app.py

import os
import subprocess
import sys

import cv2
import gdown
from PIL import Image
import numpy as np
import streamlit as st
import torch
from torchvision import transforms


def setup_env(path='Variations-of-SFANet-for-Crowd-Counting'):
    if os.path.exists(path):
        return path
    subprocess.run(
        [
            'git',
            'clone',
            f'https://github.com/Pongpisit-Thanasutives/{path}.git',
            f'{path}',
        ],
        capture_output=True,
        check=True,
        )
    sys.path.append(path)
    with open(os.path.join(path, 'models', '__init__.py'), 'w') as f:
        f.write('')
    return path


def get_model(path, weights):
    from models import M_SFANet_UCF_QNRF

    model = M_SFANet_UCF_QNRF.Model()
    model.load_state_dict(
        torch.load(weights, map_location=torch.device('cpu')))
    return model.eval()


def download_weights(
    url='https://drive.google.com/uc?id=1fGuH4o0hKbgdP1kaj9rbjX2HUL1IH0oo',
    out="Paper's_weights_UCF_QNRF.zip",
):
    weights = "Paper's_weights_UCF_QNRF/best_M-SFANet*_UCF_QNRF.pth"
    if os.path.exists(weights):
        return weights
    gdown.download(url, out)
    subprocess.run(
        ['unzip', out],
        capture_output=True,
        check=True,
        )
    return weights


def transform_image(img):
    trans = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ])
    height, width = img.size[1], img.size[0]
    height = round(height / 16) * 16
    width = round(width / 16) * 16
    img = cv2.resize(np.array(img), (width, height), cv2.INTER_CUBIC)
    return trans(Image.fromarray(img))[None, :]


def main():
    st.write("Demo of [Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting](https://arxiv.org/abs/2003.05586)")  # noqa
    path = setup_env()
    weights = download_weights()
    model = get_model(path, weights)

    image_file = st.file_uploader(
        "Upload image", type=['png', 'jpg', 'jpeg'])
    if image_file is not None:
        image = Image.open(image_file).convert('RGB')
        st.image(image)
        density_map = model(transform_image(image))
        density_map_img = density_map.detach().numpy()[0].transpose(1, 2, 0)
        st.image(density_map_img / density_map_img.max())
        st.write("Estimated count: ", torch.sum(density_map).item())
    else:
        st.write("Example image to use that you can drag and drop:")
        st.image(Image.open('crowd.jpg').convert('RGB'))


main()