347 lines
13 KiB
Python
347 lines
13 KiB
Python
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import glob
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import os
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from collections import Counter
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import matplotlib
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import numpy as np
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from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
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from sklearn.mixture import GaussianMixture
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from pyexiv2 import Image
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# 设置中文字体，解决显示问题
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matplotlib.rcParams["font.family"] = ["SimHei", ]
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matplotlib.rcParams['axes.unicode_minus'] = False # 正确显示负号
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# matplotlib.use('Agg') # 仅保存图片不显示，需配合 plt.savefig()
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import matplotlib.pyplot as plt
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# 确保输出目录存在
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os.makedirs('clustering_results', exist_ok=True)
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def read_xmp_target(p):
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"""读取图像的XMP元数据"""
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try:
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img = Image(p)
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xmp = img.read_xmp()
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# 定义需要提取的XMP标签
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xmp_tags = {
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'Xmp.drone-dji.GpsLatitude': None,
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'Xmp.drone-dji.GpsLongitude': None,
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'Xmp.drone-dji.AbsoluteAltitude': None,
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'Xmp.drone-dji.GimbalRollDegree': None,
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'Xmp.drone-dji.GimbalYawDegree': None,
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'Xmp.drone-dji.GimbalPitchDegree': None,
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'Xmp.drone-dji.FlightRollDegree': None,
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'Xmp.drone-dji.FlightYawDegree': None,
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'Xmp.drone-dji.FlightPitchDegree': None,
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'Xmp.drone-dji.LRFTargetDistance': None,
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'Xmp.drone-dji.LRFTargetLon': None,
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'Xmp.drone-dji.LRFTargetLat': None,
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'Xmp.drone-dji.LRFTargetAlt': None,
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'Xmp.drone-dji.LRFTargetAbsAlt': None,
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}
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# 提取标签值
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for key in xmp_tags.keys():
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if key in xmp:
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xmp_tags[key] = xmp[key]
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else:
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print(f"警告: 图像 {p} 中未找到标签 {key}")
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return xmp_tags
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except Exception as e:
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print(f"读取图像 {p} 的XMP数据时出错: {e}")
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return None
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def repair_neighbors_of_minus1(labels):
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"""修复标签中的-1值，使用相邻区域的多数标签填充"""
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out = np.array(labels, dtype=int)
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n = len(out)
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# 找到所有 -1 的下标
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ind = np.where(out == -1)[0]
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# 把区间切成段：[0, ind[0]), [ind[0]+1, ind[1]), ..., [ind[-1]+1, n)
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split_indices = [0] + (ind + 1).tolist() + [n]
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for k in range(len(split_indices) - 1):
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start = split_indices[k]
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end = split_indices[k + 1]
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seg = out[start:end]
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# 只统计非 -1 的标签
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votes = seg[seg != -1]
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if len(votes) == 0:
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continue
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winner = Counter(votes).most_common(1)[0][0]
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# 把区间内非 -1 标签统一成 winner
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out[start:end] = np.where(seg == -1, -1, winner)
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return out.tolist()
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def visualize_clusters(data, labels, cluster_centers, method_name):
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"""可视化数据点和聚类中心，包含-1标签和图片名称标注"""
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# 提取需要可视化的数据
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norm_lat = [d['norm_lat'] for d in data]
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norm_lon = [d['norm_lon'] for d in data]
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alt = [d['clustering_alt'] for d in data] # 使用聚类时实际用的高度
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image_names = [os.path.basename(d['path'])[-10:] for d in data] # 取图片名后10个字符
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# 创建3D图形
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fig = plt.figure(figsize=(12, 10))
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ax = fig.add_subplot(111, projection='3d')
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# 绘制数据点（使用标签区分颜色）
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unique_labels = np.unique(labels)
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colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
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for label, color in zip(unique_labels, colors):
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if label == -1:
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# -1标签点用黄色带黑色边缘突出显示
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color = [1, 1, 0, 0.8] # 黄色半透明
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edgecolor = 'black'
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size = 80
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else:
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edgecolor = 'none'
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size = 50
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mask = np.array(labels) == label
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scatter = ax.scatter(
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np.array(norm_lat)[mask],
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np.array(norm_lon)[mask],
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np.array(alt)[mask],
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c=[color], alpha=0.7, s=size, edgecolors=edgecolor,
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label=f'类别 {label}' if label != -1 else '待重新分类 (-1)'
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)
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# 标注图片名称（后10个字符）
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for i, (x, y, z, name) in enumerate(zip(norm_lat, norm_lon, alt, image_names)):
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# 只标注-1标签的数据点或每隔一定数量标注，避免过于拥挤
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if labels[i] == -1 : # 每隔5个点标注一个 or i % 5 == 0:
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ax.text(x, y, z, name, fontsize=16,
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bbox=dict(facecolor='white', alpha=0.5, boxstyle='round,pad=0.5'))
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# 如果有聚类中心，则绘制
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if cluster_centers is not None:
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centers_x, centers_y, centers_z = zip(*cluster_centers)
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ax.scatter(
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centers_x, centers_y, centers_z,
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c='red', marker='X', s=200, edgecolors='black',
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label='聚类中心'
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)
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# 设置坐标轴标签
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ax.set_xlabel('归一化纬度 (×100000)')
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ax.set_ylabel('归一化经度 (×100000)')
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ax.set_zlabel('高度')
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# 添加标题和图例
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ax.set_title(f'{method_name} 聚类结果的3D分布')
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ax.legend()
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# 调整视角，让标注更清晰
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ax.view_init(elev=30, azim=45)
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# 保存图形
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plt.tight_layout()
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plt.savefig(f'clustering_results/{method_name}_clusters.jpg', dpi=300)
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plt.show()
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# plt.close()
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def process_images_with_method(image_paths, method, method_name):
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"""使用指定的聚类方法处理图像数据"""
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# 1. 读取数据
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data = []
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for path in image_paths:
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xmp = read_xmp_target(path)
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if not xmp:
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continue
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try:
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lat = float(xmp['Xmp.drone-dji.GpsLatitude']) if xmp['Xmp.drone-dji.GpsLatitude'] else 0
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lon = float(xmp['Xmp.drone-dji.GpsLongitude']) if xmp['Xmp.drone-dji.GpsLongitude'] else 0
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alt = float(xmp['Xmp.drone-dji.AbsoluteAltitude']) if xmp['Xmp.drone-dji.AbsoluteAltitude'] else 0
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yaw = float(xmp['Xmp.drone-dji.GimbalYawDegree']) if xmp['Xmp.drone-dji.GimbalYawDegree'] else 0
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pitch = float(xmp['Xmp.drone-dji.GimbalPitchDegree']) if xmp['Xmp.drone-dji.GimbalPitchDegree'] else 0
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data.append({
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'path': path,
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'lat': lat, 'lon': lon, 'alt': alt,
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'yaw': yaw, 'pitch': pitch,
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'lrf_lat': float(xmp['Xmp.drone-dji.LRFTargetLat']) if xmp['Xmp.drone-dji.LRFTargetLat'] else 0,
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'lrf_lon': float(xmp['Xmp.drone-dji.LRFTargetLon']) if xmp['Xmp.drone-dji.LRFTargetLon'] else 0,
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'lrf_alt': float(xmp['Xmp.drone-dji.LRFTargetAlt']) if xmp['Xmp.drone-dji.LRFTargetAlt'] else 0
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})
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except Exception as e:
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print(f"处理图像 {path} 时出错: {e}")
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continue
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if not data:
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print("没有有效数据可处理")
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return None, None, None
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# 2. 归一化GPS并考虑LRF数据
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# 首先收集所有可能用到的纬度和经度（包括LRF数据）用于计算最小值
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all_latitudes = []
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all_longitudes = []
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for d in data:
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all_latitudes.append(d['lat'])
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all_latitudes.append(d['lrf_lat'])
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all_longitudes.append(d['lon'])
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all_longitudes.append(d['lrf_lon'])
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lat_min, lon_min = min(all_latitudes), min(all_longitudes)
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distance_threshold = 10.0 # 距离阈值，单位：米（可根据实际情况调整）
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for d in data:
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# 计算无人机位置与LRF目标点的距离（简化的欧氏距离计算）
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dx = (d['lrf_lon'] - d['lon']) * 111319 # 经度差转米 (1度≈111319米)
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dy = (d['lrf_lat'] - d['lat']) * 111319 # 纬度差转米
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distance = np.sqrt(dx ** 2 + dy ** 2)
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# 根据距离决定使用哪种数据
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if distance < distance_threshold and d['lrf_lat'] != 0 and d['lrf_lon'] != 0:
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# 距离近且LRF数据有效，使用LRF数据
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d['norm_lat'] = (d['lrf_lat'] - lat_min) * 100000
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d['norm_lon'] = (d['lrf_lon'] - lon_min) * 100000
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d['used_data'] = 'LRF' # 标记使用了哪种数据
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# 使用LRF的高度
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d['clustering_alt'] = d['lrf_alt']
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else:
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# 距离远或LRF数据无效，使用原始GPS数据
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d['norm_lat'] = (d['lat'] - lat_min) * 100000
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d['norm_lon'] = (d['lon'] - lon_min) * 100000
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d['used_data'] = 'GPS' # 标记使用了哪种数据
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# 使用原始高度
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d['clustering_alt'] = d['alt']
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# 3. 准备聚类数据，使用决定后的高度数据
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X = np.array([[d['norm_lat'], d['norm_lon'], d['clustering_alt']] for d in data])
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# 4. 应用聚类算法
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labels = None
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cluster_centers = None
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if isinstance(method, KMeans):
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method.fit(X)
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labels = method.labels_
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cluster_centers = method.cluster_centers_
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elif isinstance(method, DBSCAN):
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labels = method.fit_predict(X)
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# DBSCAN没有传统意义上的聚类中心，这里计算每个簇的质心作为参考
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unique_labels = np.unique(labels)
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cluster_centers = []
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for label in unique_labels:
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if label != -1: # 排除噪声点
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cluster_points = X[labels == label]
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cluster_centers.append(np.mean(cluster_points, axis=0))
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if not cluster_centers:
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cluster_centers = None
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elif isinstance(method, AgglomerativeClustering):
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labels = method.fit_predict(X)
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# 计算每个簇的质心作为参考
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unique_labels = np.unique(labels)
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cluster_centers = []
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for label in unique_labels:
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cluster_points = X[labels == label]
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cluster_centers.append(np.mean(cluster_points, axis=0))
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elif isinstance(method, GaussianMixture):
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labels = method.fit_predict(X)
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cluster_centers = method.means_
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# 5. 时序突变检测
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vec = []
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if len(data) > 1:
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for i in range(1, len(data)):
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prev, curr = data[i - 1], data[i]
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yaw_change = abs(curr['yaw'] - prev['yaw'])
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yaw_change = min(yaw_change, 360 - yaw_change) # 处理跨0°
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pitch_change = abs(curr['pitch'] - prev['pitch'])
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pos_change = np.linalg.norm([
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curr['norm_lat'] - prev['norm_lat'],
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curr['norm_lon'] - prev['norm_lon'],
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curr['clustering_alt'] - prev['clustering_alt']
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])
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vec.append([
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curr['norm_lat'] - prev['norm_lat'],
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curr['norm_lon'] - prev['norm_lon'],
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curr['clustering_alt'] - prev['clustering_alt'],
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yaw_change,
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pitch_change
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])
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# 若姿态突变但位置变化小→换面（保持标签）
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# 若两者突变→换叶片（标记需重新检查）
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if (yaw_change > 10 or pitch_change > 5) and pos_change > 10:
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labels[i] = -1 # 待重新分类
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# 6. 修复标签
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if labels is not None:
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labels = repair_neighbors_of_minus1(labels)
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# 7. 可视化聚类结果
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visualize_clusters(data, labels, cluster_centers, method_name)
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return labels, vec, data
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def compare_clustering_methods(image_paths):
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"""比较多种聚类方法"""
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# 定义要比较的聚类方法
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n_clusters = 3 # 已知目标有3类
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clustering_methods = {
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'KMeans': KMeans(n_clusters=n_clusters, random_state=0),
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'DBSCAN': DBSCAN(eps=5, min_samples=3), # 参数可根据实际数据调整
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'Agglomerative': AgglomerativeClustering(n_clusters=n_clusters),
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'GaussianMixture': GaussianMixture(n_components=n_clusters, random_state=0)
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
results = {}
|
|||
|
|
|
|||
|
|
# 对每种方法执行聚类
|
|||
|
|
for name, method in clustering_methods.items():
|
|||
|
|
print(f"正在使用 {name} 进行聚类...")
|
|||
|
|
labels, vec, data = process_images_with_method(image_paths, method, name)
|
|||
|
|
if labels is not None:
|
|||
|
|
results[name] = {
|
|||
|
|
'labels': labels,
|
|||
|
|
'vec': vec,
|
|||
|
|
'data': data
|
|||
|
|
}
|
|||
|
|
print(f"{name} 聚类完成\n")
|
|||
|
|
|
|||
|
|
return results
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
# 获取图像路径
|
|||
|
|
image_dir = r'D:\4.work\study\classify\img'
|
|||
|
|
image_paths = glob.glob(os.path.join(image_dir, '*.jpg'))
|
|||
|
|
|
|||
|
|
if not image_paths:
|
|||
|
|
print(f"在目录 {image_dir} 中未找到任何JPG图像")
|
|||
|
|
else:
|
|||
|
|
print(f"找到 {len(image_paths)} 张图像，开始聚类比较...")
|
|||
|
|
results = compare_clustering_methods(image_paths)
|
|||
|
|
|
|||
|
|
# 输出每种方法的聚类结果
|
|||
|
|
for method_name, result in results.items():
|
|||
|
|
print(f"\n{method_name} 聚类结果:")
|
|||
|
|
output_file = f'clustering_results/{method_name}_labels.txt'
|
|||
|
|
with open(output_file, 'w', encoding='utf-8') as f:
|
|||
|
|
for i, (path, label) in enumerate(zip(
|
|||
|
|
[d['path'] for d in result['data']],
|
|||
|
|
result['labels'])):
|
|||
|
|
line = f"图像: {os.path.basename(path)}, 类别: {label}, 使用数据: {result['data'][i]['used_data']}"
|
|||
|
|
if i < len(result['vec']):
|
|||
|
|
line += f", 变化向量: {result['vec'][i]}"
|
|||
|
|
print(line)
|
|||
|
|
f.write(line + '\n')
|
|||
|
|
print("\n所有聚类结果已保存到 clustering_results 目录")
|