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| 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 | |
| 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 | |
| # 1 - 2 images | |
| if len(img_array) <= 2: | |
| row_img = img_array[0] | |
| for i in img_array[1:]: | |
| row_img = np.hstack((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 = np.hstack((row1_img, i)) | |
| row2_img = img_array[2] | |
| for i in img_array[3:]: | |
| row2_img = np.hstack((row2_img, i)) | |
| for i in range(4-len(img_array)): | |
| row2_img = np.hstack((row2_img, white_img)) | |
| final_img = np.vstack((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, "Im谩genes 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 d铆a"], | |
| [426, 1, "Edificios con cielo oscuro"], | |
| [397, 1, "Edificios de d铆a"], | |
| [395, 1, "Edificios desde anfiteatro"], | |
| [281, 1, "Edificios con luces encendidas"], | |
| [230, 1, "Edificios con luces encendidas y vegetaci贸n"], | |
| [221, 1, "Edificios con vegetaci贸n"], | |
| [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 im谩genes del campus universitario de Rabanales, C贸rdoba." | |
| ) | |
| if __name__ == "__main__": | |
| app, local_url, share_url = iface.launch(debug=True) |