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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()
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