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import os
import cv2
import numpy as np
import ffmpeg
from math import ceil
from segmindapi import SD2_1, Kadinsky
import gradio as gr
from .TextPreprocessor import TextPreprocessor
from .AVCombiner import AVCombiner
class Videobook:
def __init__(self):
self.preprocessor = TextPreprocessor()
self.combiner = AVCombiner()
def get_sentences(self, story):
return self.preprocessor(story)
# def generate_voice(self, story, sentences , path = 'tts.mp3'):
# for i,n in enumerate(sentences):
# tts=gTTS(n,lang='en')
# tts.save('tts'+str(i)+'.mp3')
# lgth=[]
# for i in range(len(sentences)):
# lgth.append(MP3('tts'+str(i)+'.mp3').info.length)
# os.remove(os.path.join(os.getcwd(),'tts'+str(i)+'.mp3'))
# tts=gTTS(story,lang='en')
# tts.save(path)
# return lgth
def generate_imgs(self, sentences, steps):
imgs = []
for sentence in sentences:
sentence = self.style + ' of ' + sentence + ', ' + self.tags
imgs.append(self.pipe.generate(prompt = sentence['pos'], negative_prompt = sentence['neg'], num_inference_steps = steps))
return imgs
# def addBuffer(self, imgs, lgth):
# imgs_buff = []
# for i,img in enumerate(imgs):
# for j in range(ceil(lgth[i] * self.fps)):
# imgs_buff.append(img)
# return imgs_buff
# def imgs_to_video(self, imgs, video_name='video.mp4'):
# video_dims = (imgs[0].width, imgs[0].height)
# fourcc = cv2.VideoWriter_fourcc(*'DIVX')
# video = cv2.VideoWriter(video_name, fourcc, self.fps, video_dims)
# for img in imgs:
# tmp_img = img.copy()
# video.write(cv2.cvtColor(np.array(tmp_img), cv2.COLOR_RGB2BGR))
# video.release()
# def make_video(self, imgs, lengths, video_name = "finished_video.mp4"):
# self.imgs_to_video(self.addBuffer(imgs, lengths), 'test_video.mp4')
# input_audio = ffmpeg.input(os.path.join(os.getcwd(),'tts.mp3'))
# input_video = ffmpeg.input(os.path.join(os.getcwd(),'test_video.mp4'))
# ffmpeg.concat(input_video, input_audio, v=1, a=1).output(video_name).run(overwrite_output=True)
def generate(self, story, api_key, fps, style, tags, model, steps):
self.fps = fps
self.style = style
self.tags = tags
if model == "Stable Diffusion v2.1":
self.pipe = SD2_1(api_key)
else:
self.pipe = Kadinsky(api_key)
processed_sentences, sentences = self.get_sentences(story)
return AVCombiner()(self.generate_imgs(processed_sentences, steps), sentences, os.getcwd()) |