src/services/ai_manager.py

import asyncio
import base64
import functools
import io
import threading
import traceback
import hashlib

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import AutoImageProcessor, AutoModel, AutoProcessor
from ultralytics import YOLO
import insightface
from insightface.app import FaceAnalysis

from src.core.config import (
    MAX_IMAGE_SIZE, MAX_CROPS, YOLO_PERSON_CLASS_ID,
    YOLO_MIN_CROP_PX, YOLO_CONF_THRESHOLD,
    DET_SIZE_PRIMARY, DET_SCALES, IOU_DEDUP_THRESHOLD,
    MIN_FACE_SIZE, MAX_FACES_PER_IMAGE, FACE_QUALITY_GATE,
    FACE_DIM, ADAFACE_DIM, FUSED_FACE_DIM,
    FACE_CROP_THUMB_SIZE, FACE_CROP_QUALITY,
    FACE_CROP_PADDING, ADAFACE_CROP_PADDING,
    INFERENCE_CACHE_SIZE, ENABLE_ADAFACE, HF_TOKEN,
)

def _resize_pil(img: Image.Image, max_side: int = MAX_IMAGE_SIZE) -> Image.Image:
    w, h = img.size
    if max(w, h) <= max_side:
        return img
    scale = max_side / max(w, h)
    return img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)

def _crop_to_b64(img_bgr: np.ndarray, x1: int, y1: int, x2: int, y2: int) -> str:
    H, W = img_bgr.shape[:2]
    w, h = x2 - x1, y2 - y1
    pad_x = int(w * FACE_CROP_PADDING)
    pad_y = int(h * FACE_CROP_PADDING)
    cx1, cy1 = max(0, x1 - pad_x), max(0, y1 - pad_y)
    cx2, cy2 = min(W, x2 + pad_x), min(H, y2 + pad_y)
    crop = img_bgr[cy1:cy2, cx1:cx2]
    if crop.size == 0:
        return ""
    pil = Image.fromarray(crop[:, :, ::-1]).resize((FACE_CROP_THUMB_SIZE, FACE_CROP_THUMB_SIZE), Image.LANCZOS)
    buf = io.BytesIO()
    pil.save(buf, format="JPEG", quality=FACE_CROP_QUALITY)
    return base64.b64encode(buf.getvalue()).decode()

def _face_crop_for_adaface(img_bgr: np.ndarray, x1: int, y1: int, x2: int, y2: int) -> np.ndarray | None:
    H, W = img_bgr.shape[:2]
    w, h = x2 - x1, y2 - y1
    pad_x = int(w * ADAFACE_CROP_PADDING)
    pad_y = int(h * ADAFACE_CROP_PADDING)
    cx1, cy1 = max(0, x1 - pad_x), max(0, y1 - pad_y)
    cx2, cy2 = min(W, x2 + pad_x), min(H, y2 + pad_y)
    crop = img_bgr[cy1:cy2, cx1:cx2]
    if crop.size == 0:
        return None
    rgb = crop[:, :, ::-1].copy()
    pil = Image.fromarray(rgb).resize((112, 112), Image.LANCZOS)
    arr = np.array(pil, dtype=np.float32) / 255.0
    arr = (arr - 0.5) / 0.5
    return arr.transpose(2, 0, 1)

def _clahe_enhance(bgr: np.ndarray) -> np.ndarray:
    lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)
    l_ch, a_ch, b_ch = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    l_eq = clahe.apply(l_ch)
    return cv2.cvtColor(cv2.merge([l_eq, a_ch, b_ch]), cv2.COLOR_LAB2BGR)

def _iou(box_a: list, box_b: list) -> float:
    xa, ya = max(box_a[0], box_b[0]), max(box_a[1], box_b[1])
    xb, yb = min(box_a[2], box_b[2]), min(box_a[3], box_b[3])
    inter = max(0, xb - xa) * max(0, yb - ya)
    if inter == 0:
        return 0.0
    area_a = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1])
    area_b = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1])
    return inter / (area_a + area_b - inter)

def _dedup_faces(faces_list: list, iou_thresh: float = IOU_DEDUP_THRESHOLD) -> list:
    if not faces_list:
        return []
    faces_list = sorted(faces_list, key=lambda f: float(f.det_score), reverse=True)
    kept = []
    for face in faces_list:
        b = face.bbox.astype(int)
        box = [b[0], b[1], b[2], b[3]]
        if not any(_iou(box, [k.bbox.astype(int)[i] for i in range(4)]) > iou_thresh for k in kept):
            kept.append(face)
    return kept

class AIModelManager:
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.siglip_processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224", use_fast=True)
        self.siglip_model = AutoModel.from_pretrained("google/siglip-base-patch16-224").to(self.device).eval()
        self.dinov2_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
        self.dinov2_model = AutoModel.from_pretrained("facebook/dinov2-base").to(self.device).eval()

        if self.device == "cuda":
            self.siglip_model = self.siglip_model.half()
            self.dinov2_model = self.dinov2_model.half()

        self.yolo = YOLO("yolo11n-seg.pt")
        
        self.face_app = FaceAnalysis(name="buffalo_l", providers=["CUDAExecutionProvider", "CPUExecutionProvider"] if self.device == "cuda" else ["CPUExecutionProvider"])
        self.face_app.prepare(ctx_id=0 if self.device == "cuda" else -1, det_size=DET_SIZE_PRIMARY)
        self.face_app.get(np.zeros((112, 112, 3), dtype=np.uint8))

        self.adaface_model = None
        self._load_adaface()

        self._face_lock = threading.Lock()
        self._cache_lock = threading.Lock()
        self._cache: dict[str, list] = {}

    def _load_adaface(self) -> None:
        if not ENABLE_ADAFACE:
            return
        import os
        import sys
        REPO_ID = "minchul/cvlface_adaface_ir50_ms1mv2"
        CACHE_PATH = os.path.expanduser("~/.cvlface_cache/minchul/cvlface_adaface_ir50_ms1mv2")
        try:
            from huggingface_hub import hf_hub_download
            from transformers import AutoModel as _HFAutoModel
            os.makedirs(CACHE_PATH, exist_ok=True)
            hf_hub_download(repo_id=REPO_ID, filename="files.txt", token=HF_TOKEN, local_dir=CACHE_PATH, local_dir_use_symlinks=False)
            with open(os.path.join(CACHE_PATH, "files.txt")) as f:
                extra = [x.strip() for x in f.read().split("\n") if x.strip()]
            for fname in extra + ["config.json", "wrapper.py", "model.safetensors"]:
                if not os.path.exists(os.path.join(CACHE_PATH, fname)):
                    hf_hub_download(repo_id=REPO_ID, filename=fname, token=HF_TOKEN, local_dir=CACHE_PATH, local_dir_use_symlinks=False)
            cwd = os.getcwd()
            os.chdir(CACHE_PATH)
            sys.path.insert(0, CACHE_PATH)
            try:
                model = _HFAutoModel.from_pretrained(CACHE_PATH, trust_remote_code=True, token=HF_TOKEN)
            finally:
                os.chdir(cwd)
                if CACHE_PATH in sys.path:
                    sys.path.remove(CACHE_PATH)
            self.adaface_model = model.to(self.device).eval()
        except Exception as e:
            self.adaface_model = None

    def _adaface_embed(self, face_arr_chw: np.ndarray | None) -> np.ndarray | None:
        if self.adaface_model is None or face_arr_chw is None:
            return None
        try:
            t = torch.from_numpy(face_arr_chw).unsqueeze(0).to(self.device)
            if self.device == "cuda":
                t = t.half()
            with torch.no_grad():
                out = self.adaface_model(t)
            emb = out if isinstance(out, torch.Tensor) else out.embedding
            return F.normalize(emb.float(), p=2, dim=1)[0].cpu().numpy()
        except Exception:
            return None

    def _embed_crops_batch(self, crops: list[Image.Image]) -> list[np.ndarray]:
        if not crops:
            return []
        with torch.no_grad():
            sig_in = self.siglip_processor(images=crops, return_tensors="pt", padding=True)
            sig_in = {k: v.to(self.device) for k, v in sig_in.items()}
            if self.device == "cuda":
                sig_in = {k: v.half() if v.dtype == torch.float32 else v for k, v in sig_in.items()}
            sig_out = self.siglip_model.get_image_features(**sig_in)
            if hasattr(sig_out, "image_embeds"):
                sig_out = sig_out.image_embeds
            elif hasattr(sig_out, "pooler_output"):
                sig_out = sig_out.pooler_output
            elif hasattr(sig_out, "last_hidden_state"):
                sig_out = sig_out.last_hidden_state[:, 0, :]
            elif isinstance(sig_out, tuple):
                sig_out = sig_out[0]
            sig_vecs = F.normalize(sig_out.float(), p=2, dim=1).cpu()

            dino_in = self.dinov2_processor(images=crops, return_tensors="pt")
            dino_in = {k: v.to(self.device) for k, v in dino_in.items()}
            if self.device == "cuda":
                dino_in = {k: v.half() if v.dtype == torch.float32 else v for k, v in dino_in.items()}
            dino_out = self.dinov2_model(**dino_in)
            dino_vecs = F.normalize(dino_out.last_hidden_state[:, 0, :].float(), p=2, dim=1).cpu()

            fused = F.normalize(torch.cat([sig_vecs, dino_vecs], dim=1), p=2, dim=1)
        return [fused[i].numpy() for i in range(len(crops))]

    def _detect_and_encode_faces(self, img_np: np.ndarray) -> list[dict]:
        if self.face_app is None:
            return []
        try:
            if img_np.dtype != np.uint8:
                img_np = (img_np * 255).astype(np.uint8)
            bgr = img_np[:, :, ::-1].copy() if img_np.shape[2] == 3 else img_np.copy()
            bgr_enhanced = _clahe_enhance(bgr)

            all_raw_faces = []
            H, W = bgr.shape[:2]

            for scale in DET_SCALES:
                scale_w, scale_h = min(W, scale[0]), min(H, scale[1])
                bgr_scaled = bgr_enhanced if scale_w == W and scale_h == H else cv2.resize(bgr_enhanced, (scale_w, scale_h))
                try:
                    self.face_app.det_model.input_size = scale
                    with self._face_lock:
                        faces_at_scale = self.face_app.get(bgr_scaled)
                    sx, sy = W / scale_w, H / scale_h
                    for f in faces_at_scale:
                        if sx != 1.0 or sy != 1.0:
                            f.bbox[0] *= sx; f.bbox[1] *= sy; f.bbox[2] *= sx; f.bbox[3] *= sy
                    all_raw_faces.extend(faces_at_scale)
                except Exception:
                    pass

            bgr_flip = cv2.flip(bgr_enhanced, 1)
            try:
                self.face_app.det_model.input_size = DET_SIZE_PRIMARY
                with self._face_lock:
                    faces_flip = self.face_app.get(bgr_flip)
                for f in faces_flip:
                    x1, y1, x2, y2 = f.bbox
                    f.bbox[0], f.bbox[2] = W - x2, W - x1
                all_raw_faces.extend(faces_flip)
            except Exception:
                pass

            self.face_app.det_model.input_size = DET_SIZE_PRIMARY
            faces = _dedup_faces(all_raw_faces)

            results, accepted = [], 0
            for face in faces:
                if accepted >= MAX_FACES_PER_IMAGE:
                    break
                bbox_raw = face.bbox.astype(int)
                x1, y1, x2, y2 = bbox_raw
                x1, y1 = max(0, x1), max(0, y1)
                x2, y2 = min(bgr.shape[1], x2), min(bgr.shape[0], y2)
                w, h = x2 - x1, y2 - y1
                if w < MIN_FACE_SIZE or h < MIN_FACE_SIZE:
                    continue
                det_score = float(face.det_score) if hasattr(face, "det_score") else 1.0
                if det_score < FACE_QUALITY_GATE or face.embedding is None:
                    continue

                arcface_vec = face.embedding.astype(np.float32)
                n = np.linalg.norm(arcface_vec)
                if n > 0:
                    arcface_vec = arcface_vec / n

                face_chw = _face_crop_for_adaface(bgr, x1, y1, x2, y2)
                adaface_vec = self._adaface_embed(face_chw)

                fused_raw = np.concatenate([arcface_vec, adaface_vec]) if adaface_vec is not None else np.concatenate([arcface_vec, np.zeros(ADAFACE_DIM, dtype=np.float32)])
                n2 = np.linalg.norm(fused_raw)
                final_vec = (fused_raw / n2) if n2 > 0 else fused_raw

                results.append({
                    "type": "face", "vector": final_vec, "face_idx": accepted,
                    "bbox": [int(x1), int(y1), int(w), int(h)],
                    "face_crop": _crop_to_b64(bgr, x1, y1, x2, y2),
                    "det_score": det_score, "face_width_px": int(w),
                })
                accepted += 1
            return results
        except Exception:
            return []

    def process_image_bytes(self, image_bytes: bytes, detect_faces: bool = True) -> list[dict]:
        file_hash = hashlib.md5(image_bytes[:65536]).hexdigest()
        cache_key = f"{file_hash}_{detect_faces}"

        with self._cache_lock:
            if cache_key in self._cache:
                return list(self._cache[cache_key])

        extracted = []
        original_pil = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        img_np = np.array(original_pil)
        faces_found = False

        if detect_faces and hasattr(self, 'face_app') and self.face_app is not None:
            face_results = self._detect_and_encode_faces(img_np)
            if face_results:
                faces_found = True
                extracted.extend(face_results)

        crops: list[Image.Image] = []
        yolo_results = getattr(self, 'yolo', lambda x, **kwargs: [])(original_pil, conf=YOLO_CONF_THRESHOLD, verbose=False)

        for r in yolo_results:
            if r.masks is not None:
                for seg_idx, mask_xy in enumerate(r.masks.xy):
                    cls_id = int(r.boxes.cls[seg_idx].item())
                    if faces_found and cls_id == YOLO_PERSON_CLASS_ID:
                        continue
                    polygon = np.array(mask_xy, dtype=np.int32)
                    if len(polygon) < 3:
                        continue
                    x, y, w, h = cv2.boundingRect(polygon)
                    if w < YOLO_MIN_CROP_PX or h < YOLO_MIN_CROP_PX:
                        continue
                    crops.append(original_pil.crop((x, y, x + w, y + h)))
                    if len(crops) >= MAX_CROPS:
                        break
            elif r.boxes is not None:
                for box in r.boxes:
                    cls_id = int(box.cls.item())
                    if faces_found and cls_id == YOLO_PERSON_CLASS_ID:
                        continue
                    x1, y1, x2, y2 = box.xyxy[0].tolist()
                    if (x2 - x1) < YOLO_MIN_CROP_PX or (y2 - y1) < YOLO_MIN_CROP_PX:
                        continue
                    crops.append(original_pil.crop((x1, y1, x2, y2)))
            if len(crops) >= MAX_CROPS:
                break

        all_crops = [_resize_pil(c, MAX_IMAGE_SIZE) for c in [original_pil] + crops]
        obj_vecs = self._embed_crops_batch(all_crops)
        extracted.extend({"type": "object", "vector": v} for v in obj_vecs)

        with self._cache_lock:
            if len(self._cache) >= INFERENCE_CACHE_SIZE:
                oldest = next(iter(self._cache))
                del self._cache[oldest]
            self._cache[cache_key] = list(extracted)

        return extracted

    async def process_image_bytes_async(self, image_bytes: bytes, detect_faces: bool = True) -> list[dict]:
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(
            None,
            functools.partial(self.process_image_bytes, image_bytes, detect_faces),
        )