streamlit_project/streamlit.py - main - RainbowRay/program_engineering_lab_07

streamlit_project/streamlit.py
91 строка3 KB
Frogog
fix: flake8 errors
29 май 2026, 22:44
29 май 2026, 22:44f695ce2
from PIL import Image
from skimage import io
import numpy as np
import torch
import torch.nn.functional as F
from transformers import AutoModelForImageSegmentation
from torchvision.transforms.functional import normalize
import streamlit as st
from io import BytesIO


model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True)


def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = F.interpolate(torch.unsqueeze(im_tensor, 0), size=model_input_size, mode='bilinear')
    image = torch.divide(im_tensor, 255.0)
    image = normalize(image, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
    return image


def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear'), 0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result - mi) / (ma - mi)
    im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
    im_array = np.squeeze(im_array)
    return im_array


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)


def show_image(path: str):
    img = Image.open(path)
    img.show()


def prepare_input(path: str):
    orig_im = io.imread(path)
    orig_im_size = orig_im.shape[0:2]
    model_input_size = [1024, 1024]
    image = preprocess_image(orig_im, model_input_size).to(device)
    result = model(image)
    result_image = postprocess_image(result[0][0], orig_im_size)
    pil_mask_im = Image.fromarray(result_image)
    orig_image = Image.open(path)
    no_bg_image = orig_image.copy()
    no_bg_image.putalpha(pil_mask_im)
    no_bg_image.show()


def io_file_input(file_object):
    file_data = file_object.getvalue()
    new_bytes_io = BytesIO(file_data)

    orig_image = Image.open(new_bytes_io)
    orig_im = np.array(orig_image)

    if orig_im.shape[-1] == 4:
        orig_im = orig_im[..., :3]

    orig_im_size = orig_im.shape[0:2]
    model_input_size = [1024, 1024]

    image = preprocess_image(orig_im, model_input_size).to(device)
    with torch.no_grad():
        result = model(image)
    result_image = postprocess_image(result[0][0], orig_im_size)

    pil_mask_im = Image.fromarray(result_image)

    new_bytes_io_result = BytesIO(file_data)
    orig_image_rgba = Image.open(new_bytes_io_result).convert("RGBA")

    no_bg_image = orig_image_rgba.copy()
    no_bg_image.putalpha(pil_mask_im)
    return no_bg_image


st.title('Удаление фона на фото')
uploaded_file = st.file_uploader(label='Загрузите фотографию:', type=["jpg", "jpeg", "png"])
if st.button('Вырезать фон'):
    if uploaded_file is not None:
        res = io_file_input(uploaded_file)
        st.image(res)