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from collections import defaultdict
from pathlib import Path
import torch
from transformers import Owlv2Processor, Owlv2ForObjectDetection
from PIL import Image
from pathlib import Path
from ultralytics import YOLO
def build_localizer(model_name):
if model_name == "owl":
return OWL()
elif model_name == "yolo":
return YOLO11()
else:
raise ValueError(f"Unknown model name: {model_name}")
class OWL:
def __init__(self):
model_name = "google/owlv2-large-patch14-ensemble"
self.processor = Owlv2Processor.from_pretrained(model_name)
self.model = Owlv2ForObjectDetection.from_pretrained(model_name).to("cuda")
self.model.eval()
self.objects_f = Path(__file__).parent / "objs" / "owl.txt"
self.objects = [line.strip() for line in self.objects_f.open().readlines()]
self.device = "cuda"
def localize(self, image, threshold=0.5):
image = Image.fromarray(image)
final = defaultdict(list)
with torch.inference_mode():
inputs = self.processor(text=self.objects, images=[image], return_tensors="pt").to(self.device)
outputs = self.model(**inputs)
target_sizes = torch.Tensor([image.size[::-1]]).to(self.device)
result = self.processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=threshold)[0]
boxes, scores, labels = result["boxes"], result["scores"], result["labels"]
for box, score, label in zip(boxes, scores, labels):
final[self.objects[label]].append(box)
return final
class YOLO11:
def __init__(self):
model_name = "yolo11m.pt"
self.model = YOLO(model_name)
self.objects_f = Path(__file__).parent / "objs" / "yolo.txt"
self.objects = [line.strip() for line in self.objects_f.open().readlines()]
def localize(self, image, threshold=0.5):
result = self.model(image, conf=threshold)[0]
boxes = result.boxes
bbox_ids = boxes.cls.cpu().numpy().astype(int)
boxes_xyxy = boxes.xyxy.cpu().numpy()
final = defaultdict(list)
for label, box in zip(bbox_ids, boxes_xyxy):
final[self.objects[label]].append(box)
return final
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