app.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
import os
import pathlib
import subprocess
import tarfile

if os.getenv('SYSTEM') == 'spaces':
    import mim

    mim.uninstall('mmcv-full', confirm_yes=True)
    mim.install('mmcv-full==1.5.2', is_yes=True)
    # mim.uninstall('mmdet', confirm_yes=True)
    # mim.install('mmdet==2.20.0', is_yes=True)

    subprocess.call('pip uninstall -y opencv-python'.split())
    subprocess.call('pip uninstall -y opencv-python-headless'.split())
    subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
    subprocess.call('conda install -c conda-forge pycocotools'.split())
    subprocess.call('pip install detectron2==0.5 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html'.split())

import cv2
import gradio as gr
import numpy as np

from mmdet.apis import init_detector, inference_detector
from utils import show_result
import mmcv 
from mmcv import Config
import os.path as osp 

DESCRIPTION = '''# OpenPSG

This is an official demo for [OpenPSG](https://github.com/Jingkang50/OpenPSG).
<img id="overview" alt="overview" src="https://camo.githubusercontent.com/880346b66831a8212074787ba9a2301b4d700bd8f765ca11e4845ac0ab34c230/68747470733a2f2f6c6976652e737461746963666c69636b722e636f6d2f36353533352f35323139333837393637375f373531613465306237395f6b2e6a7067" />
'''
FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=c-liangyu.openpsg" alt="visitor badge" />'


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    return parser.parse_args()


def update_input_image(image: np.ndarray) -> dict:
    if image is None:
        return gr.Image.update(value=None)
    scale = 1500 / max(image.shape[:2])
    if scale < 1:
        image = cv2.resize(image, None, fx=scale, fy=scale)
    return gr.Image.update(value=image)


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


def infer(model, input_image, num_rel):
    result = inference_detector(model, input_image)
    return show_result(input_image,
                        result,
                        is_one_stage=True,
                        num_rel=num_rel,
                        show=True
                        )


def main():
    args = parse_args()

    model_ckt ='OpenPSG/checkpoints/epoch_60.pth'
    cfg = Config.fromfile('OpenPSG/configs/psgtr/psgtr_r50_psg_inference.py')

    model = init_detector(cfg, model_ckt, device=args.device)

    with gr.Blocks(theme=args.theme, css='style.css') as demo:
        gr.Markdown(DESCRIPTION)

        with gr.Row():
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(label='Input Image', type='numpy')
                with gr.Group():
                    with gr.Row():
                        num_rel = gr.Slider(
                            5,
                            100,
                            step=5,
                            value=20,
                            label='Number of Relations')
                with gr.Row():
                    run_button = gr.Button(value='Run')
                    # prediction_results = gr.Variable()
            with gr.Column():
                with gr.Row():
                    # visualization = gr.Image(label='Result', type='numpy')
                    result = gr.Gallery(label='Result', type='numpy')

        with gr.Row():
            paths = sorted(pathlib.Path('images').rglob('*.jpg'))
            example_images = gr.Dataset(components=[input_image],
                                        samples=[[path.as_posix()]
                                                 for path in paths])

        gr.Markdown(FOOTER)

        input_image.change(fn=update_input_image,
                           inputs=input_image,
                           outputs=input_image)
        
        run_button.click(fn=infer,
                         inputs=[
                             model, input_image
                         ],
                         outputs=result)

        example_images.click(fn=set_example_image,
                             inputs=example_images,
                             outputs=input_image)

    demo.launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()