app.py

import streamlit as st
from PIL import Image, ImageOps
import cv2
import numpy as np
import random
import time
import seaborn as sns

from cv_funcs import *
from torchvision_funcs import *


from backgroundremover.utilities import download_download_files_from_github
import torch
import os
from hsh.library.hash import Hasher
from torchvision import transforms

def load_model(model_name: str = "u2net"):
    hasher = Hasher()

    model = {
        'u2netp': (u2net.U2NETP,
                   'e4f636406ca4e2af789941e7f139ee2e',
                   '1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy',
                   'U2NET_PATH'),
        'u2net': (u2net.U2NET,
                  '09fb4e49b7f785c9f855baf94916840a',
                  '1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ',
                  'U2NET_PATH'),
        'u2net_human_seg': (u2net.U2NET,
                            '347c3d51b01528e5c6c071e3cff1cb55',
                            '1-Yg0cxgrNhHP-016FPdp902BR-kSsA4P',
                            'U2NET_PATH')
    }[model_name]

    if model_name == "u2net":
        net = u2net.U2NET(3, 1)
        path = os.environ.get(
            "U2NET_PATH",
            os.path.expanduser(os.path.join("~", ".u2net", model_name + ".pth")),
        )
        if (
            not os.path.exists(path)
            or hasher.md5(path) != "09fb4e49b7f785c9f855baf94916840a"
        ):
            download_downloadfiles_from_github(
                path, model_name
            )
    else:
        print("Choose between u2net, u2net_human_seg or u2netp", file=sys.stderr)

    try:
        if torch.cuda.is_available():
            net.load_state_dict(torch.load(path))
            net.to(torch.device("cuda"))
        else:
            net.load_state_dict(
                torch.load(
                    path,
                    map_location="cpu",
                )
            )
    except FileNotFoundError:
        raise FileNotFoundError(
            errno.ENOENT, os.strerror(errno.ENOENT), model_name + ".pth"
        )

    net.eval()

    return net

def norm_pred(d):
    ma = torch.max(d)
    mi = torch.min(d)
    dn = (d - mi) / (ma - mi)

    return dn


def preprocess(image):
    label_3 = np.zeros(image.shape)
    label = np.zeros(label_3.shape[0:2])

    if 3 == len(label_3.shape):
        label = label_3[:, :, 0]
    elif 2 == len(label_3.shape):
        label = label_3

    if 3 == len(image.shape) and 2 == len(label.shape):
        label = label[:, :, np.newaxis]
    elif 2 == len(image.shape) and 2 == len(label.shape):
        image = image[:, :, np.newaxis]
        label = label[:, :, np.newaxis]

    transform = transforms.Compose(
        [data_loader.RescaleT(320), data_loader.ToTensorLab(flag=0)]
    )
    sample = transform({"imidx": np.array([0]), "image": image, "label": label})

    return sample


def predict(net, item):
    sample = preprocess(item)

    with torch.no_grad():

        if torch.cuda.is_available():
            inputs_test = torch.cuda.FloatTensor(
                sample["image"].unsqueeze(0).cuda().float()
            )
        else:
            inputs_test = torch.FloatTensor(sample["image"].unsqueeze(0).float())

        d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)

        pred = d1[:, 0, :, :]
        predict = norm_pred(pred)

        predict = predict.squeeze()
        predict_np = predict.cpu().detach().numpy()
        img = Image.fromarray(predict_np * 255).convert("RGB")

        del d1, d2, d3, d4, d5, d6, d7, pred, predict, predict_np, inputs_test, sample

        return img

def remove_bg(img):
    img_arry = np.array(img)
    model = load_model(model_name="u2net")
    mask = predict(model, img_arry)
    mask = mask.resize(img.size)

    mask_arry = np.array(mask)
    mask_arry[mask_arry>0] = 1
    img_masked = Image.fromarray(cv2.multiply(img_arry, mask_arry))
    index_masked = np.where(np.array(mask)==0)
    return img_masked, index_masked

@st.cache
def show_generated_image(image):
    st.image(image)
    
@st.cache(suppress_st_warning=True)
def randomize_palette_colors(n_rows, n_cols, palettes=['Set1', 'Set3', 'Spectral'], seed=time.time(), n_times=10):
    random.seed(seed)
    colors = [sns.color_palette(palette, n_rows*n_cols*n_times) for palette in palettes]
    _ = [random.shuffle(color) for color in colors]
    return colors

@st.cache(suppress_st_warning=True)
def remove_image_background(image):
    #return deeplabv3_remove_bg(image)
    return remove_bg(img)

title = 'Andy Warhol like Image Generator'
st.set_page_config(page_title=title, page_icon='favicon.jpeg', layout='centered')
st.title(title)
uploaded_file = st.file_uploader('Choose an image file')
if uploaded_file is None: uploaded_file = './sample.jpg'

if uploaded_file is not None:
    im = Image.open(uploaded_file)
    im.thumbnail((1000, 1000),resample=Image.BICUBIC) # resize
    
    is_masked = st.checkbox('With background masking? (3 colors)')
    if is_masked:
       im_masked, index_masked = remove_image_background(im)
       st.image(im_masked, caption='Masked image')        
    else: st.image(im, caption='Original')
    
    im_gray =  np.array(im.convert('L'))
    thresh, _img = cv2.threshold(im_gray, 0, 255, cv2.THRESH_OTSU)
    
    n_rows, n_cols = st.number_input('Rows', value=3), st.number_input('Columns', value=3)

    thresh = st.slider('Threshold', value=thresh, min_value=0.0, max_value=255.0)        
    colors = randomize_palette_colors(n_rows, n_cols, seed=0)
    
    if st.button('Shuffle colors'):
        colors = randomize_palette_colors(n_rows, n_cols, seed=time.time())            
    
    if True or st.button('Generate'):
        ims_generated = []

        for row in range(n_rows):
            for col in range(n_cols):
                i_color = n_cols * row + col
                rgbs = [np.array(color[i_color])*np.array([255, 255, 255]).tolist() for color in colors]
                ims_col = np.empty((*im_gray.shape, 3))
                for i in range(3): # RGB
                     ims_col[:, :, i] = (im_gray <= thresh) * rgbs[0][i] + (im_gray > thresh) * rgbs[1][i]
                     if is_masked: ims_col[:, :, i][index_masked] = rgbs[2][i]
                if col == 0:
                    im_col_concat = Image.fromarray(ims_col.astype(np.uint8))
                else:
                    im_col_concat = get_concat_h(im_col_concat, Image.fromarray(ims_col.astype(np.uint8)))
            if row == 0:
                im_generated = im_col_concat
            else:
                im_generated = get_concat_v(im_generated, im_col_concat)
#     if 'im_generated' in locals():
        st.image(im_generated)