用python实现基于灰度投影的视频防抖动算法

基于灰度投影的视频防抖动算法可以通过对视频中每一帧图像进行灰度投影防抖动处理来实现。下面是用Python实现基于灰度投影的视频防抖动算法的代码：

import cv2
import numpy as np

def gray_projection_dithering(image):
    # 将彩色图像转换成灰度图像
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 计算灰度图像的投影
    horizontal = np.sum(gray, axis=1)
    vertical = np.sum(gray, axis=0)
    # 将投影值转换成二值化图像
    horizontal_image = np.zeros_like(gray)
    vertical_image = np.zeros_like(gray)
    for i in range(gray.shape[0]):
        if horizontal[i] > np.mean(horizontal):
            horizontal_image[i, :] = 255
    for j in range(gray.shape[1]):
        if vertical[j] > np.mean(vertical):
            vertical_image[:, j] = 255
    # 对二值化图像进行腐蚀操作
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    horizontal_image = cv2.erode(horizontal_image, kernel)
    vertical_image = cv2.erode(vertical_image, kernel)
    # 将水平和垂直方向的二值化图像合并
    mask = horizontal_image + vertical_image
    # 对原始图像进行掩模操作
    result = cv2.bitwise_and(image, image, mask=mask)
    return result

def video_stabilization(video_path):
    # 打开视频文件
    cap = cv2.VideoCapture(video_path)

    # 获取视频帧数和帧率
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    # 创建输出视频文件
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    out = cv2.VideoWriter('stabilized_video.avi', fourcc, fps, (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))

    # 读取第一帧图像并进行灰度投影防抖动处理
    ret, prev_frame = cap.read()
    prev_frame = gray_projection_dithering(prev_frame)

    # 循环读取视频中的每一帧图像并进行灰度投影防抖动处理
    for i in range(frame_count - 1):
        ret, curr_frame = cap.read()
        if not ret:
            break
        curr_frame = gray_projection_dithering(curr_frame)

        # 计算当前帧图像与前一帧图像之间的光流
        prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
        curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
        flow = cv2.calcOpticalFlowFarneback(prev_gray, curr_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)

        # 根据光流计算位移矩阵
        dx = np.median(flow[:, :, 0])
        dy = np.median(flow[:, :, 1])
        M = np.float32([[1, 0, dx], [0, 1, dy]])

        # 通过仿射变换对当前帧图像进行平移
        rows, cols, _ = curr_frame.shape
        curr_frame = cv2.warpAffine(curr_frame, M, (cols, rows))

        # 输出平移后的图像到输出视频文件
        out.write(curr_frame)

        # 将当前帧图像设为前一帧图像
        prev_frame = curr_frame

    # 释放视频文件和输出视频文件
    cap.release()
    out.release()

video_stabilization('input_video.avi')

上述代码中，使用OpenCV库实现了基于灰度投影的视频防抖动算法。通过对每一帧图像进行灰度投影防抖动处理，然后使用光流法计算帧与帧之间的位移矩阵，最后通过仿射变换对当前帧图像进行平移，从而实现视频防抖动。最终将平移后的图像输出到输出视频文件中。