app.py

  import gradio as gr
  import torch
  from transformers import AutoImageProcessor,
  AutoModelForDepthEstimation
  import numpy as np
  from PIL import Image
  import cv2

  # デバイス設定
  device = "cpu"
  print(f"Using device: {device}")

  # モデル読み込み
  model_name = "depth-anything/Depth-Anything-V2-Small-hf"
  processor = AutoImageProcessor.from_pretrained(model_name)
  model = AutoModelForDepthEstimation.from_pretrained(model_name)
  model.to(device)
  model.eval()
  print("Model loaded successfully")

  def predict_depth(image):
      """深度推定関数"""
      if image is None:
          return None, None

      try:
          # 画像処理
          if hasattr(image, 'convert'):
              image = image.convert('RGB')

          # サイズ調整
          max_size = 256
          if max(image.size) > max_size:
              ratio = max_size / max(image.size)
              new_size = tuple(int(dim * ratio) for dim in image.size)        
              image = image.resize(new_size, Image.Resampling.LANCZOS)        

          # 深度推定
          inputs = processor(images=image, return_tensors="pt")

          with torch.no_grad():
              outputs = model(**inputs)
              depth = outputs.predicted_depth.squeeze().cpu().numpy()

          # 可視化
          depth_norm = ((depth - depth.min()) / (depth.max() -
  depth.min()) * 255).astype(np.uint8)
          depth_colored = cv2.applyColorMap(depth_norm,
  cv2.COLORMAP_VIRIDIS)
          depth_colored = cv2.cvtColor(depth_colored, cv2.COLOR_BGR2RGB)      
          depth_image = Image.fromarray(depth_colored)

          return image, depth_image

      except Exception as e:
          print(f"Error: {e}")
          return image, None

  # Gradioインターフェース
  demo = gr.Interface(
      fn=predict_depth,
      inputs=gr.Image(type="pil"),
      outputs=[
          gr.Image(type="pil", label="Original"),
          gr.Image(type="pil", label="Depth Map")
      ],
      title="Depth Estimation API",
      description="DepthAnything V2による深度推定"
  )

  if __name__ == "__main__":
      demo.launch()