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class AnchorBox():
def __init__(self, input_shape, min_size, max_size=None, aspect_ratios=None, flip=True):
self.input_shape = input_shape
self.min_size = min_size
self.max_size = max_size
self.aspect_ratios = []
for ar in aspect_ratios:
self.aspect_ratios.append(ar)
self.aspect_ratios.append(1.0 / ar)
def call(self, layer_shape, mask=None):
# --------------------------------- #
# 获取输入进来的特征层的宽和高
# 比如38x38
# --------------------------------- #
layer_height = layer_shape[0]
layer_width = layer_shape[1]
# --------------------------------- #
# 获取输入进来的图片的宽和高
# 比如300x300
# --------------------------------- #
img_height = self.input_shape[0]
img_width = self.input_shape[1]
box_widths = []
box_heights = []
# --------------------------------- #
# self.aspect_ratios一般有两个值
# [1, 1, 2, 1/2]
# [1, 1, 2, 1/2, 3, 1/3]
# --------------------------------- #
for ar in self.aspect_ratios:
# 首先添加一个较小的正方形
if ar == 1 and len(box_widths) == 0:
box_widths.append(self.min_size)
box_heights.append(self.min_size)
# 然后添加一个较大的正方形
elif ar == 1 and len(box_widths) > 0:
box_widths.append(np.sqrt(self.min_size * self.max_size))
box_heights.append(np.sqrt(self.min_size * self.max_size))
# 然后添加长方形
elif ar != 1:
box_widths.append(self.min_size * np.sqrt(ar))
box_heights.append(self.min_size / np.sqrt(ar))
# --------------------------------- #
# 获得所有先验框的宽高1/2
# --------------------------------- #
box_widths = 0.5 * np.array(box_widths)
box_heights = 0.5 * np.array(box_heights)
# --------------------------------- #
# 每一个特征层对应的步长
# --------------------------------- #
step_x = img_width / layer_width
step_y = img_height / layer_height
# --------------------------------- #
# 生成网格中心
# --------------------------------- #
linx = np.linspace(0.5 * step_x, img_width - 0.5 * step_x,
layer_width)
liny = np.linspace(0.5 * step_y, img_height - 0.5 * step_y,
layer_height)
centers_x, centers_y = np.meshgrid(linx, liny)
centers_x = centers_x.reshape(-1, 1)
centers_y = centers_y.reshape(-1, 1)
# 每一个先验框需要两个(centers_x, centers_y)，前一个用来计算左上角，后一个计算右下角
num_anchors_ = len(self.aspect_ratios)
anchor_boxes = np.concatenate((centers_x, centers_y), axis=1)
anchor_boxes = np.tile(anchor_boxes, (1, 2 * num_anchors_))
# 获得先验框的左上角和右下角
anchor_boxes[:, ::4] -= box_widths
anchor_boxes[:, 1::4] -= box_heights
anchor_boxes[:, 2::4] += box_widths
anchor_boxes[:, 3::4] += box_heights
# --------------------------------- #
# 将先验框变成小数的形式
# 归一化
# --------------------------------- #
anchor_boxes[:, ::2] /= img_width
anchor_boxes[:, 1::2] /= img_height
anchor_boxes = anchor_boxes.reshape(-1, 4)
anchor_boxes = np.minimum(np.maximum(anchor_boxes, 0.0), 1.0)
return anchor_boxes
#---------------------------------------------------#
# 用于计算共享特征层的大小
#---------------------------------------------------#
def get_vgg_output_length(height, width):
filter_sizes = [3, 3, 3, 3, 3, 3, 3, 3]
padding = [1, 1, 1, 1, 1, 1, 0, 0]
stride = [2, 2, 2, 2, 2, 2, 1, 1]
feature_heights = []
feature_widths = []
for i in range(len(filter_sizes)):
height = (height + 2*padding[i] - filter_sizes[i]) // stride[i] + 1
width = (width + 2*padding[i] - filter_sizes[i]) // stride[i] + 1
feature_heights.append(height)
feature_widths.append(width)
return np.array(feature_heights)[-6:], np.array(feature_widths)[-6:]
def get_mobilenet_output_length(height, width):
filter_sizes = [3, 3, 3, 3, 3, 3, 3, 3, 3]
padding = [1, 1, 1, 1, 1, 1, 1, 1, 1]
stride = [2, 2, 2, 2, 2, 2, 2, 2, 2]
feature_heights = []
feature_widths = []
for i in range(len(filter_sizes)):
height = (height + 2*padding[i] - filter_sizes[i]) // stride[i] + 1
width = (width + 2*padding[i] - filter_sizes[i]) // stride[i] + 1
feature_heights.append(height)
feature_widths.append(width)
return np.array(feature_heights)[-6:], np.array(feature_widths)[-6:]
def get_anchors(input_shape = [300,300], anchors_size = [30, 60, 111, 162, 213, 264, 315], backbone = 'vgg'):
if backbone == 'vgg' or backbone == 'resnet50':
feature_heights, feature_widths = get_vgg_output_length(input_shape[0], input_shape[1])
aspect_ratios = [[1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2]]
else:
feature_heights, feature_widths = get_mobilenet_output_length(input_shape[0], input_shape[1])
aspect_ratios = [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]
anchors = []
for i in range(len(feature_heights)):
anchor_boxes = AnchorBox(input_shape, anchors_size[i], max_size = anchors_size[i+1],
aspect_ratios = aspect_ratios[i]).call([feature_heights[i], feature_widths[i]])
anchors.append(anchor_boxes)
anchors = np.concatenate(anchors, axis=0)
return anchors
if __name__ == '__main__':
import matplotlib.pyplot as plt
class AnchorBox_for_Vision():
def __init__(self, input_shape, min_size, max_size=None, aspect_ratios=None, flip=True):
# 获得输入图片的大小，300x300
self.input_shape = input_shape
# 先验框的短边
self.min_size = min_size
# 先验框的长边
self.max_size = max_size
# [1, 2] => [1, 1, 2, 1/2]
# [1, 2, 3] => [1, 1, 2, 1/2, 3, 1/3]
self.aspect_ratios = []
for ar in aspect_ratios:
self.aspect_ratios.append(ar)
self.aspect_ratios.append(1.0 / ar)
def call(self, layer_shape, mask=None):
# --------------------------------- #
# 获取输入进来的特征层的宽和高
# 比如3x3
# --------------------------------- #
layer_height = layer_shape[0]
layer_width = layer_shape[1]
# --------------------------------- #
# 获取输入进来的图片的宽和高
# 比如300x300
# --------------------------------- #
img_height = self.input_shape[0]
img_width = self.input_shape[1]
box_widths = []
box_heights = []
# --------------------------------- #
# self.aspect_ratios一般有两个值
# [1, 1, 2, 1/2]
# [1, 1, 2, 1/2, 3, 1/3]
# --------------------------------- #
for ar in self.aspect_ratios:
# 首先添加一个较小的正方形
if ar == 1 and len(box_widths) == 0:
box_widths.append(self.min_size)
box_heights.append(self.min_size)
# 然后添加一个较大的正方形
elif ar == 1 and len(box_widths) > 0:
box_widths.append(np.sqrt(self.min_size * self.max_size))
box_heights.append(np.sqrt(self.min_size * self.max_size))
# 然后添加长方形
elif ar != 1:
box_widths.append(self.min_size * np.sqrt(ar))
box_heights.append(self.min_size / np.sqrt(ar))
print("box_widths:", box_widths)
print("box_heights:", box_heights)
# --------------------------------- #
# 获得所有先验框的宽高1/2
# --------------------------------- #
box_widths = 0.5 * np.array(box_widths)
box_heights = 0.5 * np.array(box_heights)
# --------------------------------- #
# 每一个特征层对应的步长
# 3x3的步长为100
# --------------------------------- #
step_x = img_width / layer_width
step_y = img_height / layer_height
# --------------------------------- #
# 生成网格中心
# --------------------------------- #
linx = np.linspace(0.5 * step_x, img_width - 0.5 * step_x, layer_width)
liny = np.linspace(0.5 * step_y, img_height - 0.5 * step_y, layer_height)
# 构建网格
centers_x, centers_y = np.meshgrid(linx, liny)
centers_x = centers_x.reshape(-1, 1)
centers_y = centers_y.reshape(-1, 1)
if layer_height == 3:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ylim(-50,350)
plt.xlim(-50,350)
plt.scatter(centers_x,centers_y)
# 每一个先验框需要两个(centers_x, centers_y)，前一个用来计算左上角，后一个计算右下角
num_anchors_ = len(self.aspect_ratios)
anchor_boxes = np.concatenate((centers_x, centers_y), axis=1)
anchor_boxes = np.tile(anchor_boxes, (1, 2 * num_anchors_))
# 获得先验框的左上角和右下角
anchor_boxes[:, ::4] -= box_widths
anchor_boxes[:, 1::4] -= box_heights
anchor_boxes[:, 2::4] += box_widths
anchor_boxes[:, 3::4] += box_heights
print(np.shape(anchor_boxes))
if layer_height == 3:
rect1 = plt.Rectangle([anchor_boxes[4, 0],anchor_boxes[4, 1]],box_widths[0]*2,box_heights[0]*2,color="r",fill=False)
rect2 = plt.Rectangle([anchor_boxes[4, 4],anchor_boxes[4, 5]],box_widths[1]*2,box_heights[1]*2,color="r",fill=False)
rect3 = plt.Rectangle([anchor_boxes[4, 8],anchor_boxes[4, 9]],box_widths[2]*2,box_heights[2]*2,color="r",fill=False)
rect4 = plt.Rectangle([anchor_boxes[4, 12],anchor_boxes[4, 13]],box_widths[3]*2,box_heights[3]*2,color="r",fill=False)
ax.add_patch(rect1)
ax.add_patch(rect2)
ax.add_patch(rect3)
ax.add_patch(rect4)
plt.show()
# --------------------------------- #
# 将先验框变成小数的形式
# 归一化
# --------------------------------- #
anchor_boxes[:, ::2] /= img_width
anchor_boxes[:, 1::2] /= img_height
anchor_boxes = anchor_boxes.reshape(-1, 4)
anchor_boxes = np.minimum(np.maximum(anchor_boxes, 0.0), 1.0)
return anchor_boxes
# 输入图片大小为300, 300
input_shape = [300, 300]
# 指定先验框的大小，即宽高
anchors_size = [30, 60, 111, 162, 213, 264, 315]
# feature_heights [38, 19, 10, 5, 3, 1]
# feature_widths [38, 19, 10, 5, 3, 1]
feature_heights, feature_widths = get_vgg_output_length(input_shape[0], input_shape[1])
# 对先验框的数量进行一个指定 4，6
aspect_ratios = [[1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2]]
anchors = []
for i in range(len(feature_heights)):
anchors.append(AnchorBox_for_Vision(input_shape, anchors_size[i], max_size = anchors_size[i+1],
aspect_ratios = aspect_ratios[i]).call([feature_heights[i], feature_widths[i]]))
anchors = np.concatenate(anchors, axis=0)
print(np.shape(anchors))
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