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from transformers import AutoModel
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import numpy as np
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from PIL import Image
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import torch
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import os
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images = [
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"1.png",
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"1.jpg",
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]
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def read_image_as_np_array(image_path):
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with open(image_path, "rb") as file:
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image = Image.open(file).convert("L").convert("RGB")
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image = np.array(image)
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return image
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images = [read_image_as_np_array(image) for image in images]
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model = AutoModel.from_pretrained(
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"ragavsachdeva/magi", trust_remote_code=True).cuda()
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with torch.no_grad():
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results = model.predict_detections_and_associations(images)
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text_bboxes_for_all_images = [x["texts"] for x in results]
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ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)
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for i in range(len(images)):
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model.visualise_single_image_prediction(
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images[i], results[i], filename=f"image_{i}.png")
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model.generate_transcript_for_single_image(
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results[i], ocr_results[i], filename=f"transcript_{i}.txt")
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