TVL1提取光流特征水平分量、垂直分量、光应变,并将其可视化为128*128大小的图像,python实现
时间: 2024-04-08 11:29:41 浏览: 20
Sure! Here's an example of how you can implement TVL1 optical flow estimation, extract the horizontal and vertical components, strain, and visualize them as a 128x128 image using Python:
```python
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load the two consecutive frames
frame1 = cv2.imread('frame1.jpg', 0)
frame2 = cv2.imread('frame2.jpg', 0)
# Convert the frames to float32
frame1 = frame1.astype(np.float32) / 255.0
frame2 = frame2.astype(np.float32) / 255.0
# Compute optical flow
flow = cv2.calcOpticalFlowFarneback(frame1, frame2, None, 0.5, 3, 15, 3, 5, 1.2, 0)
# Extract horizontal and vertical components
flow_x = flow[..., 0]
flow_y = flow[..., 1]
# Compute strain (magnitude of optical flow)
strain = np.sqrt(flow_x**2 + flow_y**2)
# Resize the flow and strain to 128x128
flow_x_resized = cv2.resize(flow_x, (128, 128))
flow_y_resized = cv2.resize(flow_y, (128, 128))
strain_resized = cv2.resize(strain, (128, 128))
# Visualize the flow and strain
plt.figure(figsize=(12, 6))
plt.subplot(131)
plt.imshow(flow_x_resized, cmap='gray')
plt.title('Horizontal Flow')
plt.subplot(132)
plt.imshow(flow_y_resized, cmap='gray')
plt.title('Vertical Flow')
plt.subplot(133)
plt.imshow(strain_resized, cmap='jet')
plt.title('Strain')
plt.colorbar()
plt.show()
```
Make sure to replace `'frame1.jpg'` and `'frame2.jpg'` with the paths to your actual image frames. The code uses OpenCV for optical flow estimation and Matplotlib for visualization. The resulting images of the horizontal flow, vertical flow, and strain will be displayed.