VisualPlaceRecognition_EvaluationTools.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Place Recognition + Cut Graph Evaluation\n",
    "\n",
    "This code implements the Visual Place Recognition (VPR) system and the criteria to evaluate the topology of places resulting from the recursive bipartition of the Appearance Graph."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libreries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from Utils import geometry2 as geo\n",
    "from Utils import load_utils\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxliar Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_descriptor(general_path):\n",
    "    # Function to read the global descriptors of an h5py file\n",
    "    descriptors=[]\n",
    "    for suffix in os.listdir(general_path):\n",
    "        f=h5py.File(general_path+suffix,'r')\n",
    "        descriptors+=f['features']\n",
    "    return np.array(descriptors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pose_mean_cov(map_dataset):\n",
    "    # Function to calculate the mean and the covariance matrix of the distributions of poses of the groups.\n",
    "    # Also computes the mean of the appearance descriptors for each group, called centroids.\n",
    "\n",
    "    # Initialization of variables\n",
    "    mean_pose=[]\n",
    "    mean_feats=[]\n",
    "    cov_pose=[]\n",
    "\n",
    "    # The groups of the topology are traversed\n",
    "    for cl in map_dataset['clusters_list']:\n",
    "        poses=map_dataset['poses'][cl]  # Mean in group poses\n",
    "        feats=map_dataset['feats'][cl]  # Mean in group appearance, centroid\n",
    "\n",
    "        # Computes the pose and the covariance matrix in SE(2)\n",
    "        poses_mean, poses_cov = geo.get_mean_covariance(poses)\n",
    "        feats_mean=np.mean(feats,axis=0)\n",
    "\n",
    "        # Save the results in the lists:\n",
    "        mean_pose.append(poses_mean)\n",
    "        mean_feats.append(feats_mean)\n",
    "        cov_pose.append(poses_cov)\n",
    "\n",
    "        del poses_mean, poses_cov, feats_mean\n",
    "    \n",
    "    # Save the result in the dictionary of the topological map:\n",
    "    map_dataset['poses_mean']=mean_pose\n",
    "    map_dataset['feats_mean']=mean_feats\n",
    "    map_dataset['poses_cov']=cov_pose\n",
    "    \n",
    "    return mean_pose, cov_pose"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## APPEARANCE DISTANCE\n",
    "This part implements the criteria to determine the distance between image descriptors, it is used in the VPR process to assign a query to a group."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions to calculate the distance in appearance between queries and places on the topological map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "cos_sim=lambda x,y:np.dot(x,y)/(np.linalg.norm(x)*np.linalg.norm(y))    # Cosine similarity\n",
    "euc_sim= lambda x,y: np.linalg.norm(np.array(x)-np.array(y))            # Euclidean distance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cosine similarity criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def app_dist_cos(query_descriptors,map_descriptors):\n",
    "    distances=[[cos_sim(query_descriptors[j],map_descriptors[i])for i in range(len(map_descriptors))]for j in range(len(query_descriptors))]\n",
    "    return np.array(distances)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimum Euclidean distance criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def app_dist_euc(query_descriptors,map_descriptors):\n",
    "    distances=[[euc_sim(query_descriptors[j],map_descriptors[i])for i in range(len(map_descriptors))]for j in range(len(query_descriptors))]\n",
    "    return np.array(distances)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 1: Most similar centroid"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion measures the distance between each query and the centroids of the groups.\n",
    "\n",
    "Returns the group to which the query belongs based on the min/max distance to the centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_similar_centroid(query_descriptors, map_dataset, criterion='COS'):\n",
    "\n",
    "    if criterion == 'COS':      # Cosine similarity\n",
    "        distances=app_dist_cos(query_descriptors,map_dataset['feats_mean']) # Calculates cosine similarity for all queries with respect to map centroids\n",
    "        return np.argmax(np.array(distances),axis=1)    # The maximum value of the cosine similarity for each query gives us the group to which it belongs\n",
    "    \n",
    "    elif criterion == 'EUC':    # Euclidean distance \n",
    "        distances=app_dist_euc(query_descriptors,map_dataset['feats_mean']) # Calculation of the Euclidean distance for all queries with respect to the centroids of the map\n",
    "        return np.argmin(np.array(distances),axis=1)    # The minimum value of the Euclidean distance for each query gives us the group to which it belongs\n",
    "    \n",
    "    else:\n",
    "        print('CRITERION ERROR: not valid')\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 2: k-most similar images"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion measures the distance in appearance for all the elements of the topological map, it returns the closest k\n",
    "\n",
    "This criterion was used in the research but was finally discarded from the final results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_similar(query_descriptors, map_dataset, k, criterion='COS'):\n",
    "\n",
    "    if criterion=='COS':     # Cosine similarity\n",
    "        distances=app_dist_cos(query_descriptors,map_dataset['feats']) # Calculates cosine similarity for all queries with all map descriptors\n",
    "        idx=np.argsort(np.array(distances),axis=1)                     # Order the distances from least to greatest.\n",
    "        img=idx[:][-k:]      # The k closest elements are chosen\n",
    "\n",
    "        # The groups to which the k closest elements belong are calculated\n",
    "        tam=len(query_descriptors)\n",
    "        cl=np.array([[map_dataset['clusters_idx'][idx[i][-j]] for j in range(1,k+1)] for i in range(tam)])  \n",
    "\n",
    "        return cl, img\n",
    "    \n",
    "    elif criterion=='EUC':   # Euclidean distance\n",
    "        distances=app_dist_euc(query_descriptors,map_dataset['feats']) # Calculates the Euclidean distance for all queries with all map descriptors\n",
    "        idx=np.argsort(np.array(distances),axis=1)                     # Order the distances from least to greatest.\n",
    "        img=idx[:,:k]        # The k closest elements are chosen\n",
    "\n",
    "        # The groups to which the k closest elements belong are calculated\n",
    "        tam=len(query_descriptors)\n",
    "        cl=np.array([[map_dataset['clusters_idx'][idx[i][j]] for j in range(k)] for i in range(tam)])  # The groups to which the k closest elements belong are calculated\n",
    "        \n",
    "        return cl, img\n",
    "    \n",
    "    else:\n",
    "        print('CRITERION ERROR: not valid')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## POSE DISTANCE\n",
    "In this section the different criteria are implemented to determine the distance in pose between the queries and the groups of the map."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 1: Mode of K-neighbors SE2 Pose"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion measures the distance in SE(2) from the poses in the query to each pose in the map and calculates the K nearest neighbor. Returns the groups to which the majority of the k-neighbors belong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mode(List):\n",
    "    # Function to calculate the mode in a list of groups of the topological map\n",
    "    aux=np.zeros(max(List)+1).tolist()\n",
    "    for item in List:\n",
    "        aux[item]+=1\n",
    "    return np.argmax(aux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mode_K_Neighbours(query_poses, map_dataset, thr_ang, k=5):\n",
    "    # Return array, with the groups to which each query belongs\n",
    "    res=[]\n",
    "\n",
    "    # Combine the map poses into a SE(2) class\n",
    "    map_poses=geo.combine(map_dataset['poses'])\n",
    "    \n",
    "    for i in range(len(query_poses)):\n",
    "        pq=query_poses[i]   # Query pose\n",
    "\n",
    "        # Calculates the composition of poses of each query to all the poses of the map\n",
    "        incre= pq / map_poses\n",
    "        dist=[np.linalg.norm(incre.t()[i]) for i in range(len(incre))]  # Extract the translation\n",
    "        ang_dist=(incre.R().as_euler('xyz'))[:,2]                       # Extract the rotation\n",
    "\n",
    "        # Sort the distances from least to greatest\n",
    "        sort_idx=np.argsort(dist)\n",
    "        \n",
    "        assert len(dist) > k \n",
    "\n",
    "        # Loop to calculate the groups of the k closest elements within the angular distance threshold\n",
    "        temp_idx=[]\n",
    "        for j in sort_idx:\n",
    "            if np.abs(ang_dist[j]) < thr_ang:               # Ensure that it meets the threshold in the angular distance\n",
    "                cluster_idx=map_dataset['clusters_idx'][j]  # Index of the group to which the k elements belong\n",
    "                temp_idx.append(cluster_idx) \n",
    "            if len(temp_idx)>=k: break  # When the number of indices exceeds the value k the loop stops\n",
    "        \n",
    "        # Call the 'get_mode' function to calculate the mode of the k cluster indices for each query\n",
    "        res.append(get_mode(temp_idx))\n",
    "        \n",
    "    return res\n",
    "         "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 2: Most nearest image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion returns the group whose element has the smallest distance in SE(2) for each query, considering it the closest group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearest(query_poses, map_dataset, thr_ang=0.3):\n",
    "    # Return array, with the groups to which each query belongs\n",
    "    res=[]\n",
    "\n",
    "    # Combine the map poses into a Se(2) class\n",
    "    map_poses=geo.combine(map_dataset['poses'])\n",
    "    \n",
    "    # For each query:\n",
    "    for i in range(len(query_poses)):\n",
    "        pq=query_poses[i]   # Pose query\n",
    "\n",
    "        # Calculate composition of poses between the query and the elements of the topological map\n",
    "        incre= pq / map_poses\n",
    "        dist=[np.linalg.norm(incre.t()[i]) for i in range(len(incre))]  # Extract the translation\n",
    "        ang_dist=(incre.R().as_euler('xyz'))[:,2]                       # Extract the rotation\n",
    "\n",
    "        # Sort the distances from least to greatest\n",
    "        sort_idx=np.argsort(dist)\n",
    "\n",
    "        # Loop to obtain the topology group with element closest to the query\n",
    "        for j in sort_idx:\n",
    "            if np.abs(ang_dist[j]) < thr_ang:               # Ensure that it meets the threshold in the angular distance\n",
    "                res.append(map_dataset['clusters_idx'][j])  # For each query, the map group whose element is at the shortest distance is assigned.\n",
    "                break\n",
    "            \n",
    "    return res"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 3: Mahalanobis Distance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion implements the Mahalanobis distance between a set of queries and the distributions of poses of the elements of the topology.\n",
    "\n",
    "For this, the mean and the covariance matrix of each distribution are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mahdist_to_cluster(query_dataset,map_dataset,select_idx):\n",
    "    mahdist=[]\n",
    "    \n",
    "    # The query poses array is traversed:\n",
    "    for pose in query_dataset['poses'][select_idx]:\n",
    "        distances=[]\n",
    "        # The Mahalanobis distance from the query pose to each group is computed with the geometry2 library.\n",
    "        for idx in range(len(map_dataset['poses_mean'])):\n",
    "            distances.append(geo.multivariate_mahalannobis(pose,\n",
    "                                                          map_dataset['poses_mean'][idx],\n",
    "                                                          map_dataset['poses_cov'][idx]))\n",
    "        mahdist.append(np.array(distances).T)\n",
    "    \n",
    "    return np.array(mahdist)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EVALUATION CRITERIA\n",
    "In this part, the two evaluation criteria used to evaluate the proposed place topology are implemented."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 1: Multiclass Confusion Matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this criterion, the Multiclass Confusion Matrix for the VPR system is calculated.\n",
    "\n",
    "Precision and recall are extracted from it, both at the group level and at the general level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cm_analysis(y_true, y_pred, labels, ymap=None, figsize=(10,10)):\n",
    "#   Function to plot multiclass confusion matrix with annotations.\n",
    "#   - Input: \n",
    "#         y_true    ---> True value (ground-truth) of queries\n",
    "#         y_pred    ---> Group prediction of queries\n",
    "#         labels    ---> Labels of query classes, array of string\n",
    "#         figsize   ---> Plot size\n",
    "\n",
    "    # Calculate the multiclass confusion matrix with the sklearn method\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
    "    cm_perc = cm / cm_sum.astype(float) * 100\n",
    "    annot = np.empty_like(cm).astype(str)\n",
    "    nrows, ncols = cm.shape\n",
    "    for i in range(nrows):\n",
    "        for j in range(ncols):\n",
    "            c = cm[i, j]\n",
    "            p = cm_perc[i, j]\n",
    "            if i == j:\n",
    "                s = cm_sum[i]\n",
    "\n",
    "                annot[i, j] = '%d' % (c)\n",
    "            elif c == 0:\n",
    "                annot[i, j] = ''\n",
    "            else:\n",
    "                annot[i, j] = '%d' % (c)\n",
    "    cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
    "    cm.index.name = 'Ground truth'\n",
    "    cm.columns.name = 'Prediction'\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    sns.heatmap(cm, annot=annot, fmt='', ax=ax,cmap='BuPu',vmin=0, vmax=10,linewidths=1,linecolor='black')\n",
    "    plt.title(\"Multiclass confusion matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_mat(query_GT, query_predict, n_clusters):\n",
    "#   Calculate the multiclass confusion matrix for all groups in the topological map\n",
    "#   Result different from the sklearn cm method, useful for evaluation of Precision and Recall at group level\n",
    "#   - Input: \n",
    "#         query_GT          ---> True value (ground-truth) of queries\n",
    "#         query_predict     ---> Query group prediction\n",
    "#         n_cluster         ---> Total number of clusters in the topological map\n",
    "#   - Output:\n",
    "#         confm             ---> Multiclass confusion matrix\n",
    "\n",
    "    # Initialize the multiclass confusion matrix\n",
    "    tam=n_clusters # Number of total groups\n",
    "    confm=np.reshape(np.zeros(tam**2),[tam,tam])\n",
    "\n",
    "    # Fill in the boxes of the multiclass confusion matrix\n",
    "    for i in range(len(query_GT)):\n",
    "        confm[query_GT[i],np.array(query_predict)[i]]+=1\n",
    "\n",
    "    return confm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_eval(query_dataset, map_dataset, select_idx, remove=True):\n",
    "#   Function that implements the evaluation of the precision and sensitivity of the VPR system at the group level\n",
    "#   - Input: \n",
    "#         query_dataset ---> Query Dataset\n",
    "#         map_dataset   ---> Topology map dataset\n",
    "#         select_idx    ---> Indices of query sets selected for evaluation\n",
    "#         remove        ---> Boolean to determine if groups not selected in the query are removed\n",
    "#   - Output:\n",
    "#         confm         ---> Multiclass confusion matrix\n",
    "    \n",
    "    \n",
    "    # Create labels for the plot\n",
    "    n_clusters=len(map_dataset['clusters_list'])\n",
    "    label=['C%s'%(i) for i in range(n_clusters)]\n",
    "\n",
    "    # Calculate ground-truth matrix (m x n) based on the closest pose on the map / m= nº of queries, n= nº of groups\n",
    "    query_GT=nearest(query_dataset['poses'][select_idx],map_dataset)\n",
    "\n",
    "    # Calculate prediction matrix (mxn) for queries based on appearance\n",
    "    query_predict=most_similar_centroid(query_dataset['feats'][select_idx],map_dataset)\n",
    "\n",
    "    # Calculate multiclass confusion matrix\n",
    "    confm=confusion_mat(query_GT,query_predict,n_clusters)\n",
    "\n",
    "    # Calculate number of True Positive, False Positive and False Negative\n",
    "    TP=np.diag(confm)                       # TP = Value on the diagonal\n",
    "    FP=confm.sum(axis=0) - np.diag(confm)   # FP = Sum of rows minus value of TP\n",
    "    FN=confm.sum(axis=1) - np.diag(confm)   # FN = Sum of columns minus TP value\n",
    "\n",
    "    # Compute precision and recall\n",
    "    epsilon=1e-10   # Added to avoid division by 0\n",
    "    P=TP/(TP+FP+epsilon)\n",
    "    R=TP/(TP+FN+epsilon)\n",
    "\n",
    "    # Removes groups that are not selected in the ranking for clearer visual representation\n",
    "    if remove:\n",
    "        idx=np.arange(n_clusters)\n",
    "        good_idx = np.union1d(np.argwhere(confm.sum(0) > 0).reshape([-1]), np.argwhere(confm.sum(1) > 0).reshape([-1]))\n",
    "        assert len(idx)==confm.shape[0]\n",
    "\n",
    "        P=P[good_idx]\n",
    "        R=R[good_idx]\n",
    "        label=['C%s'%(i) for i in good_idx]\n",
    "    \n",
    "    return (label,P), (label,R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def general_eval(query_dataset, map_dataset, select_idx):\n",
    "#   Function that implements the evaluation of the precision, sensitivity and accuracy of the VPR system at a general level\n",
    "#   - Input: \n",
    "#         query_dataset ---> Query Dataset\n",
    "#         map_dataset   ---> Topology map dataset\n",
    "#         select_idx    ---> Indices of query sets selected on for evaluation\n",
    "#   - Output:\n",
    "#         confm         ---> Multiclass confusion matrix\n",
    "\n",
    "    # Calculate ground-truth matrix (m x n) based on the closest pose on the map / m= nº of queries, n= nº of groups\n",
    "    query_GT=nearest(query_dataset['poses'][select_idx],map_dataset)\n",
    "\n",
    "    # Calculate prediction matrix (mxn) for queries based on appearance\n",
    "    query_predict=most_similar_centroid(query_dataset['feats'][select_idx],map_dataset)\n",
    "\n",
    "    # Calculate multiclass confusion matrix with the sklearn method\n",
    "    confm=confusion_matrix(query_GT,query_predict)\n",
    "    \n",
    "    # Calculate number of True Positive, True Negative, False Positive and False Negative\n",
    "    TP = np.diag(confm).sum(0)          # TP = Diagonal sum of multiclass confusion matrix\n",
    "    FP = confm.sum(axis=0).sum() - TP   # FP = Sum of all rows of the multiclass confusion matrix minus the value of TP\n",
    "    FN = confm.sum(axis=1).sum() - TP   # FN = Sum of all columns of the multiclass confusion matrix minus the value of TP\n",
    "    TN = 0                              # TN = Sum of all rows and columns other than the class on which it is calculated.\n",
    "    for i in range(confm.shape[0]):     # Add the value for all classes\n",
    "        for k in range(confm.shape[0]):\n",
    "            for j in range(confm.shape[1]):\n",
    "                if k!=i and j!=i :\n",
    "                    TN+=confm[k][j]\n",
    "    \n",
    "    # Calculate the overall precision, sensitivity, and accuracy:\n",
    "    P=TP/(TP+FP)\n",
    "    R=TP/(TP+FN)\n",
    "    ACC = (TP+TN)/(TP+TN+FP+FN)\n",
    "\n",
    "    return P, R, ACC"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criterion 2: Silhouette score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this criterion, the silhouette score is calculated for all the elements of the topological map in order to determine the existence of Perceptual Aliasing in the classification. It is based on the calculation of the Mahalanobis distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sh_bar(score,xlabel,k=4):\n",
    "#   Function that plots the bar chart to display the result of the silhouette score\n",
    "#   - Input: \n",
    "#         score            ---> Array with SH values (silhouette score)\n",
    "#         xlabel           ---> Plot title\n",
    "#         k                ---> Number of intervals in the range [-1,1] to create the bar graph\n",
    "#   - Output:\n",
    "#         (label, graph)   ---> Labels for bar chart and bar chart\n",
    "\n",
    "    assert k>1\n",
    "\n",
    "    # Sorting the score array:\n",
    "    sorted_arr = np.sort(score)\n",
    "    n = len(sorted_arr)\n",
    " \n",
    "    # Divide the range [-1,1] into k equal parts\n",
    "    bin_edges = np.linspace(-1,1,k+1).tolist()\n",
    "    print('Bin edges: ',bin_edges)\n",
    "    graph = np.zeros(k,dtype=int)\n",
    "\n",
    "    # Increase for each score value the corresponding interval\n",
    "    for i in range(n):\n",
    "        j = np.searchsorted(bin_edges, sorted_arr[i], side='right')-1\n",
    "        graph[j] += 1\n",
    "    \n",
    "    # We create the bar chart:\n",
    "    label=['[%s, %s)'%(np.round(bin_edges[i],2),np.round(bin_edges[i+1],2)) for i in range(k)]\n",
    "    # Red and green colors for wrong and correct classifications\n",
    "    my_color=['red' for i in range(len(bin_edges)) if bin_edges[i]<0]\n",
    "    my_color.extend(['green' for i in range(len(bin_edges)-1) if bin_edges[i]>=0])\n",
    "\n",
    "    # Show plot\n",
    "    fig,ax=plt.subplots(figsize=(7,5))\n",
    "    bars=plt.bar(label,graph,width=0.5,color=my_color)\n",
    "    ax.bar_label(bars)\n",
    "    plt.title(xlabel)\n",
    "    plt.xlabel('Intervalos de SH')\n",
    "    plt.show()\n",
    "\n",
    "    return (label,graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_pose_silhouette_score(map_dataset, select_idx):\n",
    "#   This function calculates the silhouette score for the elements of the topological map.\n",
    "#   For each element, calculate the Mahalanobis distance of its pose to all pose distributions on the map. The silhouette score is calculated\n",
    "#   comparing the distances of each element with its group and the closest group other than its own.\n",
    "#   - Input: \n",
    "#         map_dataset      ---> Topology map dataset\n",
    "#         select_idx       ---> Selected map indices, usually we get all indices\n",
    "#   - Output:\n",
    "#         score            ---> Array with outline score for each map element\n",
    "\n",
    "    # SH array initialization:\n",
    "    score=np.zeros(len(select_idx))\n",
    "\n",
    "    # Calculates Mahalanobis distance for selected map elements and all map pose distributions\n",
    "    mahdist=mahdist_to_cluster(map_dataset,map_dataset,select_idx)\n",
    "\n",
    "\n",
    "    for i in range(len(select_idx)):\n",
    "        # Select Mahalanobis distance from element to its cluster: map_dataset['clusters_idx'][select_idx[i]]\n",
    "        a=mahdist[i][map_dataset['clusters_idx'][select_idx[i]]]\n",
    "        \n",
    "        # Remove Mahalanobis distance from the group to which the element belongs\n",
    "        dist=np.copy(mahdist[i])\n",
    "        dist=np.delete(dist,map_dataset['clusters_idx'][select_idx[i]])\n",
    "\n",
    "        # Sets the Mahalanobis distance of the element to the nearest group other than its own\n",
    "        sort_dist=np.sort(dist)\n",
    "        b=sort_dist[0]\n",
    "\n",
    "        # Silhouette Score Formula:\n",
    "        score[i]=(b-a)/np.max([a,b])\n",
    "\n",
    "    return score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Topology map directories\n",
    "map_json=\".../Utils/Topological_map.json\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a dictionary with the information from the query dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary creation:\n",
    "query_dict=dict(im_paths=[], poses=[], odom_poses=[], features=[], seq=[], test=[])\n",
    "\n",
    "query_path='.../dataset/query/'    # Query dataset directory\n",
    "\n",
    "# Load poses and other information:\n",
    "for json_suffix in os.listdir(query_path+\"Poses/\"):\n",
    "    json_file=json.load(open(query_path+\"Poses/\"+json_suffix)) \n",
    "    samples=len(json_file['im_paths'])\n",
    "    query_dict['im_paths']+=json_file['im_paths']\n",
    "    query_dict['poses']+=json_file['poses']\n",
    "    query_dict['odom_poses']+=json_file['odom_poses']\n",
    "    query_dict['seq']+=[json_suffix]*samples \n",
    "    \n",
    "# Load query image descriptors\n",
    "descr_path=query_path+'Descriptors/'\n",
    "features= get_descriptor(descr_path).tolist()\n",
    "query_dict['features']+=features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We process the query poses to represent them in SE(2) space\n",
    "query_dataset = load_utils.load_sequence(query_dict,verbose=False)\n",
    "pose_query = query_dataset['poses']\n",
    "feats_query = query_dataset['feats']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select query dataset indices for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "tam=len(pose_query)\n",
    "select_idx=np.arange(0,tam,5)  # Selected queries\n",
    "\n",
    "pose_test=pose_query[select_idx]\n",
    "feats_test= feats_query[select_idx]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process topological map pose information in SE(2) and information is extracted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_dataset = load_utils.load_sequence(json.load(open(map_json)),verbose=False) # Process poses in SE(2)\n",
    "\n",
    "pose_map = geo.combine(map_dataset['poses'])\n",
    "feats_map = map_dataset['feats']\n",
    "clusters_map = map_dataset['clusters_list']     # Group list\n",
    "cl_clasif = map_dataset['clusters_idx']         # Classification vector of elements by groups"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the mean of the descriptors and the mean and the covariance matrix of the poses by groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose_mean, pose_cov = pose_mean_cov(map_dataset)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Silhouette Score for the Topological Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "tam=len(pose_map)\n",
    "map_select_idx=np.arange(tam)  # Select all elements on the map\n",
    "\n",
    "# Silhouette score function call\n",
    "map_sh_score=map_pose_silhouette_score(map_dataset, map_select_idx)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of well-classified items is calculated, evaluating the sign of the silhouette punctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_sh_score_mean=(np.sign(map_sh_score)).mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Silhouette Score Bar Chart Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin edges:  [-1.0, -0.6666666666666667, -0.33333333333333337, 0.0, 0.33333333333333326, 0.6666666666666665, 1.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "\n",
    "map_pose_sh_diagram=sh_bar(map_sh_score,xlabel='Puntuación de Silueta',k=6)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VPR System Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculation and plot of the multiclass confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate ground-truth matrix (m x n) based on the closest pose on the map / m= nº of queries, n= nº of groups\n",
    "query_GT=nearest(query_dataset['poses'][select_idx],map_dataset)\n",
    "\n",
    "# Calculate prediction matrix (mxn) for queries based on appearance\n",
    "query_predict=most_similar_centroid(query_dataset['feats'][select_idx],map_dataset)\n",
    "query_predict=query_predict.tolist()\n",
    "\n",
    "# List of groups visited for labels on the multiclass confusion matrix\n",
    "visited_clusters=np.unique(np.union1d(query_GT,query_predict))\n",
    "my_labels=[\"C\"+str(i) for i in visited_clusters]\n",
    "\n",
    "# Plot multiclass confusion matrix\n",
    "cm_analysis(query_GT,query_predict,my_labels,figsize=(40,30))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and Recall at the groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "P, R= cluster_eval(query_dataset, map_dataset, select_idx, remove=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar Chart Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,5])\n",
    "plt.bar(P[0],P[1],width=0.8)\n",
    "plt.xlabel('Precisión para cada clase')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.bar(R[0],R[1],width=0.8)\n",
    "plt.xlabel('Sensibilidad para cada clase')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the mean precision and sensitivity at the group level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_mean=np.mean(P[1])\n",
    "R_mean=np.mean(R[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Precision, Recall and Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_, R_, ACC_ =general_eval(query_dataset, map_dataset,select_idx)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall evaluation result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media SH score intra map:  0.5724398301085417  ~  78.62199150542708 %\n",
      "Mean Precission in clusters:  80.11057728783581 %\n",
      "Mean Recall in clusters:  86.85835797329572 %\n",
      "Precision general:  88.6986301369863 %\n",
      "Recall general:  88.6986301369863 %\n",
      "Accuracy general:  99.27087936367653 %\n",
      "Error : 0.7291206363234659 %\n"
     ]
    }
   ],
   "source": [
    "print('Mean SH score intra map: ',map_sh_score_mean,' ~ ',100*((map_sh_score_mean+1)/2),'%')\n",
    "print('Mean Precission in clusters: ',100*P_mean,'%')\n",
    "print('Mean Recall in clusters: ',100*R_mean,'%')\n",
    "print('General Precision: ', 100*P_,'%')\n",
    "print('General Recall: ',100*R_,'%')\n",
    "print('General Accuracy: ',100*ACC_,'%')\n",
    "print('Error :',100*(1-ACC_),'%')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot VPR map"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section the topological map and the queries are plotted, indicating if they are well or badly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate ground-truth matrix (m x n) based on the closest pose on the map / m= nº of queries, n= nº of groups\n",
    "query_GT=nearest(query_dataset['poses'][select_idx],map_dataset)\n",
    "\n",
    "# Calculate prediction matrix (mxn) for queries based on appearance\n",
    "query_predict=most_similar_centroid(query_dataset['feats'][select_idx],map_dataset)\n",
    "query_predict=query_predict.tolist()\n",
    "\n",
    "# List of groups to which a query is assigned\n",
    "visited_clusters=np.unique(np.union1d(query_GT,query_predict))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-16.24954285, 6.53292785, -5.6359072, 4.6513412)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2300x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the figure:\n",
    "fig=plt.figure(figsize=(23,14))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(0,0,marker='*',color='red')    # Mark the origin of the map (0,0)\n",
    "\n",
    "# Topological map plot\n",
    "n_clusters=len(clusters_map)\n",
    "for i in range(n_clusters):\n",
    "    subgraph=np.copy(clusters_map[i])   # Copy the group\n",
    "\n",
    "    for k,item in enumerate(clusters_map[i]):   # Loop through group items\n",
    "        # Extract element position:\n",
    "        translation=geo.SE2Poses.t(pose_map[item])\n",
    "        new_x=translation[0]\n",
    "        new_y=translation[1]\n",
    "        # If it is a visited group it is plotted in blue, otherwise in grey:\n",
    "        if i in visited_clusters:\n",
    "            plt.plot(new_x,new_y,markersize=2.5,marker='o',color='b',alpha=0.4,label=\"Map Selected Group\")\n",
    "        else:\n",
    "            plt.plot(new_x,new_y,markersize=2.5,marker='o',color='black',alpha=0.3,label=\"Map Not Selected Group\")\n",
    "    # Plot a black dot to delimit the groups\n",
    "    plt.plot(new_x,new_y,markersize=6,marker='o',color='black')\n",
    "\n",
    "\n",
    "# Plot the queries:\n",
    "pose_consulta=[geo.SE2Poses.t(pose_test[j]) for j in range(len(pose_test))] # Query poses\n",
    "\n",
    "for j in range(len(pose_consulta)):\n",
    "    # If the prediction is correct, the position is plotted in green\n",
    "    if query_GT[j] == query_predict[j]:\n",
    "        plt.plot(pose_consulta[j][0],pose_consulta[j][1],markersize=4,marker='o',color='g',label='Positive Prediction')\n",
    "\n",
    "    # If the prediction is not correct, the position is plotted in red\n",
    "    else:\n",
    "        plt.plot(pose_consulta[j][0],pose_consulta[j][1],markersize=4,marker='o',color='#DC143C',label=\"Negative Prediction\")\n",
    "\n",
    "# Show plot\n",
    "ax.legend()\n",
    "handles, labels = ax.get_legend_handles_labels()  \n",
    "lgd = dict(zip(labels, handles))\n",
    "ax.legend(lgd.values(), lgd.keys(),fontsize=\"20\")\n",
    "plt.axis('off')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit",
   "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": "3.7.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}