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| import torch | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| from transformers import OwlViTProcessor, OwlViTForObjectDetection | |
| # Use GPU if available | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| model = OwlViTForObjectDetection.from_pretrained("google/owlvit-large-patch14").to(device) | |
| model.eval() | |
| processor = OwlViTProcessor.from_pretrained("google/owlvit-large-patch14") | |
| def query_image(img_url, text_queries, score_threshold): | |
| text_queries = text_queries.split(",") | |
| response = requests.get(img_url) | |
| img = Image.open(BytesIO(response.content)) | |
| img = np.array(img) | |
| target_sizes = torch.Tensor([img.shape[:2]]) | |
| 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(outputs=outputs, target_sizes=target_sizes) | |
| boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"] | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| for box, score, label in zip(boxes, scores, labels): | |
| box = [int(i) for i in box.tolist()] | |
| if score >= score_threshold: | |
| img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5) | |
| if box[3] + 25 > 768: | |
| y = box[3] - 10 | |
| else: | |
| y = box[3] + 25 | |
| img = cv2.putText( | |
| img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA | |
| ) | |
| return img | |
| description = """ | |
| Gradio demo for <a href="https://huggingface.co./docs/transformers/main/en/model_doc/owlvit">OWL-ViT</a>, | |
| introduced in <a href="https://arxiv.org/abs/2205.06230">Simple Open-Vocabulary Object Detection | |
| with Vision Transformers</a>. | |
| \n\nYou can use OWL-ViT to query images with text descriptions of any object. | |
| To use it, simply input the URL of 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: *"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=["text", "text", gr.Slider(0, 1, value=0.1)], | |
| outputs="image", | |
| title="Zero-Shot Object Detection with OWL-ViT", | |
| description=description, | |
| examples=[], | |
| ) | |
| demo.launch() | |