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import cv2
import os
from moviepy.editor import *
import gradio as gr
import re
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from nltk.corpus import wordnet
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')
def get_wordnet_pos(tag):
if tag.startswith('J'):
return wordnet.ADJ
elif tag.startswith('V'):
return wordnet.VERB
elif tag.startswith('N'):
return wordnet.NOUN
elif tag.startswith('R'):
return wordnet.ADV
else:
return wordnet.NOUN # Default to noun if the POS tag is not found
def get_lemma(word):
lemmatizer = WordNetLemmatizer()
tokens = word_tokenize(word)
tagged_words = nltk.pos_tag(tokens)
lemmas = []
for tagged_word in tagged_words:
word = tagged_word[0]
pos = tagged_word[1]
wordnet_pos = get_wordnet_pos(pos)
lemma = lemmatizer.lemmatize(word, pos=wordnet_pos)
lemmas.append(lemma)
return ' '.join(lemmas)
def apply_lemma_to_string(sentence):
words = word_tokenize(sentence)
lemmas = [get_lemma(word) for word in words]
return ' '.join(lemmas)
def parse_string(string, dataset):
parsed_list = []
start = 0
otherword=""
end = len(string)
while start < end:
max_chunk = ""
max_length = 0
for chunk in VideosNames:
if string.startswith(chunk.lower(), start) and len(chunk) > max_length:
max_chunk = chunk
max_length = len(chunk)
if max_chunk:
if len(max_chunk)>1:
parsed_list.append(max_chunk)
print(max_chunk)
else:
otherword+=max_chunk
start += len(max_chunk)
else:
parsed_list.append(otherword)
otherword=""
start += 1
return parsed_list
def remove_empty_values(lst):
return [x for x in lst if x and (not isinstance(x, (str, list, dict)) or x)]
def flatten_lists(lst):
flat_list = []
for i in lst:
if type(i) == list:
flat_list.extend(flatten_lists(i))
else:
flat_list.append(i)
return flat_list
path = 'dataset'
videos = []
VideosNames = []
myList = os.listdir(path)
print(myList)
for cu_video in myList:
current_Video = cv2.imread(f'{path}/{cu_video}')
videos.append(current_Video)
VideosNames.append((os.path.splitext(cu_video)[0]).replace("-"," ").lower())
print(VideosNames)
def texttoSign(text):
text=text+" "
text=text.lower()
text=apply_lemma_to_string(text)
text=re.sub('[^a-z]+', ' ', text)
framescount=0
listofwords=parse_string(text,VideosNames)
listofwords=remove_empty_values(listofwords)
index=0
for word in listofwords:
if word not in VideosNames:
listofwords[index]=(list(word))
index+=1
listofwords=flatten_lists(listofwords)
clips=[]
for i in range(len(listofwords)):
path="dataset/"+(listofwords[i])+".mp4"
data=cv2.VideoCapture(path)
framescount = data.get(cv2.CAP_PROP_FRAME_COUNT)
fps = data.get(cv2.CAP_PROP_FPS)
seconds = round(framescount / fps)
clips.append(VideoFileClip(path))
clips[i]=clips[i].subclip(0, seconds)
result_clip=concatenate_videoclips(clips, method='compose')
result_clip.write_videofile("combined.mp4", fps=30)
return "combined.mp4"
# except:
# pass
demo=gr.Interface(fn=texttoSign,
inputs="text",
outputs="video",
title="ASL Text To Sign",
description="This is a small text to sign language model based on ASL sign langugae standards",
examples=[["bad boy"]])
demo.launch(debug=True) |