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| #try: | |
| # import detectron2 | |
| #except: | |
| import os | |
| os.system('pip install git+https://github.com/SysCV/transfiner.git') | |
| from matplotlib.pyplot import axis | |
| import gradio as gr | |
| import requests | |
| import numpy as np | |
| from torch import nn | |
| import requests | |
| import torch | |
| from detectron2 import model_zoo | |
| from detectron2.engine import DefaultPredictor | |
| from detectron2.config import get_cfg | |
| from detectron2.utils.visualizer import Visualizer | |
| from detectron2.data import MetadataCatalog | |
| ''' | |
| url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg' | |
| r = requests.get(url1, allow_redirects=True) | |
| open("city1.jpg", 'wb').write(r.content) | |
| url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg' | |
| r = requests.get(url2, allow_redirects=True) | |
| open("city2.jpg", 'wb').write(r.content) | |
| ''' | |
| model_name='./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner.yaml' | |
| # model = model_zoo.get(model_name, trained=True) | |
| cfg = get_cfg() | |
| # add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library | |
| cfg.merge_from_file(model_name) | |
| cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model | |
| # Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as w ell | |
| cfg.MODEL.WEIGHTS = './output_3x_transfiner_r50.pth' | |
| if not torch.cuda.is_available(): | |
| cfg.MODEL.DEVICE='cpu' | |
| predictor = DefaultPredictor(cfg) | |
| def inference(image): | |
| img = np.array(image) | |
| outputs = predictor(img) | |
| v = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2) | |
| out = v.draw_instance_predictions(outputs["instances"].to("cpu")) | |
| return out.get_image() | |
| title = "Mask Transfiner R50 model" | |
| description = "demo for Mask Transfiner based on R50-FPN. 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='https://arxiv.org/abs/2012.07177'>Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022</a> | <a href='https://github.com/SysCV/transfiner'>Mask Transfiner Github</a></p>" | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="pil", label="Input")], | |
| gr.outputs.Image(type="numpy", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ["demo/sample_imgs/000000132408.jpg"], | |
| ["demo/sample_imgs/000000176037.jpg"], | |
| ["demo/sample_imgs/000000018737.jpg"], | |
| ["demo/sample_imgs/000000224200.jpg"], | |
| ["demo/sample_imgs/000000558073.jpg"], | |
| ["demo/sample_imgs/000000404922.jpg"], | |
| ["demo/sample_imgs/000000252776.jpg"], | |
| ["demo/sample_imgs/000000482477.jpg"], | |
| ["demo/sample_imgs/000000344909.jpg"] | |
| ]).launch() | |