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| import torch | |
| import gradio as gr | |
| from transformers import Owlv2Processor, Owlv2ForObjectDetection | |
| import spaces | |
| # Use GPU if available | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device) | |
| processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble") | |
| def query_image(img, text_queries, score_threshold): | |
| text_queries = text_queries | |
| text_queries = text_queries.split(",") | |
| size = max(img.shape[:2]) | |
| target_sizes = torch.Tensor([[size, size]]) | |
| inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| outputs.logits = outputs.logits.cpu() | |
| outputs.pred_boxes = outputs.pred_boxes.cpu() | |
| results = processor.post_process_object_detection(outputs=outputs, 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 | |
| result_labels.append((box, text_queries[label.item()])) | |
| return img, result_labels | |
| description = """ | |
| Try this demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/owlv2">OWLv2</a>, | |
| introduced in <a href="https://arxiv.org/abs/2306.09683">Scaling Open-Vocabulary Object Detection</a>. | |
| \n\n Compared to OWLVIT, OWLv2 performs better both in yield and performance (average precision). | |
| You can use OWLv2 to query images with text descriptions of any object. | |
| To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You | |
| can also use the score threshold slider to set a threshold to filter out low probability predictions. | |
| \n\nOWL-ViT is trained on text templates, | |
| hence you can get better predictions by querying the image with text templates used in training the original model: e.g. *"photo of a star-spangled banner"*, | |
| *"image of a shoe"*. Refer to the <a href="https://arxiv.org/abs/2103.00020">CLIP</a> paper to see the full list of text templates used to augment the training data. | |
| \n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a> | |
| """ | |
| demo = gr.Interface( | |
| query_image, | |
| inputs=[gr.Image(), "text", gr.Slider(0, 1, value=0.1)], | |
| outputs="annotatedimage", | |
| title="Zero-Shot Object Detection with OWLv2", | |
| description=description, | |
| examples=[ | |
| ["assets/astronaut.png", "human face, rocket, star-spangled banner, nasa badge", 0.11], | |
| ["assets/coffee.png", "coffee mug, spoon, plate", 0.1], | |
| ["assets/butterflies.jpeg", "orange butterfly", 0.3], | |
| ], | |
| ) | |
| demo.launch() | |