Add 16bit PNG depth map with download

app.py

@@ -19,27 +19,37 @@ def analyze_depth(image):
         return None
 
     # Run inference
-    prediction = model.inference([image], process_res=1024)
+    prediction = model.inference([image], process_res=1600)
 
     # 取得深度圖 [H, W]
     depth = prediction.depth[0]
     depth_np = depth.cpu().numpy() if torch.is_tensor(depth) else depth
 
-    # 正規化：depthMin → 255 (近/亮), depthMax → 0 (遠/暗)
+    # 正規化：depthMin → 最大值 (近/亮), depthMax → 0 (遠/暗)
     depth_min = depth_np.min()
     depth_max = depth_np.max()
     depth_normalized = (depth_max - depth_np) / (depth_max - depth_min)
-    depth_gray = (depth_normalized * 255).astype(np.uint8)
 
-    return Image.fromarray(depth_gray, mode='L')
+    # 16bit PNG (0-65535)
+    depth_16bit = (depth_normalized * 65535).astype(np.uint16)
+    depth_16bit_img = Image.fromarray(depth_16bit, mode='I;16')
+
+    # 8bit 預覽圖 (0-255)
+    depth_8bit = (depth_normalized * 255).astype(np.uint8)
+    depth_8bit_img = Image.fromarray(depth_8bit, mode='L')
+
+    return depth_8bit_img, depth_16bit_img
 
 # 建立 Gradio 介面
 demo = gr.Interface(
     fn=analyze_depth,
     inputs=gr.Image(type="pil", label="上傳圖片"),
-    outputs=gr.Image(type="pil", label="灰階深度圖"),
+    outputs=[
+        gr.Image(type="pil", label="8bit 預覽圖"),
+        gr.Image(type="pil", label="16bit 深度圖（可下載）")
+    ],
     title="Depth Anything V3 - 深度分析",
-    description="上傳圖片，輸出灰階深度圖（白色=近，黑色=遠）"
+    description="上傳圖片，輸出深度圖（白色=近，黑色=遠）。16bit PNG 保留更精準的深度值。"
 )
 
 demo.launch()