app.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from PIL import Image
import io
import cv2
from math import tau
import gradio as gr
from concurrent.futures import ThreadPoolExecutor

def fourier_transform_drawing(input_image, frames, coefficients, img_size, blur_kernel_size, desired_range, num_points, theta_points):
    # Convert PIL to OpenCV image
    img = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
    img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_AREA)
    imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(imgray, (blur_kernel_size, blur_kernel_size), 0)
    _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # find the contour with the largest area
    largest_contour_idx = np.argmax([cv2.contourArea(c) for c in contours])
    largest_contour = contours[largest_contour_idx]
    
    # def combine_all_contours(contours):
    #     combined_contour = np.array([], dtype=np.int32).reshape(0, 1, 2)
    #     for contour in contours:
    #         combined_contour = np.vstack((combined_contour, contour))
    #     return combined_contour

    # largest_contour = combine_all_contours(contours)
    verts = [tuple(coord) for coord in largest_contour.squeeze()]
    xs, ys = np.asarray(list(zip(*verts)))
    x_range, y_range = np.max(xs) - np.min(xs), np.max(ys) - np.min(ys)
    scale_x, scale_y = desired_range / x_range, desired_range / y_range
    xs = (xs - np.mean(xs)) * scale_x
    ys = (-ys + np.mean(ys)) * scale_y

    t_list = np.linspace(0, tau, len(xs))
    t_values = np.linspace(0, tau, num_points)
    f_precomputed = np.interp(t_values, t_list, xs + 1j * ys)

    def compute_cn(f_exp, n, t_values):
        coef = np.trapz(f_exp * np.exp(-n * t_values * 1j), t_values) / tau
        return coef

    N = coefficients
    indices = [0] + [j for i in range(1, N + 1) for j in (i, -i)]

    with ThreadPoolExecutor(max_workers=8) as executor:
        coefs = list(executor.map(lambda n: (compute_cn(f_precomputed, n, t_values), n), indices))

    fig, ax = plt.subplots()
    circles = [ax.plot([], [], 'b-')[0] for _ in range(-N, N + 1)]
    circle_lines = [ax.plot([], [], 'g-')[0] for _ in range(-N, N + 1)]
    drawing, = ax.plot([], [], 'r-', linewidth=2)

    ax.set_xlim(-desired_range, desired_range)
    ax.set_ylim(-desired_range, desired_range)
    ax.set_axis_off()
    ax.set_aspect('equal')
    fig.set_size_inches(15, 15)

    draw_x, draw_y = [], []
    theta = np.linspace(0, tau, theta_points)
    coefs_static = [(np.linalg.norm(c), fr) for c, fr in coefs]

    def animate(i, coefs, time):
        center = (0, 0)
        for idx, (r, fr) in enumerate(coefs_static):
            c_dynamic = coefs[idx][0] * np.exp(1j * (fr * tau * time[i]))
            x, y = center[0] + r * np.cos(theta), center[1] + r * np.sin(theta)
            circle_lines[idx].set_data([center[0], center[0] + np.real(c_dynamic)], [center[1], center[1] + np.imag(c_dynamic)])
            circles[idx].set_data(x, y)
            center = (center[0] + np.real(c_dynamic), center[1] + np.imag(c_dynamic))

        draw_x.append(center[0])
        draw_y.append(center[1])
        drawing.set_data(draw_x[:i+1], draw_y[:i+1])


        # Capture the current plot as an image
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight')
        buf.seek(0)
        image = np.array(Image.open(buf))

        # Yield the current image and a placeholder for the final animation
        yield (image, None)

    # Generate and yield images for each frame
    for frame in range(frames):
        yield from animate(frame, coefs, np.linspace(0, 1, num=frames))

    # Generate final animation as GIF
    with tempfile.NamedTemporaryFile(delete=False, suffix='.gif') as temp_file:
        anim = animation.FuncAnimation(fig, animate, frames=frames, interval=5, fargs=(coefs, np.linspace(0, 1, num=frames)))
        anim.save(temp_file.name, fps=15)

        # Read the final GIF
        with open(temp_file.name, 'rb') as gif_file:
            final_gif = np.array(Image.open(io.BytesIO(gif_file.read())))

    # Remove the temporary file
    os.remove(temp_file.name)

    # Yield the final GIF in place of the last frame
    yield (image, final_gif)

# Gradio interface setup
interface = gr.Interface(
    fn=fourier_transform_drawing,
    inputs=[
        gr.Image(label="Input Image", sources=['upload'], type="pil"),
        gr.Slider(minimum=5, maximum=500, value=100, label="Number of Frames"),
        gr.Slider(minimum=1, maximum=500, value=50, label="Number of Coefficients"),
        gr.Number(value=224, label="Image Size (px)", precision=0),
        gr.Slider(minimum=3, maximum=11, step=2, value=5, label="Blur Kernel Size (odd number)"),
        gr.Number(value=400, label="Desired Range for Scaling", precision=0),
        gr.Number(value=1000, label="Number of Points for Integration", precision=0),
        gr.Slider(minimum=50, maximum=500, value=80, label="Theta Points for Animation")
    ],
    outputs=["image", "image"],
    title="Fourier Transform Drawing",
    description="Upload an image and generate a Fourier Transform drawing animation.",
)

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