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--- |
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license: mit |
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library_name: transformers |
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pipeline_tag: image-to-text |
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--- |
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# Load model |
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from transformers import AutoProcessor, BlipForConditionalGeneration |
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processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base") |
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model = BlipForConditionalGeneration.from_pretrained("trunks/blip-image-captioning-base") |
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# prepare image for model |
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from PIL import Image |
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from IPython.display import display |
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img1 = Image.open("imagepath/img.jpeg") |
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width, height = img1.size |
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img1_resized = img1.resize((int(0.3 * width), int(0.3 * height)) |
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display(img1_resized) |
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# testing image |
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inputs = processor(images=img1, return_tensors="pt") |
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pixel_values = inputs.pixel_values |
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50) |
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generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(generated_caption) |
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