app.py

import sys
import pickle
import os
import numpy as np
import PIL.Image
import IPython.display
from IPython.display import Image
import matplotlib.pyplot as plt

import gradio as gr
import cv2

sys.path.insert(0, "StyleGAN2-GANbanales")

#from StyleGAN2-GANbanales import dnnlib
#from StyleGAN2-GANbanales import dnnlib.tflib as tflib

##############################################################################
# Generation functions

def seed2vec(Gs, seed):
    rnd = np.random.RandomState(seed)
    return rnd.randn(1, *Gs.input_shape[1:])

def init_random_state(Gs, seed):
    rnd = np.random.RandomState(seed) 
    noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
    tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]

def generate_image(Gs, z, truncation_psi, prefix="image", save=False, show=False):
    # Render images for dlatents initialized from random seeds.
    Gs_kwargs = {
        'output_transform': dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True),
        'randomize_noise': False
    }
    if truncation_psi is not None:
        Gs_kwargs['truncation_psi'] = truncation_psi

    label = np.zeros([1] + Gs.input_shapes[1][1:])
    images = Gs.run(z, label, **Gs_kwargs) # [minibatch, height, width, channel]

    if save == True:
      path = f"{prefix}.png"
      PIL.Image.fromarray(images[0], 'RGB').save(path)

    if show == True:
      return images[0]
      
##############################################################################
# Function concatenate

def concatenate(img_array):

  zeros = np.zeros([256,256,3], dtype=np.uint8)
  zeros.fill(255)
  white_img = zeros

  a = 1
  for i in img_array:
    cv2.imwrite('imagenes/' + str(a) + '.png', i)
    a+=1

  # 1 - 2 images
  if len(img_array) <= 2:
    row_img = img_array[0]
    for i in img_array[1:]:
      row_img = cv2.hconcat([row_img, i])
    
    final_img = row_img
  
  # 3 - 4 images
  elif len(img_array) >= 3 and len(img_array) <= 4:
    row1_img = img_array[0]
    for i in img_array[1:2]:
      row1_img = cv2.hconcat([row1_img, i])

    cv2.imwrite('imagenes/row1.png', row1_img)
    
    row2_img = img_array[2]
    for i in img_array[3:]:
      row2_img = cv2.hconcat([row2_img, i])

    cv2.imwrite('imagenes/row2_before.png', row2_img)
    
    for i in range(4-len(img_array)):
      row2_img = cv2.hconcat([row2_img, white_img])

    cv2.imwrite('imagenes/row2_after.png', row2_img)
    
    final_img = cv2.vconcat([row1_img, row2_img])

  # 5 - 6 images
  elif len(img_array) >= 4 and len(img_array) <= 6:
    row1_img = img_array[0]
    for i in img_array[1:3]:
      row1_img = cv2.hconcat([row1_img, i])
    
    row2_img = img_array[3]
    for i in img_array[4:]:
      row2_img = cv2.hconcat([row2_img, i])
    
    for i in range(6-len(img_array)):
      row2_img = cv2.hconcat([row2_img, white_img])
    
    final_img = cv2.vconcat([row1_img, row2_img])

  # 7 - 9 images
  elif len(img_array) >= 7:
    row1_img = img_array[0]
    for i in img_array[1:3]:
      row1_img = cv2.hconcat([row1_img, i])
    
    row2_img = img_array[3]
    for i in img_array[4:6]:
      row2_img = cv2.hconcat([row2_img, i])
    
    row3_img = img_array[6]
    for i in img_array[7:9]:
      row3_img = cv2.hconcat([row3_img, i])
    
    for i in range(9-len(img_array)):
      row3_img = cv2.hconcat([row3_img, white_img])
    
    final_img = cv2.vconcat([row1_img, row2_img])
    final_img = cv2.vconcat([final_img, row3_img])

  return final_img

##############################################################################
# Function initiate
def initiate(seed, n_imgs, text):

  pkl_file = "networks/experimento_2.pkl"
  tflib.init_tf()

  with open(pkl_file, 'rb') as pickle_file:
    _G, _D, Gs = pickle.load(pickle_file)

  img_array = []
  first_seed = seed

  for i in range(seed, seed+n_imgs):
    init_random_state(Gs, 10)
    z = seed2vec(Gs, seed)
    img = generate_image(Gs, z, 1.0, show=True)

    img_array.append(img)
    seed+=1

  final_img = concatenate(img_array)

  return final_img, "Imágenes generadas"
  
##############################################################################
# Gradio code

iface = gr.Interface(
    fn=initiate, 
    inputs=[gr.inputs.Slider(0, 99999999, "image"), gr.inputs.Slider(1, 9, "images"), "text"],
    outputs=["image", "text"],
    examples=[
        [40, 1, "Edificios al anochecer"],
        [37, 1, "Fuente de día"],
        [426, 1, "Edificios con cielo oscuro"],
        [397, 1, "Edificios de día"],
        [395, 1, "Edificios desde anfiteatro"],
        [281, 1, "Edificios con luces encendidas"],
        [230, 1, "Edificios con luces encendidas y vegetación"],
        [221, 1, "Edificios con vegetación"],
        [214, 1, "Edificios al atardecer con luces encendidas"],
        [198, 1, "Edificio al anochecer con luces en el pasillo"]
    ],
    title="GANbanales",
    description="Una GAN para generar imágenes del campus universitario de Rabanales, Córdoba."
)


if __name__ == "__main__":
    app, local_url, share_url = iface.launch(debug=True)