app.py

import spaces
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
import torch
import gradio as gr

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to(device)

@spaces.GPU
def infer(img, text_queries, score_threshold, model):
  
    if model == "dino":
        queries = ""
        for query in text_queries:
            queries += f"{query}. "

        height, width = img.shape[:2]
        target_sizes = [(height, width)]
        inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)

        with torch.no_grad():
            outputs = dino_model(**inputs)
            outputs.logits = outputs.logits.cpu()
            outputs.pred_boxes = outputs.pred_boxes.cpu()
            results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
                                                                            box_threshold=score_threshold,
                                                                            target_sizes=target_sizes)
      
        boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
        result_labels = []

        for box, score, label in zip(boxes, scores, labels):
            box = [int(i) for i in box.tolist()]
            if score < score_threshold:
                continue

            if model == "dino":
                if label != "":
                    result_labels.append((box, label))
        return result_labels

def query_image(img, text_queries, dino_threshold):
    text_queries = text_queries.split(",")
    dino_output = infer(img, text_queries, dino_threshold, "dino")
    annotations = []
    for box, label in dino_output:
        annotations.append({"label": label, "coordinates": {"x": box[0], "y": box[1], "width": box[2] - box[0], "height": box[3] - box[1]}})
    return (img, {"boxes": annotations})


dino_threshold = gr.Slider(0, 1, value=0.12, label="Grounding DINO Threshold")
dino_output = gr.AnnotatedImage(label="Grounding DINO Output")
demo = gr.Interface(
    query_image,
    inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), dino_threshold],
    outputs=[dino_output],
    title="OWLv2 ⚔ Grounding DINO",
    description="Evaluate state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection models. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
    examples=[["./warthog.jpg", "zebra, warthog", 0.16], ["./zebra.jpg", "zebra, lion", 0.16]]
)
demo.launch(debug=True)