app.py

import torch
import argparse
import numpy as np
from helper import *
from config.GlobalVariables import *
from SynthesisNetwork import SynthesisNetwork
from DataLoader import DataLoader
import convenience
import gradio as gr

#@title Demo
device = 'cpu'
num_samples = 10

net = SynthesisNetwork(weight_dim=256, num_layers=3).to(device)

if not torch.cuda.is_available():
    net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device))["model_state_dict"])
    

dl = DataLoader(num_writer=1, num_samples=10, divider=5.0, datadir='./data/writers')


writer_options = [5, 14, 15, 16, 17, 22, 25, 80, 120, 137, 147, 151]
all_loaded_data = []
avail_char = "0 1 2 3 4 5 6 7 8 9 a b c d e f g h i j k l m n o p q r s t u v w x y z A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ! ? \" ' * + - = : ; , . < > \ / [ ] ( ) # $ % &"
avail_char_list = avail_char.split(" ")
for writer_id in [120, 80]:
    loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(num_samples)))
    all_loaded_data.append(loaded_data)

default_loaded_data = all_loaded_data[-1]
mdn_words = []
mdn_mean_Ws = []
all_word_mdn_Ws = []
all_word_mdn_Cs = []

# data for writer interpolation
writer_words = []
writer_mean_Ws = []
all_word_writer_Ws = []
all_word_writer_Cs = []
weight = 0.7

def update_target_word(target_word):
    writer_words.clear()
    for word in target_word.split(" "):
        writer_words.append(word)

    writer_mean_Ws.clear()
    for loaded_data in all_loaded_data:
        mean_global_W = convenience.get_mean_global_W(net, loaded_data, device)
        writer_mean_Ws.append(mean_global_W)

    all_word_writer_Ws.clear()
    all_word_writer_Cs.clear()
    for word in writer_words:
        all_writer_Ws, all_writer_Cs = convenience.get_DSD(net, word, writer_mean_Ws, all_loaded_data, device)
        all_word_writer_Ws.append(all_writer_Ws)
        all_word_writer_Cs.append(all_writer_Cs)

    return update_writer_slider(weight)
    

# for writer interpolation
def update_writer_slider(val):
    global weight
    weight = val
    net.clamp_mdn = 0
    im = convenience.draw_words(writer_words, all_word_writer_Ws, all_word_writer_Cs, [1 - weight, weight], net)
    return im.convert("RGB")

def update_chosen_writers(writer1, writer2):
    net.clamp_mdn = 0
    id1, id2 = int(writer1.split(" ")[1]), int(writer2.split(" ")[1])
    all_loaded_data.clear()
    for writer_id in [id1, id2]:
        loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(num_samples)))
        all_loaded_data.append(loaded_data)

    return gr.Slider.update(label=f"{writer1} vs. {writer2}"), update_writer_slider(weight)

# for character blend
def interpolate_chars(c1, c2, weight):
    """Generates an image of handwritten text based on target_sentence"""

    net.clamp_mdn = 0

    letters = [c1, c2]
    character_weights = [1 - weight, weight]
    M = len(letters)
    mean_global_W = convenience.get_mean_global_W(net, all_loaded_data[0], device)

    all_Cs = torch.zeros(1, M, convenience.L, convenience.L)
    for i in range(M):  # get corners of grid
        W_vector, char_matrix =  convenience.get_DSD(net, letters[i], [mean_global_W], [default_loaded_data], device)
        all_Cs[:, i, :, :] = char_matrix

    all_Ws = mean_global_W.reshape(1, 1, convenience.L)

    all_W_c = convenience.get_character_blend_W_c(character_weights, all_Ws, all_Cs)
    all_commands = convenience.get_commands(net, letters[0], all_W_c)

    width = 60
    x_offset = 325
    im = Image.fromarray(np.zeros([160, 750]))
    dr = ImageDraw.Draw(im)
    for [x, y, t] in all_commands:
        if t == 0:
            dr.line((
                px + width/2 + x_offset,
                py - width/2,  # letters are shifted down for some reason
                x + width/2 + + x_offset,
                y - width/2), 255, 1)
        px, py = x, y

        
    return im.convert("RGB")

def choose_blend_chars(c1, c2):
   return gr.Slider.update(label=f"'{c1}' vs. '{c2}'")

# for MDN

def update_mdn_word(target_word):
    mdn_words.clear()
    for word in target_word.split(" "):
        mdn_words.append(word)

    mdn_mean_Ws.clear()
    mean_global_W = convenience.get_mean_global_W(net, default_loaded_data, device)
    mdn_mean_Ws.append(mean_global_W)

    all_word_mdn_Ws.clear()
    all_word_mdn_Cs.clear()
    for word in mdn_words:
        all_writer_Ws, all_writer_Cs = convenience.get_DSD(net, word, mdn_mean_Ws, [default_loaded_data], device)
        all_word_mdn_Ws.append(all_writer_Ws)
        all_word_mdn_Cs.append(all_writer_Cs)

    return sample_mdn(net.scale_sd, net.clamp_mdn)


def sample_mdn(maxs, maxr):
    net.clamp_mdn = maxr
    net.scale_sd = maxs
    im = convenience.draw_words(mdn_words, all_word_mdn_Ws, all_word_mdn_Cs, [1], net)
    return im.convert("RGB")


update_target_word("hello world")
update_mdn_word("hello world")

with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Blend Writers"):
          target_word = gr.Textbox(label="Target Word", value="hello world", max_lines=1)
          with gr.Row():
              left_ratio_options = ["Style " + str(id) for i, id in enumerate(writer_options) if i % 2 == 0]
              right_ratio_options = ["Style " + str(id) for i, id in enumerate(writer_options) if i % 2 == 1]
              with gr.Column():
                  writer1 = gr.Radio(left_ratio_options, value="Style 120", label="Style for first writer")
              with gr.Column():
                  writer2 = gr.Radio(right_ratio_options, value="Style 80", label="Style for second writer")
          with gr.Row():
              writer_slider = gr.Slider(0, 1, value=0.7, label="Style 120 vs. Style 80")
          with gr.Row():
              writer_submit = gr.Button("Submit")
          with gr.Row():
              writer_default_image = convenience.sample_blended_writers([0.3, 0.7], "hello world", net, all_loaded_data, device).convert("RGB")
              writer_output = gr.Image(writer_default_image)
          
          writer_submit.click(fn=update_writer_slider, inputs=[writer_slider], outputs=[writer_output])
          writer_slider.change(fn=update_writer_slider, inputs=[writer_slider], outputs=[writer_output])
          target_word.submit(fn=update_target_word, inputs=[target_word], outputs=[writer_output])

          writer1.change(fn=update_chosen_writers, inputs=[writer1, writer2], outputs=[writer_slider, writer_output])
          writer2.change(fn=update_chosen_writers, inputs=[writer1, writer2], outputs=[writer_slider, writer_output])
    

        with gr.TabItem("Blend Characters"):
            with gr.Row():
                with gr.Column():
                    char1 = gr.Dropdown(choices=avail_char_list, value="y", label="Character 1")
                with gr.Column():
                    char2 = gr.Dropdown(choices=avail_char_list, value="s", label="Character 2")
            with gr.Row():
               char_slider = gr.Slider(0, 1, value=0.7, label="'y' vs. 's'")
            with gr.Row():
               char_default_image = convenience.sample_blended_chars([0.3, 0.7], ["y", "s"], net, [default_loaded_data], device).convert("RGB")
               char_output = gr.Image(char_default_image)
            
            char_slider.change(fn=interpolate_chars, inputs=[char1, char2, char_slider], outputs=[char_output])

            char1.change(fn=choose_blend_chars, inputs=[char1, char2], outputs=[char_slider])
            char2.change(fn=choose_blend_chars, inputs=[char1, char2], outputs=[char_slider])
        
        
        with gr.TabItem("Add Randomness"):
          mdn_word = gr.Textbox(label="Target Word", value="hello world", max_lines=1)
          '''
          with gr.Row():
              radio_options3 = ["Writer " + str(n) for n in writer_options]
              writer = gr.Radio(radio_options3, value="Writer 80", label="Style for Writer")
              writer.change(fn=new_writer_mdn, inputs=[writer, slider3, slider4], outputs=[output])
          '''
          with gr.Row():
            with gr.Column():
              max_rand = gr.Slider(0, 1, value=1, label="Maximum Randomness")
            with gr.Column():
              scale_rand = gr.Slider(0, 3, value=0.5, label="Scale of Randomness")
          with gr.Row():
              mdn_sample_button = gr.Button(value="Resample!")
          with gr.Row():
              default_im = convenience.mdn_single_sample("hello world", 0.5, 1, net, [default_loaded_data], device).convert('RGB')
              mdn_output = gr.Image(default_im)

          max_rand.change(fn=sample_mdn, inputs=[scale_rand, max_rand], outputs=[mdn_output])
          scale_rand.change(fn=sample_mdn, inputs=[scale_rand, max_rand], outputs=[mdn_output])        
          mdn_sample_button.click(fn=sample_mdn, inputs=[scale_rand, max_rand], outputs=[mdn_output])
          mdn_word.submit(fn=update_mdn_word, inputs=[mdn_word], outputs=[mdn_output])

demo.launch()