src/domain/detectors/gpu.py

import torch
import torch.nn.functional as F
import logging
import os
import gc
import numpy as np
import cv2
from PIL import Image
from typing import List, Dict, Any, Tuple
from transformers import Owlv2Processor, Owlv2ForObjectDetection
from .base import BaseDetector
import time

logger = logging.getLogger(__name__)

class WeaponDetectorGPU(BaseDetector):
    """Detector de armas otimizado para GPU."""
    
    def __init__(self):
        """Inicializa o detector."""
        super().__init__()
        self.default_resolution = 640
        self.device = None  # Será configurado em _initialize
        self._initialize()
    
    def _initialize(self):
        try:
            # Configurar device
            if not torch.cuda.is_available():
                raise RuntimeError("CUDA não está disponível!")
            
            # Configurar device corretamente
            self.device = torch.device("cuda:0")  # Usar device CUDA
            
            # Carregar modelo e processador
            logger.info("Carregando modelo e processador...")
            model_name = "google/owlv2-base-patch16"
            
            self.owlv2_processor = Owlv2Processor.from_pretrained(model_name)
            self.owlv2_model = Owlv2ForObjectDetection.from_pretrained(
                model_name,
                torch_dtype=torch.float16,
                device_map={"": 0}  # Mapear todo o modelo para GPU 0
            )
            
            # Otimizar modelo
            self.owlv2_model.eval()
            
            # Processar queries
            self.text_queries = self._get_detection_queries()
            logger.info(f"Queries carregadas: {self.text_queries}")  # Log das queries
            
            self.processed_text = self.owlv2_processor(
                text=self.text_queries,
                return_tensors="pt",
                padding=True
            ).to(self.device)
            
            logger.info("Inicialização GPU completa!")
            self._initialized = True
            
        except Exception as e:
            logger.error(f"Erro na inicialização GPU: {str(e)}")
            raise

    def detect_objects(self, image: Image.Image, threshold: float = 0.3) -> List[Dict]:
        """Detecta objetos em uma imagem."""
        try:
            # Pré-processar imagem
            image = self._preprocess_image(image)
            
            # Processar imagem
            image_inputs = self.owlv2_processor(
                images=image,
                return_tensors="pt"
            ).to(self.device)
            
            # Inferência
            with torch.no_grad():
                outputs = self.owlv2_model(**{**image_inputs, **self.processed_text})
                
                target_sizes = torch.tensor([image.size[::-1]], device=self.device)
                results = self.owlv2_processor.post_process_grounded_object_detection(
                    outputs=outputs,
                    target_sizes=target_sizes,
                    threshold=threshold
                )[0]
            
            # Processar detecções
            detections = []
            if len(results["scores"]) > 0:
                for score, box, label in zip(results["scores"], results["boxes"], results["labels"]):
                    score_val = score.item()
                    if score_val >= threshold:
                        label_idx = min(label.item(), len(self.text_queries) - 1)
                        detections.append({
                            "confidence": round(score_val * 100, 2),
                            "box": [int(x) for x in box.tolist()],
                            "label": self.text_queries[label_idx]
                        })
                        logger.debug(f"Detecção: {self.text_queries[label_idx]} ({score_val * 100:.2f}%)")
            
            # Aplicar NMS nas detecções
            return self._apply_nms(detections)
            
        except Exception as e:
            logger.error(f"Erro em detect_objects: {str(e)}")
            return []

    def _get_best_device(self) -> torch.device:
        """Retorna o melhor dispositivo disponível."""
        if torch.cuda.is_available():
            return torch.device("cuda:0")
        return torch.device("cpu")

    def _clear_gpu_memory(self):
        """Limpa memória GPU."""
        torch.cuda.empty_cache()
        gc.collect()

    def process_video(self, video_path: str, fps: int = None, threshold: float = 0.3, resolution: int = 640) -> Tuple[str, Dict]:
        """Processa um vídeo para detecção de objetos."""
        metrics = {
            "total_time": 0,
            "frame_extraction_time": 0,
            "analysis_time": 0,
            "frames_analyzed": 0,
            "video_duration": 0,
            "device_type": "GPU",
            "detections": [],
            "technical": {
                "model": "owlv2-base-patch16",
                "input_size": f"{resolution}x{resolution}",
                "nms_threshold": 0.5,
                "preprocessing": "optimized",
                "early_stop": False
            }
        }
        
        try:
            start_time = time.time()
            
            # Limpar cache de GPU antes de começar
            torch.cuda.empty_cache()
            gc.collect()
            
            # Extrair frames
            t0 = time.time()
            frames = self.extract_frames(video_path, fps or 2, resolution)
            metrics["frame_extraction_time"] = time.time() - t0
            metrics["frames_analyzed"] = len(frames)
            
            if not frames:
                logger.warning("Nenhum frame extraído do vídeo")
                return video_path, metrics
            
            # Calcular duração do vídeo
            metrics["video_duration"] = len(frames) / (fps or 2)
            
            # Processar frames
            t0 = time.time()
            detections_by_frame = self._process_frames(frames, fps, threshold)
            
            # Atualizar métricas finais
            metrics["analysis_time"] = time.time() - t0
            metrics["total_time"] = time.time() - start_time
            metrics["detections"] = detections_by_frame
            
            return video_path, metrics
            
        except Exception as e:
            logger.error(f"Erro ao processar vídeo: {str(e)}")
            return video_path, metrics

    def _process_frames(self, frames: List[np.ndarray], fps: int, threshold: float) -> List[Dict]:
        """Processa frames do vídeo para detecção."""
        detections_by_frame = []
        
        for i, frame in enumerate(frames):
            try:
                # Preparar frame
                frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                frame_pil = self._preprocess_image(frame_pil)
                
                # Processar frame
                inputs = self.owlv2_processor(
                    images=frame_pil,
                    return_tensors="pt"
                ).to(self.device)
                
                # Inferência
                with torch.no_grad():
                    outputs = self.owlv2_model(**{**inputs, **self.processed_text})
                    target_sizes = torch.tensor([frame_pil.size[::-1]], device=self.device)
                    results = self.owlv2_processor.post_process_grounded_object_detection(
                        outputs=outputs,
                        target_sizes=target_sizes,
                        threshold=threshold
                    )[0]
                    
                    # Processar resultados
                    if len(results["scores"]) > 0:
                        frame_detections = []
                        for score, box, label in zip(results["scores"], results["boxes"], results["labels"]):
                            score_val = score.item()
                            if score_val >= threshold:
                                label_idx = min(label.item(), len(self.text_queries) - 1)
                                frame_detections.append({
                                    "confidence": round(score_val * 100, 2),
                                    "box": [int(x) for x in box.tolist()],
                                    "label": self.text_queries[label_idx],
                                    "frame": i,
                                    "timestamp": i / (fps or 2)
                                })
                        
                        if frame_detections:
                            detections_by_frame.extend(self._apply_nms(frame_detections))
                
            except Exception as e:
                logger.error(f"Erro ao processar frame {i}: {str(e)}")
                continue
                
            finally:
                # Liberar memória
                if 'inputs' in locals():
                    del inputs
                if 'outputs' in locals():
                    del outputs
                torch.cuda.empty_cache()
                
            # Log de progresso
            if i % 10 == 0:
                logger.info(f"Processados {i}/{len(frames)} frames")
        
        return detections_by_frame

    def _preprocess_image(self, image: Image.Image) -> Image.Image:
        """Pré-processa a imagem para o formato esperado pelo modelo."""
        try:
            # Cache de tamanho para evitar redimensionamentos desnecessários
            if hasattr(self, '_last_size') and self._last_size == image.size:
                return image

            # Converter para RGB se necessário usando conversão direta
            if image.mode != 'RGB':
                image = image.convert('RGB')

            # Redimensionar mantendo proporção com otimização de memória
            target_size = (self.default_resolution, self.default_resolution)
            if image.size != target_size:
                # Calcular novo tamanho uma única vez
                ratio = min(target_size[0] / image.size[0], target_size[1] / image.size[1])
                new_size = tuple(int(dim * ratio) for dim in image.size)
                
                # Redimensionar diretamente para o tamanho final se possível
                if new_size == target_size:
                    image = image.resize(target_size, Image.Resampling.BILINEAR)
                else:
                    # Criar imagem com padding em uma única operação
                    new_image = Image.new('RGB', target_size, (0, 0, 0))
                    image = image.resize(new_size, Image.Resampling.BILINEAR)
                    paste_x = (target_size[0] - new_size[0]) // 2
                    paste_y = (target_size[1] - new_size[1]) // 2
                    new_image.paste(image, (paste_x, paste_y))
                    image = new_image

            # Armazenar tamanho para cache
            self._last_size = image.size
            return image
            
        except Exception as e:
            logger.error(f"Erro no pré-processamento: {str(e)}")
            return image

    def _apply_nms(self, detections: List[Dict], iou_threshold: float = 0.5) -> List[Dict]:
        """Aplica Non-Maximum Suppression nas detecções."""
        try:
            if not detections or len(detections) <= 1:
                return detections

            # Extrair scores e boxes
            scores = torch.tensor([d["confidence"] for d in detections], device=self.device)
            boxes = torch.tensor([[d["box"][0], d["box"][1], d["box"][2], d["box"][3]] 
                                for d in detections], device=self.device)

            # Ordenar por score
            _, order = scores.sort(descending=True)
            keep = []

            while order.numel() > 0:
                if order.numel() == 1:
                    keep.append(order.item())
                    break

                i = order[0]
                keep.append(i.item())

                # Calcular IoU com os boxes restantes
                box1 = boxes[i]
                box2 = boxes[order[1:]]
                
                # Calcular interseção
                left = torch.max(box1[0], box2[:, 0])
                top = torch.max(box1[1], box2[:, 1])
                right = torch.min(box1[2], box2[:, 2])
                bottom = torch.min(box1[3], box2[:, 3])
                
                width = torch.clamp(right - left, min=0)
                height = torch.clamp(bottom - top, min=0)
                inter = width * height

                # Calcular união
                area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
                area2 = (box2[:, 2] - box2[:, 0]) * (box2[:, 3] - box2[:, 1])
                union = area1 + area2 - inter

                # Calcular IoU
                iou = inter / union
                mask = iou <= iou_threshold
                order = order[1:][mask]

            # Retornar detecções filtradas
            return [detections[i] for i in keep]

        except Exception as e:
            logger.error(f"Erro ao aplicar NMS: {str(e)}")
            return detections