| |
|
|
| import cv2 |
| import numpy as np |
| import tensorflow as tf |
| import gradio as gr |
|
|
| |
| model = tf.keras.models.load_model("best_model.h5", compile=False) |
| model.compile(optimizer="adam", loss="mse") |
|
|
| def process_and_denoise(image): |
| """ |
| Takes any input image (color or grayscale), converts it to 64×64 grayscale, |
| adds Gaussian noise, runs the autoencoder, and returns all three stages |
| upsampled to 128×128 for clearer display: |
| 1) Original resized |
| 2) Noisy version |
| 3) Denoised reconstruction |
| """ |
| |
| if image.ndim == 3 and image.shape[2] == 3: |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
| else: |
| gray = image[..., 0] if image.ndim == 3 else image |
|
|
| |
| small = cv2.resize(gray, (64, 64)) |
| norm = small.astype(np.float32) / 255.0 |
|
|
| |
| sigma = 0.1 |
| noisy = norm + sigma * np.random.randn(*norm.shape) |
| noisy = np.clip(noisy, 0.0, 1.0) |
|
|
| |
| inp = noisy[np.newaxis, ..., np.newaxis] |
| pred = model.predict(inp)[0, ..., 0] |
|
|
| |
| orig_disp = (norm * 255).astype(np.uint8) |
| noisy_disp = (noisy * 255).astype(np.uint8) |
| recon_disp = (pred * 255).astype(np.uint8) |
|
|
| |
| def upsample(img): |
| return cv2.resize(img, (128, 128), interpolation=cv2.INTER_LINEAR) |
|
|
| return upsample(orig_disp), upsample(noisy_disp), upsample(recon_disp) |
|
|
| demo = gr.Interface( |
| fn=process_and_denoise, |
| inputs=gr.Image(type="numpy", label="Input Image"), |
| outputs=[ |
| gr.Image(type="numpy", label="Original ▶ 128×128"), |
| gr.Image(type="numpy", label="Noisy (σ=0.1) ▶ 128×128"), |
| gr.Image(type="numpy", label="Denoised ▶ 128×128") |
| ], |
| title="Denoising Autoencoder Demo", |
| description=( |
| "Upload any image (grayscale or color).\n" |
| "- Internally resized to 64×64 for denoising.\n" |
| "- Adds Gaussian noise (σ=0.1).\n" |
| "- Displays all stages upsampled to 128×128 for clearer viewing." |
| ) |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(server_name="0.0.0.0", share=True) |
|
|
