app.py

#!/usr/bin/env python

import pathlib

import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions
from transformers import AutoImageProcessor, DetaForObjectDetection

DESCRIPTION = "# DETA (Detection Transformers with Assignment)"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

MODEL_ID = "jozhang97/deta-swin-large"
image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = DetaForObjectDetection.from_pretrained(MODEL_ID)
model.to(device)


@spaces.GPU
@torch.inference_mode()
def run(image_path: str, threshold: float) -> np.ndarray:
    image = PIL.Image.open(image_path)
    inputs = image_processor(images=image, return_tensors="pt").to(device)
    outputs = model(**inputs)
    target_sizes = torch.tensor([image.size[::-1]])
    results = image_processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[0]

    boxes = results["boxes"].cpu().numpy()
    scores = results["scores"].cpu().numpy()
    cat_ids = results["labels"].cpu().numpy().tolist()

    preds = []
    for box, score, cat_id in zip(boxes, scores, cat_ids, strict=True):
        box_int = np.round(box).astype(int)
        cat_label = model.config.id2label[cat_id]
        pred = ObjectPrediction(bbox=box_int, category_id=cat_id, category_name=cat_label, score=score)
        preds.append(pred)

    return visualize_object_predictions(np.asarray(image), preds)["image"]


with gr.Blocks(css_paths="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="Input image", type="filepath")
            threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, step=0.01, value=0.1)
            run_button = gr.Button()
        result = gr.Image(label="Result")
    gr.Examples(
        examples=[[path, 0.1] for path in sorted(pathlib.Path("images").glob("*.jpg"))],
        inputs=[image, threshold],
        outputs=result,
        fn=run,
    )

    run_button.click(
        fn=run,
        inputs=[image, threshold],
        outputs=result,
        api_name="predict",
    )

if __name__ == "__main__":
    demo.launch()