flownet中的correlation
时间: 2023-09-25 19:06:28 浏览: 39
Flownet中的correlation是指在光流估计过程中,对两个相邻图像的像素进行匹配时,使用的一种相似度度量方式。具体而言,它通过计算两个图像中每个像素周围的局部相似度来确定它们之间的匹配关系。这种相似度度量方式通常采用卷积运算来实现,即将每个像素周围的一小块图像区域与另一个图像中对应位置的区域进行卷积,得到一个相似度值。Flownet中的correlation可以有效地提高光流估计的精度和速度,并且在处理大规模图像序列时具有较好的鲁棒性和可扩展性。
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flownet中correlation代码
以下是Flownet中的correlation代码示例:
```python
from torch.nn.functional import unfold
def correlation(input1, input2, kernel_size=1, max_displacement=1, stride1=1, stride2=1, padding=0, dilation=1):
b, c, h, w = input1.size()
# Pad input2
padded_input2 = F.pad(input2, (padding, padding, padding, padding))
# Unfold input2
unfolded_input2 = unfold(padded_input2, kernel_size=kernel_size, dilation=dilation, stride=stride2)
# Reshape unfolded input2
unfolded_input2 = unfolded_input2.view(b, c, kernel_size**2, h, w)
# Create empty output tensor
output = torch.zeros(b, (2 * max_displacement + 1) ** 2, h // stride1, w // stride1).to(input1.device)
# Loop over displacements
for i in range(-max_displacement, max_displacement + 1):
for j in range(-max_displacement, max_displacement + 1):
# Shift input2
shifted_input2 = padded_input2[:, :, padding + i:h + padding + i, padding + j:w + padding + j]
# Unfold shifted input2
unfolded_shifted_input2 = unfold(shifted_input2, kernel_size=kernel_size, dilation=dilation, stride=stride2)
# Reshape unfolded shifted input2
unfolded_shifted_input2 = unfolded_shifted_input2.view(b, c, kernel_size**2, h, w)
# Compute correlation
corr = (input1 * unfolded_shifted_input2).sum(dim=1)
# Add to output tensor
output[:, (i + max_displacement) * (2 * max_displacement + 1) + j + max_displacement, :, :] = corr
return output
```
这段代码实现了correlation操作,它是Flownet中的关键操作之一。在此操作中,两个输入张量被移位并相乘,然后求和以计算它们之间的相似性。这个操作在光流估计中非常有用,因为它可以帮助找到两个图像中的相同区域。
flownet中的correlation代码实现
以下是Flownet中的correlation代码实现:
```python
import tensorflow as tf
def correlation(x1, x2, kernel_size=1, max_displacement=20, stride_1=1, stride_2=2):
with tf.variable_scope('correlation'):
x1_shape = tf.shape(x1)
x2_shape = tf.shape(x2)
# Set output tensor shape
batch_size = x1_shape[0]
height = x1_shape[1]
width = x1_shape[2]
num_channels = x1_shape[3]
out_height = height
out_width = width
out_channels = kernel_size * kernel_size * num_channels
# Pad x2 to fit kernel size and max displacement
pad_size = max_displacement + (kernel_size - 1) // 2
x2_padded = tf.pad(x2, [[0, 0], [pad_size, pad_size], [pad_size, pad_size], [0, 0]], "CONSTANT")
# Create empty output tensor
output = tf.zeros(shape=[batch_size, out_height, out_width, out_channels], dtype=tf.float32)
# Compute correlation
for y in range(0, kernel_size):
for x in range(0, kernel_size):
offset = (y * kernel_size + x) * num_channels
patch = tf.slice(x1, [0, y * stride_1, x * stride_1, 0], [-1, height, width, -1])
patch = tf.reshape(patch, [batch_size, height * width, num_channels])
patch = tf.transpose(patch, [0, 2, 1])
weights = tf.slice(x2_padded, [0, y, x, 0], [-1, out_height, out_width, -1])
weights = tf.reshape(weights, [batch_size, out_height * out_width, num_channels])
weights = tf.transpose(weights, [0, 2, 1])
correlation = tf.matmul(patch, weights)
correlation = tf.transpose(correlation, [0, 2, 1])
correlation = tf.reshape(correlation, [batch_size, out_height, out_width, -1])
output += tf.slice(correlation, [0, 0, 0, offset], [-1, -1, -1, num_channels])
return output
```
该实现使用TensorFlow实现了correlation操作。输入参数x1和x2是两个张量,kernel_size是卷积核尺寸,max_displacement是最大位移量,stride_1和stride_2是步长。输出张量output是x1和x2的相关性张量。
该实现首先计算了x2_padded,即将x2张量进行填充以适应卷积核大小和最大位移量。然后创建了一个空的输出张量,将所有相关性相加到其中。最后,通过使用tf.slice将相关性张量拆分为num_channels个部分,并将它们添加到output张量中,以获得最终的相关性张量输出。