chatgpt-ad-maker.py

import gradio as gr
import numpy as np
import cv2

def create_dot_effect(image, dot_size=10, spacing=2, invert=False):
    # Convert to grayscale if image is color
    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        gray = image
    
    # Apply adaptive thresholding to improve contrast
    gray = cv2.adaptiveThreshold(
        gray,
        255,
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY,
        25,  # Block size
        5    # Constant subtracted from mean
    )
    
    # Create a blank canvas with white background if inverted
    height, width = gray.shape
    canvas = np.zeros_like(gray) if not invert else np.full_like(gray, 255)
    
    # 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
    dot_color = 255 if not invert else 0
    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)
                
                # Dynamic dot sizing based on brightness
                relative_brightness = brightness / 255.0
                if invert:
                    relative_brightness = 1 - relative_brightness
                
                # Draw circle with size proportional to brightness
                radius = int((dot_size/2) * relative_brightness)
                if radius > 0:
                    cv2.circle(canvas, 
                             (x + dot_size//2, y + dot_size//2), 
                             radius, 
                             (dot_color), 
                             -1)
    
    return canvas

def process_video(video_path, dot_size=10, spacing=2, invert=False):
    # Read the video
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return None

    # Get video properties
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    
    # Calculate target dimensions (max 720p for performance)
    max_height = 720
    if frame_height > max_height:
        scale = max_height / frame_height
        frame_width = int(frame_width * scale)
        frame_height = max_height
    
    # Create temporary output file
    output_path = "temp_output.mp4"
    fourcc = cv2.VideoWriter_fourcc(*'avc1')
    out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height), False)

    try:
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            
            # Resize frame if needed
            if frame.shape[0] > max_height:
                frame = cv2.resize(frame, (frame_width, frame_height))
            
            # Convert BGR to RGB for processing
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            
            # Apply dot effect
            dotted_frame = create_dot_effect(frame_rgb, dot_size, spacing, invert)
            
            # Write the frame
            out.write(dotted_frame)

    finally:
        # Ensure resources are released
        cap.release()
        out.release()
    
    return output_path

# Create Gradio interface
with gr.Blocks(title="ChatGPT Ad Maker") as iface:
    gr.Markdown("# ChatGPT Ad Maker")
    gr.Markdown("Convert your image or video into a dotted pattern. Adjust dot size and spacing using the sliders.")
    
    with gr.Tab("Image"):
        image_input = gr.Image(label="Input Image")
        with gr.Row():
            img_dot_size = gr.Slider(minimum=2, maximum=20, value=10, step=1, label="Dot Size")
            img_spacing = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Dot Spacing")
        image_output = gr.Image(label="Dotted Output")
        image_button = gr.Button("Process Image")
        image_button.click(
            fn=create_dot_effect,
            inputs=[image_input, img_dot_size, img_spacing],
            outputs=image_output
        )
    
    with gr.Tab("Video"):
        video_input = gr.Video(label="Input Video")
        with gr.Row():
            vid_dot_size = gr.Slider(minimum=2, maximum=20, value=10, step=1, label="Dot Size")
            vid_spacing = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Dot Spacing")
            vid_invert = gr.Checkbox(label="Invert", value=False)
        video_output = gr.Video(label="Dotted Output", format="mp4")
        video_button = gr.Button("Process Video")
        video_button.click(
            fn=process_video,
            inputs=[video_input, vid_dot_size, vid_spacing, vid_invert],
            outputs=video_output
        )

if __name__ == "__main__":
    iface.launch()