500 lines
19 KiB
Python
500 lines
19 KiB
Python
import gpxpy
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import folium
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import os
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import math
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import glob
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import html
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import json
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from datetime import datetime
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# 定义可用的地图源
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MAP_TILES = {
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"高德卫星图": "http://webst02.is.autonavi.com/appmaptile?style=6&x={x}&y={y}&z={z}",
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"高德街道图": "http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=8&x={x}&y={y}&z={z}"
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}
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# 地图源的属性信息
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MAP_ATTRIBUTION = {
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"高德卫星图": '© <a href="http://ditu.amap.com/">高德地图</a>',
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"高德街道图": '© <a href="http://ditu.amap.com/">高德地图</a>'
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}
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# 性能配置：超过此点数不生成逐点悬停层，避免浏览器卡顿
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# MAX_POINTS_FOR_HOVER = 500
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def wgs84_to_gcj02(lng, lat):
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"""
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WGS84转GCJ02(火星坐标系)
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将GPS的WGS84坐标转换为高德地图使用的GCJ02坐标
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"""
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a = 6378245.0 # 长半轴
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ee = 0.00669342162296594323 # 扁率
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# 判断是否在国内
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if (lng < 72.004 or lng > 137.8347) or (lat < 0.8293 or lat > 55.8271):
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return lng, lat
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dlat = _transform_lat(lng - 105.0, lat - 35.0)
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dlng = _transform_lng(lng - 105.0, lat - 35.0)
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radlat = lat / 180.0 * math.pi
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magic = math.sin(radlat)
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magic = 1 - ee * magic * magic
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sqrtmagic = math.sqrt(magic)
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dlat = (dlat * 180.0) / ((a * (1 - ee)) / (magic * sqrtmagic) * math.pi)
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dlng = (dlng * 180.0) / (a / sqrtmagic * math.cos(radlat) * math.pi)
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mglat = lat + dlat
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mglng = lng + dlng
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return mglng, mglat
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def _transform_lat(lng, lat):
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ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + 0.1 * lng * lat + 0.2 * math.sqrt(abs(lng))
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ret += (20.0 * math.sin(6.0 * lng * math.pi) + 20.0 * math.sin(2.0 * lng * math.pi)) * 2.0 / 3.0
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ret += (20.0 * math.sin(lat * math.pi) + 40.0 * math.sin(lat / 3.0 * math.pi)) * 2.0 / 3.0
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ret += (160.0 * math.sin(lat / 12.0 * math.pi) + 320 * math.sin(lat * math.pi / 30.0)) * 2.0 / 3.0
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return ret
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def _transform_lng(lng, lat):
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ret = 300.0 + lng + 2.0 * lat + 0.1 * lng * lng + 0.1 * lng * lat + 0.1 * math.sqrt(abs(lng))
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ret += (20.0 * math.sin(6.0 * lng * math.pi) + 20.0 * math.sin(2.0 * lng * math.pi)) * 2.0 / 3.0
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ret += (20.0 * math.sin(lng * math.pi) + 40.0 * math.sin(lng / 3.0 * math.pi)) * 2.0 / 3.0
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ret += (150.0 * math.sin(lng / 12.0 * math.pi) + 300.0 * math.sin(lng / 30.0 * math.pi)) * 2.0 / 3.0
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return ret
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def format_datetime(dt):
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"""
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格式化日期时间，去掉时区信息
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"""
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if dt:
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return dt.strftime("%Y-%m-%d %H:%M:%S")
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return "未知"
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def format_duration(seconds):
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"""
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格式化持续时间
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"""
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if seconds is None:
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return "未知"
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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secs = int(seconds % 60)
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if hours > 0:
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return f"{hours}小时{minutes}分{secs}秒"
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elif minutes > 0:
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return f"{minutes}分{secs}秒"
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else:
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return f"{secs}秒"
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def parse_gpx_file(gpx_file_path):
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"""
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解析GPX文件，优化了速度平滑处理和运动时间计算（自动剔除暂停时间）
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"""
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# 配置参数
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MAX_SPEED_THRESHOLD = 50.0 # 最高时速上限 (km/h)，超过此值可能是漂移
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PAUSE_THRESHOLD_SECONDS = 3 # 采样间隔超过3秒视为暂停
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SPEED_WINDOW_SIZE = 3 # 速度平滑窗口大小
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try:
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# 尝试多种编码，避免非UTF-8文件导致崩溃
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gpx_content = None
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for encoding in ['utf-8', 'utf-8-sig', 'gbk', 'gb2312', 'latin-1']:
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try:
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with open(gpx_file_path, 'r', encoding=encoding) as f:
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gpx_content = f.read()
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break
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except (UnicodeDecodeError, UnicodeError):
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continue
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if gpx_content is None:
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print(f"无法解码文件: {gpx_file_path}")
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return None
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gpx = gpxpy.parse(gpx_content)
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points = []
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total_distance = 0
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moving_time_seconds = 0 # 净运动时间
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point_moving_time = 0 # 每个轨迹点的累计运动时间（排除暂停）
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start_time = None
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end_time = None
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# 临时存储速度用于平滑处理
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recent_speeds = []
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for track in gpx.tracks:
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for segment in track.segments:
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previous_point = None
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for i, point in enumerate(segment.points):
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# 坐标转换
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gcj_lng, gcj_lat = wgs84_to_gcj02(point.longitude, point.latitude)
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segment_distance = 0
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raw_speed = 0
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time_diff = 0
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if previous_point:
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# 计算距离 (使用WGS84原始坐标计算距离更准确，再转换用于显示)
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segment_distance = gpxpy.geo.haversine_distance(
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previous_point['raw_lat'], previous_point['raw_lng'],
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point.latitude, point.longitude
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)
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total_distance += segment_distance
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# 计算时间差
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if point.time and previous_point['time']:
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time_diff = (point.time - previous_point['time']).total_seconds()
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# --- 改进2：暂停检测逻辑 ---
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# 如果时间间隔在阈值内，计入运动时间
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if 0 < time_diff < PAUSE_THRESHOLD_SECONDS:
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moving_time_seconds += time_diff
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point_moving_time += time_diff
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# 计算原始速度
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if time_diff > 0:
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raw_speed = (segment_distance / time_diff) * 3.6 # km/h
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# --- 改进1：速度平滑处理 ---
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# 剔除极端错误的数字，用0替代而非前一个值（防止异常值传播）
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if raw_speed > MAX_SPEED_THRESHOLD:
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raw_speed = 0.0
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recent_speeds.append(raw_speed)
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if len(recent_speeds) > SPEED_WINDOW_SIZE:
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recent_speeds.pop(0)
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# 取窗口平均值作为当前点的瞬时速度
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smoothed_speed_kmh = sum(recent_speeds) / len(recent_speeds) if recent_speeds else 0.0
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# 记录时间
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if i == 0 and point.time and not start_time:
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start_time = point.time
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point_data = {
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'latitude': gcj_lat,
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'longitude': gcj_lng,
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'raw_lat': point.latitude, # 保留原始坐标用于距离计算
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'raw_lng': point.longitude,
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'elevation': point.elevation,
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'time': point.time,
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'cumulative_distance': total_distance,
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'instantaneous_speed': smoothed_speed_kmh / 3.6, # 转回 m/s 保持统一
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'duration_from_start': point_moving_time,
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'segment_distance': segment_distance
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}
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points.append(point_data)
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previous_point = point_data
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if not points: return None
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# 安全获取结束时间（点可能没有时间戳）
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end_time = None
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for p in reversed(points):
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if p['time'] is not None:
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end_time = p['time']
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break
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# 如果仍未找到开始时间，从第一个有时间戳的点获取
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if start_time is None:
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for p in points:
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if p['time'] is not None:
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start_time = p['time']
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break
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# 计算总时长（包含暂停），只有两者都不为None才计算
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total_duration_hours = 0.0
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if start_time and end_time:
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total_duration_hours = (end_time - start_time).total_seconds() / 3600
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# 计算运动时长（剔除暂停）
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moving_duration_hours = moving_time_seconds / 3600
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# 其他统计逻辑 (海拔等) 保持不变...
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elevations = [p['elevation'] for p in points if p['elevation'] is not None]
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elevation_gain = 0
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if len(elevations) > 1:
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for i in range(1, len(elevations)):
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if elevations[i] > elevations[i-1]:
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elevation_gain += elevations[i] - elevations[i-1]
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speeds_kmh = [p['instantaneous_speed'] * 3.6 for p in points]
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max_speed = max(speeds_kmh) if speeds_kmh else 0
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avg_speed = (total_distance / 1000) / moving_duration_hours if moving_duration_hours > 0 else 0
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return {
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'points': points,
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'total_distance': total_distance / 1000,
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'start_time': format_datetime(start_time),
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'end_time': format_datetime(end_time),
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'num_points': len(points),
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'min_elevation': min(elevations) if elevations else None,
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'max_elevation': max(elevations) if elevations else None,
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'avg_elevation': sum(elevations)/len(elevations) if elevations else None,
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'elevation_gain': elevation_gain,
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'duration_hours': moving_duration_hours, # 这里改为返回运动时间
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'total_elapsed_hours': total_duration_hours, # 保留一个总耗时
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'avg_speed': avg_speed,
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'max_speed': max_speed
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}
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except Exception as e:
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print(f"解析GPX文件时出错: {e}")
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return None
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def create_map_with_track(gpx_data, output_file='gpx_track_map.html'):
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"""
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使用folium创建带有轨迹的地图，默认提供多个地图源选项
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添加鼠标悬停显示路程、速度和时间功能
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轨迹始终显示，没有选择框控制
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"""
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if not gpx_data or not gpx_data['points']:
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print("没有轨迹数据可显示")
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return
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points = gpx_data['points']
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# 计算地图中心点
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center_lat = sum(p['latitude'] for p in points) / len(points)
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center_lon = sum(p['longitude'] for p in points) / len(points)
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# 创建地图 - 使用高德街道图作为默认地图
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m = folium.Map(
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location=[center_lat, center_lon],
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zoom_start=13,
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tiles=None # 不设置默认瓦片
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)
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# 添加所有地图源作为可选图层
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for tile_name, tile_url in MAP_TILES.items():
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folium.TileLayer(
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tiles=tile_url,
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attr=MAP_ATTRIBUTION[tile_name],
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name=tile_name
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).add_to(m)
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# 创建轨迹线的坐标列表和对应的工具提示数据
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track_coordinates = []
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tooltip_data = []
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for point in points:
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track_coordinates.append((point['latitude'], point['longitude']))
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# 准备工具提示信息（使用 html.escape 防止 XSS）
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distance_km = point['cumulative_distance'] / 1000
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speed_kmh = point['instantaneous_speed'] * 3.6 # 转换为km/h
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tooltip = (
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f'<div style="font-size:12px;font-family:Arial,sans-serif;">'
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f'<b>位置信息</b><br>'
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f'路程: {distance_km:.2f} km<br>'
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f'速度: {speed_kmh:.1f} km/h<br>'
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f'时间: {html.escape(format_datetime(point["time"]))}<br>'
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f'持续时间: {html.escape(format_duration(point["duration_from_start"]))}'
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f'</div>'
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)
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tooltip_data.append(tooltip)
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# 创建轨迹线 - 直接添加到地图
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track_line = folium.PolyLine(
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track_coordinates,
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color='red',
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weight=5,
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opacity=0.8,
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popup='GPS轨迹',
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tooltip='鼠标悬停查看详情'
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)
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track_line.add_to(m)
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# 使用 JavaScript 实现高效的鼠标悬停（替代创建大量透明PolyLine）
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# 只在点数合理时启用逐点悬停，否则降级为简单提示
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# if len(points) <= MAX_POINTS_FOR_HOVER:
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if True: # 始终启用逐点悬停功能
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# 将坐标和提示数据注入 JS
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coords_json = json.dumps([[p['latitude'], p['longitude']] for p in points])
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tooltips_json = json.dumps(tooltip_data, ensure_ascii=False)
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hover_js = f"""
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<script>
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(function() {{
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var trackCoords = {coords_json};
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var trackTooltips = {tooltips_json};
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function getMapInstance() {{
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for (var key in window) {{
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try {{
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if (window[key] instanceof L.Map) return window[key];
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}} catch (e) {{}}
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}}
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var mapEl = document.querySelector('.folium-map');
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if (mapEl) {{
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var inner = mapEl.querySelector('div[id]');
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if (inner) {{
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var id = inner.id;
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if (window[id] && window[id] instanceof L.Map) return window[id];
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if (window['map_' + id] && window['map_' + id] instanceof L.Map) return window['map_' + id];
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}}
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}}
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return null;
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}}
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function findNearestPoint(latlng) {{
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var minDist = Infinity, minIdx = 0;
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for (var i = 0; i < trackCoords.length; i++) {{
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var dLat = trackCoords[i][0] - latlng.lat;
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var dLng = trackCoords[i][1] - latlng.lng;
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var d = dLat * dLat + dLng * dLng;
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if (d < minDist) {{ minDist = d; minIdx = i; }}
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}}
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return minIdx;
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}}
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function initHover() {{
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var mapInstance = getMapInstance();
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if (!mapInstance) {{
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setTimeout(initHover, 300);
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return;
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}}
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var hoverTooltip = L.tooltip({{className: 'track-hover-tooltip', direction: 'top', offset: [0, -10]}});
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var hoverLine = L.polyline(trackCoords, {{
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color: 'transparent',
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weight: 25,
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opacity: 0.01,
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interactive: true
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}}).addTo(mapInstance);
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hoverLine.on('mousemove', function(e) {{
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var idx = findNearestPoint(e.latlng);
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hoverTooltip
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.setLatLng(trackCoords[idx])
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.setContent(trackTooltips[idx])
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.addTo(mapInstance);
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}});
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hoverLine.on('mouseout', function() {{
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try {{ mapInstance.removeLayer(hoverTooltip); }} catch (e) {{}}
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}});
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}}
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setTimeout(initHover, 200);
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}})();
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</script>
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"""
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m.get_root().html.add_child(folium.Element(hover_js))
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# 添加起点标记
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start_point = points[0]
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folium.Marker(
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[start_point['latitude'], start_point['longitude']],
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popup=f"起点\n时间: {format_datetime(start_point['time'])}",
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tooltip="起点",
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icon=folium.Icon(color='green', icon='play', prefix='fa')
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).add_to(m)
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# 添加终点标记
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end_point = points[-1]
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folium.Marker(
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[end_point['latitude'], end_point['longitude']],
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popup=f"终点\n时间: {format_datetime(end_point['time'])}",
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tooltip="终点",
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icon=folium.Icon(color='red', icon='stop', prefix='fa')
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).add_to(m)
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# 在轨迹上添加关键点标记（每25%的距离添加一个）
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num_key_points = 4
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for i in range(1, num_key_points):
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target_distance = (gpx_data['total_distance'] * 1000) * (i / num_key_points)
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# 找到最接近目标距离的点
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closest_point = min(points, key=lambda x: abs(x['cumulative_distance'] - target_distance))
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folium.CircleMarker(
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[closest_point['latitude'], closest_point['longitude']],
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radius=4,
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popup=f"路程: {closest_point['cumulative_distance']/1000:.2f} km",
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tooltip=f"{i*25}% 路程点",
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color='blue',
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fill=True,
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fillColor='blue'
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).add_to(m)
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# 添加信息框 - 缩小尺寸并添加最高速度
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info_html = f"""
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<div style="position: fixed;
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top: 10px; left: 10px; width: 280px; height: auto;
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background-color: white; border:2px solid grey; z-index:9999;
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padding: 8px; font-size:12px; border-radius: 5px;
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box-shadow: 0 2px 6px rgba(0,0,0,0.3);">
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<b style="font-size:13px;">轨迹信息</b><br>
|
||
总距离: {gpx_data['total_distance']:.2f} km<br>
|
||
轨迹点数: {gpx_data['num_points']}<br>
|
||
开始时间: {gpx_data['start_time']}<br>
|
||
结束时间: {gpx_data['end_time']}<br>
|
||
"""
|
||
|
||
# 添加海拔信息（如果存在）
|
||
if gpx_data['min_elevation'] is not None:
|
||
info_html += f"最低海拔: {gpx_data['min_elevation']:.1f} m<br>"
|
||
info_html += f"最高海拔: {gpx_data['max_elevation']:.1f} m<br>"
|
||
info_html += f"平均海拔: {gpx_data['avg_elevation']:.1f} m<br>"
|
||
info_html += f"累计爬升: {gpx_data['elevation_gain']:.0f} m<br>"
|
||
|
||
# 添加时间和速度信息
|
||
if gpx_data['duration_hours'] > 0:
|
||
hours = int(gpx_data['duration_hours'])
|
||
minutes = int((gpx_data['duration_hours'] - hours) * 60)
|
||
info_html += f"持续时间: {hours}时{minutes}分<br>"
|
||
info_html += f"平均速度: {gpx_data['avg_speed']:.1f} km/h<br>"
|
||
info_html += f"<b>最高速度: {gpx_data['max_speed']:.1f} km/h</b><br>"
|
||
|
||
info_html += "<br><b>使用提示:</b> 鼠标悬停轨迹查看详情"
|
||
info_html += "</div>"
|
||
m.get_root().html.add_child(folium.Element(info_html))
|
||
|
||
# 添加图层控制（只控制地图源，不控制轨迹）
|
||
folium.LayerControl().add_to(m)
|
||
|
||
# 保存地图
|
||
m.save(output_file)
|
||
return m
|
||
|
||
def batch_process_gpx_files(folder_path):
|
||
"""
|
||
批量处理文件夹中的所有GPX文件
|
||
"""
|
||
gpx_files = glob.glob(os.path.join(folder_path, "*.gpx"))
|
||
|
||
if not gpx_files:
|
||
print("在指定文件夹中未找到GPX文件")
|
||
return
|
||
|
||
processed_count = 0
|
||
|
||
for gpx_file in gpx_files:
|
||
print(f"正在处理: {os.path.basename(gpx_file)}")
|
||
gpx_data = parse_gpx_file(gpx_file)
|
||
|
||
if gpx_data:
|
||
output_filename = os.path.splitext(gpx_file)[0] + '_map.html'
|
||
create_map_with_track(gpx_data, output_filename)
|
||
processed_count += 1
|
||
print(f" 已生成: {os.path.basename(output_filename)}")
|
||
|
||
return processed_count
|
||
|
||
if __name__ == "__main__":
|
||
print("GPX轨迹可视化工具")
|
||
print("正在处理当前文件夹中的GPX文件...")
|
||
|
||
# 获取当前程序所在文件夹路径
|
||
current_folder = os.path.dirname(os.path.abspath(__file__))
|
||
|
||
# 批量处理当前文件夹中的所有GPX文件
|
||
processed_count = batch_process_gpx_files(current_folder)
|
||
|
||
print(f"处理完成！共生成 {processed_count} 个地图文件")
|
||
print("请在浏览器中打开生成的HTML文件查看轨迹地图")
|
||
print("提示：您可以在右上角切换不同的地图图层")
|
||
print("新功能：鼠标悬停在轨迹线上可以查看实时路程、速度和时间信息") |