{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4ce7b4b-3994-45ba-812e-a2bf53544df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import pandas as pd\n",
    "\n",
    "def compare_depth_maps(dir1, dir2):\n",
    "    # 读取dir2中的图像并调整尺寸\n",
    "    img2 = cv2.imread(dir2, cv2.IMREAD_GRAYSCALE)\n",
    "    img2_resized = cv2.resize(img2, (512, 512))\n",
    "\n",
    "    mse_list = []\n",
    "    for file in os.listdir(dir1):\n",
    "        if file.endswith(\".png\"):   # 假设所有深度图都是.png格式\n",
    "            # 读取dir1中的图像\n",
    "            img1 = cv2.imread(os.path.join(dir1, file), cv2.IMREAD_GRAYSCALE)\n",
    "            # 计算MSE并添加到列表中\n",
    "            mse = mean_squared_error(img1, img2_resized)\n",
    "            mse_list.append([file, mse])\n",
    "\n",
    "    # 将结果写入Excel\n",
    "    df = pd.DataFrame(mse_list, columns=['Image', 'MSE'])\n",
    "    df.to_excel(os.path.join(dir1, 'mse_results.xlsx'), index=False)\n",
    "\n",
    "# 使用函数\n",
    "compare_depth_maps('D:\\\\SubDiffusion\\\\928\\\\lunwen\\\\f\\\\f-dp\\\\fake', 'D:\\\\SubDiffusion\\\\928\\\\lunwen\\\\f\\\\f-dp\\\\real\\\\f-4.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfef9116-928c-4aab-9b89-6ed2979170b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}