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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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current_dir = os.getcwd() |
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images = [ |
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os.path.join(current_dir, "test", "1.png"), |
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os.path.join(current_dir, "test", "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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print("Done") |
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