import spaces from transformers import Owlv2Processor, Owlv2ForObjectDetection, 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("cuda") @spaces.GPU def infer(img, text_queries, score_threshold, model): if model == "dino": queries="" for query in text_queries: queries += f"{query}. " width, height = img.shape[:2] target_sizes=[(width, height)] 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 text_queries = text_queries.split(",") dino_output = infer(img, text_queries, dino_threshold, "dino") return (img, dino_output) 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=[["./deer.jpg", "zebra, deer, goat", 0.16], ["./zebra.jpg", "zebra, lion, deer", 0.16]] ) demo.launch(debug=True)