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| import numpy as np | |
| import cv2 | |
| import gradio as gr | |
| PCA_MODEL_PATH = "pca_texture_model.npy" | |
| COMPONENT_NAMES_PATH = "component_names.txt" | |
| # Load PCA model | |
| pca = np.load(PCA_MODEL_PATH, allow_pickle=True).item() | |
| mean_texture = pca.mean_ | |
| components = pca.components_ | |
| explained_variance = pca.explained_variance_ | |
| n_components = components.shape[0] | |
| TEXTURE_SIZE = int(np.sqrt(mean_texture.shape[0] // 3)) | |
| # Calculate slider ranges | |
| slider_ranges = [3 * np.sqrt(var) for var in explained_variance] | |
| # Load component names if available | |
| try: | |
| with open(COMPONENT_NAMES_PATH, "r") as f: | |
| component_names = [f"Component {i+1} ({line.strip()})" if line.strip() else f"Component {i+1}" for i, line in enumerate(f.readlines())] | |
| if len(component_names) < n_components: | |
| component_names += [f"Component {i+1}" for i in range(len(component_names), n_components)] | |
| except FileNotFoundError: | |
| component_names = [f"Component {i+1}" for i in range(n_components)] | |
| def generate_texture(*component_values): | |
| component_values = np.array(component_values) | |
| new_texture = mean_texture + np.dot(component_values, components) | |
| new_texture = np.clip(new_texture, 0, 255).astype(np.uint8) | |
| new_texture = new_texture.reshape((TEXTURE_SIZE, TEXTURE_SIZE, 3)) | |
| new_texture = cv2.cvtColor(new_texture, cv2.COLOR_BGR2RGB) | |
| return new_texture | |
| def randomize_texture(): | |
| sampled_coefficients = np.random.normal(0, np.sqrt(explained_variance), size=n_components) | |
| return sampled_coefficients.tolist() | |
| def update_texture(*component_values): | |
| texture = generate_texture(*component_values) | |
| return texture | |
| def on_random_click(): | |
| random_values = randomize_texture() | |
| texture = generate_texture(*random_values) | |
| # Prepare updates for sliders and the image | |
| updates = [gr.update(value=value) for value in random_values] | |
| updates.append(texture) | |
| return updates | |
| def on_image_upload(image): | |
| # Process the uploaded image and get PCA coefficients | |
| coefficients = process_uploaded_image(image) | |
| texture = generate_texture(*coefficients) | |
| # Update sliders and image | |
| updates = [gr.update(value=value) for value in coefficients] | |
| updates.append(texture) | |
| return updates | |
| # Create Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| sliders = [] | |
| for i in range(n_components): | |
| range_limit = slider_ranges[i] | |
| slider = gr.Slider( | |
| minimum=-range_limit, | |
| maximum=range_limit, | |
| step=10, | |
| value=0, | |
| label=component_names[i] | |
| ) | |
| sliders.append(slider) | |
| with gr.Column(): | |
| output_image = gr.Image( | |
| label="Generated Texture" | |
| ) | |
| random_button = gr.Button("Randomize Texture") | |
| upload_image = gr.Image( | |
| label="Upload Image", | |
| sources=['upload', 'clipboard'], | |
| type="numpy" | |
| ) | |
| # Update texture when any slider changes | |
| for slider in sliders: | |
| slider.change( | |
| fn=update_texture, | |
| inputs=sliders, | |
| outputs=output_image | |
| ) | |
| # Randomize texture and update sliders and image | |
| random_button.click( | |
| fn=on_random_click, | |
| inputs=None, | |
| outputs=[*sliders, output_image] | |
| ) | |
| # Update sliders and image based on uploaded image | |
| upload_image.change( | |
| fn=on_image_upload, | |
| inputs=upload_image, | |
| outputs=[*sliders, output_image] | |
| ) | |
| demo.launch() | |