求最佳缝合线的python代码

以下是一些示例代码，用于找到最佳缝合线：

1. 动态规划

def find_best_seam(image):
    # 计算能量图
    energy_map = compute_energy_map(image)
    # 初始化路径图
    path_map = np.zeros_like(energy_map, dtype=np.int)
    # 动态规划
    for i in range(1, energy_map.shape[0]):
        for j in range(0, energy_map.shape[1]):
            # 处理边界情况
            if j == 0:
                path_map[i][j] = j + np.argmin(energy_map[i-1][j:j+2])
            elif j == energy_map.shape[1] - 1:
                path_map[i][j] = j - 1 + np.argmin(energy_map[i-1][j-1:j+1])
            else:
                path_map[i][j] = j - 1 + np.argmin(energy_map[i-1][j-1:j+2])
            # 更新能量值
            energy_map[i][j] += energy_map[i-1][path_map[i][j]]
    # 找出最佳缝合线
    seam = []
    j = np.argmin(energy_map[-1])
    for i in range(path_map.shape[0]-1, -1, -1):
        seam.append((i, j))
        j = path_map[i][j]
    seam.reverse()
    return seam

2. 拓扑排序

def find_best_seam(image):
    # 计算能量图
    energy_map = compute_energy_map(image)
    # 构建图
    graph = build_graph(energy_map)
    # 拓扑排序
    topo_order = topo_sort(graph)
    # 初始化路径图和能量值
    path_map = np.zeros_like(energy_map, dtype=np.int)
    energy = np.zeros_like(energy_map)
    energy[0] = energy_map[0]
    # 计算最佳缝合线
    for node in topo_order:
        if node[0] == 0:
            continue
        energy[node] = energy_map[node] + np.min([energy[pred] for pred in graph[node]])
        path_map[node] = graph[node][np.argmin([energy[pred] for pred in graph[node]])][1]
    seam = []
    j = np.argmin(energy[-1])
    for i in range(path_map.shape[0]-1, -1, -1):
        seam.append((i, j))
        j = path_map[i][j]
    seam.reverse()
    return seam

3. 双向动态规划

def find_best_seam(image):
    # 计算能量图
    energy_map = compute_energy_map(image)
    # 初始化路径图和能量值
    path_map = np.zeros_like(energy_map, dtype=np.int)
    energy = np.zeros_like(energy_map)
    energy[0] = energy_map[0]
    # 双向动态规划
    for i in range(1, energy_map.shape[0]):
        for j in range(1, energy_map.shape[1]-1):
            energy[i][j] = energy_map[i][j] + np.min([energy[i-1][j-1:j+2]])
            path_map[i][j] = j - 1 + np.argmin(energy[i-1][j-1:j+2])
        energy[i][0] = energy_map[i][0] + np.min([energy[i-1][0], energy[i-1][1]])
        path_map[i][0] = np.argmin([energy[i-1][0], energy[i-1][1]])
        j = energy_map.shape[1] - 1
        energy[i][j] = energy_map[i][j] + np.min([energy[i-1][j-1:j+1]])
        path_map[i][j] = j - 1 + np.argmin(energy[i-1][j-1:j+1])
    # 找出最佳缝合线
    seam = []
    j = np.argmin(energy[-1])
    for i in range(path_map.shape[0]-1, -1, -1):
        seam.append((i, j))
        j = path_map[i][j]
    seam.reverse()
    return seam