python代码 基于图像配准的视频消抖算法

以下是基于图像配准的视频消抖算法的 Python 代码，使用的是 OpenCV 库：

import cv2
import numpy as np

def stabilize_video(input_file, output_file):
    cap = cv2.VideoCapture(input_file)

    # Get first frame
    ret, prev_frame = cap.read()

    # Convert to grayscale
    prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)

    # Get height and width of frame
    height, width = prev_gray.shape[:2]

    # Define transformation to identity matrix
    transform = np.eye(3)

    # Define motion model
    motion_model = cv2.MOTION_TRANSLATION

    # Define termination criteria
    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)

    # Create video writer object
    fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
    out = cv2.VideoWriter(output_file, fourcc, 30.0, (width, height))

    while True:
        # Read next frame
        ret, curr_frame = cap.read()

        if not ret:
            break

        # Convert to grayscale
        curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)

        # Estimate motion between frames
        (transform, _) = cv2.findTransformECC(prev_gray, curr_gray, transform, motion_model, criteria)

        # Apply motion to current frame
        stabilized_frame = cv2.warpPerspective(curr_frame, transform, (width, height), flags=cv2.INTER_LINEAR)

        # Write stabilized frame to output video
        out.write(stabilized_frame)

        # Set current frame as previous frame for next iteration
        prev_gray = curr_gray.copy()

    cap.release()
    out.release()
    cv2.destroyAllWindows()

该算法的基本思路是，通过图像配准技术来估计相邻帧之间的运动，并将当前帧根据估计出的运动进行纠正，从而达到视频消抖的效果。具体实现中，我们使用了 OpenCV 库中的 `cv2.findTransformECC()` 函数来估计运动模型，然后用 `cv2.warpPerspective()` 函数将当前帧进行纠正。最后，我们将纠正后的帧写入输出视频中。