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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 | |
| from datetime import datetime | |
| 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> | |
| <p>🚀 Some examples of cropped handwritten word images can be found <a href="https://huggingface.co./spaces/ankankbhunia/HWT/tree/main/files/example_data/style-1" target="_blank">here</a>. | |
| """ | |
| 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. | |
| try: | |
| text_copy = text | |
| if images: | |
| style_log = images | |
| style_inputs, width_length = load_itw_samples(images) | |
| elif folder: | |
| style_log = 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') | |
| current_datetime = datetime.now() | |
| formatted_datetime = current_datetime.strftime("%Y-%m-%d %H:%M:%S") | |
| print (f'{formatted_datetime}: input_string - {text_copy}, style_input - {style_log}\n') | |
| return input_styles, page_val | |
| except: | |
| print ('ERROR! Try again.') | |
| return None, None | |
| input_text_string = "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." | |
| # Define Gradio Interface | |
| iface = gr.Interface( | |
| fn=generate_image, | |
| inputs=[ | |
| gr.Textbox(value = input_text_string, 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!", | |
| # examples = [["The sun dipped below the horizon, painting the sky in hues of orange and pink. A gentle breeze rustled the leaves, whispering secrets to the ancient trees. In that fleeting moment, nature's beauty spoke louder than words ever could.", 'files/example_data/style-30', None, None], | |
| # ["Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum eget lectus eu ex iaculis tristique. Nullam vestibulum, odio vel tincidunt aliquet, sapien quam efficitur risus, nec hendrerit justo quam eget elit. Sed vel augue a lacus facilisis venenatis. ", 'files/example_data/style-31', None, None], | |
| # ["The sun dipped below the horizon, painting the sky in hues of orange and pink. A gentle breeze rustled the leaves, whispering secrets to the ancient trees. In that fleeting moment, nature's beauty spoke louder than words ever could.", None, None, glob.glob('files/example_data/style-1/*')], | |
| # ["Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum eget lectus eu ex iaculis tristique. Nullam vestibulum, odio vel tincidunt aliquet, sapien quam efficitur risus, nec hendrerit justo quam eget elit. Sed vel augue a lacus facilisis venenatis. ", None, None, glob.glob('files/example_data/style-2/*')]] | |
| ) | |
| # Launch the Gradio Interface | |
| iface.launch(debug=True, share=True) | |