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| import os | |
| import cv2 | |
| import torch | |
| import warnings | |
| import numpy as np | |
| import gradio as gr | |
| import paddlehub as hub | |
| from PIL import Image | |
| from methods.img2pixl import pixL | |
| from examples.pixelArt.combine import combine | |
| from methods.media import Media | |
| warnings.filterwarnings("ignore") | |
| U2Net = hub.Module(name='U2Net') | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device, size=512) | |
| model = torch.hub.load("bryandlee/animegan2-pytorch", "generator", device=device).eval() | |
| def initilize(media,pixel_size,checkbox1): | |
| #Author: Alican Akca | |
| if media.name.endswith('.gif'): | |
| return Media().split(media.name,pixel_size) | |
| elif media.name.endswith('.mp4'): | |
| return Media().split(media.name,pixel_size) | |
| else: | |
| media = Image.open(media.name).convert("RGB") | |
| media = cv2.cvtColor(np.asarray(face2paint(model, media)), cv2.COLOR_BGR2RGB) | |
| if checkbox1: | |
| result = U2Net.Segmentation(images=[media], | |
| paths=None, | |
| batch_size=1, | |
| input_size=320, | |
| output_dir='output', | |
| visualization=True) | |
| result = combine.combiner(images = pixL().toThePixL([result[0]['front'][:,:,::-1], result[0]['mask']], | |
| pixel_size), | |
| background_image = media) | |
| else: | |
| result = pixL().toThePixL([media], pixel_size) | |
| result = Image.fromarray(result) | |
| result.save('cache.png') | |
| return [None, result, 'cache.png'] | |
| inputs = [gr.File(label="Media"), | |
| gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"), | |
| gr.Checkbox(label="Object-Oriented Inference", value=False)] | |
| outputs = [gr.Video(label="Pixed Media"), | |
| gr.Image(label="Pixed Media"), | |
| gr.File(label="Download")] | |
| title = "Pixera: Create your own Pixel Art" | |
| description = """Mobile applications will have released soon ^^ """ | |
| gr.Interface(fn = initilize, | |
| inputs = inputs, | |
| outputs = outputs, | |
| title=title, | |
| description=description).launch() | |