File size: 1,807 Bytes
9a72e69 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
#!/usr/bin/env python
# coding: utf-8
# In[6]:
import os
import cv2
import paddlehub as hub
import gradio as gr
import torch
import urllib.request
# In[7]:
# Fetch image for analysis
img_url = "http://claireye.com.tw/img/230212a.jpg"
urllib.request.urlretrieve(img_url, "pose.jpg")
model = hub.Module(name='U2Net')
# In[8]:
def infer(webcam, img,option):
if option == "webcam":
webcam.save('temp.jpg')
result = model.Segmentation(
images=[cv2.imread("temp.jpg")],
paths=None,
batch_size=1,
input_size=320,
output_dir='output',
visualization=True)
else:
img.save('temp.jpg')
result = model.Segmentation(
images=[cv2.imread("temp.jpg")],
paths=None,
batch_size=1,
input_size=320,
output_dir='output',
visualization=True)
return result[0]['front'][:,:,::-1], result[0]['mask']
# In[9]:
inputs = [gr.inputs.Image(source="webcam", label="Webcam", type="pil",optional=True),gr.inputs.Image(source="upload", label="Input Image", type="pil",optional=True),gr.inputs.Radio(choices=["webcam","Image"], type="value", default="Image", label="Input Type")]
outputs = [
gr.outputs.Image(type="numpy",label="Front"),
gr.outputs.Image(type="numpy",label="Mask")
]
title = "U^2-Net"
description = "demo for U^2-Net. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='http://claireye.com.tw'>Claireye</a> | 2023</p>"
examples = [
['pose.jpg','pose.jpg','Image'],
]
# In[10]:
gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()
# In[ ]:
|