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from transformers import YolosImageProcessor, YolosForObjectDetection |
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from PIL import Image |
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import torch |
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import requests |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny', local_files_only=True) |
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image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny", local_files_only=True) |
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inputs = image_processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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bboxes = outputs.pred_boxes |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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box = [round(i, 2) for i in box.tolist()] |
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print( |
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f"Detected {model.config.id2label[label.item()]} with confidence " |
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f"{round(score.item(), 3)} at location {box}" |
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) |
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