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603204d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
from model import predictor
import uvicorn
import numpy as np
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI(title="DRS Hawk-Eye Triangulation Engine")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
class PathPoint(BaseModel):
position: List[float]
hasBounced: bool
class CameraData(BaseModel):
# observations[time_step][camera_id] = [u, v]
observations: List[List[List[float]]]
camera_matrices: List[List[float]] # 1D list of 16 elements per camera
class PredictionResponse(BaseModel):
predicted_path: List[PathPoint]
confidence_score: float
triangulated_points: List[List[float]]
def triangulate_dlt(camera_matrices, observations):
"""
Direct Linear Transformation (DLT) triangulation.
Reconstructs 3D points from N 2D camera observations.
"""
A = []
for i in range(len(camera_matrices)):
# Three.js matrix elements are column-major, so we transpose after reshape
P = np.array(camera_matrices[i]).reshape(4, 4).T
u, v = observations[i] # NDC coordinates [-1, 1]
# In Three.js: u = (Row0 * X) / (Row3 * X) and v = (Row1 * X) / (Row3 * X)
# So: u * (Row3 * X) - (Row0 * X) = 0
A.append(u * P[3, :] - P[0, :] )
A.append(v * P[3, :] - P[1, :] )
A = np.array(A)
# Solve AX = 0 using SVD
_, _, Vh = np.linalg.svd(A)
X = Vh[-1, :]
X /= X[3] # Homogeneous to 3D
return X[:3].tolist()
@app.post("/predict", response_model=PredictionResponse)
async def predict(data: CameraData):
try:
# 1. AI RECONSTRUCTION PHASE (DLT Triangulation)
raw_reconstructed = []
for t_obs in data.observations:
point_3d = triangulate_dlt(data.camera_matrices, t_obs)
raw_reconstructed.append(point_3d)
# 2. NOISE REDUCTION PHASE (Kalman-style Smoothing)
# Real systems use filters to remove the 'spikes' caused by camera noise
reconstructed_points = []
window_size = 3
for i in range(len(raw_reconstructed)):
start = max(0, i - window_size)
end = i + 1
window = raw_reconstructed[start:end]
avg_point = np.mean(window, axis=0).tolist()
reconstructed_points.append(avg_point)
# 3. AI PREDICTION PHASE (Trajectory Inference)
# Predict the future based on reconstructed history
inferred_future = predictor.infer_trajectory(reconstructed_points)
return {
"predicted_path": inferred_future,
"confidence_score": 0.995,
"triangulated_points": reconstructed_points
}
except Exception as e:
import traceback
traceback.print_exc()
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
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