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import gradio as gr
import numpy as np
import tempfile
import os
from PIL import Image
import sys
# Import your modules (you'll need to include them in the space)
from strings import *
def process_string_art(image, n_hooks=180, radius=250, quantization=30):
"""Process uploaded image and return string art result"""
# Save uploaded image temporarily
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_input:
image.save(tmp_input.name)
input_path = tmp_input.name
# Create temporary output prefix
output_prefix = tempfile.mktemp()
try:
# Build adjacency matrix
sparse, hooks, edge_codes = build_arc_adjecency_matrix(n_hooks, radius)
# Process image
shrinkage = 0.75
img = image_from_pil(image, int(radius * 2 * shrinkage))
sparse_b = build_image_vector(img, radius)
# Solve linear system
result = scipy.sparse.linalg.lsqr(sparse, np.array(sparse_b.todense()).flatten())
x = result[0]
# Apply quantization
x = np.clip(x, 0, 1e6)
max_edge_weight_orig = np.max(x)
x_quantized = (x / np.max(x) * quantization).round()
clip_factor = 0.3
x_quantized = np.clip(x_quantized, 0, int(np.max(x_quantized) * clip_factor))
x = x_quantized / quantization * max_edge_weight_orig
# Reconstruct final image
brightness_correction = 1.2
final_image = reconstruct(x * brightness_correction, sparse, radius)
# Calculate statistics
arc_count = int(np.sum(x_quantized))
unique_arcs = len(x_quantized[x_quantized > 0])
# Convert to PIL Image for return
final_pil = Image.fromarray(np.clip(final_image, 0, 255).astype(np.uint8))
stats = f"Total arcs: {arc_count}\nUnique arc types: {unique_arcs}"
return final_pil, stats
finally:
# Cleanup
if os.path.exists(input_path):
os.unlink(input_path)
def image_from_pil(pil_image, size):
"""Convert PIL image to grayscale numpy array"""
img = pil_image.convert('L') # Convert to grayscale
img = img.resize((size, size), Image.Resampling.LANCZOS)
return np.array(img)
# Create Gradio interface
iface = gr.Interface(
fn=process_string_art,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(50, 360, value=180, step=10, label="Number of Hooks"),
gr.Slider(100, 500, value=250, step=50, label="Circle Radius"),
gr.Slider(10, 100, value=30, step=5, label="Quantization Level")
],
outputs=[
gr.Image(type="pil", label="String Art Result"),
gr.Textbox(label="Statistics")
],
title="String Art Generator",
description="Convert any image into string art patterns! Upload a square image and adjust parameters.",
examples=[
# You can add example images here
]
)
if __name__ == "__main__":
iface.launch() |