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| 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") | |
| 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=[["./warthog.jpg", "zebra, warthog", 0.16], ["./zebra.jpg", "zebra, lion", 0.16]] | |
| ) | |
| demo.launch(debug=True) | |