根据运动模型生成运动模糊图像:对一副灰度图像实现运动模糊,包含主程序、模糊程序。

好的，下面是一个简单的运动模糊图像生成程序：

主程序代码：

import cv2
import numpy as np

# 读取原始图像
img = cv2.imread('lena.jpg', 0)
height, width = img.shape

# 生成运动模糊核
kernel_size = 15
theta = 45.0  # 运动方向（角度）
blur_kernel = np.zeros((kernel_size, kernel_size))
center = kernel_size // 2
slope_tan = np.tan(np.deg2rad(theta))
slope_cot = 1 / slope_tan
if slope_tan <= 1:
    for i in range(kernel_size):
        x = i - center
        y = round(x * slope_tan)
        y += center
        if y >= kernel_size or y < 0:
            continue
        blur_kernel[y, i] = 1
else:
    for i in range(kernel_size):
        y = i - center
        x = round(y * slope_cot)
        x += center
        if x >= kernel_size or x < 0:
            continue
        blur_kernel[i, x] = 1
blur_kernel /= kernel_size

# 运动模糊
blur_img = cv2.filter2D(img, -1, blur_kernel)

# 显示原始图像和运动模糊图像
cv2.imshow('Original', img)
cv2.imshow('Motion Blur', blur_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

模糊程序代码：

import numpy as np
import cv2

def motion_blur(img, kernel_size, theta):
    # 生成运动模糊核
    blur_kernel = np.zeros((kernel_size, kernel_size))
    center = kernel_size // 2
    slope_tan = np.tan(np.deg2rad(theta))
    slope_cot = 1 / slope_tan
    if slope_tan <= 1:
        for i in range(kernel_size):
            x = i - center
            y = round(x * slope_tan)
            y += center
            if y >= kernel_size or y < 0:
                continue
            blur_kernel[y, i] = 1
    else:
        for i in range(kernel_size):
            y = i - center
            x = round(y * slope_cot)
            x += center
            if x >= kernel_size or x < 0:
                continue
            blur_kernel[i, x] = 1
    blur_kernel /= kernel_size

    # 运动模糊
    blur_img = cv2.filter2D(img, -1, blur_kernel)

    return blur_img

其中，`motion_blur`函数接收三个参数：原始图像、核大小和运动方向。函数返回运动模糊图像。可以在主程序中调用该函数实现图像运动模糊。