Shape_Model_2D/pm2d/web/server.py

"""FastAPI webapp standalone per PM2D.

Endpoint:
  GET  /                    → HTML UI
  POST /upload              → upload immagine (multipart)
  POST /match               → JSON params + ids → results
  GET  /image/{id}/raw      → PNG originale
  GET  /image/{id}/annotated → PNG con overlay match
"""
from __future__ import annotations

import base64
import io
import time
import uuid
from pathlib import Path
from typing import Any

import cv2
import numpy as np
from fastapi import FastAPI, File, HTTPException, UploadFile
from fastapi.responses import FileResponse, HTMLResponse, Response, StreamingResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel

from pm2d.line_matcher import LineShapeMatcher, Match
from pm2d.auto_tune import auto_tune


WEB_DIR = Path(__file__).parent
STATIC_DIR = WEB_DIR / "static"
STATIC_DIR.mkdir(exist_ok=True)

# In-memory image store: id → np.ndarray BGR
_IMAGES: dict[str, np.ndarray] = {}
# Cache del last annotated: (image_id, matches_hash) → png bytes
_ANNOT_CACHE: dict[tuple[str, int], bytes] = {}

app = FastAPI(title="PM2D Webapp", version="1.0.0")


def _store_image(img: np.ndarray) -> str:
    iid = uuid.uuid4().hex[:12]
    _IMAGES[iid] = img
    return iid


def _encode_png(img: np.ndarray) -> bytes:
    ok, buf = cv2.imencode(".png", img)
    if not ok:
        raise RuntimeError("PNG encode failed")
    return buf.tobytes()


def _draw_matches(scene: np.ndarray, matches: list[Match],
                   template_gray: np.ndarray | None) -> np.ndarray:
    out = scene.copy()
    H, W = scene.shape[:2]
    palette = [
        (0, 255, 0), (0, 200, 255), (255, 100, 100), (255, 200, 0),
        (200, 0, 255), (100, 255, 200), (255, 0, 0), (0, 255, 255),
    ]
    for i, m in enumerate(matches):
        color = palette[i % len(palette)]
        if template_gray is not None:
            t = template_gray
            th, tw = t.shape
            edge = cv2.Canny(t, 50, 150)
            cx_t = (tw - 1) / 2.0; cy_t = (th - 1) / 2.0
            M = cv2.getRotationMatrix2D((cx_t, cy_t), m.angle_deg, m.scale)
            M[0, 2] += m.cx - cx_t
            M[1, 2] += m.cy - cy_t
            warped = cv2.warpAffine(edge, M, (W, H),
                                     flags=cv2.INTER_NEAREST, borderValue=0)
            mask = warped > 0
            if mask.any():
                overlay = np.zeros_like(out)
                overlay[mask] = color
                out[mask] = (0.3 * out[mask] + 0.7 * overlay[mask]).astype(np.uint8)
        poly = m.bbox_poly.astype(np.int32).reshape(-1, 1, 2)
        cv2.polylines(out, [poly], True, color, 2, cv2.LINE_AA)
        p0 = tuple(m.bbox_poly[0].astype(int))
        p1 = tuple(m.bbox_poly[1].astype(int))
        cv2.line(out, p0, p1, color, 4, cv2.LINE_AA)
        cx, cy = int(round(m.cx)), int(round(m.cy))
        cv2.drawMarker(out, (cx, cy), color, cv2.MARKER_CROSS, 22, 2, cv2.LINE_AA)
        L = int(np.linalg.norm(m.bbox_poly[1] - m.bbox_poly[0])) // 2
        a = np.deg2rad(m.angle_deg)
        cv2.arrowedLine(out, (cx, cy),
                        (int(cx + L * np.cos(a)), int(cy - L * np.sin(a))),
                        color, 2, cv2.LINE_AA, tipLength=0.2)
        label = f"#{i+1} {m.angle_deg:.0f}d s={m.scale:.2f} {m.score:.2f}"
        cv2.putText(out, label, (cx + 8, cy - 8),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2, cv2.LINE_AA)
    return out


# ---------------- Models ----------------

class UploadResp(BaseModel):
    id: str
    width: int
    height: int


class MatchParams(BaseModel):
    model_id: str
    scene_id: str
    roi: list[int]  # [x, y, w, h] nell'immagine modello
    angle_min: float = 0.0
    angle_max: float = 360.0
    angle_step: float = 5.0
    scale_min: float = 1.0
    scale_max: float = 1.0
    scale_step: float = 0.1
    min_score: float = 0.55
    max_matches: int = 25
    nms_radius: int = 0
    num_features: int = 96
    weak_grad: float = 30.0
    strong_grad: float = 60.0
    spread_radius: int = 5
    pyramid_levels: int = 3
    verify_threshold: float = 0.4


class MatchResult(BaseModel):
    cx: float
    cy: float
    angle_deg: float
    scale: float
    score: float
    bbox_poly: list[list[float]]


class MatchResp(BaseModel):
    matches: list[MatchResult]
    train_time: float
    find_time: float
    num_variants: int
    annotated_id: str


class TuneParams(BaseModel):
    model_id: str
    roi: list[int]


# ---------- User-facing (simple) params ----------

SYMMETRY_TO_ANGLE_MAX = {
    "nessuna": 360.0,
    "bilaterale": 180.0,
    "rot_3": 120.0,
    "rot_4": 90.0,
    "rot_6": 60.0,
    "rot_8": 45.0,
}

SCALE_PRESETS = {
    "fissa":  (1.0, 1.0, 0.1),
    "mini":   (0.9, 1.1, 0.05),    # ±10%
    "medio":  (0.75, 1.25, 0.05),  # ±25%
    "max":    (0.5, 1.5, 0.05),    # ±50%
}

PRECISION_ANGLE_STEP = {
    "veloce": 10.0,
    "normale": 5.0,
    "preciso": 2.0,
}


class SimpleMatchParams(BaseModel):
    model_id: str
    scene_id: str
    roi: list[int]
    tipo: str = "intero"              # "intero" | "parziale"
    simmetria: str = "nessuna"        # chiave SYMMETRY_TO_ANGLE_MAX
    scala: str = "fissa"              # chiave SCALE_PRESETS
    precisione: str = "normale"       # chiave PRECISION_ANGLE_STEP
    min_score: float = 0.70
    max_matches: int = 25


def _simple_to_technical(
    p: SimpleMatchParams, roi_img: np.ndarray,
) -> dict:
    """Converti parametri user-facing → tecnici usando analisi della ROI."""
    from pm2d.auto_tune import auto_tune as _auto

    tune = _auto(roi_img)
    h, w = roi_img.shape[:2]
    min_side = min(h, w)

    # Feature count: parziale = meno feature (area minore)
    nf = tune["num_features"]
    if p.tipo == "parziale":
        nf = max(32, int(nf * 0.6))

    # Piramide derivata da dimensione ROI
    if min_side < 60:
        pyr = 1
    elif min_side < 150:
        pyr = 2
    elif min_side < 400:
        pyr = 3
    else:
        pyr = 4

    # Spread radius ~2-3% del lato minimo
    spread = max(3, min(10, int(round(min_side * 0.03))))

    angle_max = SYMMETRY_TO_ANGLE_MAX.get(p.simmetria, 360.0)
    smin, smax, sstep = SCALE_PRESETS.get(p.scala, (1.0, 1.0, 0.1))
    ang_step = PRECISION_ANGLE_STEP.get(p.precisione, 5.0)

    return {
        "num_features": nf,
        "weak_grad": tune["weak_grad"],
        "strong_grad": tune["strong_grad"],
        "spread_radius": spread,
        "pyramid_levels": pyr,
        "angle_min": 0.0,
        "angle_max": angle_max,
        "angle_step": ang_step,
        "scale_min": smin,
        "scale_max": smax,
        "scale_step": sstep,
        "min_score": p.min_score,
        "max_matches": p.max_matches,
        "nms_radius": 0,
        # Verify NCC più permissivo per match con variazione intensità
        # (foro su sfondo variabile, parti di oggetto ecc.)
        "verify_threshold": 0.25,
    }


# ---------------- Endpoints ----------------

@app.get("/", response_class=HTMLResponse)
def index():
    html_path = STATIC_DIR / "index.html"
    return HTMLResponse(html_path.read_text(encoding="utf-8"))


@app.post("/upload", response_model=UploadResp)
async def upload(file: UploadFile = File(...)):
    data = await file.read()
    arr = np.frombuffer(data, dtype=np.uint8)
    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
    if img is None:
        raise HTTPException(400, "Immagine non valida")
    iid = _store_image(img)
    return UploadResp(id=iid, width=img.shape[1], height=img.shape[0])


@app.get("/image/{iid}/raw")
def image_raw(iid: str):
    img = _IMAGES.get(iid)
    if img is None:
        raise HTTPException(404, "Image not found")
    return Response(_encode_png(img), media_type="image/png")


@app.post("/match", response_model=MatchResp)
def match(p: MatchParams):
    model = _IMAGES.get(p.model_id)
    scene = _IMAGES.get(p.scene_id)
    if model is None or scene is None:
        raise HTTPException(404, "Immagini non trovate")
    x, y, w, h = p.roi
    x = max(0, x); y = max(0, y)
    w = max(1, min(w, model.shape[1] - x))
    h = max(1, min(h, model.shape[0] - y))
    roi_img = model[y:y + h, x:x + w]

    m = LineShapeMatcher(
        num_features=p.num_features,
        weak_grad=p.weak_grad, strong_grad=p.strong_grad,
        angle_range_deg=(p.angle_min, p.angle_max),
        angle_step_deg=p.angle_step,
        scale_range=(p.scale_min, p.scale_max),
        scale_step=p.scale_step,
        spread_radius=p.spread_radius,
        pyramid_levels=p.pyramid_levels,
    )
    t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
    nms = p.nms_radius if p.nms_radius > 0 else None
    t0 = time.time()
    matches = m.find(
        scene, min_score=p.min_score, max_matches=p.max_matches,
        nms_radius=nms, verify_threshold=p.verify_threshold,
    )
    t_find = time.time() - t0

    # Render annotated image
    tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
    annotated = _draw_matches(scene, matches, tg)
    ann_id = _store_image(annotated)

    return MatchResp(
        matches=[MatchResult(
            cx=m_.cx, cy=m_.cy, angle_deg=m_.angle_deg, scale=m_.scale,
            score=m_.score,
            bbox_poly=m_.bbox_poly.tolist(),
        ) for m_ in matches],
        train_time=t_train, find_time=t_find,
        num_variants=n, annotated_id=ann_id,
    )


@app.post("/match_simple", response_model=MatchResp)
def match_simple(p: SimpleMatchParams):
    """Match con parametri user-facing (tipo/simmetria/scala/precisione).

    Il server deriva i parametri tecnici (num_features, soglie gradiente,
    piramide, ecc.) dall'analisi automatica della ROI.
    """
    model = _IMAGES.get(p.model_id)
    scene = _IMAGES.get(p.scene_id)
    if model is None or scene is None:
        raise HTTPException(404, "Immagini non trovate")
    x, y, w, h = p.roi
    x = max(0, x); y = max(0, y)
    w = max(1, min(w, model.shape[1] - x))
    h = max(1, min(h, model.shape[0] - y))
    roi_img = model[y:y + h, x:x + w]

    tech = _simple_to_technical(p, roi_img)

    m = LineShapeMatcher(
        num_features=tech["num_features"],
        weak_grad=tech["weak_grad"], strong_grad=tech["strong_grad"],
        angle_range_deg=(tech["angle_min"], tech["angle_max"]),
        angle_step_deg=tech["angle_step"],
        scale_range=(tech["scale_min"], tech["scale_max"]),
        scale_step=tech["scale_step"],
        spread_radius=tech["spread_radius"],
        pyramid_levels=tech["pyramid_levels"],
    )
    t0 = time.time(); n = m.train(roi_img); t_train = time.time() - t0
    nms = tech["nms_radius"] if tech["nms_radius"] > 0 else None
    t0 = time.time()
    matches = m.find(
        scene, min_score=tech["min_score"], max_matches=tech["max_matches"],
        nms_radius=nms, verify_threshold=tech["verify_threshold"],
    )
    t_find = time.time() - t0

    tg = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
    annotated = _draw_matches(scene, matches, tg)
    ann_id = _store_image(annotated)

    return MatchResp(
        matches=[MatchResult(
            cx=mt.cx, cy=mt.cy, angle_deg=mt.angle_deg, scale=mt.scale,
            score=mt.score, bbox_poly=mt.bbox_poly.tolist(),
        ) for mt in matches],
        train_time=t_train, find_time=t_find,
        num_variants=n, annotated_id=ann_id,
    )


@app.post("/auto_tune")
def tune(p: TuneParams):
    model = _IMAGES.get(p.model_id)
    if model is None:
        raise HTTPException(404, "Immagine non trovata")
    x, y, w, h = p.roi
    roi_img = model[y:y + h, x:x + w]
    t = auto_tune(roi_img)
    return {k: v for k, v in t.items() if not k.startswith("_")}


# Mount static
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")


def serve(host: str = "127.0.0.1", port: int = 8080):
    import uvicorn
    uvicorn.run(app, host=host, port=port, log_level="info")


if __name__ == "__main__":
    serve()