82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
import gradio as gr
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import numpy as np
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from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
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from datasets import load_dataset
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from PIL import Image, ImageDraw, ImageFont
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
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model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd")
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labels = ['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER']
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id2label = {0: 'O', 1: 'B-HEADER', 2: 'I-HEADER', 3: 'B-QUESTION', 4: 'I-QUESTION', 5: 'B-ANSWER', 6: 'I-ANSWER'}
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label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
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def unnormalize_box(bbox, width, height):
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return [
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width * (bbox[0] / 1000),
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height * (bbox[1] / 1000),
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width * (bbox[2] / 1000),
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height * (bbox[3] / 1000),
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]
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def iob_to_label(label):
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label = label[2:]
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if not label:
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return 'other'
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return label
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def process_image(image):
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width, height = image.size
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# encode
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encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
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offset_mapping = encoding.pop('offset_mapping')
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# forward pass
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outputs = model(**encoding)
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# get predictions
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predictions = outputs.logits.argmax(-1).squeeze().tolist()
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token_boxes = encoding.bbox.squeeze().tolist()
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# only keep non-subword predictions
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is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
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true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
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true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
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# draw predictions over the image
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draw = ImageDraw.Draw(image)
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font = ImageFont.load_default()
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for prediction, box in zip(true_predictions, true_boxes):
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predicted_label = iob_to_label(prediction).lower()
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draw.rectangle(box, outline=label2color[predicted_label])
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draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
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return image
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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with gr.Row():
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with gr.Column():
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image_input =gr.inputs.Image(type="pil", label="图片")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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image_output = gr.Image(label="图片")
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button.click(fn=process_image, inputs=image_input, outputs=image_output)
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examples = gr.Examples(examples=[['document.png']], inputs=[image_input], label="例子")
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0")
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