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