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import os
import torch
from torchvision import transforms
from PIL import Image
import io
from model import get_model
class EndpointHandler:
def __init__(self, path: str = ""):
"""
Initialize the handler. Load the Faster R-CNN model.
"""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model_weights_path = os.path.join(path, "model.pt")
# Load the model
self.model = get_model(num_classes=4)
print(f"Loading weights from: {self.model_weights_path}")
checkpoint = torch.load(self.model_weights_path, map_location=self.device)
self.model.load_state_dict(checkpoint["model_state_dict"])
self.model.to(self.device)
self.model.eval()
# Define image preprocessing
self.transform = transforms.Compose([
transforms.Resize((640, 640)),
transforms.ToTensor(),
])
def __call__(self, data):
"""
Process the incoming request and return object detection predictions.
"""
try:
if "image" not in data:
return [{"error": "No 'image' provided in request."}]
image_bytes = data["image"].encode("latin1")
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
input_tensor = self.transform(image).unsqueeze(0).to(self.device)
with torch.no_grad():
outputs = self.model(input_tensor)
boxes = outputs[0]["boxes"].cpu().tolist()
labels = outputs[0]["labels"].cpu().tolist()
scores = outputs[0]["scores"].cpu().tolist()
threshold = 0.5
predictions = [
{"box": box, "label": label, "score": score}
for box, label, score in zip(boxes, labels, scores)
if score > threshold
]
return [{"predictions": predictions}]
except Exception as e:
return [{"error": str(e)}] |