dataset/UAV/prepara_dataset.py

import torch
from torch.utils.data import Dataset
import os
import cv2
# @Time : 2023-02-13 22:56
# @Author : Wang Zhen
# @Email : frozenzhencola@163.com
# @File : SatelliteTool.py
# @Project : TGRS_seqmatch_2023_1
import numpy as np
import random
from utils.geo import BoundaryBox, Projection
from osm.tiling import TileManager,MapTileManager
from pathlib import Path
from torchvision import transforms
from tqdm import tqdm
import time
import math
import random
from geopy import Point, distance
from osm.viz import Colormap, plot_nodes

def generate_random_coordinate(latitude, longitude, dis):
    # 生成一个随机方向角
    random_angle = random.uniform(0, 360)
    # print("random_angle",random_angle)
    # 计算目标点的经纬度
    start_point = Point(latitude, longitude)
    destination = distance.distance(kilometers=dis/1000).destination(start_point, random_angle)

    return destination.latitude, destination.longitude

def rotate_corp(src,angle):
    # 原图的高、宽 以及通道数
    rows, cols, channel = src.shape

    # 绕图像的中心旋转
    # 参数：旋转中心 旋转度数 scale
    M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
    # rows, cols=700,700
    # 自适应图片边框大小
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
    new_w = rows * sin + cols * cos
    new_h = rows * cos + cols * sin
    M[0, 2] += (new_w - cols) * 0.5
    M[1, 2] += (new_h - rows) * 0.5
    w = int(np.round(new_w))
    h = int(np.round(new_h))
    rotated = cv2.warpAffine(src, M, (w, h))

    # rotated = cv2.warpAffine(src, M, (cols, rows))

    c=int(w / 2)
    w=int(rows*math.sqrt(2)/4)
    rotated2=rotated[c-w:c+w,c-w:c+w,:]
    return rotated2

class SatelliteGeoTools:
    """
    用于读取卫星图tfw文件，执行 像素坐标-Mercator-GPS坐标 的转化
    """
    def __init__(self, tfw_path):
        self.SatelliteParameter=self.Parsetfw(tfw_path)
    def Parsetfw(self, tfw_path):
        info = []
        f = open(tfw_path)
        for _ in range(6):
            line = f.readline()
            line = line.strip('\n')
            info.append(float(line))
        f.close()
        return info
    def Pix2Geo(self, x, y):
        A, D, B, E, C, F = self.SatelliteParameter
        x1 = A * x + B * y + C
        y1 = D * x + E * y + F
        # print(x1,y1)
        s_long, s_lat = self.MercatorTolonlat(x1, y1)
        return s_long, s_lat

    def Geo2Pix(self, lon, lat):
        """
        https://baike.baidu.com/item/TFW%E6%A0%BC%E5%BC%8F/6273151?fr=aladdin
        x'=Ax+By+C
        y'=Dx+Ey+F
        :return:
        """
        x1, y1 = self.LonlatToMercator(lon, lat)
        A, D, B, E, C, F = self.SatelliteParameter
        M = np.array([[A, B, C],
                      [D, E, F],
                      [0, 0, 1]])
        M_INV = np.linalg.inv(M)
        XY = np.matmul(M_INV, np.array([x1, y1, 1]).T)
        return int(XY[0]), int(XY[1])
    def MercatorTolonlat(self,mx,my):
        x = mx/20037508.3427892*180
        y = my/20037508.3427892*180
        # y= 180/math.pi*(2*math.atan(math.exp(y*math.pi/180))-math.pi/2)
        y = 180.0 / np.pi * (2.0 * np.arctan(np.exp(y * np.pi / 180.0)) - np.pi / 2.0)
        return x,y
    def LonlatToMercator(self,lon, lat):
        x = lon * 20037508.342789 / 180
        y = np.log(np.tan((90 + lat) * np.pi / 360)) / (np.pi / 180)
        y = y * 20037508.34789 / 180
        return x, y

def geodistance(lng1, lat1, lng2, lat2):
    lng1, lat1, lng2, lat2 = map(np.radians, [lng1, lat1, lng2, lat2])
    dlon = lng2 - lng1
    dlat = lat2 - lat1
    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
    distance = 2 * np.arcsin(np.sqrt(a)) * 6371 * 1000  # 地球平均半径，6371km
    return distance

class PreparaDataset:
    def __init__(
        self,
        root: Path,
        city:str,
        patch_size:int,
        tile_size_meters:float
    ):
        super().__init__()

        # self.root = root

        # city = 'Manhattan'
        # root = '/root/DATASET/CrossModel/'
        imagepath = root/city/ '{}.tif'.format(city)
        tfwpath = root/city/'{}.tfw'.format(city)

        self.osmpath = root/city/'{}.osm'.format(city)

        self.TileManager=MapTileManager(self.osmpath)
        image = cv2.imread(str(imagepath))
        self.image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

        self.ST = SatelliteGeoTools(str(tfwpath))

        self.patch_size=patch_size
        self.tile_size_meters=tile_size_meters



    def get_osm(self,prior_latlon,uav_latlon):
        latlon = np.array(prior_latlon)
        proj = Projection(*latlon)
        center = proj.project(latlon)

        uav_latlon=np.array(uav_latlon)

        XY=proj.project(uav_latlon)
        # tile_size_meters = 128
        bbox = BoundaryBox(center, center) + self.tile_size_meters
        # bbox= BoundaryBox(center, center)
        # Query OpenStreetMap for this area
        self.pixel_per_meter = 1
        start_time = time.time()
        canvas = self.TileManager.from_bbox(proj, bbox, self.pixel_per_meter)
        end_time = time.time()
        execution_time = end_time - start_time
        # print("方法执行时间：", execution_time, "秒")
        # canvas = tiler.query(bbox)
        XY=[XY[0]+self.tile_size_meters,-XY[1]+self.tile_size_meters]
        return canvas,XY
    def random_corp(self):

        # 根据随机裁剪尺寸计算出裁剪区域的左上角坐标
        x = random.randint(1000, self.image.shape[1] - self.patch_size-1000)
        y = random.randint(1000, self.image.shape[0] - self.patch_size-1000)
        x1 = x + self.patch_size
        y1 = y + self.patch_size
        return x,x1,y,y1

    def generate(self):
        x,x1,y,y1 = self.random_corp()
        uav_center_x,uav_center_y=int((x+x1)//2),int((y+y1)//2)
        uav_center_long,uav_center_lat=self.ST.Pix2Geo(uav_center_x,uav_center_y)
        # print(uav_center_long,uav_center_lat)
        self.image_patch = self.image[y:y1, x:x1]

        map_center_lat, map_center_long = generate_random_coordinate(uav_center_lat, uav_center_long, self.tile_size_meters)
        map,XY=self.get_osm([map_center_lat,map_center_long],[uav_center_lat, uav_center_long])


        yaw=np.random.random()*360
        self.image_patch=rotate_corp(self.image_patch,yaw)
        # return self.image_patch,self.osm_patch
        # XY=[X+self.tile_size_meters
        return {
            'uav_image':self.image_patch,
            'uav_long_lat':[uav_center_long,uav_center_lat],
            'map_long_lat': [map_center_long,map_center_lat],
            'tile_size_meters': map.raster.shape[1],
            'pixel_per_meter':self.pixel_per_meter,
            'yaw':yaw,
            'map':map.raster,
            "uv":XY
        }
if __name__ == '__main__':

    import argparse

    parser = argparse.ArgumentParser(description='manual to this script')
    parser.add_argument('--city', type=str, default=None,required=True)
    parser.add_argument('--num', type=int, default=10000)
    args = parser.parse_args()


    root=Path('/root/DATASET/OrienterNet/UavMap/')
    city=args.city
    dataset = PreparaDataset(
        root=root,
        city=city,
        patch_size=512,
        tile_size_meters=128,
    )

    uav_path=root/city/'uav'
    if not uav_path.exists():
        uav_path.mkdir(parents=True)

    map_path = root / city / 'map'
    if not map_path.exists():
        map_path.mkdir(parents=True)

    map_vis_path = root / city / 'map_vis'
    if not map_vis_path.exists():
        map_vis_path.mkdir(parents=True)

    info_path = root / city / 'info.csv'

    # num=1000
    num = args.num
    info=[['id','uav_name','map_name','uav_long','uav_lat','map_long','map_lat','tile_size_meters','pixel_per_meter','u','v','yaw']]
    # info =[]
    for i in tqdm(range(num)):
        data=dataset.generate()
        # print(str(uav_path/"{:05d}.jpg".format(i)))

        cv2.imwrite(str(uav_path/"{:05d}.jpg".format(i)),cv2.cvtColor(data['uav_image'],cv2.COLOR_RGB2BGR))

        np.save(str(map_path/"{:05d}.npy".format(i)),data['map'])

        map_viz, label = Colormap.apply(data['map'])
        map_viz = map_viz * 255
        map_viz = map_viz.astype(np.uint8)
        cv2.imwrite(str(map_vis_path / "{:05d}.jpg".format(i)), cv2.cvtColor(map_viz, cv2.COLOR_RGB2BGR))


        uav_center_long, uav_center_lat=data['uav_long_lat']
        map_center_long, map_center_lat = data['map_long_lat']
        info.append([
             i,
             "{:05d}.jpg".format(i),
             "{:05d}.npy".format(i),
             uav_center_long,
             uav_center_lat,
             map_center_long,
             map_center_lat,
             data["tile_size_meters"],
             data["pixel_per_meter"],
             data['uv'][0],
             data['uv'][1],
             data['yaw']
             ])
        # print(info)
        np.savetxt(info_path,info,delimiter=',',fmt="%s")