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import cv2
import gradio as gr
import numpy as np
from paddleocr import PaddleOCR
from PIL import Image
from transformers import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
PIPE = pipeline("document-question-answering", "impira/layoutlm-document-qa")
OCR = PaddleOCR(
use_angle_cls=True,
lang="en",
det_limit_side_len=10_000,
det_db_score_mode="slow",
enable_mlkdnn=True,
)
PADDLE_OCR_LABEL = "PaddleOCR (en)"
TESSERACT_LABEL = "Tesseract (HF default)"
def predict(image: Image.Image, question: str, ocr_engine: str):
image_np = np.asarray(image)
if ocr_engine == PADDLE_OCR_LABEL:
ocr_result = OCR.ocr(image_np)[0]
words = [x[1][0] for x in ocr_result]
boxes = np.asarray([x[0] for x in ocr_result]) # (n_boxes, 4, 2)
for box in boxes:
cv2.polylines(image_np, [box.reshape(-1, 1, 2).astype(int)], True, (0, 255, 255), 3)
x1 = boxes[:, :, 0].min(1) * 1000 / image.width
y1 = boxes[:, :, 1].min(1) * 1000 / image.height
x2 = boxes[:, :, 0].max(1) * 1000 / image.width
y2 = boxes[:, :, 1].max(1) * 1000 / image.height
# (n_boxes, 4) in xyxy format
boxes = np.stack([x1, y1, x2, y2], axis=1).astype(int)
elif ocr_engine == TESSERACT_LABEL:
words, boxes = apply_tesseract(image, None, "")
for x1, y1, x2, y2 in boxes:
x1 = int(x1 * image.width / 1000)
y1 = int(y1 * image.height / 1000)
x2 = int(x2 * image.width / 1000)
y2 = int(y2 * image.height / 1000)
cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 255), 3)
else:
raise ValueError(f"Unsupported ocr_engine={ocr_engine}")
word_boxes = list(zip(words, boxes))
result = PIPE(image, question, word_boxes)[0]
return result["answer"], result["score"], image_np
gr.Interface(
fn=predict,
inputs=[
gr.Image(type="pil"),
"text",
gr.Radio([PADDLE_OCR_LABEL, TESSERACT_LABEL]),
],
outputs=[
gr.Textbox(label="Answer"),
gr.Number(label="Score"),
gr.Image(label="OCR results"),
],
).launch()
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