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import streamlit as st
import cv2
import pytesseract
from googletrans import Translator
from PIL import Image, ImageDraw, ImageFont
import numpy as np
# Step 1: Load the image
def load_image(image_file):
# Read the image using OpenCV
img = cv2.imdecode(np.fromstring(image_file.read(), np.uint8), cv2.IMREAD_COLOR)
return img
# Step 2: Extract text and coordinates using pytesseract
def extract_text_with_position(image):
# Convert the image to grayscale for better OCR performance
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Use pytesseract to get OCR results along with bounding boxes
details = pytesseract.image_to_data(gray_image, output_type=pytesseract.Output.DICT)
text_data = []
for i in range(len(details['text'])):
if details['text'][i].strip() != '':
text_data.append({
'text': details['text'][i],
'left': details['left'][i],
'top': details['top'][i],
'width': details['width'][i],
'height': details['height'][i]
})
return text_data
# Step 3: Translate the extracted text using Google Translate
def translate_text(text, target_language='en'):
translator = Translator()
translation = translator.translate(text, dest=target_language)
return translation.text
# Step 4: Recreate the image with translated text
def recreate_image_with_translated_text(original_img, text_data, target_language):
# Convert OpenCV image to Pillow image for easier manipulation
pil_img = Image.fromarray(cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(pil_img)
try:
# Load a default font (you can use a specific font if you want)
font = ImageFont.load_default()
except IOError:
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 30)
for item in text_data:
# Translate each piece of text
translated_text = translate_text(item['text'], target_language)
# Draw the translated text on the new image at the same position
draw.text((item['left'], item['top']), translated_text, font=font, fill="black")
# Convert the image back to an OpenCV format
output_img = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
return output_img
# Step 5: Display or download the translated image
def display_or_download_image(output_img):
# Convert image to bytes to allow for download
_, img_bytes = cv2.imencode('.png', output_img)
st.image(output_img, channels="BGR", caption="Translated Image", use_column_width=True)
# Provide a button to download the image
st.download_button(
label="Download Translated Image",
data=img_bytes.tobytes(),
file_name="translated_image.png",
mime="image/png"
)
# Streamlit Interface
def main():
st.title("Image Translation App")
# Upload image
image_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if image_file is not None:
# Load the image and show it
img = load_image(image_file)
st.image(img, caption="Uploaded Image", channels="BGR", use_column_width=True)
# Get the target language from the user
target_language = st.text_input("Enter the target language code (e.g., 'es' for Spanish):")
if target_language:
# Extract text from image
text_data = extract_text_with_position(img)
# Recreate the image with translated text
translated_img = recreate_image_with_translated_text(img, text_data, target_language)
# Display or allow download of the translated image
display_or_download_image(translated_img)
if __name__ == "__main__":
main() |