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# server.py
import sys
from pathlib import Path
from typing import Union
import numpy as np
import uvicorn
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from PIL import Image
sys.path.append("..")
from pydantic import BaseModel
from ui.api import ImageMatchingAPI
from ui.utils import DEVICE
class ImageInfo(BaseModel):
image_path: str
max_keypoints: int
reference_points: list
class ImageMatchingService:
def __init__(self, conf: dict, device: str):
self.api = ImageMatchingAPI(conf=conf, device=device)
self.app = FastAPI()
self.register_routes()
def register_routes(self):
@self.app.post("/v1/match")
async def match(
image0: UploadFile = File(...), image1: UploadFile = File(...)
):
try:
image0_array = self.load_image(image0)
image1_array = self.load_image(image1)
output = self.api(image0_array, image1_array)
skip_keys = ["image0_orig", "image1_orig"]
pred = self.filter_output(output, skip_keys)
return JSONResponse(content=pred)
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
@self.app.post("/v1/extract")
async def extract(image: UploadFile = File(...)):
try:
image_array = self.load_image(image)
output = self.api.extract(image_array)
skip_keys = ["descriptors", "image", "image_orig"]
pred = self.filter_output(output, skip_keys)
return JSONResponse(content=pred)
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
@self.app.post("/v2/extract")
async def extract_v2(image_path: ImageInfo):
img_path = image_path.image_path
try:
safe_path = Path(img_path).resolve(strict=False)
image_array = self.load_image(str(safe_path))
output = self.api.extract(image_array)
skip_keys = ["descriptors", "image", "image_orig"]
pred = self.filter_output(output, skip_keys)
return JSONResponse(content=pred)
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500)
def load_image(self, file_path: Union[str, UploadFile]) -> np.ndarray:
"""
Reads an image from a file path or an UploadFile object.
Args:
file_path: A file path or an UploadFile object.
Returns:
A numpy array representing the image.
"""
if isinstance(file_path, str):
file_path = Path(file_path).resolve(strict=False)
else:
file_path = file_path.file
with Image.open(file_path) as img:
image_array = np.array(img)
return image_array
def filter_output(self, output: dict, skip_keys: list) -> dict:
pred = {}
for key, value in output.items():
if key in skip_keys:
continue
if isinstance(value, np.ndarray):
pred[key] = value.tolist()
return pred
def run(self, host: str = "0.0.0.0", port: int = 8001):
uvicorn.run(self.app, host=host, port=port)
if __name__ == "__main__":
conf = {
"feature": {
"output": "feats-superpoint-n4096-rmax1600",
"model": {
"name": "superpoint",
"nms_radius": 3,
"max_keypoints": 4096,
"keypoint_threshold": 0.005,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1600,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
"matcher": {
"output": "matches-NN-mutual",
"model": {
"name": "nearest_neighbor",
"do_mutual_check": True,
"match_threshold": 0.2,
},
},
"dense": False,
}
service = ImageMatchingService(conf=conf, device=DEVICE)
service.run()
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