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Update app.py
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app.py
CHANGED
@@ -1,98 +1,68 @@
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import os
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import io
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import cv2
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import matplotlib.animation as animation
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import matplotlib.pyplot as plt
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import numpy as np
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import
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from math import tau
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from PIL import Image
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from concurrent.futures import ThreadPoolExecutor
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# Convert PIL to OpenCV image(array)
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input_image = np.array(input_image)
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img = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
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# processing
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# resize the image to a smaller size for faster processing
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dim = (img_size, img_size)
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img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
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imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(imgray, (
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# find the contour with the largest area
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# largest_contour_idx = np.argmax([cv2.contourArea(c) for c in contours])
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# largest_contour = contours[largest_contour_idx]
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#
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def combine_all_contours(contours):
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combined_contour = np.array([], dtype=np.int32).reshape(0, 1, 2)
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for contour in contours:
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combined_contour = np.vstack((combined_contour, contour))
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return combined_contour
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largest_contour = combine_all_contours(contours)
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verts = [tuple(coord) for coord in largest_contour.squeeze()]
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#
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x_range = np.max(xs) - np.min(xs)
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# determine the scale factors
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desired_range = 400
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scale_x = desired_range / x_range
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scale_y = desired_range / y_range
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# apply scaling
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# ys needs to be flipped vertically
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xs = (xs - np.mean(xs)) * scale_x
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ys = (-ys + np.mean(ys)) * scale_y
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#
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num_points = 1000 # how many points to use for numerical integration
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t_values = np.linspace(0, tau, num_points)
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t_list = np.linspace(0, tau, len(xs))
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def compute_cn(f_exp, n, t_values):
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"""
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Integrate the contour along axis (-1) using the composite trapezoidal rule.
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https://numpy.org/doc/stable/reference/generated/numpy.trapz.html#r7aa6c77779c0-2
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"""
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coef = np.trapz(f_exp * np.exp(-n * t_values * 1j), t_values) / tau
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return coef
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# pre-compute the interpolated values
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f_precomputed = np.interp(t_values, t_list, xs + 1j * ys)
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N = coefficients
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indices = [0] + [j for i in range(1, N + 1) for j in (i, -i)]
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#
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with ThreadPoolExecutor(max_workers=8) as executor:
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print("Number of threads used:", executor._max_workers)
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coefs = list(executor.map(lambda n: (compute_cn(f_precomputed, n, t_values), n), indices))
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coefs = [(coefs[0][0], 0)] + coefs[1:]
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#
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fig, ax = plt.subplots()
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circles = [ax.plot([], [], 'b-')[0] for _ in range(-N, N+1)]
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circle_lines = [ax.plot([], [], 'g-')[0] for _ in range(-N, N+1)]
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drawing, = ax.plot([], [], 'r-', linewidth=2)
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ax.set_xlim(-desired_range, desired_range)
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@@ -102,49 +72,47 @@ def fourier_transform_drawing(input_image, frames, coefficients, img_size):
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fig.set_size_inches(15, 15)
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draw_x, draw_y = [], []
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# pre-compute static values outside the animate function
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theta = np.linspace(0, tau, 80)
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coefs_static = [(np.linalg.norm(c), fr) for c, fr in coefs]
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def animate(i, coefs, time):
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center = (0, 0)
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c_dynamic = coefs[_][0] * np.exp(1j * (fr * tau * time[i]))
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x, y = center[0] + r * np.cos(theta), center[1] + r * np.sin(theta)
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circle_lines[
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circles[
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center = (center[0] + np.real(c_dynamic), center[1] + np.imag(c_dynamic))
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draw_x.append(center[0])
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draw_y.append(center[1])
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drawing.set_data(draw_x[:i+1], draw_y[:i+1])
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anim = animation.FuncAnimation(fig, animate, frames=frames, interval=5, fargs=(coefs, time))
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# save the animation as an MP4 file
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output_animation = "output.mp4"
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anim.save(output_animation, fps=15)
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plt.close(fig)
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return output_animation
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# Gradio interface
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interface = gr.Interface(
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fn=fourier_transform_drawing,
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inputs=[
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gr.Image(label="Input Image", sources=['upload'], type="pil"),
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gr.Slider(minimum=5, maximum=500, value=100, label="Number of Frames"),
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gr.Slider(minimum=1, maximum=500, value=50, label="Number of Coefficients"),
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gr.Number(value=224, label="Image
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],
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outputs=gr.Video(),
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title="Fourier Transform Drawing",
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description="Upload an image and generate a Fourier Transform drawing animation.
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examples=[["Fourier2.jpg", 100, 200, 224], ["Luffy.png", 100, 100, 224]]
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)
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if __name__ == "__main__":
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import os
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import io
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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from PIL import Image
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from math import tau
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from concurrent.futures import ThreadPoolExecutor
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import gradio as gr
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def fourier_transform_drawing(input_image, frames, coefficients, img_size, blur_kernel_size, desired_range, num_points, theta_points):
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# Convert PIL to OpenCV image
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img = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
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# Resize the image for faster processing
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img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_AREA)
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# Image processing
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imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(imgray, (blur_kernel_size, blur_kernel_size), 0)
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_, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# find the contour with the largest area
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# largest_contour_idx = np.argmax([cv2.contourArea(c) for c in contours])
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# largest_contour = contours[largest_contour_idx]
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# Combine all contours
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def combine_all_contours(contours):
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combined_contour = np.array([], dtype=np.int32).reshape(0, 1, 2)
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for contour in contours:
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combined_contour = np.vstack((combined_contour, contour))
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return combined_contour
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combined_contour = combine_all_contours(contours)
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verts = [tuple(coord) for coord in combined_contour.squeeze()]
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xs, ys = np.asarray(list(zip(*verts)))
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# Scale the coordinates
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x_range, y_range = np.max(xs) - np.min(xs), np.max(ys) - np.min(ys)
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scale_x, scale_y = desired_range / x_range, desired_range / y_range
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xs = (xs - np.mean(xs)) * scale_x
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ys = (-ys + np.mean(ys)) * scale_y
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# Compute Fourier coefficients
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t_list = np.linspace(0, tau, len(xs))
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t_values = np.linspace(0, tau, num_points)
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f_precomputed = np.interp(t_values, t_list, xs + 1j * ys)
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def compute_cn(f_exp, n, t_values):
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coef = np.trapz(f_exp * np.exp(-n * t_values * 1j), t_values) / tau
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return coef
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N = coefficients
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indices = [0] + [j for i in range(1, N + 1) for j in (i, -i)]
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# Parallel computation of coefficients
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with ThreadPoolExecutor(max_workers=8) as executor:
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coefs = list(executor.map(lambda n: (compute_cn(f_precomputed, n, t_values), n), indices))
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# Animation setup
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fig, ax = plt.subplots()
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circles = [ax.plot([], [], 'b-')[0] for _ in range(-N, N + 1)]
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circle_lines = [ax.plot([], [], 'g-')[0] for _ in range(-N, N + 1)]
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drawing, = ax.plot([], [], 'r-', linewidth=2)
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ax.set_xlim(-desired_range, desired_range)
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fig.set_size_inches(15, 15)
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draw_x, draw_y = [], []
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theta = np.linspace(0, tau, theta_points)
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coefs_static = [(np.linalg.norm(c), fr) for c, fr in coefs]
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# Animation function
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def animate(i, coefs, time):
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center = (0, 0)
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for idx, (r, fr) in enumerate(coefs_static):
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c_dynamic = coefs[idx][0] * np.exp(1j * (fr * tau * time[i]))
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x, y = center[0] + r * np.cos(theta), center[1] + r * np.sin(theta)
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circle_lines[idx].set_data([center[0], center[0] + np.real(c_dynamic)], [center[1], center[1] + np.imag(c_dynamic)])
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circles[idx].set_data(x, y)
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center = (center[0] + np.real(c_dynamic), center[1] + np.imag(c_dynamic))
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draw_x.append(center[0])
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draw_y.append(center[1])
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drawing.set_data(draw_x[:i+1], draw_y[:i+1])
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# Create and save the animation
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anim = animation.FuncAnimation(fig, animate, frames=frames, interval=5, fargs=(coefs, np.linspace(0, 1, num=frames)))
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output_animation = "output.mp4"
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anim.save(output_animation, fps=15)
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plt.close(fig)
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return output_animation
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# Gradio interface setup
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interface = gr.Interface(
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fn=fourier_transform_drawing,
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inputs=[
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gr.Image(label="Input Image", sources=['upload'], type="pil"),
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gr.Slider(minimum=5, maximum=500, value=100, label="Number of Frames"),
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gr.Slider(minimum=1, maximum=500, value=50, label="Number of Coefficients"),
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gr.Number(value=224, label="Image Size (px)", precision=0),
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gr.Slider(minimum=3, maximum=11, step=2, value=5, label="Blur Kernel Size (odd number)"),
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gr.Number(value=400, label="Desired Range for Scaling", precision=0),
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gr.Number(value=1000, label="Number of Points for Integration", precision=0),
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gr.Slider(minimum=50, maximum=500, value=80, label="Theta Points for Animation")
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],
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outputs=gr.Video(),
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title="Fourier Transform Drawing",
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description="Upload an image and generate a Fourier Transform drawing animation.",
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examples=[["Fourier2.jpg", 100, 200, 224, 5, 400, 1000, 80], ["Luffy.png", 100, 100, 224, 5, 400, 1000, 80]]
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)
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if __name__ == "__main__":
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