Arisa_PCRegist_SIFT-checkloopresults.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "644afa5a",
   "metadata": {},
   "source": [
    "****\n",
    "# Import transformation(30102023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6657f846",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  [0.0, 0.0, 1.0, -0.00700003],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[0.95348503, 0.15261709, 0.25995063, 0.19682084],\n",
       "  [-0.0138774, 0.88367132, -0.46790214, -0.35920217],\n",
       "  [-0.30112078, 0.44253025, 0.8446853, -0.12937736],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[1.0, 0.0, 0.0, -0.00409579],\n",
       "  [0.0, 1.0, 0.0, 0.007364],\n",
       "  [0.0, 0.0, 1.0, -0.01200003],\n",
       "  [0.0, 0.0, 0.0, 1.0]],\n",
       " [[1.0, 0.0, 0.0, -0.18333028],\n",
       "  [0.0, 1.0, 0.0, -0.04376846],\n",
       "  [0.0, 0.0, 1.0, -0.02999997],\n",
       "  [0.0, 0.0, 0.0, 1.0]]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%run Mylib_import.ipynb\n",
    "exp_dir = \"Arisa_NNSIFT\"\n",
    "\n",
    "# Specify the path to your Excel file\n",
    "folder_name = 'cap_1' #'coffee_mug_1'\n",
    "experiment_fol = 'test30102023'\n",
    "excel_file_path = f'{exp_dir}/{experiment_fol}/Wahington-Lai_results_{folder_name}.xlsx'\n",
    "\n",
    "# Load the Excel file into a DataFrame\n",
    "df = pd.read_excel(excel_file_path)\n",
    "all_T_arrays = []\n",
    "all_src_name =[]\n",
    "all_tar_name=[]\n",
    "# Loop through each row in the DataFrame\n",
    "for index, row in df.iterrows():\n",
    "    # Get the transformation string\n",
    "    transformation_str = row['Transformation']\n",
    "    \n",
    "    # Split the string into lines\n",
    "    lines = transformation_str.splitlines()\n",
    "    \n",
    "    # Process each line\n",
    "    transformation_array = []\n",
    "    for line in lines:\n",
    "        # Remove brackets and extra spaces\n",
    "        line = line.replace(\"[\", \"\").replace(\"]\", \"\").strip()\n",
    "        \n",
    "        # Skip empty lines\n",
    "        if line:\n",
    "            # Split the line into elements\n",
    "            elements = line.split()\n",
    "            \n",
    "            # Convert each element to a float if it's not 'None'\n",
    "            float_elements = []\n",
    "            for e in elements:\n",
    "                try:\n",
    "                    float_elements.append(float(e))\n",
    "                except ValueError:\n",
    "                    float_elements.append(None)  # or you can skip this element entirely\n",
    "            \n",
    "            transformation_array.append(float_elements)\n",
    "            \n",
    "    all_src_name.append(row['Source File'])\n",
    "    all_tar_name.append(row['Target File'])\n",
    "    all_T_arrays.append(transformation_array)\n",
    "    # Convert the list of lists to a numpy array\n",
    "    transformation_array = np.array(transformation_array)\n",
    "    \n",
    "#     # Save the numpy array to a text file\n",
    "#     np.savetxt(f\"{exp_dir}/{experiment_fol}/transformation/{folder_name}/{row['Source File']}_{row['Target File']}.txt\", transformation_array, fmt=\"%s\")\n",
    "all_T_arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3015b73e",
   "metadata": {},
   "source": [
    "****\n",
    "# Check results(31102023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d87128da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source: D:/Users/msari/datasets/Wahington-Lai-rgbd/cap/cap_1/cap_1_1_151.ply, \n",
      "target: D:/Users/msari/datasets/Wahington-Lai-rgbd/cap/cap_1/cap_1_1_156.ply\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[1.0, 0.0, 0.0, 0.00315425],\n",
       " [0.0, 1.0, 0.0, 0.00671351],\n",
       " [0.0, 0.0, 1.0, -0.00700003],\n",
       " [0.0, 0.0, 0.0, 1.0]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loop_n = 139\n",
    "source_dir_test = f\"D:/Users/msari/datasets/Wahington-Lai-rgbd/{folder_name[:-2]}/{folder_name}/{all_src_name[loop_n]}.ply\"\n",
    "target_dir_test = f\"D:/Users/msari/datasets/Wahington-Lai-rgbd/{folder_name[:-2]}/{folder_name}/{all_tar_name[loop_n]}.ply\"\n",
    "print(f\"source: {source_dir_test}, \\ntarget: {target_dir_test}\")\n",
    "src_pcd = o3d.io.read_point_cloud(source_dir_test)\n",
    "tar_pcd = o3d.io.read_point_cloud(target_dir_test)\n",
    "all_T_arrays[loop_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c271414c",
   "metadata": {},
   "outputs": [],
   "source": [
    "src_ = copy.deepcopy(src_pcd)\n",
    "tar_ = copy.deepcopy(tar_pcd)\n",
    "transrc_= copy.deepcopy(src_pcd)\n",
    "o3d.visualization.draw_geometries([src_, tar_], width=800, height=600)\n",
    "transrc_.transform(all_T_arrays[loop_n])\n",
    "o3d.visualization.draw_geometries([transrc_, tar_], width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8fecbc94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder 'Arisa_NNSIFT' already exists.\n"
     ]
    }
   ],
   "source": [
    "%run Mylib_import.ipynb\n",
    "exp_dir = \"Arisa_NNSIFT\"\n",
    "create_folder(exp_dir)\n",
    "\n",
    "def process_point_clouds_dim(src, tar, imdim):   \n",
    "    check_cl_src = check_pc_has_color(src)\n",
    "    check_cl_tar = check_pc_has_color(tar)\n",
    "\n",
    "    def compute_transformation_using_fpfh(src, tar):\n",
    "        # Compute FPFH features\n",
    "        source_fpfh = compute_fpfh_features(src)\n",
    "        target_fpfh = compute_fpfh_features(tar)\n",
    "\n",
    "        # Match the features\n",
    "        matches = o3d.registration.registration_ransac_based_on_feature_matching(\n",
    "            src, tar, source_fpfh, target_fpfh,\n",
    "            max_correspondence_distance=0.1,\n",
    "            estimation_method=o3d.registration.TransformationEstimationPointToPoint(False),\n",
    "            ransac_n=4,\n",
    "            checkers=[o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),\n",
    "                      o3d.registration.CorrespondenceCheckerBasedOnDistance(0.1)],\n",
    "            criteria=o3d.registration.RANSACConvergenceCriteria(4000000, 500)\n",
    "        )\n",
    "\n",
    "        return matches.transformation, 0\n",
    "\n",
    "    if check_cl_src == 'No' or check_cl_tar == 'No':\n",
    "        print(\"----- Non-colored point clouds ------\")\n",
    "        transformation, if_col = compute_transformation_using_fpfh()\n",
    "        imdim = 0\n",
    "        num_keypoints = [0, 0]\n",
    "        \n",
    "    else:\n",
    "        print(\"----- Colored point clouds ------\")\n",
    "        # calculates img_dim referring pointclouds sizes\n",
    "        #imdim = 50\n",
    "        \n",
    "        # Generate pseudo-images (color maps) for both point clouds:    \n",
    "        src_colormap, src_pointmap = point_cloud_to_color_and_point_map(src, img_dim=imdim)\n",
    "        tar_colormap, tar_pointmap  = point_cloud_to_color_and_point_map(tar, img_dim=imdim)\n",
    "\n",
    "        # Extracting SIFT keypoints and descriptors: from the pseudo-images.   \n",
    "        src_keypoints, src_descriptors = extract_sift_feat(src_colormap)\n",
    "        tar_keypoints, tar_descriptors = extract_sift_feat(tar_colormap)\n",
    "        print(f\"Number of source keypoints extracted: {len(src_keypoints), len(tar_keypoints)}\")\n",
    "        draw_keypoints(src_colormap, src_keypoints, 'Source pseudo-image Keypoints')\n",
    "        draw_keypoints(tar_colormap, tar_keypoints, 'Target pseudo-image Keypoints')\n",
    "        cv2.waitKey(0)\n",
    "        cv2.destroyAllWindows()\n",
    "        \n",
    "        if len(src_keypoints) <= 2 or len(tar_keypoints) <= 2:\n",
    "            print(\"----- Keypoints are not proper detected ------\")\n",
    "            transformation, if_col = compute_transformation_using_fpfh()\n",
    "            num_keypoints = [len(src_keypoints), len(tar_keypoints)]\n",
    "        else:\n",
    "            if_col = 1\n",
    "            # Project the keypoints from 2D to 3D using the point map\n",
    "            src_keypoints_3d, src_pseudesc = project_keypoints_to_3D(src_keypoints, src_descriptors, src_pointmap)\n",
    "            tar_keypoints_3d, tar_pseudesc = project_keypoints_to_3D(tar_keypoints, tar_descriptors, tar_pointmap)\n",
    "\n",
    "            # Debugging: Printing the number of 3D keypoints generated\n",
    "            print(f\"Number of 3D keypoints(src, tar): {len(src_keypoints_3d), len(tar_keypoints_3d)}\")\n",
    "            num_keypoints = [len(src_keypoints_3d), len(tar_keypoints_3d)]\n",
    "\n",
    "            # Assuming src_descriptors and tar_descriptors are your source and target descriptors\n",
    "            good_matches = find_good_matches(src_pseudesc, tar_pseudesc)\n",
    "\n",
    "            # Assuming good_matches contains your filtered matches\n",
    "            matched_src_keypoints, matched_tar_keypoints, matched_src_descriptors, matched_tar_descriptors = \\\n",
    "                extract_matched_keypoints_descriptors(good_matches, src_keypoints_3d, tar_keypoints_3d, src_pseudesc, tar_pseudesc)\n",
    "\n",
    "            # Applying RANSAC\n",
    "            transformation = apply_ransac(matched_src_keypoints, matched_tar_keypoints, matched_src_descriptors, matched_tar_descriptors)\n",
    "    \n",
    "    # Check if the transformation was successful\n",
    "    if transformation is not None:\n",
    "        transformed_src = copy.deepcopy(src)\n",
    "        transformed_src.transform(transformation)\n",
    "    else:\n",
    "        print(\"Transformation failed.\")\n",
    "        print(transformation)\n",
    "        return None, None, None, None, None, imdim, num_keypoints, if_col\n",
    "\n",
    "    # Compute and print RMSE for quantitative evaluation\n",
    "    init_rmse = compute_rmse(src, tar)\n",
    "    rmse = compute_rmse(transformed_src, tar)\n",
    "\n",
    "    # Compute color differences\n",
    "    mean_color_init,mean_point_init  = compute_differences(src, tar)\n",
    "    mean_color_regist, mean_point_regist = compute_differences(transformed_src, tar)\n",
    "    \n",
    "    return transformed_src, rmse, mean_color_regist, mean_point_regist, transformation, imdim, num_keypoints, if_col\n",
    "\n",
    "###############################\n",
    "dim = 40\n",
    "transformed_src, rmse, mean_color_regist, mean_point_regist, transformation, imdim, num_keypoints, if_col = process_point_clouds_dim(src_, tar_, dim)\n",
    "\n",
    "src_ = copy.deepcopy(src_pcd)\n",
    "tar_ = copy.deepcopy(tar_pcd)\n",
    "print(f\"source:{len(src_.points)} points, target:{len(tar_.points)} points\")\n",
    "transrc_= copy.deepcopy(src_pcd)\n",
    "o3d.visualization.draw_geometries([src_, tar_], width=800, height=600)\n",
    "transrc_.transform(transformation)\n",
    "o3d.visualization.draw_geometries([transrc_, tar_], width=800, height=600)\n",
    "o3d.visualization.draw_geometries([transformed_src, tar_], width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5a26783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source:16681 points, target:16331 points\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 9.82705233e-01,  1.84871946e-01,  1.06202166e-02,\n",
       "         1.05112037e-02],\n",
       "       [-1.84178382e-01,  9.81745695e-01, -4.74732895e-02,\n",
       "        -3.55883946e-02],\n",
       "       [-1.92028313e-02,  4.46962357e-02,  9.98816048e-01,\n",
       "        -3.79827136e-04],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.00000000e+00]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_transformation_using_fpfh(src, tar):\n",
    "    # Compute FPFH features\n",
    "    source_fpfh = compute_fpfh_features(src)\n",
    "    target_fpfh = compute_fpfh_features(tar)\n",
    "\n",
    "    # Match the features\n",
    "    matches = o3d.registration.registration_ransac_based_on_feature_matching(\n",
    "        src, tar, source_fpfh, target_fpfh,\n",
    "        max_correspondence_distance=0.1,\n",
    "        estimation_method=o3d.registration.TransformationEstimationPointToPoint(False),\n",
    "        ransac_n=4,\n",
    "        checkers=[o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),\n",
    "                  o3d.registration.CorrespondenceCheckerBasedOnDistance(0.1)],\n",
    "        criteria=o3d.registration.RANSACConvergenceCriteria(4000000, 500)\n",
    "    )\n",
    "\n",
    "    return matches.transformation, 0\n",
    "T,_ = compute_transformation_using_fpfh(src_, tar_)\n",
    "\n",
    "src_ = copy.deepcopy(src_pcd)\n",
    "tar_ = copy.deepcopy(tar_pcd)\n",
    "print(f\"source:{len(src_.points)} points, target:{len(tar_.points)} points\")\n",
    "transrc_= copy.deepcopy(src_pcd)\n",
    "o3d.visualization.draw_geometries([src_, tar_], width=800, height=600)\n",
    "transrc_.transform(T)\n",
    "o3d.visualization.draw_geometries([transrc_, tar_], width=800, height=600)\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "650d073e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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