app.py

#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_X_101_32x8d_FPN_3x.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.resize((1024,1024)))
    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 = "Detectron2-MaskRCNN X101"
description = "demo for Detectron2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\
    </br><b>Model: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml</b>"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.07177'>Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation</a> | <a href='https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md'>Detectron model ZOO</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/000000224200.jpg"],
            ["demo/sample_imgs/000000344909.jpg"]
        ]).launch()