Update app.py

app.py

@@ -8,21 +8,19 @@ import numpy as np
 import gradio as gr
 from huggingface_hub import hf_hub_download
 
-# ================== 必须最早 import spaces ==================
 try:
-    import spaces  # type: ignore
+    import spaces  
     gpu_decorator = spaces.GPU
 except Exception:
     gpu_decorator = lambda f: f
 
-# ================== 工程路径：确保能 import networks ==================
-# 适配：无论你从哪里启动 python app.py，都能找到项目根目录
+
 PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(PROJECT_ROOT)
 
 from networks.models import make  # noqa: E402
 
-# ================== HF 模型配置 ==================
+
 WEIGHTS_REPO = "Insta360-Research/DAP-weights"
 WEIGHTS_FILE = "model.pth"
 CONFIG_PATH = os.path.join(PROJECT_ROOT, "config", "infer.yaml")
@@ -30,7 +28,7 @@ CONFIG_PATH = os.path.join(PROJECT_ROOT, "config", "infer.yaml")
 model = None
 device = "cpu"
 
-# ================== 固定颜色映射（颜色一致） ==================
+
 import matplotlib
 
 def colorize_depth_fixed(depth_u8: np.ndarray, cmap: str = "Spectral") -> np.ndarray:
@@ -43,7 +41,7 @@ def colorize_depth_fixed(depth_u8: np.ndarray, cmap: str = "Spectral") -> np.nda
     colored = (colored * 255).astype(np.uint8)
     return np.ascontiguousarray(colored)
 
-# ================== 模型加载 ==================
+
 def load_model(config_path: str):
     import torch
     import torch.nn as nn
@@ -77,17 +75,17 @@ def load_model(config_path: str):
     print("✅ Model loaded.")
     return m
 
-# ================== 启动时加载一次模型 ==================
+
 model = load_model(CONFIG_PATH)
 
-# ================== 加载标度尺图片 ==================
+
 COLORBAR_DIR = os.path.join(PROJECT_ROOT, "colorbars")
 colorbar_100m_color = cv2.imread(os.path.join(COLORBAR_DIR, "colorbar_100m_color.png"))
 colorbar_100m_gray = cv2.imread(os.path.join(COLORBAR_DIR, "colorbar_100m_gray.png"))
 colorbar_10m_color = cv2.imread(os.path.join(COLORBAR_DIR, "colorbar_10m_color.png"))
 colorbar_10m_gray = cv2.imread(os.path.join(COLORBAR_DIR, "colorbar_10m_gray.png"))
 
-# 转换为RGB（Gradio需要RGB格式）
+
 if colorbar_100m_color is not None:
     colorbar_100m_color = cv2.cvtColor(colorbar_100m_color, cv2.COLOR_BGR2RGB)
 if colorbar_100m_gray is not None:
@@ -97,7 +95,7 @@ if colorbar_10m_color is not None:
 if colorbar_10m_gray is not None:
     colorbar_10m_gray = cv2.cvtColor(colorbar_10m_gray, cv2.COLOR_BGR2RGB)
 
-# ================== 推理函数 ==================
+
 @gpu_decorator
 def infer_raw(img_rgb: np.ndarray):
     if img_rgb is None:
@@ -105,7 +103,6 @@ def infer_raw(img_rgb: np.ndarray):
 
     import torch
 
-    # 保持你原逻辑：不 resize，直接喂入
     img = img_rgb.astype(np.float32) / 255.0
     tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(device)
 
@@ -119,7 +116,6 @@ def infer_raw(img_rgb: np.ndarray):
                 outputs["pred_depth"][~mask] = 1
             pred = outputs["pred_depth"][0].cpu().squeeze().numpy()
         else:
-            # 保持你原逻辑的 fallback
             pred = outputs[0].cpu().squeeze().numpy()
 
     return pred.astype(np.float32)
@@ -152,11 +148,10 @@ def visualize_10m(pred: np.ndarray):
 
 @gpu_decorator
 def infer_and_vis_100m(img_rgb: np.ndarray):
-    pred = infer_raw(img_rgb)                 # 跑模型一次（GPU）
-    color, gray, npy, cbar_color, cbar_gray = visualize_100m(pred)   # 默认100m显示（CPU）
+    pred = infer_raw(img_rgb)              
+    color, gray, npy, cbar_color, cbar_gray = visualize_100m(pred)  
     return pred, color, gray, npy, cbar_color, cbar_gray
 
-# ================== Gradio UI ==================
 example_paths = [
     "hfdemo/01.jpg",
     "hfdemo/02.jpg",
@@ -196,12 +191,10 @@ with gr.Blocks() as demo:
     )
     gr.Markdown("# Official Depth Prediction demo for **[DAP](https://insta360-research-team.github.io/DAP_website/)**")
 
-    raw_depth = gr.State()  # 🔑 保存模型输出
+    raw_depth = gr.State() 
 
     with gr.Row():
     
-        # ========== Left ==========
-        # 左侧列（Input Image）
         with gr.Column(scale=10):
             inp = gr.Image(
                 type="numpy",
@@ -231,29 +224,23 @@ with gr.Blocks() as demo:
                 elem_id="vis_hint",
             )
     
-        # ========== Right ==========
-        # 右侧整体（包含 中间列 + colorbar 列）
         with gr.Column(scale=11):
     
             # -------- Row 1: Color Depth --------
             with gr.Row():
-                # 中间列（必须和左侧等宽）
                 with gr.Column(scale=10):
                     out_color = gr.Image(
                         label="Depth (Color)",
                         height=260
                     )
     
-                # colorbar 列（很窄）
                 with gr.Column(scale=1, min_width=80):
                     colorbar_color = gr.Image(
                         label="Scale",
                         height=260,
-                        show_label=False,
-                        show_download_button=False
+                        show_label=False
                     )
     
-            # -------- Row 2: Gray Depth --------
             with gr.Row():
                 with gr.Column(scale=10):
                     out_gray = gr.Image(
@@ -265,28 +252,24 @@ with gr.Blocks() as demo:
                     colorbar_gray = gr.Image(
                         label="Scale",
                         height=260,
-                        show_label=False,
-                        show_download_button=False
+                        show_label=False
                     )
     
             out_npy = gr.File(label="Depth (.npy)")
 
 
-    # 1️⃣ 跑模型
     btn_infer.click(
         fn=infer_and_vis_100m,
         inputs=inp,
         outputs=[raw_depth, out_color, out_gray, out_npy, colorbar_color, colorbar_gray],
     )
 
-    # 2️⃣ 100m
     btn_100m.click(
         fn=visualize_100m,
         inputs=raw_depth,
         outputs=[out_color, out_gray, out_npy, colorbar_color, colorbar_gray],
     )
 
-    # 3️⃣ 10m
     btn_10m.click(
         fn=visualize_10m,
         inputs=raw_depth,
@@ -298,9 +281,9 @@ if __name__ == "__main__":
     host = os.environ.get("HOST", "0.0.0.0")
     port = int(os.environ.get("PORT", "7860"))
 
-    demo.queue(  # ✅ 开启排队
-        max_size=32,          # 队列最大长度（可选）
-        default_concurrency_limit=1,  # 每个函数默认并发（可选，GPU任务通常设 1）
+    demo.queue(
+        max_size=32,         
+        default_concurrency_limit=1,  
     ).launch(
         server_name=host,
         server_port=port,