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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
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):
"""Inicializa o modelo."""
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()
self.processed_text = self.owlv2_processor(
text=self.text_queries,
return_tensors="pt",
padding=True
)
self.processed_text = {
key: val.to(self.device)
for key, val in self.processed_text.items()
}
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"
)
image_inputs = {
key: val.to(self.device)
for key, val in image_inputs.items()
}
# Inferência
with torch.no_grad():
inputs = {**image_inputs, **self.processed_text}
outputs = self.owlv2_model(**inputs)
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:
scores = results["scores"]
boxes = results["boxes"]
labels = results["labels"]
for score, box, label in zip(scores, boxes, labels):
if score.item() >= threshold:
detections.append({
"confidence": score.item(),
"box": [int(x) for x in box.tolist()],
"label": self.text_queries[label]
})
# Aplicar NMS nas detecções
detections = self._apply_nms(detections)
return 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."""
metrics = {
"total_time": 0,
"frames_analyzed": 0,
"detections": []
}
try:
frames = self.extract_frames(video_path, fps or 2, resolution)
metrics["frames_analyzed"] = len(frames)
for i, frame in enumerate(frames):
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_pil = Image.fromarray(frame_rgb)
detections = self.detect_objects(frame_pil, threshold)
if detections:
metrics["detections"].append({
"frame": i,
"detections": detections
})
return video_path, metrics
return video_path, metrics
except Exception as e:
logger.error(f"Erro ao processar vídeo: {str(e)}")
return video_path, metrics
def _preprocess_image(self, image: Image.Image) -> Image.Image:
"""Pré-processa a imagem para o formato esperado pelo modelo."""
try:
# Converter para RGB se necessário
if image.mode != 'RGB':
image = image.convert('RGB')
# Redimensionar mantendo proporção
target_size = (self.default_resolution, self.default_resolution)
if image.size != target_size:
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)
image = image.resize(new_size, Image.Resampling.LANCZOS)
# Adicionar padding se necessário
if new_size != target_size:
new_image = Image.new('RGB', target_size, (0, 0, 0))
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
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 |