main.py

import gradio as gr
import cv2
import gradio as gr
import torch
from torchvision import transforms
import requests
from PIL import Image
from demo import Demo,read_input_image_test,show_result,vis_image_feature
from osm.tiling import TileManager
from osm.viz import Colormap, plot_nodes
from utils.viz_2d import plot_images
import numpy as np
from utils.viz_2d import features_to_RGB
from utils.viz_localization import (
    likelihood_overlay,
    plot_dense_rotations,
    add_circle_inset,
)
from osm.viz import GeoPlotter
import matplotlib.pyplot as plt
import random
from geopy.distance import geodesic

experiment_or_path = "weight/last-step-checkpointing.ckpt"
# experiment_or_path="experiments/maplocanet_0906_diffhight/last-step-checkpointing.ckpt"
image_path = 'images/00000.jpg'

# prior_latlon = (37.75704325989902, -122.435941445631)
# tile_size_meters = 128
model = Demo(experiment_or_path=experiment_or_path, num_rotations=128, device='cpu')

def demo_localize(image,long,lat,tile_size_meters):
    # inp = Image.fromarray(inp.astype('uint8'), 'RGB')
    # inp = transforms.ToTensor()(inp).unsqueeze(0)
    prior_latlon=(lat,long)
    image, camera, gravity, proj, bbox, true_prior_latlon = read_input_image_test(
        image,
        prior_latlon=prior_latlon,
        tile_size_meters=tile_size_meters,  # try 64, 256, etc.
    )
    tiler = TileManager.from_bbox(projection=proj, bbox=bbox, ppm=1, tile_size=tile_size_meters)
    # tiler = TileManager.from_bbox(projection=proj, bbox=bbox + 10,ppm=1,path=root/city/'{}.osm'.format(city), tile_size=1)
    canvas = tiler.query(bbox)
    uv, yaw, prob, neural_map, image_rectified, data_, pred = model.localize(
        image, camera, canvas)
    prior_latlon_pred = proj.unproject(canvas.to_xy(uv))

    map_viz = Colormap.apply(canvas.raster)
    map_vis_image_result = map_viz * 255
    map_vis_image_result =show_result(map_vis_image_result.astype(np.uint8), uv, yaw)
    # map_vis_image_result = show_result(map_vis_image_result.astype(np.uint8), True_uv,
    #                                    uv,
    #                                    90.0 - yaw_T,
    #                                    yaw)
    # return prior_latlon_pred
    uab_feature_rgb = vis_image_feature(pred['features_image'][0].cpu().numpy())
    map_viz = cv2.resize(map_viz, (prob.numpy().shape[0], prob.numpy().shape[1]))
    overlay = likelihood_overlay(prob.numpy().max(-1), map_viz.mean(-1, keepdims=True))
    (neural_map_rgb,) = features_to_RGB(neural_map.numpy())
    fig=plot_images([image, map_vis_image_result / 255, overlay, uab_feature_rgb, neural_map_rgb],
                    titles=["UAV image", "map","likelihood","UAV feature","map feature"])
    # plot_images([overlay, neural_map_rgb], titles=["prediction", "neural map"])
    # ax = plt.gcf().axes[2]
    # ax.scatter(*canvas.to_uv(bbox.center), s=5, c="red")
    # plot_dense_rotations(ax, prob, w=0.005, s=1 / 25)
    # add_circle_inset(ax, uv)

    # Plot as interactive figure
    bbox_latlon = proj.unproject(canvas.bbox)
    plot2 = GeoPlotter(zoom=16.5)
    plot2.raster(map_viz, bbox_latlon, opacity=0.5)
    plot2.raster(likelihood_overlay(prob.numpy().max(-1)), proj.unproject(bbox))
    plot2.points(prior_latlon[:2], "red", name="location prior", size=10)
    plot2.points(proj.unproject(canvas.to_xy(uv)), "black", name="argmax", size=10)
    plot2.bbox(bbox_latlon, "blue", name="map tile")
    # plot2.fig.show()
    return fig,plot2.fig,str(prior_latlon_pred)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
#标题
title = "MapLocNet"
#标题下的描述，支持md格式
description = "UAV Vision-based Geo-Localization Using Vectorized Maps"

# outputs = gr.outputs.Label(num_top_classes=3)
outputs = gr.Plot()
interface = gr.Interface(fn=demo_localize,
                         inputs=["image",
                                 gr.Number(label="Prior location-longitude)"),
                                 gr.Number(label="Prior location-longitude)"),
                                 gr.Radio([64, 128, 256], label="Search radius (meters)", info="vectorized map size"),
                                 # gr.inputs.RadioGroup(label="Search radius (meters)",["English", "French", "Spanish"]),
                                 # gr.Slider(64, 512,label='Search radius (meters)')
                                 ],
                         outputs=["plot","plot","text"],
                         title=title,
                         description=description,
                         examples=[['images/00000.jpg',-122.435941445631,37.75704325989902,128]])
interface.launch(share=True)