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CarDentIQ — FastAPI backend
Developer: Saksham Pathak (github.com/parthmax2)
"""
import base64
import io
import json
import numpy as np
from fastapi import FastAPI, File, Form, UploadFile
from fastapi.staticfiles import StaticFiles
from PIL import Image
from src.detector import CLASS_COLORS, CLASS_NAMES, DEFAULT_THRESHOLDS, detect
app = FastAPI(title="CarDentIQ API")
@app.get("/api/classes")
def get_classes():
return {
"classes": [
{
"id": cid,
"name": CLASS_NAMES[cid],
"color": CLASS_COLORS[cid],
"default_threshold": DEFAULT_THRESHOLDS[cid],
}
for cid in CLASS_NAMES
]
}
@app.post("/api/detect")
async def api_detect(
image: UploadFile = File(...),
resize: bool = Form(False),
thresholds: str = Form(...),
):
thresholds_map = {int(k): float(v) for k, v in json.loads(thresholds).items()}
raw = await image.read()
img = np.array(Image.open(io.BytesIO(raw)).convert("RGB"))
img_h, img_w = img.shape[:2]
result = detect(img, resize, thresholds_map)
def encode(arr) -> str:
buf = io.BytesIO()
Image.fromarray(arr).save(buf, format="PNG")
return f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
csv_lines = ["Class,Confidence,cx,cy,w,h"]
yolo_lines = []
for d in result["detections"]:
cx, cy, w, h = d["box_yolo"]
csv_lines.append(f'{d["class_name"]},{d["confidence"]},{cx},{cy},{w},{h}')
yolo_lines.append(f'{d["class_id"]} {cx:.6f} {cy:.6f} {w:.6f} {h:.6f}')
return {
"annotated_image": encode(result["annotated"]),
"detections": result["detections"],
"summary": result["summary"],
"image_info": {
"width": img_w,
"height": img_h,
"size_bytes": len(raw),
"filename": image.filename,
},
"csv": "\n".join(csv_lines),
"yolo_txt": "\n".join(yolo_lines),
}
# ── static frontend (must be mounted last — acts as a catch-all) ──
app.mount("/", StaticFiles(directory="static", html=True), name="static")
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