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import cv2
import numpy as np
import gradio as gr
from PIL import Image
import paddlehub as hub
from methods.img2pixl import pixL
from examples.pixelArt.combine import combine
from examples.pixelArt.white_box_cartoonizer.cartoonize import WB_Cartoonize
model = hub.Module(name='U2Net')
pixl = pixL()
combine = combine()
def GIF(fname,pixel_size):
print(fname)
gif = Image.open(fname)
frames = []
for i in range(gif.n_frames):
gif.seek(i)
frame = Image.new('RGB', gif.size)
frame.paste(gif)
frame = np.array(frame)
frames.append(frame)
print(len(frames))
result = pixl.toThePixL(frames, pixel_size)
print(len(result), result[0].shape)
frames = []
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('new.gif', append_images=frames, save_all=True, loop=1)
return Image.open('cache.gif')
def func_tab1(image,pixel_size, checkbox1):
if image.name.endswith('.gif'):
GIF(image.name,pixel_size)
else:
image = cv2.imread(image.name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = WB_Cartoonize().infer(image)
image = np.array(image)
image = cv2.cvtColor(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 result
inputs_tab1 = [gr.inputs.Image(type='file', label="Image"),
gr.Slider(4, 100, value=12, step = 2, label="Pixel Size"),
gr.Checkbox(label="Object-Oriented Inference", value=False)]
outputs_tab1 = [gr.Image(type="file",label="Front")]
gr.Interface(fn = func_tab1,
inputs = inputs_tab1,
outputs = outputs_tab1).launch()
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