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from transformers import pipeline, SamModel, SamProcessor |
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import torch |
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import numpy as np |
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import spaces |
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checkpoint = "google/owlvit-base-patch16" |
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detector = pipeline(model=checkpoint, task="zero-shot-object-detection") |
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sam_model = SamModel.from_pretrained("facebook/sam-vit-base").to("cuda") |
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sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-base") |
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@spaces.GPU |
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def query(image, texts, threshold): |
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texts = texts.split(",") |
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predictions = detector( |
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image, |
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candidate_labels=texts, |
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threshold=threshold |
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) |
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result_labels = [] |
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for pred in predictions: |
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box = pred["box"] |
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score = pred["score"] |
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label = pred["label"] |
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box = [round(pred["box"]["xmin"], 2), round(pred["box"]["ymin"], 2), |
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round(pred["box"]["xmax"], 2), round(pred["box"]["ymax"], 2)] |
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inputs = sam_processor( |
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image, |
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input_boxes=[[[box]]], |
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return_tensors="pt" |
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).to("cuda") |
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with torch.no_grad(): |
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outputs = sam_model(**inputs) |
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mask = sam_processor.image_processor.post_process_masks( |
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outputs.pred_masks.cpu(), |
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inputs["original_sizes"].cpu(), |
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inputs["reshaped_input_sizes"].cpu() |
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)[0][0][0].numpy() |
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mask = mask[np.newaxis, ...] |
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result_labels.append((mask, label)) |
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return image, result_labels |
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import gradio as gr |
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description = "This Space combines OWLv2, the state-of-the-art zero-shot object detection model with SAM, the state-of-the-art mask generation model. SAM normally doesn't accept text input. Combining SAM with OWLv2 makes SAM text promptable. Try the example or input an image and comma separated candidate labels to segment." |
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demo = gr.Interface( |
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query, |
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inputs=[gr.Image(type="pil", label="Image Input"), gr.Textbox(label = "Candidate Labels"), gr.Slider(0, 1, value=0.05, label="Confidence Threshold")], |
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outputs="annotatedimage", |
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title="OWL π€ SAM", |
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description=description, |
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examples=[ |
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["./cats.png", "cat", 0.1], |
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], |
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cache_examples=True |
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) |
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demo.launch(debug=True) |
