Spaces:
Running
Running
File size: 1,690 Bytes
1e4d453 8105001 1e4d453 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import cv2
import numpy as np
import gradio as gr
import paddlehub as hub
from methods.img2pixl import pixL
from methods.combine import combine
from methods.white_box_cartoonizer.cartoonize import WB_Cartoonize
model = hub.Module(name='U2Net')
pixl = pixL()
combine = combine()
def func_tab1(image,pixel_size, checkbox1):
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
def func_tab2():
pass
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="numpy",label="Front")]
inputs_tab2 = [gr.Video()]
outputs_tab2 = [gr.Video()]
tab1 = gr.Interface(fn = func_tab1,
inputs = inputs_tab1,
outputs = outputs_tab1)
#Pixera for Videos
tab2 = gr.Interface(fn = func_tab2,
inputs = inputs_tab2,
outputs = outputs_tab2)
gr.TabbedInterface([tab1], ["Pixera for Images"]).launch()
|