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import gradio as gr
from PIL import Image
import numpy as np
from io import BytesIO
import glob
import os
import time
from data.dataset import load_itw_samples, crop_
import torch
import cv2
import os
import numpy as np
from models.model import TRGAN
from params import *
from torch import nn
from data.dataset import get_transform
import pickle
from PIL import Image
import tqdm
import shutil
wellcomingMessage = """
<h1>💥 Handwriting Synthesis - Generate text in anyone's handwriting 💥 </h1>
<p>🚀 This app is a demo for the ICCV'21 paper "Handwriting Transformer". Visit our github paper for more information - <a href="https://github.com/ankanbhunia/Handwriting-Transformers" target="_blank">https://github.com/ankanbhunia/Handwriting-Transformers</a></p>
<p>🚀 You can either choose from an existing style gallery or upload your own handwriting. If you choose to upload, please ensure that you provide a sufficient number of (~15) cropped handwritten word images for the model to work effectively. The demo is made available for research purposes, and any other use is not intended.</p>
"""
model_path = 'files/iam_model.pth'
batch_size = 1
print ('(1) Loading model...')
model = TRGAN(batch_size = batch_size)
model.netG.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')) )
print (model_path+' : Model loaded Successfully')
model.eval()
# Define a function to generate an image based on text and images
def generate_image(text,folder, _ch3, images):
# Your image generation logic goes here (replace with your actual implementation)
# For demonstration purposes, we'll just concatenate the uploaded images horizontally.
if images:
style_inputs, width_length = load_itw_samples(images)
elif folder:
style_inputs, width_length = load_itw_samples(folder)
else:
return None
# Load images
text = text.replace("\n", "").replace("\t", "")
text_encode = [j.encode() for j in text.split(' ')]
eval_text_encode, eval_len_text = model.netconverter.encode(text_encode)
eval_text_encode = eval_text_encode.to(DEVICE).repeat(batch_size, 1, 1)
input_styles, page_val = model._generate_page(style_inputs.to(DEVICE).clone(), width_length, eval_text_encode, eval_len_text, no_concat = True)
page_val = crop_(page_val[0]*255)
input_styles = crop_(input_styles[0]*255)
max_width = max(page_val.shape[1],input_styles.shape[1])
if page_val.shape[1]!=max_width:
page_val = np.concatenate([page_val, np.ones((page_val.shape[0],max_width-page_val.shape[1]))*255], 1)
else:
input_styles = np.concatenate([input_styles, np.ones((input_styles.shape[0],max_width-input_styles.shape[1]))*255], 1)
upper_pad = np.ones((45,input_styles.shape[1]))*255
input_styles = np.concatenate([upper_pad, input_styles], 0)
page_val = np.concatenate([upper_pad, page_val], 0)
page_val = Image.fromarray(page_val).convert('RGB')
input_styles = Image.fromarray(input_styles).convert('RGB')
return input_styles, page_val
# Define Gradio Interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(value = "In the quiet hum of everyday life, the dance of existence unfolds. Time, an ever-flowing river, carries the stories of triumph and heartache. Each fleeting moment is a brushstroke on the canvas of our memories.",label = "Input text"),
gr.Dropdown(value = "files/example_data/style-30", choices=glob.glob('files/example_data/*'), label="Choose from provided writer styles"),
gr.Markdown("### OR"),
gr.File(label="Upload multiple word images", file_count="multiple")
],
outputs=[#gr.Markdown("## Output"),
gr.Image(type="pil", label="Style Image"),
gr.Image(type="pil", label="Generated Image")],
description = wellcomingMessage,
thumbnail = "Handwriting Synthesis - Mimic anyone's handwriting!"
)
# Launch the Gradio Interface
iface.launch(debug=True, share=True)
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