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import numpy as np
import torch
import gradio as gr
from infer import detections
import os
os.system("mkdir data")
os.system("mkdir data/models")
'''
os.system("wget https://www.cs.cmu.edu/~walt/models/walt_people.pth -O data/models/walt_people.pth")
'''
os.system("wget https://www.cs.cmu.edu/~walt/models/walt_vehicle.pth -O data/models/walt_vehicle.pth")
def walt_demo(input_img, confidence_threshold):
#detect_people = detections('configs/walt/walt_people.py', 'cuda:0', model_path='data/models/walt_people.pth')
if torch.cuda.is_available() == False:
device='cpu'
else:
device='cuda:0'
#detect_people = detections('configs/walt/walt_people.py', device, model_path='data/models/walt_people.pth')
detect = detections('configs/walt/walt_vehicle.py', device, model_path='data/models/walt_vehicle.pth', threshold=confidence_threshold)
count = 0
#img = detect_people.run_on_image(input_img)
output_img = detect.run_on_image(input_img)
#try:
#except:
# print("detecting on image failed")
return output_img
description = """
WALT Demo on WALT dataset. After watching and automatically learning for several days, this approach shows significant performance improvement in detecting and segmenting occluded people and vehicles, over human-supervised amodal approaches</b>.
<center>
<a href="https://www.cs.cmu.edu/~walt/">
<img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-WALT-green">
</a>
<a href="https://www.cs.cmu.edu/~walt/pdf/walt.pdf"><img style="display:inline" src="https://img.shields.io/badge/Paper-Pdf-red"></a>
<a href="https://github.com/dineshreddy91/WALT"><img style="display:inline" src="https://img.shields.io/github/stars/dineshreddy91/WALT?style=social"></a>
</center>
"""
title = "WALT:Watch And Learn 2D Amodal Representation using Time-lapse Imagery"
article="""
<center>
<img src='https://visitor-badge.glitch.me/badge?page_id=anhquancao.MonoScene&left_color=darkmagenta&right_color=purple' alt='visitor badge'>
</center>
"""
examples = [
['demo/images/img_1.jpg',0.8],
]
'''
examples = [
['demo/images/img_1.jpg',0.8]
['demo/images/img_2.jpg',0.8],
['demo/images/img_4.png',0.85],
]
import cv2
filename='demo/images/img_1.jpg'
img=cv2.imread(filename)
img=walt_demo(img)
cv2.imwrite(filename.replace('/images/','/results/'),img)
cv2.imwrite('check.png',img)
'''
confidence_threshold = gr.Slider(minimum=0.3,
maximum=1.0,
step=0.01,
value=1.0,
label="Amodal Detection Confidence Threshold")
inputs = [gr.Image(), confidence_threshold]
demo = gr.Interface(walt_demo,
outputs="image",
inputs=inputs,
article=article,
title=title,
enable_queue=True,
examples=examples,
description=description)
#demo.launch(server_name="0.0.0.0", server_port=7000)
demo.launch()
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