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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 | |
model_name='./configs/transfiner/mask_rcnn_R_101_FPN_3x_deform.yaml' | |
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 | |
cfg.VIS_PERIOD = 100 | |
# 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' | |
cfg.MODEL.WEIGHTS = './output_3x_transfiner_r101_deform.pth' | |
if not torch.cuda.is_available(): | |
cfg.MODEL.DEVICE='cpu' | |
predictor = DefaultPredictor(cfg) | |
def inference(image): | |
width, height = image.size | |
if width > 1300: | |
ratio = float(height) / float(width) | |
width = 1300 | |
height = int(ratio * width) | |
image = image.resize((width, height)) | |
img = np.asarray(image) | |
#img = np.array(image) | |
outputs = predictor(img) | |
v = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0])) | |
out = v.draw_instance_predictions(outputs["instances"].to("cpu")) | |
return out.get_image() | |
title = "Mask Transfiner [CVPR, 2022]" | |
description = "Demo for <a target='_blank' href='https://arxiv.org/abs/2111.13673'>Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022</a> based on R101-FPN. To use it, simply upload your image, or click one of the examples to load them. Note that it runs in <b>CPU environment</b> provided by Hugging Face so the processing speed may be slow." | |
article = "<p style='text-align: center'><a target='_blank' href='https://arxiv.org/abs/2111.13673'>Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022</a> | <a target='_blank' href='https://github.com/SysCV/transfiner'>Mask Transfiner Github Code</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/000000131444.jpg"], | |
["demo/sample_imgs/000000157365.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() | |