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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(1, seconds/2) | |
| 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="Urdu Text To Sign", | |
| description="This is a small text to sign language model based on Urdu sign langugae standards", | |
| examples=[["good boy"]]) | |
| demo.launch(debug=True) |