Shock Wave Imaging - Optical Flow Analysis

optical_flow.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

cap = cv2.VideoCapture('video.mp4')

ret, prev_frame = cap.read()
if not ret:
    raise ValueError("Could not read video.")
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
    # Compute dense optical flow using Farneback's algorithm
    flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None,
                                        pyr_scale=0.5, levels=3, winsize=15,
                                        iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
    
    # Prepare grid for visualization
    h, w = gray.shape
    step = 16  # grid spacing
    y, x = np.mgrid[step//2:h:step, step//2:w:step].astype(int)
    fx, fy = flow[y, x].T
    
    plt.figure(figsize=(8, 8))
    plt.imshow(gray, cmap='gray')
    plt.quiver(x, y, fx, fy, color='red', angles='xy', scale_units='xy', scale=1)
    plt.title('Optical Flow Displacement Field')
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.pause(0.001)
    plt.clf()
    
    prev_gray = gray.copy()

cap.release()
plt.close()

Concept:
Another approach is to use optical flow, which computes the apparent motion between consecutive frames. Even without a static reference image, methods like the Farneback algorithm can capture the local displacements that might be induced by the shock wave. This approach works well when the shock wave produces subtle, rapid changes between successive frames.

• Optical flow is computed between each pair of consecutive frames, capturing any transient shifts.

• The resulting vector field may include contributions from overall scene or camera motion. Additional stabilization or filtering might be needed to isolate the shock-induced displacements.