#!/usr/bin/env python # -*- coding: utf-8 -*- """ 飞行轨迹可视化模块 - 在多架次GPS数据上绘制飞行轨迹 支持功能： 1. 读取多个.gps文件（每个文件代表一个架次） 2. 在高光谱假彩色影像上绘制飞行轨迹 3. 不同架次使用不同颜色 4. 图例显示架次起始到结束的时间段 5. 添加指北针、比例尺 6. 保存为PNG图像 """ import numpy as np import pandas as pd import matplotlib.pyplot as plt from pathlib import Path from typing import Optional, Tuple, List, Dict, Union import warnings from matplotlib.patches import FancyArrowPatch import matplotlib.patheffects as path_effects from datetime import datetime from matplotlib.colors import ListedColormap import matplotlib.patches as mpatches # 性能优化配置 plt.rcParams['agg.path.chunksize'] = 10000 plt.rcParams['path.simplify'] = True plt.rcParams['path.simplify_threshold'] = 0.1 # 导入GDAL用于影像读写 try: from osgeo import gdal, osr GDAL_AVAILABLE = True except ImportError: GDAL_AVAILABLE = False print("警告: GDAL未安装，地理坐标转换功能可能无法正常工作") class FlightPathVisualizer: """飞行轨迹可视化类 - 在高光谱假彩色影像上绘制多架次飞行轨迹""" # 预定义颜色方案（不同架次使用不同颜色） FLIGHT_COLORS = [ '#FF0000', # 红色 '#00FF00', # 绿色 '#0000FF', # 蓝色 '#FF00FF', # 紫色 '#00FFFF', # 青色 '#FFFF00', # 黄色 '#FF8000', # 橙色 '#8000FF', # 紫罗兰 '#0080FF', # 天蓝 '#FF0080', # 粉红 ] def __init__(self, output_dir: str = "./flight_paths"): """ 初始化飞行轨迹可视化器 Args: output_dir: 输出目录，用于保存生成的轨迹图 """ self.output_dir = Path(output_dir) self.output_dir.mkdir(parents=True, exist_ok=True) # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans', 'Arial Unicode MS'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.size'] = 12 warnings.filterwarnings('ignore') def create_flight_path_map(self, gps_folder: str, hyperspectral_path: str, output_filename: Optional[str] = None, rgb_bands: Optional[List[int]] = None, line_width: int = 2, show_north_arrow: bool = True, show_scale_bar: bool = True, dpi: int = 300) -> str: """ 创建飞行轨迹地图：在高光谱假彩色影像上绘制多架次飞行轨迹 Args: gps_folder: GPS文件夹路径，包含多个.gps文件 hyperspectral_path: 高光谱影像文件路径 (.dat, .bsq, .tif等) output_filename: 输出文件名（如果为None则自动生成） rgb_bands: 用于RGB合成的三个波段索引 [R, G, B]，默认为None自动选择(650,550,460nm) line_width: 轨迹线宽 show_north_arrow: 是否显示指北针 show_scale_bar: 是否显示比例尺 dpi: 输出图像分辨率 Returns: 生成的地图文件路径 """ if not GDAL_AVAILABLE: raise ImportError("GDAL未安装，无法处理地理坐标转换") print(f"正在生成飞行轨迹地图...") # 读取高光谱影像 hyperspectral_img, geotransform, projection, width, height = self._read_hyperspectral( hyperspectral_path, rgb_bands) # 读取所有GPS文件 flight_data = self._read_gps_files(gps_folder) if not flight_data: raise ValueError(f"未在 {gps_folder} 中找到有效的.gps文件") # 将GPS坐标转换为像素坐标 flight_pixels = self._convert_flights_to_pixels( flight_data, geotransform, width, height, projection) # 创建地图 if output_filename is None: folder_name = Path(gps_folder).name hs_name = Path(hyperspectral_path).stem output_filename = f"{hs_name}_{folder_name}_flight_paths.png" output_path = self.output_dir / output_filename self._create_map_visualization( hyperspectral_img, flight_pixels, flight_data, str(output_path), line_width, show_north_arrow, show_scale_bar, dpi, geotransform, width, height ) print(f"飞行轨迹地图已保存: {output_path}") return str(output_path) def _read_hyperspectral(self, hyperspectral_path: str, rgb_bands: Optional[List[int]] = None) -> Tuple[np.ndarray, tuple, str, int, int]: """读取高光谱影像 - 使用650/550/460nm波长""" dataset = gdal.Open(hyperspectral_path) if dataset is None: raise ValueError(f"无法打开高光谱影像: {hyperspectral_path}") width = dataset.RasterXSize height = dataset.RasterYSize band_count = dataset.RasterCount # 确定要读取的波段 - 使用指定波长 650, 550, 460nm if rgb_bands is None: if band_count >= 3: try: from src.utils.util import find_band_number rgb_bands = [ find_band_number(650.0, hyperspectral_path), # Red ~650nm find_band_number(550.0, hyperspectral_path), # Green ~550nm find_band_number(460.0, hyperspectral_path) # Blue ~460nm ] print(f" 根据波长选择RGB波段: R={rgb_bands[0]}, G={rgb_bands[1]}, B={rgb_bands[2]}") except Exception as e: print(f" 波长查找失败 ({e})，使用默认索引") rgb_bands = [min(band_count-1, int(band_count*0.25)), min(band_count-1, int(band_count*0.15)), min(band_count-1, int(band_count*0.05))] else: rgb_bands = [0, 0, 0] # 读取RGB波段 rgb_data = [] for band_idx in rgb_bands: band = dataset.GetRasterBand(band_idx + 1) band_data = band.ReadAsArray().astype(np.float32) rgb_data.append(band_data) # 堆叠为RGB图像 if len(rgb_data) == 3: image_array = np.stack(rgb_data, axis=2) else: image_array = np.stack([rgb_data[0]]*3, axis=2) geotransform = dataset.GetGeoTransform() projection = dataset.GetProjection() dataset = None print(f" 读取影像: {width}x{height}x{image_array.shape[2]} (RGB)") if projection: proj_type = "投影坐标系" if "PROJCS" in projection else "地理坐标系" print(f" 影像投影: {proj_type}") return image_array, geotransform, projection, width, height def _read_gps_files(self, gps_folder: str) -> Dict[str, pd.DataFrame]: """ 读取文件夹中的所有.gps文件 文件格式: 日期、时间、三个姿态角、经度、纬度、高程 列: [date, time, pitch, roll, yaw, longitude, latitude, altitude] """ gps_folder_path = Path(gps_folder) if not gps_folder_path.exists(): raise FileNotFoundError(f"GPS文件夹不存在: {gps_folder}") gps_files = list(gps_folder_path.glob("*.gps")) if not gps_files: print(f"警告: 在 {gps_folder} 中未找到.gps文件") return {} print(f"找到 {len(gps_files)} 个GPS文件") flight_data = {} for gps_file in gps_files: try: # 读取GPS文件（制表符分隔） df = pd.read_csv(gps_file, sep='\t', header=None, names=['date', 'time', 'pitch', 'roll', 'yaw', 'longitude', 'latitude', 'altitude']) # 确保数值类型 df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce') df['latitude'] = pd.to_numeric(df['latitude'], errors='coerce') df['altitude'] = pd.to_numeric(df['altitude'], errors='coerce') # 删除无效坐标 df = df.dropna(subset=['longitude', 'latitude']) if len(df) > 0: flight_data[gps_file.stem] = df print(f" ✓ 读取 {gps_file.name}: {len(df)} 个轨迹点") # 显示时间范围 start_time = df.iloc[0]['time'] end_time = df.iloc[-1]['time'] print(f" 时间范围: {start_time} - {end_time}") else: print(f" ✗ {gps_file.name}: 无有效数据") except Exception as e: print(f" ✗ 读取 {gps_file.name} 失败: {e}") return flight_data def _convert_flights_to_pixels(self, flight_data: Dict[str, pd.DataFrame], geotransform: tuple, width: int, height: int, projection: str = "") -> Dict[str, List[Tuple[float, float]]]: """将所有飞行轨迹的地理坐标转换为像素坐标""" if geotransform is None: print("警告: 无地理变换信息，无法转换坐标") return {} gt = geotransform flight_pixels = {} # 检查是否需要投影转换 needs_transform = projection and ("PROJCS" in projection or "GEOGCS" in projection) transform = None if needs_transform and GDAL_AVAILABLE: try: src_srs = osr.SpatialReference() src_srs.ImportFromEPSG(4326) # WGS84 dst_srs = osr.SpatialReference() dst_srs.ImportFromWkt(projection) transform = osr.CoordinateTransformation(src_srs, dst_srs) print(" ✓ 已创建WGS84到影像投影的坐标转换") except Exception as e: print(f" ⚠ 坐标转换创建失败: {e}") for flight_name, df in flight_data.items(): pixel_coords = [] for _, row in df.iterrows(): lon = float(row['longitude']) lat = float(row['latitude']) if transform is not None: try: proj_x, proj_y, _ = transform.TransformPoint(lat, lon) x = (proj_x - gt[0]) / gt[1] y = (proj_y - gt[3]) / gt[5] except: x = (lon - gt[0]) / gt[1] y = (lat - gt[3]) / gt[5] else: x = (lon - gt[0]) / gt[1] y = (lat - gt[3]) / gt[5] # 限制在图像范围内 x = max(0, min(x, width - 1)) y = max(0, min(y, height - 1)) pixel_coords.append((x, y)) flight_pixels[flight_name] = pixel_coords print(f" 已转换 {len(flight_pixels)} 个架次的坐标") return flight_pixels def _create_false_color_image(self, image_array: np.ndarray) -> np.ndarray: """创建假彩色RGB图像 - 应用线性拉伸""" if image_array.shape[2] != 3: if len(image_array.shape) == 2 or image_array.shape[2] == 1: if len(image_array.shape) == 2: image_array = np.stack([image_array]*3, axis=2) else: image_array = np.repeat(image_array, 3, axis=2) def simple_linear_stretch(data, min_percent=1, max_percent=99): valid_data = data[np.isfinite(data)] if len(valid_data) == 0: return np.zeros_like(data, dtype=np.float32) p_low = np.percentile(valid_data, min_percent) p_high = np.percentile(valid_data, max_percent) if p_high - p_low < 1e-8: data_min = valid_data.min() data_max = valid_data.max() if data_max > data_min: stretched = (data - data_min) / (data_max - data_min) else: stretched = np.zeros_like(data, dtype=np.float32) else: stretched = (data - p_low) / (p_high - p_low) stretched = np.clip(stretched, 0.0, 1.0) return stretched r_stretched = simple_linear_stretch(image_array[:, :, 0]) g_stretched = simple_linear_stretch(image_array[:, :, 1]) b_stretched = simple_linear_stretch(image_array[:, :, 2]) rgb_image = np.stack([r_stretched, g_stretched, b_stretched], axis=2) rgb_image = np.nan_to_num(rgb_image, nan=0.0) rgb_image = np.clip(rgb_image, 0.0, 1.0) # Gamma校正增加亮度 gamma = 0.85 rgb_image = np.power(rgb_image, gamma) # 映射到0-255 rgb_image = (rgb_image * 255).astype(np.uint8) return rgb_image def _create_map_visualization(self, image_array: np.ndarray, flight_pixels: Dict[str, List[Tuple[float, float]]], flight_data: Dict[str, pd.DataFrame], output_path: str, line_width: int, show_north_arrow: bool, show_scale_bar: bool, dpi: int, geotransform: tuple, width: int, height: int): """创建地图可视化""" figsize = (14, 10) fig, ax = plt.subplots(figsize=figsize, dpi=150) # 处理背景图像 rgb_image = self._create_false_color_image(image_array) ax.imshow(rgb_image, interpolation='bilinear') # 绘制飞行轨迹 - 不同架次不同颜色 legend_elements = [] for idx, (flight_name, pixel_coords) in enumerate(flight_pixels.items()): if len(pixel_coords) < 2: continue # 选择颜色 color = self.FLIGHT_COLORS[idx % len(self.FLIGHT_COLORS)] # 提取x,y坐标 x_coords = [p[0] for p in pixel_coords] y_coords = [p[1] for p in pixel_coords] # 绘制轨迹线 ax.plot(x_coords, y_coords, color=color, linewidth=line_width, alpha=0.8, solid_capstyle='round') # 标记起点和终点 ax.plot(x_coords[0], y_coords[0], 'o', color=color, markersize=8, markeredgecolor='white', markeredgewidth=1) ax.plot(x_coords[-1], y_coords[-1], 's', color=color, markersize=8, markeredgecolor='white', markeredgewidth=1) # 获取时间范围用于图例 df = flight_data[flight_name] start_time = df.iloc[0]['time'] end_time = df.iloc[-1]['time'] # 创建图例元素 legend_label = f"{flight_name}: {start_time} - {end_time}" legend_elements.append( mpatches.Patch(color=color, label=legend_label) ) # 添加图例 if legend_elements: ax.legend(handles=legend_elements, loc='lower right', frameon=True, facecolor='white', edgecolor='gray', fontsize=9, title='飞行轨迹 (起点→终点)') # 添加指北针 if show_north_arrow: self._add_north_arrow(ax, width, height) # 添加比例尺 if show_scale_bar and geotransform is not None: self._add_scale_bar(ax, geotransform, width, height) # 设置标题 ax.set_title('多架次飞行轨迹图', fontsize=16, fontweight='bold', pad=20) # 隐藏坐标轴刻度 ax.set_xticks([]) ax.set_yticks([]) # 添加网格 ax.grid(True, alpha=0.2, linestyle='--') plt.tight_layout() plt.savefig(output_path, dpi=dpi, bbox_inches='tight', pad_inches=0.1, facecolor='white') plt.close(fig) def _add_north_arrow(self, ax, width: int, height: int): """添加指北针""" arrow_x = width * 0.92 arrow_y = height * 0.88 arrow = FancyArrowPatch((arrow_x, arrow_y), (arrow_x, arrow_y - height*0.08), color='black', linewidth=3, arrowstyle='->', mutation_scale=20) ax.add_patch(arrow) ax.text(arrow_x, arrow_y - height*0.1, 'N', fontsize=14, fontweight='bold', color='black', ha='center', va='center', path_effects=[path_effects.withStroke(linewidth=3, foreground='white')]) def _add_scale_bar(self, ax, geotransform: tuple, width: int, height: int): """添加比例尺""" if geotransform is None: return pixel_size_x = abs(geotransform[1]) image_width_meters = width * pixel_size_x scale_length_m = image_width_meters / 4 scale_options = [1000, 500, 200, 100, 50, 20, 10, 5, 2, 1] scale_meters = next((s for s in scale_options if s <= scale_length_m), 1) scale_pixels = int(scale_meters / pixel_size_x) bar_x = width * 0.08 bar_y = height * 0.92 ax.plot([bar_x, bar_x + scale_pixels], [bar_y, bar_y], color='black', linewidth=4) ax.plot([bar_x, bar_x], [bar_y, bar_y + 8], color='black', linewidth=2) ax.plot([bar_x + scale_pixels, bar_x + scale_pixels], [bar_y, bar_y + 8], color='black', linewidth=2) ax.text(bar_x + scale_pixels/2, bar_y + 15, f'{scale_meters} m', fontsize=11, ha='center', va='bottom', fontweight='bold', bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', pad=1)) def batch_create_maps(self, gps_folder: str, hyperspectral_folder: str, output_subdir: str = "flight_paths") -> Dict[str, str]: """ 批量创建飞行轨迹地图 Args: gps_folder: 包含多个子文件夹（每个子文件夹包含.gps文件）的文件夹 hyperspectral_folder: 包含高光谱影像的文件夹 output_subdir: 输出子目录 Returns: 生成的地图文件路径字典 """ gps_folder_path = Path(gps_folder) hs_folder_path = Path(hyperspectral_folder) if not gps_folder_path.exists(): raise FileNotFoundError(f"GPS文件夹不存在: {gps_folder}") if not hs_folder_path.exists(): raise FileNotFoundError(f"高光谱文件夹不存在: {hyperspectral_folder}") output_dir = self.output_dir / output_subdir output_dir.mkdir(parents=True, exist_ok=True) map_paths = {} # 查找所有包含.gps文件的子文件夹 gps_subfolders = [d for d in gps_folder_path.iterdir() if d.is_dir() and list(d.glob("*.gps"))] # 查找高光谱影像 hs_files = [] for ext in ['*.dat', '*.bsq', '*.tif', '*.tiff']: hs_files.extend(list(hs_folder_path.glob(ext))) if not hs_files: print(f"警告: 在 {hyperspectral_folder} 中未找到高光谱影像") return map_paths print(f"找到 {len(gps_subfolders)} 个GPS子文件夹和 {len(hs_files)} 个高光谱影像") # 简单匹配：使用第一个高光谱影像与所有GPS文件夹组合 hs_file = hs_files[0] for gps_subfolder in gps_subfolders: try: output_filename = f"{hs_file.stem}_{gps_subfolder.name}_flight_paths.png" map_path = self.create_flight_path_map( gps_folder=str(gps_subfolder), hyperspectral_path=str(hs_file), output_filename=output_filename, dpi=200 ) map_paths[gps_subfolder.name] = map_path print(f"✓ 生成: {gps_subfolder.name}") except Exception as e: print(f"✗ 处理 {gps_subfolder.name} 失败: {e}") print(f"批量生成完成，共生成 {len(map_paths)} 个飞行轨迹图") return map_paths # 测试代码 if __name__ == "__main__": print("FlightPathVisualizer类已创建") print("用法示例:") print(" visualizer = FlightPathVisualizer(output_dir='./flight_maps')") print(" map_path = visualizer.create_flight_path_map(") print(" gps_folder='./gps_data',") print(" hyperspectral_path='./hyperspectral.dat'") print(" )") visualizer = FlightPathVisualizer(output_dir=r"E:\code\WQ\pipeline_result\work_dir\14_visualization\flight_maps") # 生成飞行轨迹图 map_path = visualizer.create_flight_path_map( gps_folder=r"D:\BaiduNetdiskDownload\20250902\gps", # GPS文件夹路径 hyperspectral_path=r"E:\code\WQ\pipeline_result\work_dir\3_deglint\deglint_goodman.bsq", # 高光谱影像路径 output_filename="flight_paths.png", line_width=2, dpi=300 )