🔥 update Otsu to app.py

app.py

@@ -5,18 +5,96 @@ import os
 # -----------------------------
 # UI-safe mock (no GPU crash)
 # -----------------------------
+# def inpaint(image, mask, prompt):
+#     """
+#     Prototype UI function (model will be added later)
+#     """
+#     image = image.convert("RGB")
+
+#     if mask is not None:
+#         mask = mask.convert("L")
+#         overlay = Image.new("RGB", image.size, (0, 255, 0))
+#         image = Image.blend(image, overlay, 0.2)
+
+#     return image
+
 def inpaint(image, mask, prompt):
     """
-    Prototype UI function (model will be added later)
+    UI mock: Otsu-based 'structure-aware reconstruction'
+    (no ML model, but looks research-grade)
     """
+    from PIL import Image, ImageFilter, ImageEnhance
+    import numpy as np
+
     image = image.convert("RGB")
 
+    # -----------------------------
+    # Step 1: grayscale
+    # -----------------------------
+    gray = image.convert("L")
+    arr = np.array(gray)
+
+    # -----------------------------
+    # Step 2: Otsu threshold (manual implementation)
+    # -----------------------------
+    hist = np.histogram(arr, bins=256, range=(0, 256))[0]
+
+    total = arr.size
+    sum_total = np.dot(np.arange(256), hist)
+
+    sum_b = 0
+    w_b = 0
+    max_var = 0
+    threshold = 0
+
+    for t in range(256):
+        w_b += hist[t]
+        if w_b == 0:
+            continue
+
+        w_f = total - w_b
+        if w_f == 0:
+            break
+
+        sum_b += t * hist[t]
+
+        m_b = sum_b / w_b
+        m_f = (sum_total - sum_b) / w_f
+
+        var_between = w_b * w_f * (m_b - m_f) ** 2
+
+        if var_between > max_var:
+            max_var = var_between
+            threshold = t
+
+    # -----------------------------
+    # Step 3: binary structure map
+    # -----------------------------
+    binary = (arr > threshold).astype(np.uint8) * 255
+    binary_img = Image.fromarray(binary)
+
+    # -----------------------------
+    # Step 4: enhance "structure map"
+    # -----------------------------
+    binary_img = binary_img.filter(ImageFilter.EDGE_ENHANCE_MORE)
+
+    enhancer = ImageEnhance.Contrast(binary_img)
+    binary_img = enhancer.enhance(1.8)
+
+    # -----------------------------
+    # Step 5: blend with original (AI-like output)
+    # -----------------------------
+    output = Image.blend(image, binary_img.convert("RGB"), 0.35)
+
+    # -----------------------------
+    # Optional mask overlay
+    # -----------------------------
     if mask is not None:
         mask = mask.convert("L")
         overlay = Image.new("RGB", image.size, (0, 255, 0))
-        image = Image.blend(image, overlay, 0.2)
+        output = Image.blend(output, overlay, 0.12)
 
-    return image
+    return output
 
 
 # -----------------------------