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")