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import gradio as gr
import numpy as np
def dot_effect(input_image, dot_size, threshold, dot_color):
"""
Convert input image to customizable dotted effect
Args:
input_image (ndarray): Input image
dot_size (int): Size of dots
threshold (float): Brightness threshold for dot appearance
dot_color (list): RGB color of dots
Returns:
ndarray: Image with dotted effect
"""
# Convert input to grayscale
gray_image = np.mean(input_image, axis=2)
# Normalize pixel values
normalized = (gray_image - gray_image.min()) / (gray_image.max() - gray_image.min())
# Create dot mask
height, width = gray_image.shape
dot_mask = np.zeros((height, width, 3), dtype=np.uint8)
for y in range(0, height, dot_size):
for x in range(0, width, dot_size):
if normalized[y, x] > threshold:
radius = int(dot_size * normalized[y, x])
center_x, center_y = x + dot_size//2, y + dot_size//2
for dy in range(-radius, radius):
for dx in range(-radius, radius):
if dx*dx + dy*dy <= radius*radius:
px, py = center_x + dx, center_y + dy
if 0 <= px < width and 0 <= py < height:
dot_mask[py, px] = dot_color
return dot_mask
# Create Gradio interface
iface = gr.Interface(
fn=dot_effect,
inputs=[
gr.Image(type="numpy", label="Input Image"),
gr.Slider(minimum=1, maximum=20, value=5, label="Dot Size"),
gr.Slider(minimum=0, maximum=1, value=0.3, label="Dot Threshold"),
gr.ColorPicker(label="Dot Color", value="#FFFFFF")
],
outputs=gr.Image(type="numpy"),
title="ChatGPT Ad Maker",
description="Transform images into customizable dotted effect"
)
# Launch the app
iface.launch() |