Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# -----------------------------
+# KERNELS
+# -----------------------------
+kernels = {
+    "Blur": np.ones((3, 3), np.float32) / 9,
+
+    "Sharpen": np.array([
+        [0, -1, 0],
+        [-1, 5, -1],
+        [0, -1, 0]
+    ]),
+
+    "Edge Detection": np.array([
+        [-1, -1, -1],
+        [-1,  8, -1],
+        [-1, -1, -1]
+    ]),
+
+    "Emboss": np.array([
+        [-2, -1, 0],
+        [-1,  1, 1],
+        [0,   1, 2]
+    ]),
+
+    "Sobel X": np.array([
+        [-1, 0, 1],
+        [-2, 0, 2],
+        [-1, 0, 1]
+    ]),
+
+    "Sobel Y": np.array([
+        [-1, -2, -1],
+        [0,   0,  0],
+        [1,   2,  1]
+    ])
+}
+
+
+# -----------------------------
+# MAIN FUNCTION
+# -----------------------------
+def process_image(image, kernel_name):
+
+    # Convert RGB → Grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+    # Get selected kernel
+    kernel = kernels[kernel_name]
+
+    # Apply convolution
+    filtered = cv2.filter2D(gray, -1, kernel)
+
+    # Histogram Equalization
+    equalized = cv2.equalizeHist(gray)
+
+    # Thresholding
+    _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+
+    # Edge Detection
+    canny = cv2.Canny(gray, 100, 200)
+
+    # -----------------------------
+    # VISUALIZATION FIGURE
+    # -----------------------------
+    fig, axs = plt.subplots(2, 3, figsize=(12, 8))
+
+    axs[0, 0].imshow(image)
+    axs[0, 0].set_title("Original Image")
+    axs[0, 0].axis("off")
+
+    axs[0, 1].imshow(gray, cmap='gray')
+    axs[0, 1].set_title("Grayscale")
+    axs[0, 1].axis("off")
+
+    axs[0, 2].imshow(filtered, cmap='gray')
+    axs[0, 2].set_title(f"{kernel_name} Output")
+    axs[0, 2].axis("off")
+
+    axs[1, 0].imshow(equalized, cmap='gray')
+    axs[1, 0].set_title("Histogram Equalization")
+    axs[1, 0].axis("off")
+
+    axs[1, 1].imshow(binary, cmap='gray')
+    axs[1, 1].set_title("Binary Threshold")
+    axs[1, 1].axis("off")
+
+    axs[1, 2].imshow(canny, cmap='gray')
+    axs[1, 2].set_title("Canny Edge Detection")
+    axs[1, 2].axis("off")
+
+    plt.tight_layout()
+
+    return fig
+
+
+# -----------------------------
+# GRADIO UI
+# -----------------------------
+demo = gr.Interface(
+    fn=process_image,
+
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+
+        gr.Dropdown(
+            choices=list(kernels.keys()),
+            value="Edge Detection",
+            label="Select Kernel"
+        )
+    ],
+
+    outputs=gr.Plot(label="Visualization"),
+
+    title="Kernel Matrix Visualization for Image Processing",
+
+    description="""
+Upload an image and visualize:
+- Grayscale conversion
+- Convolution kernels
+- Edge detection
+- Thresholding
+- Histogram Equalization
+- CNN-style preprocessing
+"""
+)
+
+demo.launch()