Update app.py

app.py

@@ -1,43 +1,43 @@
-import gradio as gr
 import cv2
 import numpy as np
-
-# Function to apply pencil sketch effect with adjustable outline intensity
-def apply_pencil_sketch(image, threshold1=50, threshold2=150, ksize=5):
+import gradio as gr
+def pencil_sketch(image, blur, threshold, stroke_width):
     # Convert to grayscale
-    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    
-    # Apply Canny edge detection to get the pencil outlines
-    edges = cv2.Canny(gray_image, threshold1, threshold2)
-    
-    # Optionally, apply a Gaussian blur to smooth the edges (to control the pencil stroke effect)
-    blurred_edges = cv2.GaussianBlur(edges, (ksize, ksize), 0)
-    
-    # Invert the edges (for a black background and white strokes)
-    inverted_edges = cv2.bitwise_not(blurred_edges)
-    
-    return inverted_edges
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # Apply Gaussian blur
+    ksize = blur * 2 + 1
+    blurred = cv2.GaussianBlur(gray, (ksize, ksize), 0)
+    # Detect edges using Canny
+    edges = cv2.Canny(blurred, threshold, threshold * 2)
+    # Dilate edges to simulate thicker pencil strokes
+    kernel = np.ones((stroke_width, stroke_width), np.uint8)
+    edges_dilated = cv2.dilate(edges, kernel, iterations=1)
+    # Invert edges to create a sketch effect
+    sketch = cv2.bitwise_not(edges_dilated)
+    # Convert back to BGR
+    sketch_bgr = cv2.cvtColor(sketch, cv2.COLOR_GRAY2BGR)
+    return sketch_bgr
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        image_input = gr.Image(source='upload', type='numpy', label="Upload Image")
+        image_output = gr.Image(type='numpy', label="Pencil Sketch Output")
+    with gr.Row():
+        blur_slider = gr.Slider(1, 10, step=1, label="Blur", value=1)
+        threshold_slider = gr.Slider(50, 255, step=1, label="Threshold", value=100)
+        stroke_width_slider = gr.Slider(1, 10, step=1, label="Stroke Width", value=1)
+
+    def update(image, blur, threshold, stroke_width):
+        if image is None:
+            return None
+        return pencil_sketch(image, blur, threshold, stroke_width)
 
-# Gradio interface to upload an image and apply pencil sketch effect with real-time outline control
-def interactive_sketch(image, threshold1, threshold2, ksize):
-    # Convert the uploaded image to a NumPy array
-    image = np.array(image)
-    
-    # Apply the pencil sketch filter to get the outlines
-    result = apply_pencil_sketch(image, threshold1=threshold1, threshold2=threshold2, ksize=ksize)
-    
-    # Return the resulting image with pencil outlines
-    return result
+    inputs = [image_input, blur_slider, threshold_slider, stroke_width_slider]
+    image_input.change(fn=update, inputs=inputs, outputs=image_output)
+    blur_slider.change(fn=update, inputs=inputs, outputs=image_output)
+    threshold_slider.change(fn=update, inputs=inputs, outputs=image_output)
+    stroke_width_slider.change(fn=update, inputs=inputs, outputs=image_output)
 
-# Create Gradio interface with updated syntax
-gr.Interface(
-    fn=interactive_sketch,
-    inputs=[
-        gr.Image(type="numpy", label="Upload Image"),  # Image upload button
-        gr.Slider(minimum=0, maximum=255, step=1, value=50, label="Canny Edge Threshold 1"),  # Slider for low threshold (Canny)
-        gr.Slider(minimum=0, maximum=255, step=1, value=150, label="Canny Edge Threshold 2"),  # Slider for high threshold (Canny)
-        gr.Slider(minimum=3, maximum=15, step=2, value=5, label="Gaussian Blur Kernel Size")  # Slider for kernel size (blur)
-    ],
-    outputs="image",  # Display the processed image with pencil outlines
-    live=True  # Update image in real-time
-).launch()
+if __name__ == "__main__":
+    demo.launch()