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from typing import Optional
from fastapi import FastAPI
from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Request
from pydantic import BaseModel
import base64, cv2, numpy as np, math
from pupil_apriltags import Detector
from scipy.spatial.transform import Rotation as R
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
detector = Detector(
families="tagStandard41h12",
nthreads=4,
quad_decimate=1.0,
quad_sigma=0.0,
refine_edges=True,
decode_sharpening=0.25,
debug=False,
)
# --- models ------------------------------------------------------------
class CameraParams(BaseModel):
fx: float
fy: float
cx: float
cy: float
class DetectionRequest(BaseModel):
image_base64: str
camera_params: CameraParams
tag_size: float
class TagDetection(BaseModel):
tag_id: int
corners: list[list[float]]
center: list[float]
translation_x: float
translation_y: float
translation_z: float
quaternion_w: float
quaternion_x: float
quaternion_y: float
quaternion_z: float
class DetectionResponse(BaseModel):
detections: list[TagDetection]
# Pose with explicit float fields for Unity/C# consumption
class Pose(BaseModel):
x: float
y: float
z: float
qw: float
qx: float
qy: float
qz: float
class TransformRequest(BaseModel):
headset_local: Pose # headset pose in local frame
detection: TagDetection # single tag detection
global_tag_pose: Pose # known tag pose in world frame
house_id : Optional[str] = None # optional house ID for future use
class TransformResponse(BaseModel):
headset_global: Pose # headset pose in world frame
local_to_global: Pose # transform local->world
global_to_local: Pose # transform world->local
@app.middleware("http")
async def log_requests(request: Request, call_next):
print(f"[REQ] {request.method} {request.url}")
return await call_next(request)
@app.get("/")
async def root():
return {"message": "Hello, Localization!"}
# --- endpoint: detect -------------------------------------------------
@app.post("/detect_apriltag", response_model=DetectionResponse)
def detect_apriltag(req: DetectionRequest):
img = cv2.imdecode(
np.frombuffer(base64.b64decode(req.image_base64), np.uint8),
cv2.IMREAD_GRAYSCALE
)
#img_ud = cv2.undistort(img, cameraMatrix=np.array([req.camera_params.fx, 0, req.camera_params.cx]))
tags = detector.detect(
img,
estimate_tag_pose=True,
camera_params=(
req.camera_params.fx,
req.camera_params.fy,
req.camera_params.cx,
req.camera_params.cy
),
tag_size=req.tag_size
)
out = []
for t in tags:
tx, ty, tz = t.pose_t.flatten().tolist()
mat = t.pose_R
qw = math.sqrt(max(0.0, 1 + mat[0,0] + mat[1,1] + mat[2,2]))/2
qx = math.copysign(math.sqrt(max(0.0, 1 + mat[0,0] - mat[1,1] - mat[2,2]))/2, mat[2,1] - mat[1,2])
qy = math.copysign(math.sqrt(max(0.0, 1 - mat[0,0] + mat[1,1] - mat[2,2]))/2, mat[0,2] - mat[2,0])
qz = math.copysign(math.sqrt(max(0.0, 1 - mat[0,0] - mat[1,1] + mat[2,2]))/2, mat[1,0] - mat[0,1])
out.append(TagDetection(
tag_id=t.tag_id,
corners=t.corners.tolist(),
center=t.center.tolist(),
translation_x=tx,
translation_y=ty,
translation_z=tz,
quaternion_w=qw,
quaternion_x=qx,
quaternion_y=qy,
quaternion_z=qz,
))
return DetectionResponse(detections=out)
# --- endpoint: compute transform ---------------------------------------
@app.post("/compute_transform", response_model=TransformResponse)
def compute_transform(req: TransformRequest):
# unpack detection: tag->camera
d = req.detection
t_ct = np.array([d.translation_x, d.translation_y, d.translation_z])
R_ct = R.from_quat([d.quaternion_x, d.quaternion_y, d.quaternion_z, d.quaternion_w]).as_matrix()
# invert: camera->tag
R_tc = R_ct.T
t_tc = -R_tc.dot(t_ct)
# unpack global tag pose: tag->world
gt = req.global_tag_pose
t_wt = np.array([gt.x, gt.y, gt.z])
R_wt = R.from_quat([gt.qx, gt.qy, gt.qz, gt.qw]).as_matrix()
# compute camera->world
R_wc = R_wt.dot(R_tc)
t_wc = R_wt.dot(t_tc) + t_wt
headset_global = Pose(
x=float(t_wc[0]), y=float(t_wc[1]), z=float(t_wc[2]),
qw=float(R.from_matrix(R_wc).as_quat()[3]),
qx=float(R.from_matrix(R_wc).as_quat()[0]),
qy=float(R.from_matrix(R_wc).as_quat()[1]),
qz=float(R.from_matrix(R_wc).as_quat()[2])
)
# unpack local headset pose: local->headset
hl = req.headset_local
t_lh = np.array([hl.x, hl.y, hl.z])
R_lh = R.from_quat([hl.qx, hl.qy, hl.qz, hl.qw]).as_matrix()
# invert: headset->local
R_hl = R_lh.T
t_hl = -R_hl.dot(t_lh)
# compute local->world
R_wl = R_wc.dot(R_hl)
t_wl = R_wc.dot(t_hl) + t_wc
local_to_global = Pose(
x=float(t_wl[0]), y=float(t_wl[1]), z=float(t_wl[2]),
qw=float(R.from_matrix(R_wl).as_quat()[3]),
qx=float(R.from_matrix(R_wl).as_quat()[0]),
qy=float(R.from_matrix(R_wl).as_quat()[1]),
qz=float(R.from_matrix(R_wl).as_quat()[2])
)
# compute global->local
R_lw = R_wl.T
t_lw = -R_lw.dot(t_wl)
global_to_local = Pose(
x=float(t_lw[0]), y=float(t_lw[1]), z=float(t_lw[2]),
qw=float(R.from_matrix(R_lw).as_quat()[3]),
qx=float(R.from_matrix(R_lw).as_quat()[0]),
qy=float(R.from_matrix(R_lw).as_quat()[1]),
qz=float(R.from_matrix(R_lw).as_quat()[2])
)
return TransformResponse(
headset_global=headset_global,
local_to_global=local_to_global,
global_to_local=global_to_local
)
if __name__ == "__main__":
import uvicorn
uvicorn.run("api.mapping_server:app", host="0.0.0.0", port=8000) |