REVERT

app.py

@@ -49,66 +49,11 @@ def depth_to_normal(depth):
     normal = (normal + 1) / 2
     return normal
 
-# ===============================
-# NEW: Dominant color from non-transparent pixels
-# ===============================
-def dominant_color_from_image(img_rgba: Image.Image, max_samples: int = 10000, k: int = 3):
-    """
-    Returns (rgb_tuple, hex_string) for the dominant color considering only alpha>0 pixels.
-    Uses K-means on a random subset of pixels for speed.
-    """
-    arr = np.array(img_rgba.convert("RGBA"))
-    alpha = arr[..., 3]
-    mask = alpha > 0
-
-    if not np.any(mask):
-        # Fallback if everything is transparent
-        rgb = (255, 255, 255)
-        hex_color = "#{:02X}{:02X}{:02X}".format(*rgb)
-        return rgb, hex_color
-
-    rgb_pixels = arr[mask][:, :3].astype(np.float32)
-
-    # Subsample for speed
-    if rgb_pixels.shape[0] > max_samples:
-        idx = np.random.choice(rgb_pixels.shape[0], max_samples, replace=False)
-        rgb_pixels = rgb_pixels[idx]
-
-    # K-means
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0)
-    attempts = 3
-    compactness, labels, centers = cv2.kmeans(
-        data=rgb_pixels,
-        K=k,
-        bestLabels=None,
-        criteria=criteria,
-        attempts=attempts,
-        flags=cv2.KMEANS_PP_CENTERS
-    )
-
-    # Pick the largest cluster
-    counts = np.bincount(labels.flatten(), minlength=k)
-    dominant_idx = int(np.argmax(counts))
-    dom = centers[dominant_idx].clip(0, 255).astype(np.uint8)
-    rgb = (int(dom[0]), int(dom[1]), int(dom[2]))
-    hex_color = "#{:02X}{:02X}{:02X}".format(*rgb)
-    return rgb, hex_color
-
 # ===============================
 # CORE PROCESSING FUNCTION
 # ===============================
 def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
-    # ORIGINAL (kept as comment): This converts alpha to black (0,0,0)
-    # img_pil = base_image.convert("RGB")
-
-    # NEW: Matte transparent pixels to the dominant color from the image itself
-    base_rgba = base_image.convert("RGBA")
-    fill_rgb, fill_hex = dominant_color_from_image(base_rgba)
-    bg = Image.new("RGBA", base_rgba.size, fill_rgb + (255,))
-    img_pil = Image.alpha_composite(bg, base_rgba).convert("RGB")  # <- alpha-aware RGB conversion
-    # (Optional) If you want to inspect which color was used:
-    # print("Dominant matte color:", fill_hex)
-
+    img_pil = base_image.convert("RGB")
     img_np = np.array(img_pil)
 
     # Prepare tensor
@@ -184,6 +129,7 @@ def _process_saree_core(base_image: Image.Image, pattern_image: Image.Image):
     mask_binary = (mask_alpha > k/100).astype(np.uint8) * 255  # slightly stricter threshold
     mask_eroded = cv2.erode(mask_binary, kernel, iterations=3)  # balanced erosion
 
+
     # 2. Feather edges (blur)
     mask_blurred = cv2.GaussianBlur(mask_eroded, (15, 15), sigmaX=3, sigmaY=3)
 
@@ -322,4 +268,4 @@ async def unhandled_exception_handler(request: Request, exc: Exception):
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)