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vision_for_anchors.py
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vision_for_anchors.py
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import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
s = 1 / (1 + np.exp(-x))
return s
#---------------------------------------------------#
# 将预测值的每个特征层调成真实值
#---------------------------------------------------#
# 13x13
def yolo_head(feats, anchors, num_classes):
# 3
num_anchors = len(anchors)
# [1, 1, 1, num_anchors, 2]
anchors_tensor = np.reshape(anchors, [1, 1, 1, num_anchors, 2]) / 32
# 获得x，y的网格
# (13,13, 1, 2)
grid_shape = np.shape(feats)[1:3] # height, width
print(grid_shape)
grid_y = np.tile(np.reshape(np.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
[1, grid_shape[1], 1, 1])
grid_x = np.tile(np.reshape(np.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
[grid_shape[0], 1, 1, 1])
grid = np.concatenate([grid_x, grid_y],-1)
print(np.shape(grid))
# (batch_size,13,13,3,85)
feats = np.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
# 将预测值调成真实值
# box_xy对应框的中心点
# box_wh对应框的宽和高
box_xy = (sigmoid(feats[..., :2]) + grid)
box_wh = np.exp(feats[..., 2:4]) * anchors_tensor
box_confidence = sigmoid(feats[..., 4:5])
box_class_probs = sigmoid(feats[..., 5:])
fig = plt.figure()
ax = fig.add_subplot(121)
plt.ylim(-2,15)
plt.xlim(-2,15)
plt.scatter(grid_x,grid_y)
plt.scatter(5,5,c='black')
plt.gca().invert_yaxis()
anchor_left = grid_x - anchors_tensor/2
anchor_top = grid_y - anchors_tensor/2
print(np.shape(anchors_tensor))
rect1 = plt.Rectangle([anchor_left[0,5,5,0,0],anchor_top[0,5,5,0,1]],anchors_tensor[0,0,0,0,0],anchors_tensor[0,0,0,0,1],color="r",fill=False)
rect2 = plt.Rectangle([anchor_left[0,5,5,1,0],anchor_top[0,5,5,1,1]],anchors_tensor[0,0,0,1,0],anchors_tensor[0,0,0,1,1],color="r",fill=False)
rect3 = plt.Rectangle([anchor_left[0,5,5,2,0],anchor_top[0,5,5,2,1]],anchors_tensor[0,0,0,2,0],anchors_tensor[0,0,0,2,1],color="r",fill=False)
ax.add_patch(rect1)
ax.add_patch(rect2)
ax.add_patch(rect3)
ax = fig.add_subplot(122)
plt.ylim(-2,15)
plt.xlim(-2,15)
plt.scatter(grid_x,grid_y)
plt.scatter(5,5,c='black')
plt.scatter(box_xy[0,5,5,:,0],box_xy[0,5,5,:,1],c='r')
plt.gca().invert_yaxis()
pre_left = box_xy[...,0] - box_wh[...,0]/2
pre_top = box_xy[...,1] - box_wh[...,1]/2
rect1 = plt.Rectangle([pre_left[0,5,5,0],pre_top[0,5,5,0]],box_wh[0,5,5,0,0],box_wh[0,5,5,0,1],color="r",fill=False)
rect2 = plt.Rectangle([pre_left[0,5,5,1],pre_top[0,5,5,1]],box_wh[0,5,5,1,0],box_wh[0,5,5,1,1],color="r",fill=False)
rect3 = plt.Rectangle([pre_left[0,5,5,2],pre_top[0,5,5,2]],box_wh[0,5,5,2,0],box_wh[0,5,5,2,1],color="r",fill=False)
ax.add_patch(rect1)
ax.add_patch(rect2)
ax.add_patch(rect3)
plt.show()
#
feat = np.random.normal(0,0.5,[4,13,13,75])
anchors = [[142, 110],[192, 243],[459, 401]]
yolo_head(feat,anchors,20)