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| import cv2 | |
| import torch | |
| 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 | |
| model = 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 GIF(fname,pixel_size): | |
| gif = Image.open(fname) | |
| frames = [] | |
| for i in range(gif.n_frames): #First Step: Splitting the GIF into frames | |
| gif.seek(i) | |
| frame = Image.new('RGB', gif.size) | |
| frame.paste(gif) | |
| frame = np.array(frame) | |
| frames.append(frame) | |
| result = pixL().toThePixL(frames, pixel_size) | |
| for frame in result: #Second Step: Adding Cartoon Effect to each frame | |
| frame = Image.fromarray(frame) | |
| frame = cv2.cvtColor(np.asarray(face2paint(model, frame)), cv2.COLOR_BGR2RGB) | |
| frames = [] | |
| <<<<<<< HEAD | |
| for frame in result: #Third Step: Combining the frames into a GIF | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| frame = Image.fromarray(frame) | |
| frames.append(frame) | |
| frames[0].save('cache.gif', append_images=frames, save_all=True, loop=1) | |
| cache = Image.open('cache.gif') | |
| return cache | |
| ======= | |
| for frame in result: | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| frame = Image.fromarray(frame) | |
| frames.append(frame) | |
| print(type(frames), len(frames), type(frames[0]), frames[0].size) | |
| frames[0].save('cache.gif', append_images=frames, save_all=True, loop=1) | |
| return Image.open('cache.gif') | |
| >>>>>>> a2e31df8118400de0782a9feb55255620b93cd83 | |
| def initilize(image,pixel_size,checkbox1): | |
| if image.name.endswith('.gif'): | |
| GIF(image.name,pixel_size) | |
| else: | |
| image = Image.open(image.name).convert("RGB") | |
| image = cv2.cvtColor(np.asarray(face2paint(model, image)), cv2.COLOR_BGR2RGB) | |
| if checkbox1: | |
| result = model.Segmentation( | |
| images=[image], | |
| 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 = image) | |
| else: | |
| result = pixL().toThePixL([image], pixel_size) | |
| return Image.fromarray(result) | |
| inputs = ["file", | |
| gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"), | |
| gr.Checkbox(label="Object-Oriented Inference", value=False)] | |
| outputs = [gr.Image(type="pil",label="Front")] | |
| gr.Interface(fn = initilize, | |
| inputs = inputs, | |
| outputs = outputs).launch() |