使用光流进行注册

使用光流进行图像配准的演示。

根据定义，光流是向量场 (u, v) 满足 image1(x+u, y+v) = image0(x, y) ，其中 (image0, image1) 是序列中连续的两个二维帧。这个向量场随后可以用于通过图像扭曲进行配准。

为了显示配准结果，通过将配准结果分配给红色通道，并将目标图像分配给绿色和蓝色通道来构建RGB图像。完美的配准结果将生成一幅灰度图像，而配准错误的像素在构建的RGB图像中呈现为彩色。

import numpy as np
from matplotlib import pyplot as plt
from skimage.color import rgb2gray
from skimage.data import stereo_motorcycle, vortex
from skimage.transform import warp
from skimage.registration import optical_flow_tvl1, optical_flow_ilk

# --- Load the sequence
image0, image1, disp = stereo_motorcycle()

# --- Convert the images to gray level: color is not supported.
image0 = rgb2gray(image0)
image1 = rgb2gray(image1)

# --- Compute the optical flow
v, u = optical_flow_tvl1(image0, image1)

# --- Use the estimated optical flow for registration

nr, nc = image0.shape

row_coords, col_coords = np.meshgrid(np.arange(nr), np.arange(nc), indexing='ij')

image1_warp = warp(image1, np.array([row_coords + v, col_coords + u]), mode='edge')

# build an RGB image with the unregistered sequence
seq_im = np.zeros((nr, nc, 3))
seq_im[..., 0] = image1
seq_im[..., 1] = image0
seq_im[..., 2] = image0

# build an RGB image with the registered sequence
reg_im = np.zeros((nr, nc, 3))
reg_im[..., 0] = image1_warp
reg_im[..., 1] = image0
reg_im[..., 2] = image0

# build an RGB image with the registered sequence
target_im = np.zeros((nr, nc, 3))
target_im[..., 0] = image0
target_im[..., 1] = image0
target_im[..., 2] = image0

# --- Show the result

fig, (ax0, ax1, ax2) = plt.subplots(3, 1, figsize=(5, 10))

ax0.imshow(seq_im)
ax0.set_title("Unregistered sequence")
ax0.set_axis_off()

ax1.imshow(reg_im)
ax1.set_title("Registered sequence")
ax1.set_axis_off()

ax2.imshow(target_im)
ax2.set_title("Target")
ax2.set_axis_off()

fig.tight_layout()

估计的向量场 (u, v) 也可以用箭头图显示。

在下面的例子中，迭代Lukas-Kanade算法（iLK）应用于粒子图像测速（PIV）中的粒子图像。该序列是来自 PIV挑战2001 的案例B。

image0, image1 = vortex()

# --- Compute the optical flow
v, u = optical_flow_ilk(image0, image1, radius=15)

# --- Compute flow magnitude
norm = np.sqrt(u**2 + v**2)

# --- Display
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))

# --- Sequence image sample

ax0.imshow(image0, cmap='gray')
ax0.set_title("Sequence image sample")
ax0.set_axis_off()

# --- Quiver plot arguments

nvec = 20  # Number of vectors to be displayed along each image dimension
nl, nc = image0.shape
step = max(nl // nvec, nc // nvec)

y, x = np.mgrid[:nl:step, :nc:step]
u_ = u[::step, ::step]
v_ = v[::step, ::step]

ax1.imshow(norm)
ax1.quiver(x, y, u_, v_, color='r', units='dots', angles='xy', scale_units='xy', lw=3)
ax1.set_title("Optical flow magnitude and vector field")
ax1.set_axis_off()
fig.tight_layout()

plt.show()