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#import libraries
import numpy as np
import os
import cv2
from dotenv import load_dotenv
from pathlib import Path
env_path = Path('.') / '.env'
from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
load_dotenv(dotenv_path=env_path)
path = {
'ROTATED_IMAGE_FOLDER_PATH': str(os.getenv('ROTATED_IMAGE_FOLDER_PATH')),
}
# Traves rotated images
for img_name in os.listdir(path["ROTATED_IMAGE_FOLDER_PATH"]):
# perform ocr
file_name = img_name.split(".")[0]
result = ocr.ocr(os.path.join(path["ROTATED_IMAGE_FOLDER_PATH"],img_name), cls=True)
ocr_output_paddle = []
for i in result:
ocr_output_paddle.append(" ".join([line[1][0] for line in i]))
#store ocr in OCR_LABEL_DATA folder
if result is not None:
try:
Path('runs').mkdir(parents=True, exist_ok=True)
Path(os.path.join('runs', 'segment')).mkdir(parents=True, exist_ok=True)
Path(os.path.join('runs', 'segment', 'inference')).mkdir(parents=True, exist_ok=True)
Path(os.path.join('runs', 'segment', 'inference', 'ocr_label_data')).mkdir(parents=True, exist_ok=True)
except OSError as error:
print(error)
with open(os.path.join('runs', 'segment', 'inference', 'ocr_label_data', img_name) +'.txt',"w+") as f:
f.write("\n".join(ocr_output_paddle)) |