In the world of artificial intelligence, Generative Adversarial Networks (GANs) have revolutionized how we generate realistic images. One of the most exciting applications of GANs is the creation of human faces, with endless possibilities for customization. This simple web application leverages GAN technology to offer users the ability to generate human faces based on three features. The user can choose one feature, a combination of two features, or all three.
The GAN web application allows users to fine-tune three features in a generated human face. Through simple checkboxes, users can specify these preferences to adjust the appearance of the face. These options include:
This customizable approach demonstrates that users can create unique, lifelike faces tailored to specific needs.
GANs operate through a unique mechanism involving two neural networks—the generator and the discriminator. The generator creates fake images, while the discriminator evaluates them against real images. Through this adversarial process, the generator improves, eventually producing realistic images that are indistinguishable from real photographs.
The GAN web application uses a pre-trained model, enabling it to generate realistic human faces based on user inputs. The key to its customization lies in integrating a user-friendly interface that translates user preferences into adjustments within the GAN's parameters.
Upon accessing the application, users are greeted with a clean and simple interface. A grid of checkboxes allows users to toggle different features like eyewear and lipstick. Each time a user adjusts a preference, the backend generates a new face image reflecting their selected features.
The backend of the GAN web application is powered by a pre-trained GAN model, which is accessed via an API. Here's a simplified code snippet for handling the model request when a user selects their preferences:
import numpy as np from keras.models import load_model from matplotlib import pyplot from numpy import load from numpy import mean from numpy import hstack from numpy import expand_dims import matplotlib.pyplot as plt import os import base64 @app.route('/resultGAN/', methods=['GET', 'POST']) def resultGAN(): # Loads the latent points from the .txt file loaded_latent_points = np.loadtxt('./static/latent_points.txt') latent_points = loaded_latent_points # average list of latent space vectors def average_points(points, ix): # convert to zero offset points zero_ix = [i-1 for i in ix] # retrieve required points vectors = points[zero_ix] # average the vectors avg_vector = mean(vectors, axis=0) # combine original and avg vectors # all_vectors = vstack((vectors, avg_vector)) return avg_vector # load model model = load_model('./static/model/generator_model_100.h5') # Identify a few images from classes of interest adult_with_glasses = [18, 45, 88, 94] adult_no_glasses = [1, 31, 42, 46, 47, 91] person_with_lipstick = [12, 41, 42, 45, 50, 57, 58, 86, 97] #Reassign classes of interest to new variables... just to make it easy not # to change names all the time getting interested in new features. feature1_ix = adult_with_glasses feature2_ix = adult_no_glasses feature3_ix = person_with_lipstick # average vectors for each class feature1 = average_points(latent_points, feature1_ix) feature2 = average_points(latent_points, feature2_ix) feature3 = average_points(latent_points, feature3_ix) # get data from the checkboxes of the web app feat1 = request.form.get('feature1') feat2 = request.form.get('feature2') feat3 = request.form.get('feature3') # Check the state of each checkbox and update result_vector accordingly if only one checkbox is checked if feat1 == "on": result_vector = feature1.copy() if feat2 == "on": result_vector = feature2.copy() if feat3 == "on": result_vector = feature3.copy() # Handle combinations of checkbox states if feat1 == "on" and feat2 == "on": result_vector = feature1 + feature2 if feat1 == "on" and feat3 == "on": result_vector = feature1 + feature3 if feat2 == "on" and feat3 == "on": result_vector = feature2 + feature3 # Handle the case where all checkboxes are "on" if feat1 == feat2 == feat3 == "on": result_vector = feature1 + feature2 + feature3 # generate image using the new calculated vector result_vector = expand_dims(result_vector, 0) result_image = model.predict(result_vector) # scale pixel values for plotting result_image = (result_image + 1) / 2.0 plt.imshow(result_image[0]) plot_pathnb = 'static/resultGAN.png' if os.path.exists(plot_pathnb): os.remove(plot_pathnb) plt.savefig(plot_pathnb, format='png') with open(plot_pathnb, 'rb') as img_file: encoded_imgnb = base64.b64encode(img_file.read()).decode('utf-8') return render_template('resultGAN.html', resultGAN=encoded_imgnb)
Explanation: This code loads a pre-trained GAN model, adjusts the input based on user-selected preferences (such as eyeglasses or lipstick), and generates the corresponding face image. The face image is then returned to the front end for display.
This GAN web application opens up a world of possibilities for both personal and professional use. It can be a tool for artists and designers looking to quickly prototype character faces or avatars. It can also be useful in creating diverse human representations for research, gaming, or virtual reality applications. The possibilities are endless when users are able to control and modify features at their fingertips.
With this GAN web application, users can explore the vast potential of AI-generated faces, customizing every detail to suit their needs. The real-time feedback and intuitive interface create a user-friendly experience, making this tool accessible to both AI enthusiasts and those simply seeking fun or practical applications for human face generation.
This technology is a step toward a future where AI and human creativity blend seamlessly to produce unique, customizable content that can be used across a variety of industries—from gaming and animation to digital marketing and beyond.
