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| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| def create_dot_effect(image, dot_size=10, spacing=2): | |
| # Convert to grayscale if image is color | |
| if len(image.shape) == 3: | |
| gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
| else: | |
| gray = image | |
| # Create a blank canvas | |
| height, width = gray.shape | |
| canvas = np.zeros_like(gray) | |
| # Calculate number of dots based on spacing | |
| y_dots = range(0, height, dot_size + spacing) | |
| x_dots = range(0, width, dot_size + spacing) | |
| # Create dots based on brightness | |
| for y in y_dots: | |
| for x in x_dots: | |
| # Get the average brightness of the region | |
| region = gray[y:min(y+dot_size, height), x:min(x+dot_size, width)] | |
| if region.size > 0: | |
| brightness = np.mean(region) | |
| # Draw circle if the region is bright enough | |
| if brightness > 30: # Threshold can be adjusted | |
| cv2.circle(canvas, | |
| (x + dot_size//2, y + dot_size//2), | |
| dot_size//2, | |
| (255), | |
| -1) | |
| return canvas | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=create_dot_effect, | |
| inputs=[ | |
| gr.Image(label="Input Image"), | |
| gr.Slider(minimum=2, maximum=20, value=10, step=1, label="Dot Size"), | |
| gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Dot Spacing") | |
| ], | |
| outputs=gr.Image(label="Dotted Image"), | |
| title="ChatGPT Ad Maker", | |
| description="Convert your image into a dotted pattern. Adjust dot size and spacing using the sliders." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |