Initial FastAPI face-lock scaffold
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.env.example
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.env.example
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ENV=dev
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.gitignore
vendored
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.gitignore
vendored
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__pycache__/
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*.pyc
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.venv/
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.env
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.pytest_cache/
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.coverage
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htmlcov/
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13
README.md
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README.md
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# face-lock
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FastAPI microservice that finds the primary subject in an image, draws a square around it, and returns a buffered crop.
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## Dev
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```bash
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cp .env.example .env
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pip install -r requirements.txt
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uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
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```
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Set `ENV=dev` to enable the Tailwind UI at `/`.
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app/__init__.py
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app/__init__.py
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app/config.py
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app/config.py
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from dataclasses import dataclass
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from dotenv import load_dotenv
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import os
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load_dotenv()
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@dataclass(frozen=True)
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class Settings:
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env: str = os.getenv("ENV", "prod").strip().lower()
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settings = Settings()
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88
app/main.py
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app/main.py
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from fastapi import FastAPI, File, HTTPException, UploadFile
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from fastapi.responses import HTMLResponse, StreamingResponse
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from app.config import settings
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app = FastAPI(title="face-lock", version="0.1.0")
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@app.get("/health")
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def health():
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return {"ok": True, "env": settings.env}
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@app.get("/", response_class=HTMLResponse)
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def index():
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if settings.env != "dev":
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return HTMLResponse("<h1>face-lock</h1><p>Set ENV=dev for the UI.</p>")
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return HTMLResponse(
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"""
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<script src="https://cdn.tailwindcss.com"></script>
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<title>face-lock</title>
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</head>
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<body class="bg-slate-950 text-slate-100 min-h-screen">
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<main class="mx-auto max-w-5xl p-6">
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<div class="mb-6">
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<h1 class="text-3xl font-bold">face-lock</h1>
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<p class="text-slate-400">Drop an image, get the primary subject squared and cropped.</p>
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</div>
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<div class="grid gap-6 md:grid-cols-2">
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<section class="rounded-2xl border border-slate-800 bg-slate-900 p-4">
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<input id="file" type="file" accept="image/*" class="block w-full rounded-lg border border-slate-700 bg-slate-950 p-3" />
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<button id="go" class="mt-4 rounded-lg bg-cyan-500 px-4 py-2 font-semibold text-slate-950">Process</button>
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<pre id="meta" class="mt-4 whitespace-pre-wrap rounded-lg bg-slate-950 p-3 text-xs text-slate-300"></pre>
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</section>
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<section class="rounded-2xl border border-slate-800 bg-slate-900 p-4">
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<div class="mb-3 text-sm font-semibold text-slate-400">Result</div>
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<img id="result" class="hidden w-full rounded-xl border border-slate-800" />
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</section>
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</div>
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</main>
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<script>
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const file = document.getElementById('file');
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const go = document.getElementById('go');
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const result = document.getElementById('result');
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const meta = document.getElementById('meta');
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go.onclick = async () => {
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if (!file.files.length) return;
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const form = new FormData();
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form.append('file', file.files[0]);
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meta.textContent = 'Working...';
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const resp = await fetch('/api/focus', { method: 'POST', body: form });
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const data = await resp.json();
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meta.textContent = JSON.stringify(data, null, 2);
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result.src = data.crop_data_url;
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result.classList.remove('hidden');
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};
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</script>
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</body>
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</html>
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"""
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)
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@app.post("/api/focus")
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async def focus(file: UploadFile = File(...)):
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from app.vision import process_image
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try:
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payload = await file.read()
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return process_image(payload, file.filename or "upload")
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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@app.post("/api/focus/image")
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async def focus_image(file: UploadFile = File(...)):
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from app.vision import process_image
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try:
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payload = await file.read()
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result = process_image(payload, file.filename or "upload")
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return StreamingResponse(result["crop_bytes_io"], media_type=result["mime_type"])
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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149
app/vision.py
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app/vision.py
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from __future__ import annotations
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from dataclasses import dataclass
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from io import BytesIO
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from typing import Any
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import cv2
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import numpy as np
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@dataclass(frozen=True)
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class BBox:
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x: int
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y: int
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w: int
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h: int
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@property
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def right(self) -> int:
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return self.x + self.w
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@property
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def bottom(self) -> int:
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return self.y + self.h
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HOG = cv2.HOGDescriptor()
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HOG.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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def decode_image(image_bytes: bytes) -> np.ndarray:
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data = np.frombuffer(image_bytes, dtype=np.uint8)
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image = cv2.imdecode(data, cv2.IMREAD_COLOR)
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if image is None:
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raise ValueError("could not decode image")
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return image
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def select_primary_bbox(image: np.ndarray) -> tuple[BBox, str]:
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person_bbox = detect_person(image)
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if person_bbox is not None:
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return person_bbox, "person_hog"
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contour_bbox = detect_salient_object(image)
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if contour_bbox is not None:
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return contour_bbox, "contour"
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h, w = image.shape[:2]
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side = int(min(w, h) * 0.8)
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x = max(0, (w - side) // 2)
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y = max(0, (h - side) // 2)
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return BBox(x=x, y=y, w=side, h=side), "center_fallback"
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def detect_person(image: np.ndarray) -> BBox | None:
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rects, weights = HOG.detectMultiScale(image, winStride=(8, 8), padding=(8, 8), scale=1.05)
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if len(rects) == 0:
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return None
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best = max(zip(rects, weights), key=lambda item: int(item[0][2]) * int(item[0][3]))[0]
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x, y, w, h = map(int, best)
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return BBox(x=x, y=y, w=w, h=h)
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def detect_salient_object(image: np.ndarray) -> BBox | None:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(gray, (7, 7), 0)
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edges = cv2.Canny(blurred, 40, 120)
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kernel = np.ones((5, 5), np.uint8)
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closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel, iterations=2)
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contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return None
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h, w = image.shape[:2]
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image_area = h * w
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candidates: list[tuple[int, BBox]] = []
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for contour in contours:
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x, y, bw, bh = cv2.boundingRect(contour)
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area = bw * bh
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if area < max(500, int(image_area * 0.01)):
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continue
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candidates.append((area, BBox(x=x, y=y, w=bw, h=bh)))
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if not candidates:
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return None
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return max(candidates, key=lambda item: item[0])[1]
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def square_bbox(bbox: BBox, image_shape: tuple[int, int, int], buffer_ratio: float = 0.15) -> BBox:
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image_h, image_w = image_shape[:2]
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side = int(round(max(bbox.w, bbox.h) * (1 + buffer_ratio * 2)))
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side = max(1, min(side, image_w, image_h))
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cx = bbox.x + bbox.w / 2
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cy = bbox.y + bbox.h / 2
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x = int(round(cx - side / 2))
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y = int(round(cy - side / 2))
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x = max(0, min(x, image_w - side))
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y = max(0, min(y, image_h - side))
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return BBox(x=x, y=y, w=side, h=side)
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def draw_square(image: np.ndarray, bbox: BBox) -> np.ndarray:
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annotated = image.copy()
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cv2.rectangle(annotated, (bbox.x, bbox.y), (bbox.right, bbox.bottom), (0, 255, 0), 3)
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return annotated
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def crop_image(image: np.ndarray, bbox: BBox) -> np.ndarray:
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return image[bbox.y:bbox.bottom, bbox.x:bbox.right]
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def encode_image(image: np.ndarray, ext: str = ".jpg") -> tuple[bytes, str]:
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ok, encoded = cv2.imencode(ext, image)
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if not ok:
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raise ValueError("could not encode image")
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mime_type = "image/jpeg" if ext.lower() in {".jpg", ".jpeg"} else "image/png"
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return encoded.tobytes(), mime_type
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def _data_url(image_bytes: bytes, mime_type: str) -> str:
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import base64
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return f"data:{mime_type};base64,{base64.b64encode(image_bytes).decode('ascii')}"
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def process_image(image_bytes: bytes, filename: str) -> dict[str, Any]:
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image = decode_image(image_bytes)
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bbox, method = select_primary_bbox(image)
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square = square_bbox(bbox, image.shape)
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crop = crop_image(image, square)
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annotated = draw_square(image, square)
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crop_bytes, mime_type = encode_image(crop, ".jpg")
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annotated_bytes, _ = encode_image(annotated, ".jpg")
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return {
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"filename": filename,
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"method": method,
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"detected_bbox": bbox.__dict__,
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"square_bbox": square.__dict__,
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"source_size": {"width": int(image.shape[1]), "height": int(image.shape[0])},
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"crop_data_url": _data_url(crop_bytes, mime_type),
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"annotated_data_url": _data_url(annotated_bytes, mime_type),
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"mime_type": mime_type,
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"crop_bytes_io": BytesIO(crop_bytes),
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}
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5
requirements.txt
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requirements.txt
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fastapi
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uvicorn[standard]
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opencv-python-headless
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python-dotenv
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python-multipart
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30
tests/test_vision.py
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tests/test_vision.py
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import numpy as np
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from app.vision import BBox, crop_image, detect_salient_object, square_bbox
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def test_square_bbox_is_square_and_inside_bounds():
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bbox = BBox(x=10, y=20, w=40, h=20)
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square = square_bbox(bbox, (100, 120, 3), buffer_ratio=0.1)
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assert square.w == square.h
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assert square.x >= 0
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assert square.y >= 0
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assert square.right <= 120
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assert square.bottom <= 100
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def test_crop_image_uses_bbox():
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image = np.zeros((60, 80, 3), dtype=np.uint8)
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image[10:30, 20:50] = 255
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bbox = BBox(x=20, y=10, w=30, h=20)
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crop = crop_image(image, bbox)
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assert crop.shape[:2] == (20, 30)
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def test_detect_salient_object_finds_rectangle():
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image = np.zeros((100, 100, 3), dtype=np.uint8)
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image[25:75, 30:80] = 255
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bbox = detect_salient_object(image)
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assert bbox is not None
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assert bbox.w >= 45
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assert bbox.h >= 45
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