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app.py

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+import os
+
+# Silence TF logs
+os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2")
+os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS", "0")
+
+import gradio as gr
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from skimage.metrics import structural_similarity as ssim
+
+# =======================
+# Load trained model
+# =======================
+MODEL_PATH = "image_denoising_autoencoder.h5"
+model = load_model(MODEL_PATH, compile=False)
+
+# =======================
+# Utility Functions
+# =======================
+def preprocess_gray(image):
+    image = cv2.resize(image, (128, 128))
+    image = image.astype("float32") / 255.0
+    return image.reshape(128, 128, 1)
+
+def add_noise(image, noise_factor):
+    noisy = image + noise_factor * np.random.normal(
+        loc=0.0, scale=1.0, size=image.shape
+    )
+    return np.clip(noisy, 0.0, 1.0)
+
+def psnr(original, denoised):
+    mse = np.mean((original - denoised) ** 2)
+    if mse == 0:
+        return 100
+    return 10 * np.log10(1.0 / mse)
+
+def interpret_psnr(value):
+    if value < 15:
+        return "Poor ❌"
+    elif value < 25:
+        return "Moderate ⚠️"
+    else:
+        return "Good ✅"
+
+def interpret_ssim(value):
+    if value < 0.5:
+        return "Low ❌"
+    elif value < 0.8:
+        return "Moderate ⚠️"
+    else:
+        return "High ✅"
+
+# =======================
+# 🔥 POST-PROCESSING
+# =======================
+def enhance_clarity(image, strength=1.0):
+    image_uint8 = (image * 255).astype(np.uint8)
+    blurred = cv2.GaussianBlur(image_uint8, (5, 5), 0)
+    sharpened = cv2.addWeighted(
+        image_uint8, 1 + strength, blurred, -strength, 0
+    )
+    return np.clip(sharpened.astype("float32") / 255.0, 0, 1)
+
+# =======================
+# Inference Function
+# =======================
+def denoise_image(input_image, noise_level, mode, clarity_strength):
+    if input_image is None:
+        return None, None, None, "Upload an image to start."
+
+    # -------- Grayscale --------
+    if mode == "Grayscale":
+        if input_image.ndim == 3:
+            gray = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
+        else:
+            gray = input_image
+
+        clean = preprocess_gray(gray)
+        noisy = add_noise(clean, noise_level)
+        denoised = model.predict(noisy.reshape(1, 128, 128, 1))[0]
+
+        enhanced = enhance_clarity(
+            denoised.reshape(128, 128),
+            strength=clarity_strength
+        )
+
+        psnr_val = psnr(clean.reshape(128, 128), denoised.reshape(128, 128))
+        ssim_val = ssim(
+            clean.reshape(128, 128),
+            denoised.reshape(128, 128),
+            data_range=1.0
+        )
+
+        metrics = (
+            f"PSNR: {psnr_val:.2f} dB ({interpret_psnr(psnr_val)})\n"
+            f"SSIM: {ssim_val:.3f} ({interpret_ssim(ssim_val)})\n"
+            f"Clarity Strength: {clarity_strength:.2f}"
+        )
+
+        return (
+            clean.reshape(128, 128),
+            noisy.reshape(128, 128),
+            enhanced,
+            metrics
+        )
+
+    # -------- RGB --------
+    resized = cv2.resize(input_image, (128, 128))
+    resized = resized.astype("float32") / 255.0
+
+    noisy_rgb = add_noise(resized, noise_level)
+    denoised_rgb = np.zeros_like(noisy_rgb)
+
+    for c in range(3):
+        channel = noisy_rgb[:, :, c].reshape(128, 128, 1)
+        denoised_rgb[:, :, c] = model.predict(
+            channel.reshape(1, 128, 128, 1)
+        )[0].reshape(128, 128)
+
+    enhanced_rgb = np.zeros_like(denoised_rgb)
+    for c in range(3):
+        enhanced_rgb[:, :, c] = enhance_clarity(
+            denoised_rgb[:, :, c],
+            strength=clarity_strength
+        )
+
+    psnr_val = psnr(resized, denoised_rgb)
+    ssim_val = ssim(
+        resized,
+        denoised_rgb,
+        channel_axis=2,
+        data_range=1.0
+    )
+
+    metrics = (
+        f"PSNR: {psnr_val:.2f} dB ({interpret_psnr(psnr_val)})\n"
+        f"SSIM: {ssim_val:.3f} ({interpret_ssim(ssim_val)})\n"
+        f"Clarity Strength: {clarity_strength:.2f}"
+    )
+
+    return resized, noisy_rgb, enhanced_rgb, metrics
+
+# =======================
+# Gradio UI
+# =======================
+with gr.Blocks(title="Image Denoising Autoencoder") as demo:
+    gr.Markdown(
+        """
+        # 🧠 Image Denoising using U-Net Autoencoder  
+        🎚 Sliders update output automatically
+        """
+    )
+
+    with gr.Row():
+        input_image = gr.Image(label="Upload Image", type="numpy", height=320)
+
+        with gr.Column():
+            noise_slider = gr.Slider(0.05, 0.5, 0.3, 0.05, label="Noise Level")
+            clarity_slider = gr.Slider(0.0, 2.0, 1.0, 0.1, label="Clarity Strength")
+            mode_selector = gr.Radio(
+                ["Grayscale", "RGB"], value="Grayscale", label="Mode"
+            )
+
+    denoise_btn = gr.Button("Denoise Image 🚀")
+
+    with gr.Row():
+        original_out = gr.Image(label="Original", height=260)
+        noisy_out = gr.Image(label="Noisy", height=260)
+        denoised_out = gr.Image(label="Denoised", height=260)
+
+    metrics_box = gr.Textbox(label="Quality Metrics", lines=4)
+
+    inputs = [input_image, noise_slider, mode_selector, clarity_slider]
+    outputs = [original_out, noisy_out, denoised_out, metrics_box]
+
+    # 🔁 AUTO UPDATE
+    noise_slider.change(denoise_image, inputs, outputs)
+    clarity_slider.change(denoise_image, inputs, outputs)
+    mode_selector.change(denoise_image, inputs, outputs)
+
+    # 🔘 MANUAL BUTTON
+    denoise_btn.click(denoise_image, inputs, outputs)
+
+# =======================
+# Launch App
+# =======================
+if __name__ == "__main__":
+    demo.launch(debug=True)

image_denoising_autoencoder.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3209805b004092354c8ffbafea7a2dd2ec8cdbe39f577a46247844829ac416a0
+size 22675608