app.py

# Import general purpose libraries
import os, sys, re
import streamlit as st
import PIL
from PIL import Image
import cv2
import numpy as np
import uuid
from zipfile import ZipFile, ZIP_DEFLATED
from io import BytesIO
from stqdm import stqdm

# Import util functions from deoldify
# NOTE:  This must be the first call in order to work properly!
from deoldify import device
from deoldify.device_id import DeviceId
#choices:  CPU, GPU0...GPU7
device.set(device=DeviceId.CPU)
from deoldify.visualize import *

# Import util functions from app_utils
from app_utils import get_model_bin



####### INPUT PARAMS ###########
model_folder = 'models/'
max_img_size = 800
################################

@st.cache(allow_output_mutation=True, show_spinner=False)
def load_model(model_dir, option):
    if option.lower() == 'artistic':
        model_url = 'https://data.deepai.org/deoldify/ColorizeArtistic_gen.pth'
        get_model_bin(model_url, os.path.join(model_dir, "ColorizeArtistic_gen.pth"))
        colorizer = get_image_colorizer(artistic=True)
    elif option.lower() == 'stable':
        model_url = "https://www.dropbox.com/s/usf7uifrctqw9rl/ColorizeStable_gen.pth?dl=0"
        get_model_bin(model_url, os.path.join(model_dir, "ColorizeStable_gen.pth"))
        colorizer = get_image_colorizer(artistic=False)

    return colorizer

def resize_img(input_img, max_size):
    img = input_img.copy()
    img_height, img_width = img.shape[0],img.shape[1]

    if max(img_height, img_width) > max_size:
        if img_height > img_width:
            new_width = img_width*(max_size/img_height)
            new_height = max_size
            resized_img = cv2.resize(img,(int(new_width), int(new_height)))
            return resized_img

        elif img_height <= img_width:
            new_width = img_height*(max_size/img_width)
            new_height = max_size
            resized_img = cv2.resize(img,(int(new_width), int(new_height)))
            return resized_img

    return img

def get_image_download_link(img, filename, button_text):
    button_uuid = str(uuid.uuid4()).replace('-', '')
    button_id = re.sub('\d+', '', button_uuid)

    buffered = BytesIO()
    img.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode()

    return get_button_html_code(img_str, filename, 'txt', button_id, button_text)

def get_button_html_code(data_str, filename, filetype, button_id, button_txt='Download file'):
    custom_css = f""" 
    <style>
        #{button_id} {{
            background-color: rgb(255, 255, 255);
            color: rgb(38, 39, 48);
            padding: 0.25em 0.38em;
            position: relative;
            text-decoration: none;
            border-radius: 4px;
            border-width: 1px;
            border-style: solid;
            border-color: rgb(230, 234, 241);
            border-image: initial;

        }} 
        #{button_id}:hover {{
            border-color: rgb(246, 51, 102);
            color: rgb(246, 51, 102);
        }}
        #{button_id}:active {{
            box-shadow: none;
            background-color: rgb(246, 51, 102);
            color: white;
            }}
    </style> """
    
    href =  custom_css + f'<a href="data:file/{filetype};base64,{data_str}" id="{button_id}" download="{filename}">{button_txt}</a>'
    return href

def display_single_image(uploaded_file, img_size=800):
    print('Type: ', type(uploaded_file))
    st_title_message.markdown("**Processing your image, please wait** ⌛")
    img_name = uploaded_file.name

    # Open the image
    pil_img = PIL.Image.open(uploaded_file)
    img_rgb = np.array(pil_img)
    resized_img_rgb = resize_img(img_rgb, img_size)
    resized_pil_img = PIL.Image.fromarray(resized_img_rgb)

    # Send the image to the model
    output_pil_img = colorizer.plot_transformed_pil_image(resized_pil_img, render_factor=35, compare=False)

    # Plot images
    st_input_img.image(resized_pil_img, 'Input image', use_column_width=True)
    st_output_img.image(output_pil_img, 'Output image', use_column_width=True)

    # Show download button
    st_download_button.markdown(get_image_download_link(output_pil_img, img_name, 'Download Image'), unsafe_allow_html=True)

    # Reset the message
    st_title_message.markdown("**To begin, please upload an image** 👇")

def process_multiple_images(uploaded_files, img_size=800):

    num_imgs = len(uploaded_files)

    output_images_list = []
    img_names_list = []
    idx = 1

    st_progress_bar.progress(0)

    for idx, uploaded_file in stqdm(enumerate(uploaded_files, start=1), st_container=st_progress_bar):
        st_title_message.markdown("**Processing image {}/{}. Please wait** ⌛".format(idx,
                                                                                    num_imgs))

        img_name = uploaded_file.name
        img_type = uploaded_file.type

        # Open the image
        pil_img = PIL.Image.open(uploaded_file)
        img_rgb = np.array(pil_img)
        resized_img_rgb = resize_img(img_rgb, img_size)
        resized_pil_img = PIL.Image.fromarray(resized_img_rgb)

        # Send the image to the model
        output_pil_img = colorizer.plot_transformed_pil_image(resized_pil_img, render_factor=35, compare=False)

        output_images_list.append(output_pil_img)
        img_names_list.append(img_name.split('.')[0])

        percent = int((idx / num_imgs)*100)
        st_progress_bar.progress(percent)

    # Zip output files
    zip_path = 'processed_images.zip'
    zip_buf = zip_multiple_images(output_images_list, img_names_list, zip_path)

    st_download_button.download_button(
        label='Download ZIP file',
        data=zip_buf.read(),
        file_name=zip_path,
        mime="application/zip"
    )

    # Show message
    st_title_message.markdown("**Images are ready for download** 💾")

def zip_multiple_images(pil_images_list, img_names_list, dest_path):
    # Create zip file on memory
    zip_buf = BytesIO()

    with ZipFile(zip_buf, 'w', ZIP_DEFLATED) as zipObj:
        for pil_img, img_name in zip(pil_images_list, img_names_list):
            with BytesIO() as output:
                # Save image in memory
                pil_img.save(output, format="PNG")
                
                # Read data
                contents = output.getvalue()

                # Write it to zip file
                zipObj.writestr(img_name+".png", contents)
    zip_buf.seek(0)
    return zip_buf



###########################
###### STREAMLIT CODE #####
###########################

# General configuration
# st.set_page_config(layout="centered")
st.set_page_config(layout="wide")
st.set_option('deprecation.showfileUploaderEncoding', False)
st.markdown('''
<style>
    .uploadedFile {display: none}
<style>''',
unsafe_allow_html=True)

# Main window configuration
st.title("Black and white colorizer")
st.markdown("This app puts color into your black and white pictures")
st_title_message = st.empty()
st_progress_bar = st.empty()
st_file_uploader = st.empty()
st_input_img = st.empty()
st_output_img = st.empty()
st_download_button = st.empty()

st_title_message.markdown("**Model loading, please wait** ⌛")

# # Sidebar
st_color_option = st.sidebar.selectbox('Select colorizer mode',
                                    ('Artistic', 'Stable'))
                                    
# st.sidebar.title('Model parameters')
# det_conf_thres = st.sidebar.slider("Detector confidence threshold", 0.1, 0.9, value=0.5, step=0.1)
# det_nms_thres = st.sidebar.slider("Non-maximum supression IoU", 0.1, 0.9, value=0.4, step=0.1)

# Load models
try:
    print('before loading the model')
    colorizer = load_model(model_folder, st_color_option)
    print('after loading the model')

except Exception as e: 
    colorizer = None
    print('Error while loading the model. Please refresh the page')
    print(e)
    st_title_message.markdown("**Error while loading the model. Please refresh the page**")

if colorizer is not None:
    st_title_message.markdown("**To begin, please upload an image** 👇")

    #Choose your own image
    uploaded_files = []
    uploaded_files = st_file_uploader.file_uploader("Upload a black and white photo", 
                                                    type=['png', 'jpg', 'jpeg'],
                                                    accept_multiple_files=True)

    if len(uploaded_files) == 1:
        display_single_image(uploaded_files[0], max_img_size)
    elif len(uploaded_files) > 1:
        process_multiple_images(uploaded_files, max_img_size)