Create app.py

app.py

@@ -0,0 +1,179 @@
+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)