python依据经纬坐标计算两点距离并求图像横纵分辨率

依据经纬坐标计算两点距离并求图像分辨率
输入：geotiff图像文件
输出：两点距离和图像分辨率
缺点：未将投影方式纳入考虑范围， 处理高纬地区可能精度失真。

import math, gdal
from geopy.distance import geodesic
from osgeo import gdal, osr
import numpy as np
from math import sin, asin, cos, radians, fabs, sqrt
 
EARTH_RADIUS = 6371  # 地球平均半径，6371km

def hav(theta):
    s = sin(theta / 2)
    return s * s
 
def get_distance_hav(lat0, lng0, lat1, lng1):
    """用haversine公式计算球面两点间的距离。"""
    # 经纬度转换成弧度
    lat0 = radians(lat0)
    lat1 = radians(lat1)
    lng0 = radians(lng0)
    lng1 = radians(lng1)
 
    dlng = fabs(lng0 - lng1)
    dlat = fabs(lat0 - lat1)
    h = hav(dlat) + cos(lat0) * cos(lat1) * hav(dlng)
    distance = 2 * EARTH_RADIUS * asin(sqrt(h))
    return distance

def radi(d):
  return d * 3.1415926 / 180

def read_img(filename):
    dataset = gdal.Open(filename) #打开文件
    im_width = dataset.RasterXSize  #栅格矩阵的列数
    im_height = dataset.RasterYSize  #栅格矩阵的行数
    im_bands = dataset.RasterCount   #波段数
    im_geotrans = dataset.GetGeoTransform()  #仿射矩阵，左上角像素的大地坐标和像素分辨率
    im_proj = dataset.GetProjection() #地图投影信息，字符串表示
    im_data = dataset.ReadAsArray(0,0,im_width,im_height)

    # del dataset 
    print(im_bands, im_height, im_width, im_geotrans, im_proj)

    return im_bands, im_data, dataset

def getDist(lat1, lat2, lon1, lon2):
    radlat = radi(lat1) - radi(lat2)
    radlon = radi(lon1) - radi(lon2)
    print(radlat, radlon)
    dist = 2*math.asin(math.sqrt(math.pow(math.sin(radlat/2), 2) + math.cos(radi(lat1)) * math.cos(radi(lat2))*math.pow(math.sin(radlon/2), 2))) #math.pow
    dist = math.floor(dist * 6378137 * 10000) / 10000
    return dist

def getDist2(lat1, lat2, lon1, lon2):
    # dist = geodesic((30.28708, 120.12802999999997), (28.7427, 115.86572000000001)).km  #
    dist = geodesic((lat1, lon1), (lat2, lon2))
    return dist
 
def getSRSPair(dataset):
    '''
    获得给定数据的投影参考系和地理参考系
    :param dataset: GDAL地理数据
    :return: 投影参考系和地理参考系
    '''
    prosrs = osr.SpatialReference()
    prosrs.ImportFromWkt(dataset.GetProjection())
    geosrs = prosrs.CloneGeogCS()
    return prosrs, geosrs

def geo2lonlat(dataset, x, y):
    '''
    将投影坐标转为经纬度坐标（具体的投影坐标系由给定数据确定）
    :param dataset: GDAL地理数据
    :param x: 投影坐标x
    :param y: 投影坐标y
    :return: 投影坐标(x, y)对应的经纬度坐标(lon, lat)
    '''
    prosrs, geosrs = getSRSPair(dataset)
    ct = osr.CoordinateTransformation(prosrs, geosrs)
    coords = ct.TransformPoint(x, y)
    return coords[:2]

def resolu(filename):

    im_bands, im_data, dataset = read_img(filename)
    trans = dataset.GetGeoTransform()
    x0, y0, xResolution, yResolution = trans[0], trans[3], trans[1], trans[5]
    coords = geo2lonlat(dataset, x0, y0)
    print('(%s, %s)->(%s, %s)' % (trans[0], trans[3], coords[0], coords[1]))
    # y0, x0 = coords[0], coords[1]
    y1 = y0 - yResolution * im_data.shape[1]
    y0 = coords[0]
    x0 = coords[1]
    coords = geo2lonlat(dataset, x0, y1)
    print('(%s, %s)->(%s, %s)' % (x0, y1, coords[0], coords[1]))
    print(x0, y0, y1, xResolution, yResolution, im_data.shape)
    y1 = coords[0]
    print(y0, y1, x0, x0)
    ylength1 = getDist(y0, y1, x0, x0)
    
    
    ylength = getDist2(y0, y1, x0, x0)
    print('ylength, ylength1 :: ', ylength, ylength1)
    yResolu = ylength1 / im_data.shape[1]

    x1 = x0 + xResolution * im_data.shape[2]
    # xlength = getDist(y0, y0, x0, x1)
    xlength = getDist2(y0, y0, x0, x1)
    xResolu = xlength / im_data.shape[2]
    return xResolu, yResolu, xlength, ylength



if __name__ == '__main__':
    # 依据经纬坐标计算两点距离并求图像分辨率
    xResolu, yResolu, xlength, ylength = resolu('D:/111/Rectangle_03_卫图/Rectangle_03_卫图_Level_18.tif')
    print('xResolu, yResolu :: ', xResolu, yResolu, xlength, ylength)