python实现匈牙利匹配

现在针对某个项目，利用python实现DBSCAN和Kmeans算法。项目简介：利用某传感器可以采集场景中的点云，每一帧都可以采集数量不等的点(x,y,z)。想要利用DBSCAN和Kmeans对点云进行无监督式的聚类，并利用匈牙利匹配对不同帧的点云簇进行匹配，从而实现跟踪效果。项目备注：这是别人拜托我来写的，我花了一点点时间。从我的角度，这种方法解决该项目，简直是胡扯。。。不过，项目和人不靠谱，并不影响代码的有效性，权当一种消遣。#数据格式点云数据用csv格式文件存储，格式如下：第1行 Frame # | X | Y | Z第2行 1 -0.4 1.04 0.11第100行 1 15.4 7.45 0.16第101行 2 89.3 4.78 3.65第114行 2 34.4 6.04 0.56.........这里不贴出数据，有关数据部分的代码，可以调整为你自己所需的格式。

#DBSCAN算法代码

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实现功能：对点云进行DBSCAN聚类，并得到每一次聚类的点云簇的个数

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加载所需的库

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import DBSCAN

from sklearn.preprocessing import StandardScaler

从数据中不断按帧数来读取数据，从frame_start读，最多不能超过frame_end，直到读取点的数量达到num_threshold后停止。可以理解为，自适应地读取一定数量的点云，从而使得点云总数拓充到一个可以聚类的程度。

def adaption_frame(data, frame_start, frame_end, num_threshold=1000):

data_x = []

data_y = []

data_z = []

for i in range(frame_start, frame_end):

target_frame = i # 替换为你想要读取的Frame值

# 筛选出指定Frame值的点云数据

table_data = data[data['Frame #'] == target_frame]

x_arr = table_data['X'].values

data_x = np.concatenate((data_x, x_arr), axis=0)

y_arr = table_data['Y'].values

data_y = np.concatenate((data_y, y_arr), axis=0)

z_arr = table_data['Z'].values

data_z = np.concatenate((data_z, z_arr), axis=0)

if data_x.shape[0] > num_threshold:

break

return data_x, data_y, data_z

利用坐标值，简单的对点云进行去噪

def valid_data(data_x, data_y, data_z):

# 创建一个布尔数组，检查每个元素是否在 -2 到 2 之间

# 使用 & 操作符来确保 A、B、C 的对应元素都满足条件

condition = (data_x >= -5) & (data_x <= 5) & (data_y>= -5) & (data_y <= 5) & (data_z >= -5) & (data_z <= 5)

# 使用布尔数组来索引 A、B、C，过滤出满足条件的元素

data_x_valid = data_x[condition]

data_y_valid = data_y[condition]

data_z_valid = data_z[condition]

# 输出新的数组大小

# print("x valid shape:", data_x_valid.shape)

# print("y valid shape:", data_y_valid.shape)

# print("z valid shape:", data_z_valid.shape)

return data_x_valid, data_y_valid, data_z_valid

用于点云的绘图

def draw_data_origin(data_x, data_y, data_z):

# 创建3D绘图

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

# 绘制点云

ax.scatter(data_x, data_y, data_z, s=0.1) # s控制点的大小

# 设置轴标签

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

ax.set_title(f'Point Cloud at Frame {1}')

# 显示图形

plt.show()

DBSCAN代码

def dbscan(data_x, data_y, data_z):

# 将 X, Y, Z 合并成一个二维数组

data_input = np.column_stack((data_x, data_y, data_z))

# 标准化数据（对于许多聚类算法来说，标准化是一个好习惯）

scaler = StandardScaler()

data_scaled = scaler.fit_transform(data_input)

# 初始化 DBSCAN，这里 eps 和 min_samples 是两个重要的参数，需要根据数据特性进行调整

# eps 是邻域的半径大小，min_samples 是成为核心对象所需的最小邻居数

dbscan = DBSCAN(eps=0.3, min_samples=5)

# 进行聚类

labels = dbscan.fit_predict(data_scaled)

# 计算不同标签的数量，即点簇的个数

num_clusters = len(set(labels)) - (1 if -1 in labels else 0)

return num_clusters, labels,

对每一次的聚类结果，按照点数大小降序排列。例如：某次聚类结果分为了3类，label为2的点云簇点云数为100，label为2的点云簇点云数为30，label为3的点云簇点云数为50。结果就是对他们进行降序排列。

def order_cluster(clusters_num, labels):

unique_labels, inverse_indices = np.unique(labels, return_inverse=True)

print(unique_labels.shape)

print(inverse_indices.shape)

# 使用 numpy.bincount 统计每个标签出现的次数

counts = np.bincount(inverse_indices)

# 按照出现次数降序排列

sorted_indices = np.argsort(counts)[::-1] # 获取降序排列的索引

sorted_labels = unique_labels[sorted_indices] # 根据索引重新排列标签

sorted_counts = counts[sorted_indices] # 根据索引重新排列计数

# 打印结果

for label, count in zip(sorted_labels, sorted_counts):

print(f"类别 {label}: {count} 次")

A = []

for i in range(unique_labels.shape[0]):

# 首先找到个数最多的标签

most_common_label = sorted_labels[i]

# 然后找到这个标签在原始 labels 数组中的位置

positions_most_common = np.where(labels == most_common_label)[0]

A.append(positions_most_common)

return A

第一次的聚类结果，需要进行特殊的处理。认为点云数量超过human_size，才可以成为一个有效簇。用这种方式得到第一次聚类结果，存在多少个有效簇，并返回最小簇的点云数。

def getFirstJudge(clusters_num, labels_order, human_size):

num = 0

for i in range(clusters_num):

size = labels_order[i].shape[0]

if size > human_size:

num = num + 1

points_num_min = size

return num, points_num_min

每一次的聚类结果进行处理。如果这一次的聚类结果，有某一次的点云簇点云数大于上一次的最小点数，认为簇的个数可以增加；否则更新最新的最小簇代表的点云个数。

def adaption_cluster(clusters_num, labels_order, num_last, points_num_min, human_size):

print("上一帧个数:" + str(num_last)+ " 最小的点簇:"+str(points_num_min))

for i in range(clusters_num):

shape = labels_order[i].shape

if i <= num_last-1:

if labels_order[i].shape[0] < human_size:

num_last = i + 1

break

else:

points_num_min = labels_order[i].shape

else:

if labels_order[i].shape[0] > human_size:

num_last = num_last + 1

points_num_min = labels_order[i].shape

else:

break;

return num_last, points_num_min

主函数的实现流程：1.读取数据2.积累一定帧数的点云，随后聚类3.对每一次的聚类结果，进行处理

if __name__ == "__main__":

# 参数

human_size = 100

csv_file = 'data/1.csv' # 替换为你的CSV文件名

data = pd.read_csv(csv_file)

frame_start = data['Frame #'][0]

frame_end = data['Frame #'][data['Frame #'].shape[0]-1]

for i in range(100000):

frame_start = data['Frame #'][i]

if frame_start < frame_end:

break

print(frame_start)

print(frame_end)

# frame_start = 0

# frame_end = 120

num_last = 0 # 上一帧的人数

points_num_min = 0 # 满足此个数才是一个人

flag = 0

for i in range(frame_start, frame_end):

data_x, data_y, data_z = adaption_frame(data, frame_start, frame_end, num_threshold=1000)

data_x, data_y, data_z = valid_data(data_x, data_y, data_z)

clusters_num, labels = dbscan(data_x, data_y, data_z)

# draw_data_origin(data_x, data_y, data_z)

# 使用 numpy.unique 获取唯一标签和它们在原始数组中的索引

labels_order = order_cluster(clusters_num, labels)

print(labels_order[0].shape)

print(labels_order[1].shape)

if flag == 0:

num_last, points_num_min = getFirstJudge(clusters_num, labels_order, human_size)

flag = 1

else:

num_last, points_num_min = adaption_cluster(clusters_num, labels_order, num_last, points_num_min, human_size)

print("第 "+str(frame_start) + " 帧有 :" + str(num_last)+" 个人")

if frame_start + 10 > frame_end:

break

else:

frame_start = frame_start + 1

#Kmeans算法代码实现功能：设定K值，对点云进行Kmeans聚类，

加载所需的包

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import DBSCAN

from sklearn.preprocessing import StandardScaler

from sklearn.cluster import KMeans

from scipy.optimize import linear_sum_assignment

from scipy.spatial.distance import cdist

以下代码同之前的DBSCAN一样，在这里不赘述

def adaption_frame(data, frame_start, frame_end, num_threshold=1000):

def valid_data(data_x, data_y, data_z):

def draw_data_origin(data_x, data_y, data_z):

def dbscan(data_x, data_y, data_z):

def order_cluster(clusters_num, labels):

def getFirstJudge(clusters_num, labels_order, human_size):

def adaption_cluster(clusters_num, labels_order, num_last, points_num_min, human_size):

Kmeans进行聚类

def cluster_kmeans(value, data_x, data_y, data_z):

data_x = data_x.reshape(-1, 1)

data_y = data_y.reshape(-1, 1)

data_z = data_z.reshape(-1, 1)

# 将三个数组组合成一个(n, 3)的点云数组

points = np.hstack((data_x, data_y, data_z))

kmeans = KMeans(n_clusters=value, random_state=0).fit(points)

return kmeans

从聚类结果中，提取一些特征，用做之后的匈牙利匹配。这里，提取了三个特征：点云簇的均值、点云数、以及点云排序id

def extract_feature(K, labels_order, data_x, data_y, data_z):

features = []

for i in range(K):

one_feature = []

data_x_k = data_x[labels_order[i]]

data_y_k = data_y[labels_order[i]]

data_z_k = data_z[labels_order[i]]

# print(data_x_k.shape)

# print(data_y_k.shape)

# print(data_z_k.shape)

x_mean = np.mean(data_x_k, axis=0)

y_mean = np.mean(data_y_k, axis=0)

z_mean = np.mean(data_z_k, axis=0)

cluster_mean = np.hstack((x_mean, y_mean, z_mean))

cluster_points_size = labels_order[i].shape

one_feature.append(cluster_mean)

one_feature.append(cluster_points_size)

one_feature.append(i)

features.append(one_feature)

return features

用提取的特征进行匈牙利匹配

def hungarian_match(features_last, features_now):

# 提取点云中心和点云数

centers_last = np.array([a[0] for a in features_last])

counts_last = np.array([a[1][0] for a in features_last])

centers_now = np.array([b[0] for b in features_now])

counts_now = np.array([b[1][0] for b in features_now])

# 计算点云中心之间的欧氏距离

distance_matrix = cdist(centers_last, centers_now)

# 定义基于点云数和距离的成本函数

# 这里我们简单地使用距离的倒数和点云数差异的绝对值作为成本

# 你可能需要根据你的具体需求来调整这个成本函数

# cost_matrix = 1.0 / distance_matrix + np.abs(counts_last[:, np.newaxis] - counts_now)

cost_matrix = np.abs(counts_last[:, np.newaxis] - counts_now) + distance_matrix * 10

# 应用匈牙利算法找到最小成本匹配

row_ind, col_ind = linear_sum_assignment(cost_matrix)

# 打印匹配结果

matches = [(features_last[row_ind[i]], features_now[col_ind[i]]) for i in range(len(row_ind))]

for match in matches:

print(f"Match: last={match[0][0]} (count={match[0][1][0]}), (label={match[0][2]}), now={match[1][0]} (count={match[1][1][0]}), (label={match[1][2]})")

return matches

主函数

if __name__ == "__main__":

csv_file = 'data/2.csv' # 替换为你的CSV文件名

K = 2

# 参数

human_size = 100

data = pd.read_csv(csv_file)

frame_start = data['Frame #'][0]

frame_end = data['Frame #'][data['Frame #'].shape[0]-1]

for i in range(100000):

frame_start = data['Frame #'][i]

if frame_start < frame_end:

break

frame_start = 0

frame_end = 120

num_last = 0 # 上一帧的人数

points_num_min = 0 # 满足此个数才是一个人

flag = 0

features_last = []

data_x_all= [[] for _ in range(K)]

data_y_all = [[] for _ in range(K)]

data_z_all = [[] for _ in range(K)]

for i in range(frame_start, frame_end):

data_x, data_y, data_z = adaption_frame(data, frame_start, frame_end, num_threshold=1000)

data_x, data_y, data_z = valid_data(data_x, data_y, data_z)

result_kmeans = cluster_kmeans(K, data_x, data_y, data_z)

# 输出每个点的label

labels = result_kmeans.labels_

labels_order = order_cluster(K, labels)

features = extract_feature(K, labels_order, data_x, data_y, data_z)

print(features)

frame_start = frame_start + 1

if flag == 0:

features_last = features

flag = 1

continue

else:

matches = hungarian_match(features_last, features)

for k in range(K):

# 第一维代表匹配对数，第二维0代表features_last，1代表features

# 第三维代表特征维度，第四维每个特征的参数

data_x_all[k].extend(data_x[labels_order[matches[k][0][2]]])

data_y_all[k].extend(data_y[labels_order[matches[k][0][2]]])

data_z_all[k].extend(data_z[labels_order[matches[k][0][2]]])

# print(len(data_x_all[k]))

features_last = features

# 创建颜色列表，这里使用RGB颜色

colors = ['r', 'g', 'b'] # 红色、绿色、蓝色

# 创建一个3D图形

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

# 遍历每组数据并绘制

for k in range(K):

x = data_x_all[k]

y = data_y_all[k]

z = data_z_all[k]

color = colors[k % len(colors)] # 使用循环颜色，以防K大于颜色数量

ax.scatter(x, y, z, c=color, label=f'Group {k+1}')

# 添加图例

ax.legend()

# 设置坐标轴标签

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

# 显示图形

plt.show()

结果

以下就是匈牙利匹配后的结果。红色和绿色分别代表，经过匈牙利匹配后的点云簇，统一了时间维度画在一张图上的结果。如果需要，可以按照时间序列一步步来画，这样可以看到红色和绿色沿着各自的动线前进