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import cv2 |
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import gradio as gr |
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import numpy as np |
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from paddleocr import PaddleOCR |
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from PIL import Image |
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from transformers import pipeline |
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from transformers.pipelines.document_question_answering import apply_tesseract |
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PIPE = pipeline("document-question-answering", "impira/layoutlm-document-qa") |
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OCR = PaddleOCR( |
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use_angle_cls=True, |
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lang="en", |
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det_limit_side_len=10_000, |
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det_db_score_mode="slow", |
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enable_mlkdnn=True, |
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) |
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PADDLE_OCR_LABEL = "PaddleOCR (en)" |
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TESSERACT_LABEL = "Tesseract (HF default)" |
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def predict(image: Image.Image, question: str, ocr_engine: str): |
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image_np = np.asarray(image) |
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if ocr_engine == PADDLE_OCR_LABEL: |
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ocr_result = OCR.ocr(image_np)[0] |
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words = [x[1][0] for x in ocr_result] |
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boxes = np.asarray([x[0] for x in ocr_result]) |
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for box in boxes: |
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cv2.polylines(image_np, [box.reshape(-1, 1, 2).astype(int)], True, (0, 255, 255), 3) |
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x1 = boxes[:, :, 0].min(1) * 1000 / image.width |
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y1 = boxes[:, :, 1].min(1) * 1000 / image.height |
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x2 = boxes[:, :, 0].max(1) * 1000 / image.width |
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y2 = boxes[:, :, 1].max(1) * 1000 / image.height |
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boxes = np.stack([x1, y1, x2, y2], axis=1).astype(int) |
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elif ocr_engine == TESSERACT_LABEL: |
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words, boxes = apply_tesseract(image, None, "") |
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for x1, y1, x2, y2 in boxes: |
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x1 = int(x1 * image.width / 1000) |
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y1 = int(y1 * image.height / 1000) |
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x2 = int(x2 * image.width / 1000) |
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y2 = int(y2 * image.height / 1000) |
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cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 255), 3) |
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else: |
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raise ValueError(f"Unsupported ocr_engine={ocr_engine}") |
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word_boxes = list(zip(words, boxes)) |
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result = PIPE(image, question, word_boxes)[0] |
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return result["answer"], result["score"], image_np |
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gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Image(type="pil"), |
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"text", |
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gr.Radio([PADDLE_OCR_LABEL, TESSERACT_LABEL]), |
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], |
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outputs=[ |
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gr.Textbox(label="Answer"), |
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gr.Number(label="Score"), |
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gr.Image(label="OCR results"), |
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], |
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).launch(server_name="0.0.0.0", server_port=7860) |
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