update

app.py

@@ -18,9 +18,22 @@ def show_preview(image):
         arr = np.array(image)
         # RGBA → RGB
         if arr.ndim == 3 and arr.shape[2] == 4:
-            return image.convert("RGB")
-        # RGB → as-is
+            image = image.convert("RGB")
+            arr = np.array(image)
+        # RGB
         if arr.ndim == 3 and arr.shape[2] == 3:
+            # If high bit-depth or non-uint8, normalize per-channel for visualization
+            if arr.dtype != np.uint8 or np.max(arr) > 255:
+                a = np.nan_to_num(arr.astype(np.float64), nan=0.0, posinf=0.0, neginf=0.0)
+                vis = np.empty_like(a, dtype=np.float64)
+                for c in range(3):
+                    vmin = np.percentile(a[..., c], 1.0)
+                    vmax = np.percentile(a[..., c], 99.0)
+                    if not np.isfinite(vmin) or not np.isfinite(vmax) or vmax <= vmin:
+                        vmin, vmax = float(np.min(a[..., c])), float(np.max(a[..., c]))
+                    denom = max(vmax - vmin, 1e-6)
+                    vis[..., c] = np.clip((a[..., c] - vmin) / denom, 0.0, 1.0) * 255.0
+                return Image.fromarray(vis.astype(np.uint8), mode='RGB')
             return image
         # Single-channel or higher bit-depth
         if arr.ndim == 2 or (arr.ndim == 3 and arr.shape[2] == 1):

sorghum_pipeline/data/preprocessor.py

@@ -31,7 +31,8 @@ class ImagePreprocessor:
         boxes = [(j, i, j + d, i + d)
                  for i, j in product(range(0, pil_img.height, d),
                                     range(0, pil_img.width,  d))]
-        stack = np.stack([np.array(pil_img.crop(b), float) for b in boxes], axis=-1)
+        # Ensure each quadrant is single-channel (grayscale) so bands are 2D
+        stack = np.stack([np.array(pil_img.crop(b).convert('L'), float) for b in boxes], axis=-1)
         green, red, red_edge, nir = np.split(stack, 4, axis=-1)
 
         # Build BGR composite so that displayed RGB = (red, red_edge, green)