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import display_gloss as dg
import synonyms_preprocess as sp
from NLP_Spacy_base_translator import NlpSpacyBaseTranslator
from flask import Flask, render_template, Response, request, send_file
import io
import cv2
import numpy as np
import os
import requests
app = Flask(__name__, static_folder='static')
app.config['TITLE'] = 'Sign Language Translate'
nlp, dict_docs_spacy = sp.load_spacy_values()
dataset, list_2000_tokens = dg.load_data()
def translate_korean_to_english(text):
try:
url = "https://translate.googleapis.com/translate_a/single"
params = {
"client": "gtx",
"sl": "ko",
"tl": "en",
"dt": "t",
"q": text.strip() # 입력 텍스트의 앞뒤 공백 제거
}
response = requests.get(url, params=params)
if response.status_code == 200:
# 전체 번역 결과를 하나의 문자열로 결합
translated_text = ' '.join(item[0] for item in response.json()[0] if item[0])
return translated_text
else:
raise Exception(f"Translation API returned status code: {response.status_code}")
except Exception as e:
print(f"Translation error: {e}")
return text
def generate_complete_video(gloss_list, dataset, list_2000_tokens):
frames = []
for frame in dg.generate_video(gloss_list, dataset, list_2000_tokens):
frame_data = frame.split(b'\r\n\r\n')[1]
nparr = np.frombuffer(frame_data, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
frames.append(img)
height, width = frames[0].shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
temp_path = os.path.join('/tmp', 'temp.mp4')
out = cv2.VideoWriter(temp_path, fourcc, 25, (width, height))
for frame in frames:
out.write(frame)
out.release()
with open(temp_path, 'rb') as f:
video_bytes = f.read()
os.remove(temp_path)
return video_bytes
@app.route('/')
def index():
return render_template('index.html', title=app.config['TITLE'])
@app.route('/translate/', methods=['POST'])
def result():
if request.method == 'POST':
input_text = request.form['inputSentence'].strip()
if not input_text:
return render_template('error.html', error="Please enter text to translate")
try:
english_text = translate_korean_to_english(input_text)
if not english_text:
raise Exception("Translation failed")
eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=english_text)
generated_gloss = eng_to_asl_translator.translate_to_gloss()
gloss_list_lower = [gloss.lower() for gloss in generated_gloss.split() if gloss.isalnum()]
gloss_sentence_before_synonym = " ".join(gloss_list_lower)
gloss_list = [sp.find_synonyms(gloss, nlp, dict_docs_spacy, list_2000_tokens)
for gloss in gloss_list_lower]
gloss_sentence_after_synonym = " ".join(gloss_list)
return render_template('result.html',
title=app.config['TITLE'],
original_sentence=input_text,
english_translation=english_text,
gloss_sentence_before_synonym=gloss_sentence_before_synonym,
gloss_sentence_after_synonym=gloss_sentence_after_synonym)
except Exception as e:
return render_template('error.html', error=f"Translation error: {str(e)}")
@app.route('/video_feed')
def video_feed():
sentence = request.args.get('gloss_sentence_to_display', '')
gloss_list = sentence.split()
return Response(dg.generate_video(gloss_list, dataset, list_2000_tokens),
mimetype='multipart/x-mixed-replace; boundary=frame')
@app.route('/download_video/<gloss_sentence>')
def download_video(gloss_sentence):
gloss_list = gloss_sentence.split()
video_bytes = generate_complete_video(gloss_list, dataset, list_2000_tokens)
return send_file(
io.BytesIO(video_bytes),
mimetype='video/mp4',
as_attachment=True,
download_name='sign_language.mp4'
)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860, debug=True) |