return plt.Rectangle(
(box[0], box[1]), (box[2]-box[0]), (box[3]-box[1]),
fill=False, edgecolor=color, linewidth=linewidth)
colors = ['blue', 'green', 'red', 'black', 'magenta']# 3+3-1=5个
plt.imshow(nd.ones((n, n, 3)).asnumpy())
anchors = boxes[20, 20, :, :]
for i in range(anchors.shape[0]):
plt.gca().add_patch(box_to_rect(anchors[i,:]*n, colors[i]))
plt.show()