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import torch |
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import cv2 |
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import gradio as gr |
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import numpy as np |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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from transformers import OwlViTProcessor, OwlViTForObjectDetection |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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else: |
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device = torch.device("cpu") |
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model = OwlViTForObjectDetection.from_pretrained("google/owlvit-large-patch14").to(device) |
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model.eval() |
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processor = OwlViTProcessor.from_pretrained("google/owlvit-large-patch14") |
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def query_image(img, text_queries, score_threshold): |
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text_queries = text_queries.split(",") |
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img = np.array(img) |
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target_sizes = torch.Tensor([img.shape[:2]]) |
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inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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outputs.logits = outputs.logits.cpu() |
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outputs.pred_boxes = outputs.pred_boxes.cpu() |
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results = processor.post_process(outputs=outputs, target_sizes=target_sizes) |
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boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"] |
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font = cv2.FONT_HERSHEY_SIMPLEX |
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for box, score, label in zip(boxes, scores, labels): |
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box = [int(i) for i in box.tolist()] |
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if score >= score_threshold: |
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img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5) |
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if box[3] + 25 > 768: |
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y = box[3] - 10 |
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else: |
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y = box[3] + 25 |
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img = cv2.putText( |
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img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA |
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) |
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return img |
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description = """ |
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\n\nYou can use OWL-ViT to query images with text descriptions of any object. |
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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 |
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can also use the score threshold slider to set a threshold to filter out low probability predictions. |
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\n\nOWL-ViT is trained on text templates, |
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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"*, |
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*"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. |
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\n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a> |
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""" |
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demo = gr.Interface( |
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query_image, |
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inputs=[gr.Image(source="upload"), |
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"text", |
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gr.Slider(0, 1, value=0.1)], |
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outputs="image", |
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title="Zero-Shot Object Detection with OWL-ViT", |
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description=description, |
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examples=[], |
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) |
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demo.launch() |
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