Delete app.py

app.py

@@ -1,179 +0,0 @@
-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")
-
-import dnnlib
-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, share=True)