Spaces:
Sleeping
Sleeping
File size: 4,170 Bytes
50e5689 013effe 7567b06 013effe 7567b06 013effe 7567b06 013effe 7567b06 013effe 7567b06 013effe 7567b06 013effe 50e5689 013effe 50e5689 4f3b907 50e5689 b18f98f 50e5689 6c0c447 1591fd2 50e5689 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
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
from nltk.tag import pos_tag
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
def get_lemma(word):
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
lemmatizer = WordNetLemmatizer()
exceptions = {
'are': 'are', # Preserve 'are' as-is
'have': 'have', # Preserve 'have' as-is
'do': 'do', # Preserve 'do' as-is
'am':'am'
}
tokens = word_tokenize(word)
tagged_words = pos_tag(tokens)
lemmas = []
for tagged_word in tagged_words:
word = tagged_word[0]
pos = tagged_word[1]
wordnet_pos = get_wordnet_pos(pos)
if word in exceptions:
lemma = exceptions[word]
else:
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) |