app.py

import gradio as gr
import cv2, numpy as np
from PIL import Image

# -------------------------------------------------
# 🔧 CORE FUNCTION
# -------------------------------------------------
def remove_folds(image, intensity=0.5):
    if image is None:
        return None

    img = np.array(image.convert("RGB"))
    img_lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
    l, a, b = cv2.split(img_lab)

    # --- (1) Smooth illumination correction ---
    ksize = int(49 + intensity * 100)  # kernel size changes with intensity
    ksize = ksize + 1 if ksize % 2 == 0 else ksize  # must be odd
    l_blur = cv2.GaussianBlur(l, (ksize, ksize), 0)
    l_equal = cv2.divide(l, l_blur, scale=128)

    # --- (2) Frequency-domain smoothing ---
    freq = np.fft.fft2(l_equal)
    freqshift = np.fft.fftshift(freq)
    rows, cols = l_equal.shape
    crow, ccol = rows // 2, cols // 2
    mask = np.ones((rows, cols), np.uint8)
    r = int(10 + intensity * 30)  # notch size grows with intensity
    mask[crow - r:crow + r, ccol - r:ccol + r] = 0
    freqshift = freqshift * mask
    ishift = np.fft.ifftshift(freqshift)
    l_flat = np.abs(np.fft.ifft2(ishift))
    l_flat = cv2.normalize(l_flat, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)

    # --- (3) Merge back and convert ---
    img_lab = cv2.merge([l_flat, a, b])
    result = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
    return Image.fromarray(result)

# -------------------------------------------------
# 🎛️ GRADIO UI
# -------------------------------------------------
title = "🪄 Saree Fold Remover (Lighting Only, No Distortion)"
description = """
Upload a flat saree or fabric image.<br>
Adjust the **Fold Intensity** slider — higher values remove deeper folds.<br>
The process equalizes lighting only, keeping weave and motifs intact.
"""

iface = gr.Interface(
    fn=remove_folds,
    inputs=[
        gr.Image(label="Upload Saree Image", type="pil"),
        gr.Slider(
            0.0,
            1.0,
            value=0.5,
            step=0.05,
            label="Fold Intensity",
            info="Higher = stronger flattening (lighting only)",
            interactive=True
        ),
    ],
    outputs=gr.Image(label="Flat, Fold-Free Output"),
    title=title,
    description=description,
    live=False,        # prevents rerun while moving
    throttle=0.8       # waits 0.8 s before triggering
)

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