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# coding: utf-8
import os
import os.path
import time
from pix2text import set_logger, read_img
from pix2text.latex_ocr import *
logger = set_logger()
def test_download_model():
latex_ocr = LatexOCR()
image_fps = [
'docs/examples/formula.jpg',
'docs/examples/math-formula-42.png',
]
start_time = time.time()
outs = latex_ocr.recognize(image_fps)
logger.info(f'average cost time: {(time.time() - start_time) / len(image_fps):.4f} seconds')
for img, out in zip(image_fps, outs):
logger.info(f'- image: {img}, out: \n\t{out}')
def test_infer_with_transformers():
from PIL import Image
from transformers import TrOCRProcessor
from optimum.onnxruntime import ORTModelForVision2Seq
model_dir = os.path.expanduser('~/.pix2text/1.1/mfr-1.5-onnx')
processor = TrOCRProcessor.from_pretrained(model_dir)
model = ORTModelForVision2Seq.from_pretrained(model_dir, use_cache=False)
image_fps = [
'docs/examples/formula.jpg',
'docs/examples/math-formula-42.png',
]
images = [read_img(fp, return_type='Image') for fp in image_fps]
pixel_values = processor(images=images, return_tensors="pt").pixel_values
# print(f'pixel_values', pixel_values)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(f'generated_ids: {generated_ids}, \ngenerated text: {generated_text}')
def test_infer():
more_model_configs = {}
latex_ocr = LatexOCR(more_model_configs=more_model_configs)
image_fps = [
'docs/examples/formula.jpg',
'docs/examples/math-formula-42.png',
]
start_time = time.time()
outs = latex_ocr.recognize(image_fps, batch_size=2)
logger.info(f'average cost time: {(time.time() - start_time) / len(image_fps):.4f} seconds')
for img, out in zip(image_fps, outs):
logger.info(f'- image: {img}, out: \n\t{out}') |