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import easyocr
from PIL import Image
import re
from torchvision import transforms
import matplotlib.pyplot as plt
# data={"image_path":"", "text":"", "preprocess_image":""}
# to preprocess the text extracted from the meme
def preprocess_text(text):
emoji_pattern = re.compile(
"["
"\U0001F600-\U0001F64F" # emoticons
"\U0001F300-\U0001F5FF" # symbols & pictographs
"\U0001F680-\U0001F6FF" # transport & map symbols
"\U0001F1E0-\U0001F1FF" # flags
"\U00002700-\U000027BF" # dingbats
"\U0001F900-\U0001F9FF" # supplemental symbols
"\U00002600-\U000026FF" # miscellaneous symbols
"\U00002B00-\U00002BFF" # arrows, etc.
"\U0001FA70-\U0001FAFF" # extended symbols
"]+",
flags=re.UNICODE
)
text = emoji_pattern.sub(r'', text)
text = text.lower().strip()
text = re.sub(r'[^a-z0-9\s]', '', text)
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\b\w\b', '', text)
text = re.sub(r'[^\w\s]', '', text)
return text
# to extract and preprocess text from image using OCR
def ocr_extract_text(image_path):
reader = easyocr.Reader(['en', 'tl'], gpu=True)
result = reader.readtext(image_path, detail=0)
final_text = " ".join(result)
preprocess_txt= preprocess_text(final_text)
return final_text, preprocess_txt
# to resize and normalize image for model input
def resize_normalize_image(image, target_size= (224, 224)):
preprocess_image= transforms.Compose([
transforms.Resize(target_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
image = Image.open(image).convert('RGB')
image = preprocess_image(image)
image = image.unsqueeze(0)
return image
# if __name__ == "__main__":
# input_image= "backend/test_image.jpg"
# data["image_path"]= input_image
# data["text"]= ocr_extract_text(input_image)
# data["preprocess_image"]= resize_normalize_image(input_image)
# print(data)
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