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晕了 import networkx as nx import matplotlib.pyplot as plt n_clique, n_path = 10, 10 clique1 = nx.complete_graph(n_clique) clique1_pos = nx.circular_layout(clique1) clique2 = nx.complete_graph(n_clique) clique2_mapping = {node: node + n_clique for node in clique2} nx.relabel_nodes(clique2, clique2_mapping, copy=False) # avoids repeated nodes x_diff, y_diff = 8, -1 clique2_pos = {node: clique1_pos[node-n_clique] + (x_diff, y_diff) for node in clique2} path = nx.path_graph(n_path) path_mapping = {node: node + 2 * n_clique for node in path} nx.relabel_nodes(path, path_mapping, copy=False) # avoids repeated nodes path_nodes = list(path.nodes) path_half1_nodes = path_nodes[:n_path//2] path_half2_nodes = path_nodes[n_path//2:] path_dist = 0.9 clique2_entry = n_clique + n_clique // 2 path_half1_pos = {node: clique1_pos[0] + (path_dist + i * path_dist, 0) for i, node in enumerate(path_half1_nodes)} path_half2_pos = {node: clique2_pos[clique2_entry] - (path_dist + i * path_dist, 0) for i, node in enumerate(path_half2_nodes[::-1])} path_pos = {**path_half1_pos, **path_half2_pos} barbell = nx.Graph() barbell.add_edges_from(clique1.edges) barbell.add_edges_from(clique2.edges) barbell.add_edges_from(path.edges) barbell.add_edges_from([(path_half1_nodes[0], 0), (path_half2_nodes[-1], clique2_entry)]) clique_pos = {**clique1_pos, **clique2_pos} barbell_pos = {**clique_pos, **path_pos} plt.figure(figsize=(20, 6)) nx.draw(barbell, pos=barbell_pos, with_labels=True) ...

最近遇到一个问题，如果需要计算区域内的面积，比如说要计算下图中类似三角形区域内的面积 之前的做法是用Canny算子提取边缘，再用HoughLines拟合直线，然后求出交点坐标并计算三角形面积，其中，边缘提取后的图像如下图所示： 我们可以很明显的看出这不是一个标准的三角形，所以如果想要更精确的获得三角形，就需要对图片进行轮廓提取，然后计算轮廓内区域的面积。这里给出代码： {% codeblock %} import cv2 import numpy as np # Input image img = cv2.imread('cut.jpeg', cv2.IMREAD_GRAYSCALE) # Needed due to JPG artifacts _, temp = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY) # Dilate to better detect contours temp = cv2.dilate(temp, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))) # Find largest contour _, cnts, _ = cv2.findContours(temp, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) largestCnt = [] for cnt in cnts: if len(cnt) > len(largestCnt): largestCnt = cnt # Determine center of area of largest contour M = cv2.moments(largestCnt) x = int(M["m10"] / M["m00"]) y = int(M["m01"] / M["m00"]) # Initiale mask for flood filling width, height = temp.shape mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255 mask[1:width, 1:height] = 0 # Generate intermediate image, draw largest contour, flood filled temp = np.zeros(temp.shape, np.uint8) temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED) _, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255) temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))) # Count pixels in desired region area = cv2.countNonZero(temp) # Put result on original image img = cv2.putText(img, str(area), (x, y), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, 255) cv2.imshow('Input', img) cv2.imshow('Temp image', temp) cv2.waitKey(0) {% endcodeblock %} ...