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import numpy as np |
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import torch |
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import gradio as gr |
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from infer import detections |
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import os |
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os.system("mkdir data") |
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os.system("mkdir data/models") |
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if not os.path.exists("data/models/walt_people.pth"): |
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os.system("wget https://www.cs.cmu.edu/~walt/models/walt_people.pth -O data/models/walt_people.pth") |
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if not os.path.exists("data/models/walt_vehicle.pth"): |
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os.system("wget https://www.cs.cmu.edu/~walt/models/walt_vehicle.pth -O data/models/walt_vehicle.pth") |
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''' |
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''' |
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def walt_demo(input_img, confidence_threshold): |
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if torch.cuda.is_available() == False: |
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device='cpu' |
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else: |
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device='cuda:0' |
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detect = detections('configs/walt/walt_vehicle.py', device, model_path='data/models/walt_vehicle.pth', threshold=confidence_threshold) |
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count = 0 |
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output_img = detect.run_on_image(input_img) |
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return output_img |
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description = """ |
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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>. |
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<center> |
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<a href="https://www.cs.cmu.edu/~walt/"> |
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<img style="display:inline" alt="Project page" src="https://img.shields.io/badge/Project%20Page-WALT-green"> |
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</a> |
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<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> |
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<a href="https://github.com/dineshreddy91/WALT"><img style="display:inline" src="https://img.shields.io/github/stars/dineshreddy91/WALT?style=social"></a> |
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</center> |
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""" |
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title = "WALT:Watch And Learn 2D Amodal Representation using Time-lapse Imagery" |
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article=""" |
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<center> |
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<img src='https://visitor-badge.glitch.me/badge?page_id=anhquancao.MonoScene&left_color=darkmagenta&right_color=purple' alt='visitor badge'> |
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</center> |
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""" |
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examples = [ |
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['demo/images/img_1.jpg',0.8], |
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['demo/images/img_2.jpg',0.8], |
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['demo/images/img_4.png',0.85], |
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] |
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''' |
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import cv2 |
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filename='demo/images/img_1.jpg' |
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img=cv2.imread(filename) |
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img=walt_demo(img) |
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cv2.imwrite(filename.replace('/images/','/results/'),img) |
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cv2.imwrite('check.png',img) |
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''' |
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confidence_threshold = gr.Slider(minimum=0.3, |
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maximum=1.0, |
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step=0.01, |
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value=1.0, |
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label="Amodal Detection Confidence Threshold") |
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inputs = [gr.Image(), confidence_threshold] |
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demo = gr.Interface(walt_demo, |
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outputs="image", |
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inputs=inputs, |
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article=article, |
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title=title, |
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enable_queue=True, |
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examples=examples, |
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description=description) |
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demo.launch(share=True) |
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