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Add dockerized detector and UI cleanup

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This commit is contained in:
Luna
2026-04-11 16:45:22 +02:00
parent 3b5a9e8635
commit ecbf948a74
7 changed files with 148 additions and 26 deletions
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53
app/main.py
View File

@@ -2,7 +2,7 @@ from fastapi import FastAPI, File, Form, HTTPException, UploadFile
from fastapi.responses import HTMLResponse, StreamingResponse
from app.config import settings
app = FastAPI(title="face-lock", version="0.1.0")
app = FastAPI(title="face-lock", version="0.2.0")
@app.get("/health")
@@ -25,16 +25,30 @@ def index():
<title>face-lock</title>
</head>
<body class="bg-slate-950 text-slate-100 min-h-screen">
<main class="mx-auto max-w-5xl p-6">
<main class="mx-auto max-w-6xl p-6">
<div class="mb-6">
<h1 class="text-3xl font-bold">face-lock</h1>
<p class="text-slate-400">Drop an image, get the primary subject squared and cropped.</p>
<p class="text-slate-400">Auto-detect the subject, square it up, and crop with buffer.</p>
</div>
<div class="grid gap-6 md:grid-cols-2">
<section class="rounded-2xl border border-slate-800 bg-slate-900 p-4">
<input id="file" type="file" accept="image/*" class="block w-full rounded-lg border border-slate-700 bg-slate-950 p-3" />
<label class="mt-4 block text-sm text-slate-400">Buffer ratio</label>
<input id="buffer_ratio" type="number" step="0.05" min="0" max="0.5" value="0.15" class="block w-full rounded-lg border border-slate-700 bg-slate-950 p-3" />
<label class="block text-sm text-slate-400">Image</label>
<input id="file" type="file" accept="image/*" class="mt-2 block w-full rounded-lg border border-slate-700 bg-slate-950 p-3" />
<div class="mt-4 grid gap-4 sm:grid-cols-2">
<div>
<label class="block text-sm text-slate-400">Detector</label>
<select id="detector" class="mt-2 block w-full rounded-lg border border-slate-700 bg-slate-950 p-3">
<option value="auto">Auto</option>
<option value="face">Face</option>
<option value="person">Person</option>
<option value="salient">Subject</option>
</select>
</div>
<div>
<label class="block text-sm text-slate-400">Buffer ratio</label>
<input id="buffer_ratio" type="number" step="0.05" min="0" max="0.6" value="0.20" class="mt-2 block w-full rounded-lg border border-slate-700 bg-slate-950 p-3" />
</div>
</div>
<button id="go" class="mt-4 rounded-lg bg-cyan-500 px-4 py-2 font-semibold text-slate-950">Process</button>
<pre id="meta" class="mt-4 whitespace-pre-wrap rounded-lg bg-slate-950 p-3 text-xs text-slate-300"></pre>
</section>
@@ -63,11 +77,20 @@ def index():
if (!file.files.length) return;
const form = new FormData();
form.append('file', file.files[0]);
form.append('detector', document.getElementById('detector').value);
form.append('buffer_ratio', document.getElementById('buffer_ratio').value);
meta.textContent = 'Working...';
const resp = await fetch('/api/focus', { method: 'POST', body: form });
const data = await resp.json();
meta.textContent = JSON.stringify(data, null, 2);
meta.textContent = JSON.stringify({
filename: data.filename,
detector: data.detector,
method: data.method,
buffer_ratio: data.buffer_ratio,
detected_bbox: data.detected_bbox,
square_bbox: data.square_bbox,
source_size: data.source_size,
}, null, 2);
crop.src = data.crop_data_url;
annotated.src = data.annotated_data_url;
crop.classList.remove('hidden');
@@ -81,23 +104,31 @@ def index():
@app.post("/api/focus")
async def focus(file: UploadFile = File(...), buffer_ratio: float = Form(0.15)):
async def focus(
file: UploadFile = File(...),
buffer_ratio: float = Form(0.15),
detector: str = Form("auto"),
):
from app.vision import process_image
try:
payload = await file.read()
return process_image(payload, file.filename or "upload", buffer_ratio=buffer_ratio)
return process_image(payload, file.filename or "upload", buffer_ratio=buffer_ratio, detector=detector)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
@app.post("/api/focus/image")
async def focus_image(file: UploadFile = File(...), buffer_ratio: float = Form(0.15)):
async def focus_image(
file: UploadFile = File(...),
buffer_ratio: float = Form(0.15),
detector: str = Form("auto"),
):
from app.vision import process_image
try:
payload = await file.read()
result = process_image(payload, file.filename or "upload", buffer_ratio=buffer_ratio)
result = process_image(payload, file.filename or "upload", buffer_ratio=buffer_ratio, detector=detector)
return StreamingResponse(result["crop_bytes_io"], media_type=result["mime_type"])
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc

73
app/vision.py
View File

@@ -2,6 +2,7 @@ from __future__ import annotations
from dataclasses import dataclass
from io import BytesIO
from pathlib import Path
from typing import Any
import cv2
@@ -24,6 +25,9 @@ class BBox:
return self.y + self.h
FACE_CASCADE = cv2.CascadeClassifier(
str(Path(cv2.data.haarcascades) / "haarcascade_frontalface_default.xml")
)
HOG = cv2.HOGDescriptor()
HOG.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
@@ -36,20 +40,59 @@ def decode_image(image_bytes: bytes) -> np.ndarray:
return image
def select_primary_bbox(image: np.ndarray) -> tuple[BBox, str]:
def select_primary_bbox(image: np.ndarray, detector: str = "auto") -> tuple[BBox, str]:
detector = (detector or "auto").strip().lower()
if detector == "face":
face_bbox = detect_face(image)
if face_bbox is not None:
return face_bbox, "face_cascade"
return fallback_bbox(image), "center_fallback"
if detector == "person":
person_bbox = detect_person(image)
if person_bbox is not None:
return person_bbox, "person_hog"
return fallback_bbox(image), "center_fallback"
if detector == "salient":
salient_bbox = detect_salient_object(image)
if salient_bbox is not None:
return salient_bbox, "salient_contour"
return fallback_bbox(image), "center_fallback"
face_bbox = detect_face(image)
if face_bbox is not None:
return face_bbox, "face_cascade"
person_bbox = detect_person(image)
if person_bbox is not None:
return person_bbox, "person_hog"
contour_bbox = detect_salient_object(image)
if contour_bbox is not None:
return contour_bbox, "contour"
salient_bbox = detect_salient_object(image)
if salient_bbox is not None:
return salient_bbox, "salient_contour"
return fallback_bbox(image), "center_fallback"
def fallback_bbox(image: np.ndarray) -> BBox:
h, w = image.shape[:2]
side = int(min(w, h) * 0.8)
side = int(min(w, h) * 0.85)
side = max(1, min(side, w, h))
x = max(0, (w - side) // 2)
y = max(0, (h - side) // 2)
return BBox(x=x, y=y, w=side, h=side), "center_fallback"
return BBox(x=x, y=y, w=side, h=side)
def detect_face(image: np.ndarray) -> BBox | None:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
faces = FACE_CASCADE.detectMultiScale(gray, scaleFactor=1.08, minNeighbors=5, minSize=(24, 24))
if len(faces) == 0:
return None
x, y, w, h = max((map(int, face) for face in faces), key=lambda rect: rect[2] * rect[3])
return BBox(x=x, y=y, w=w, h=h)
def detect_person(image: np.ndarray) -> BBox | None:
@@ -63,10 +106,11 @@ def detect_person(image: np.ndarray) -> BBox | None:
def detect_salient_object(image: np.ndarray) -> BBox | None:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (7, 7), 0)
edges = cv2.Canny(blurred, 40, 120)
kernel = np.ones((5, 5), np.uint8)
closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel, iterations=2)
blurred = cv2.GaussianBlur(gray, (9, 9), 0)
edges = cv2.Canny(blurred, 30, 110)
kernel = np.ones((13, 13), np.uint8)
expanded = cv2.dilate(edges, kernel, iterations=1)
closed = cv2.morphologyEx(expanded, cv2.MORPH_CLOSE, kernel, iterations=1)
contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return None
@@ -77,7 +121,7 @@ def detect_salient_object(image: np.ndarray) -> BBox | None:
for contour in contours:
x, y, bw, bh = cv2.boundingRect(contour)
area = bw * bh
if area < max(500, int(image_area * 0.01)):
if area < max(1000, int(image_area * 0.015)):
continue
candidates.append((area, BBox(x=x, y=y, w=bw, h=bh)))
@@ -89,7 +133,7 @@ def detect_salient_object(image: np.ndarray) -> BBox | None:
def square_bbox(bbox: BBox, image_shape: tuple[int, int, int], buffer_ratio: float = 0.15) -> BBox:
image_h, image_w = image_shape[:2]
buffer_ratio = max(0.0, min(buffer_ratio, 0.5))
buffer_ratio = max(0.0, min(buffer_ratio, 0.6))
side = int(round(max(bbox.w, bbox.h) * (1 + buffer_ratio * 2)))
side = max(1, min(side, image_w, image_h))
@@ -127,9 +171,9 @@ def _data_url(image_bytes: bytes, mime_type: str) -> str:
return f"data:{mime_type};base64,{base64.b64encode(image_bytes).decode('ascii')}"
def process_image(image_bytes: bytes, filename: str, buffer_ratio: float = 0.15) -> dict[str, Any]:
def process_image(image_bytes: bytes, filename: str, buffer_ratio: float = 0.15, detector: str = "auto") -> dict[str, Any]:
image = decode_image(image_bytes)
bbox, method = select_primary_bbox(image)
bbox, method = select_primary_bbox(image, detector=detector)
square = square_bbox(bbox, image.shape, buffer_ratio=buffer_ratio)
crop = crop_image(image, square)
annotated = draw_square(image, square)
@@ -139,6 +183,7 @@ def process_image(image_bytes: bytes, filename: str, buffer_ratio: float = 0.15)
return {
"filename": filename,
"detector": detector,
"method": method,
"buffer_ratio": buffer_ratio,
"detected_bbox": bbox.__dict__,