基于图的图像分割算法python

图像分割是计算机视觉领域的一个重要任务，它的目标是将图像划分成多个区域，使得每个区域内的像素具有相似的特征。基于图的图像分割算法是一种常用的分割方法，它通过构建图来描述图像中的像素之间的关系，然后基于图上的最小割或最大流算法来实现分割。

下面是一个基于图的图像分割算法的Python实现：

import numpy as np
import cv2
import networkx as nx

def build_graph(img):
    height, width, channels = img.shape
    graph = nx.DiGraph()
    for i in range(height):
        for j in range(width):
            node_num = i * width + j
            if j < width - 1:
                weight = np.sum(np.abs(img[i, j] - img[i, j + 1]))
                graph.add_edge(node_num, node_num + 1, weight=weight)
            if i < height - 1:
                weight = np.sum(np.abs(img[i, j] - img[i + 1, j]))
                graph.add_edge(node_num, node_num + width, weight=weight)
            if i < height - 1 and j < width - 1:
                weight = np.sum(np.abs(img[i, j] - img[i + 1, j + 1]))
                graph.add_edge(node_num, node_num + width + 1, weight=weight)
            if i < height - 1 and j > 0:
                weight = np.sum(np.abs(img[i, j] - img[i + 1, j - 1]))
                graph.add_edge(node_num, node_num + width - 1, weight=weight)
    return graph

def segment_image(img, k):
    graph = build_graph(img)
    height, width, channels = img.shape
    nodes = graph.nodes()
    edges = graph.edges()
    num_nodes = len(nodes)

    node_map = {}
    for i, node in enumerate(nodes):
        node_map[node] = i

    weights = np.array([graph[u][v]['weight'] for u, v in edges])
    max_weight = np.max(weights)
    min_weight = np.min(weights)

    normalized_weights = (weights - min_weight) / (max_weight - min_weight)
    normalized_weights = k * normalized_weights

    graph = nx.Graph()
    graph.add_nodes_from(range(num_nodes))
    for i, (u, v) in enumerate(edges):
        weight = normalized_weights[i]
        graph.add_edge(node_map[u], node_map[v], weight=weight)

    segments = nx.algorithms.cut_normalized(graph)
    groups = {}
    for i, seg in enumerate(segments):
        groups[i] = set([nodes[n] for n in seg])
    
    output_img = np.zeros((height, width), dtype=np.uint8)
    for i, group in groups.items():
        for node in group:
            i, j = divmod(node, width)
            output_img[i, j] = i + j

    return output_img

# 示例
img = cv2.imread('test.png')
segments = segment_image(img, 100)
cv2.imshow('Segmented Image', segments)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个例子中，我们首先使用`build_graph`函数来构建一个图，其中图的节点表示图像中的每个像素，边表示相邻像素之间的关系。然后我们使用`segment_image`函数对图像进行分割，其中参数`k`表示对边权值的缩放因子。

最后，我们将分割结果显示出来。