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import torch
from torchvision import transforms
from PIL import Image
import io
MODEL_PATH = "model_checkpoint.pt"
NUM_CLASSES = 4
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Load Faster R-CNN model
def load_model(model_path, num_classes):
from torchvision.models.detection import fasterrcnn_resnet50_fpn
model = fasterrcnn_resnet50_fpn(pretrained=False, num_classes=num_classes)
checkpoint = torch.load(model_path, map_location=DEVICE)
model.load_state_dict(checkpoint["model_state_dict"])
model.to(DEVICE)
model.eval()
return model
transform = transforms.Compose([
transforms.Resize((640, 640)),
transforms.ToTensor(),
])
model = load_model(MODEL_PATH, NUM_CLASSES)
def detect_objects(image_bytes):
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
input_tensor = transform(image).unsqueeze(0).to(DEVICE)
with torch.no_grad():
predictions = model(input_tensor)
boxes = predictions[0]["boxes"].cpu().tolist()
labels = predictions[0]["labels"].cpu().tolist()
scores = predictions[0]["scores"].cpu().tolist()
confidence_threshold = 0.5
results = [
{"box": box, "label": label, "score": score}
for box, label, score in zip(boxes, labels, scores)
if score > confidence_threshold
]
return {"predictions": results}
def inference(payload):
try:
if "image" not in payload:
return {"error": "No image provided. Please send an image."}
image_bytes = payload["image"].encode("latin1")
results = detect_objects(image_bytes)
return results
except Exception as e:
return {"error": str(e)} |