给出实现seam carving算法的代码

以下是一个简单的实现seam carving算法的示例代码（使用Python和NumPy库）：

import numpy as np
import cv2

def energy(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
    energy = np.sqrt(sobelx**2 + sobely**2)
    return energy

def find_seam(energy):
    rows, cols = energy.shape
    seam = np.zeros((rows,), dtype=np.int64)
    seam_energy = np.zeros((rows, cols), dtype=np.float64)
    
    seam_energy[0] = energy[0]
    
    for i in range(1, rows):
        for j in range(cols):
            if j == 0:
                seam_energy[i, j] = energy[i, j] + min(seam_energy[i-1, j], seam_energy[i-1, j+1])
            elif j == cols-1:
                seam_energy[i, j] = energy[i, j] + min(seam_energy[i-1, j-1], seam_energy[i-1, j])
            else:
                seam_energy[i, j] = energy[i, j] + min(seam_energy[i-1, j-1], seam_energy[i-1, j], seam_energy[i-1, j+1])
    
    min_energy = np.min(seam_energy[-1])
    seam[-1] = np.argmin(seam_energy[-1])
    
    for i in range(rows-2, -1, -1):
        if seam[i+1] == 0:
            seam[i] = np.argmin(seam_energy[i, :2])
        elif seam[i+1] == cols-1:
            seam[i] = np.argmin(seam_energy[i, -2:]) + cols-2
        else:
            seam[i] = np.argmin(seam_energy[i, seam[i+1]-1:seam[i+1]+2]) + seam[i+1] - 1
    
    return seam

def remove_seam(img, seam):
    rows, cols, _ = img.shape
    new_img = np.zeros((rows, cols-1, 3), dtype=np.uint8)
    
    for i in range(rows):
        new_img[i] = np.delete(img[i], seam[i], 0)
    
    return new_img

def seam_carving(img, num_seams):
    for _ in range(num_seams):
        energy_map = energy(img)
        seam = find_seam(energy_map)
        img = remove_seam(img, seam)
    
    return img

# 示例用法
image_path = 'input.jpg'
output_path = 'output.jpg'
img = cv2.imread(image_path)
resized_img = seam_carving(img, 50) # 将图像宽度减少50像素
cv2.imwrite(output_path, resized_img)

这是一个基本的seam carving算法的实现，它通过计算能量图和找到最小能量路径来实现图像的缩小。请注意，该示例代码仅适用于RGB图像。如果要处理灰度图像或其他图像类型，可能需要进行适当的修改。