APP

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import cv2, numpy as np
+from PIL import Image
+
+def uniform_lighting(image):
+    # Convert PIL → OpenCV
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+
+    # 1️⃣ Convert to grayscale to estimate illumination
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # 2️⃣ Smooth illumination field (large Gaussian kernel)
+    illumination = cv2.GaussianBlur(gray, (51, 51), 0)
+
+    # 3️⃣ Compute normalization map using brightest region
+    brightest = np.max(illumination)
+    illum_norm = brightest / (illumination + 1e-5)
+    illum_norm = np.repeat(illum_norm[:, :, np.newaxis], 3, axis=2)
+
+    # 4️⃣ Apply correction
+    corrected = np.clip(img.astype(np.float32) * illum_norm, 0, 255).astype(np.uint8)
+
+    # Convert back to PIL for Gradio display
+    corrected_rgb = cv2.cvtColor(corrected, cv2.COLOR_BGR2RGB)
+    return Image.fromarray(corrected_rgb)
+
+title = "🧵 Saree AI – Uniform Lighting Correction"
+description = """
+Upload a saree image with uneven lighting.
+This tool will automatically detect illumination gradients and normalize brightness
+using the brightest region as reference, giving you a flat, uniform look for catalog or dataset use.
+"""
+
+demo = gr.Interface(
+    fn=uniform_lighting,
+    inputs=gr.Image(label="Upload Saree Image", type="pil"),
+    outputs=gr.Image(label="Corrected Image"),
+    title=title,
+    description=description,
+    examples=[],
+)
+
+if __name__ == "__main__":
+    demo.launch()