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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], | |
| ['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() | |