unittest_partaware.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5d21c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import seaborn as sns\n",
    "\n",
    "# Base Metric\n",
    "from metrics.base.dice_score_coefficient import dsc\n",
    "\n",
    "# Match\n",
    "from metrics.part_aware.partpq import create_match_dict, get_all_matches, optimal_matching, thing_cls_counts, thing_cls_scores, stuff_cls_scores, PartPQ\n",
    "\n",
    "# tools\n",
    "from metrics.tools.labelling import label\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e31612b",
   "metadata": {},
   "source": [
    "# Case Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a175469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummy Case\n",
    "pred = torch.zeros((30, 30)).cuda()\n",
    "gt = torch.zeros((30, 30)).cuda()\n",
    "\n",
    "# Stuff Classes first (lowest hirearchy)\n",
    "pred[1:30, 1:30] = 4\n",
    "\n",
    "# Thing Classes next (medium hirearchy)\n",
    "pred[1:6, 1:6] = 1\n",
    "pred[7:20, 7:20] = 1\n",
    "pred[22:27, 22:27] = 3\n",
    "\n",
    "# Part Classes last (highest hirearchy)\n",
    "pred[2:5, 2:5] = 2\n",
    "pred[2:5, 8:10] = 2\n",
    "pred[8:12, 8:12] = 2\n",
    "pred[14:19, 14:19] = 2\n",
    "pred[10:15, 22:25] = 2\n",
    "\n",
    "# Same ordering for ground truth\n",
    "# Stuff Classes first\n",
    "gt[1:30, 1:30] = 4\n",
    "\n",
    "# Thing Classes next\n",
    "gt[1:6, 1:6] = 1\n",
    "gt[7:20, 7:20] = 1\n",
    "gt[22:27, 22:27] = 3\n",
    "\n",
    "# Part Classes last\n",
    "gt[2:5, 2:5] = 2\n",
    "gt[14:19, 14:19] = 2\n",
    "\n",
    "# Class definitions\n",
    "stuff_cls_list = [4]\n",
    "thing_cls_list = [1, 3]\n",
    "part_cls_list = [2]\n",
    "\n",
    "#! Unfortunately, I am not sure how to avoid defining this hierarchy style thing. But we dont need to because the output is expected to be in hierarchy already"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe02c538",
   "metadata": {},
   "source": [
    "# Visualise sample case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7037a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_case(pred, gt, stuff_cls_list, thing_cls_list, part_cls_list, class_names_dict=None):\n",
    "\n",
    "    sns.set(style=\"whitegrid\", context=\"talk\")\n",
    "    plt.rcParams[\"axes.edgecolor\"] = \"0.2\"\n",
    "    plt.rcParams[\"axes.linewidth\"] = 1.5\n",
    "\n",
    "    pred_np = pred.cpu().numpy() if hasattr(pred, \"cpu\") else pred\n",
    "    gt_np = gt.cpu().numpy() if hasattr(gt, \"cpu\") else gt\n",
    "\n",
    "    all_classes = sorted(set(thing_cls_list + part_cls_list + stuff_cls_list))\n",
    "    max_class_id = max(all_classes) + 1  # max possible ID + 1\n",
    "\n",
    "    background_color = [0.97, 0.97, 0.97, 1]\n",
    "    pastel_colors = [\n",
    "        [0.85, 0.41, 0.43, 1.0],\n",
    "        [0.88, 0.58, 0.40, 1.0],\n",
    "        [0.92, 0.77, 0.5, 1.0],\n",
    "        [0.6, 0.75, 0.55, 1.0],\n",
    "        [0.55, 0.67, 0.83, 1.0],\n",
    "        [0.65, 0.5, 0.75, 1.0],\n",
    "        [0.75, 0.55, 0.75, 1.0],\n",
    "        [0.7, 0.9, 0.9, 1.0],\n",
    "    ]\n",
    "\n",
    "    # Create color mapping that works with any class IDs\n",
    "    class_colors = [background_color] * max_class_id  # Initialize with background color\n",
    "    color_idx = 0\n",
    "    for class_id in all_classes:\n",
    "        if color_idx < len(pastel_colors):\n",
    "            class_colors[class_id] = pastel_colors[color_idx]\n",
    "        else:\n",
    "            # Generate additional colors if needed\n",
    "            h = (color_idx * 0.618033988749895) % 1  # Golden ratio method for color distribution\n",
    "            r, g, b = plt.cm.hsv(h)\n",
    "            class_colors[class_id] = [r, g, b, 1.0]\n",
    "        color_idx += 1\n",
    "    \n",
    "    # Custom colormap for class visualization\n",
    "    class_cmap = mcolors.ListedColormap(class_colors)\n",
    "    \n",
    "    # Custom normalization to map each class ID to its specific color\n",
    "    class_bounds = np.arange(-0.5, max_class_id + 0.5, 1)\n",
    "    class_norm = mcolors.BoundaryNorm(class_bounds, len(class_colors))\n",
    "\n",
    "    # TP / FP / FN Confusion map colors\n",
    "    tp_color = [0.77, 0.60, 0.43, 1.0]  # True Positive\n",
    "    fp_color = [0.95, 0.70, 0.80, 1.0]  # False Positive\n",
    "    fn_color = [0.85, 0.71, 0.85, 1.0]  # False Negative\n",
    "\n",
    "    # Create confusion visualization\n",
    "    confusion_vis = np.zeros_like(pred_np)\n",
    "    confusion_colors = [background_color] * (3 * max_class_id)  # Initialize with background\n",
    "\n",
    "    # For each class, set colors for TP, FP, FN\n",
    "    for i, c in enumerate(all_classes):\n",
    "        # Find TP, FP, FN for each class\n",
    "        tp = (pred_np == c) & (gt_np == c)\n",
    "        fp = (pred_np == c) & (gt_np != c)\n",
    "        fn = (pred_np != c) & (gt_np == c)\n",
    "\n",
    "        # Set values in confusion visualization\n",
    "        confusion_vis[tp] = c\n",
    "        confusion_vis[fp] = c + max_class_id\n",
    "        confusion_vis[fn] = c + 2 * max_class_id\n",
    "\n",
    "        # Set colors for each case\n",
    "        confusion_colors[c] = tp_color\n",
    "        confusion_colors[c + max_class_id] = fp_color\n",
    "        confusion_colors[c + 2 * max_class_id] = fn_color\n",
    "\n",
    "    confusion_cmap = mcolors.ListedColormap(confusion_colors)\n",
    "    confusion_bounds = np.arange(-0.5, 3 * max_class_id + 0.5, 1)\n",
    "    confusion_norm = mcolors.BoundaryNorm(confusion_bounds, len(confusion_colors))\n",
    "\n",
    "    # Create figure with 3 subplots\n",
    "    fig, ax = plt.subplots(1, 3, figsize=(30, 8))\n",
    "    plt.subplots_adjust(wspace=0.4)\n",
    "\n",
    "    # Plot prediction, ground truth, and confusion map\n",
    "    ax[0].imshow(pred_np, cmap=class_cmap, norm=class_norm, interpolation=\"nearest\")\n",
    "    ax[0].set_title(\"Prediction\")\n",
    "\n",
    "    ax[1].imshow(gt_np, cmap=class_cmap, norm=class_norm, interpolation=\"nearest\")\n",
    "    ax[1].set_title(\"Ground Truth\")\n",
    "\n",
    "    ax[2].imshow(confusion_vis, cmap=confusion_cmap, norm=confusion_norm, interpolation=\"nearest\")\n",
    "    ax[2].set_title(\"Confusion Map\")\n",
    "\n",
    "    # Format axes with grids\n",
    "    for a in ax:\n",
    "        h, w = pred_np.shape\n",
    "        a.set_xticks(np.arange(-0.5, w, 1), minor=True)\n",
    "        a.set_yticks(np.arange(-0.5, h, 1), minor=True)\n",
    "        a.grid(which='minor', color='gray', linestyle='--', linewidth=0.5)\n",
    "        a.tick_params(which='minor', bottom=False, left=False)\n",
    "        a.set_xticks([])\n",
    "        a.set_yticks([])\n",
    "        for spine in a.spines.values():\n",
    "            spine.set_visible(True)\n",
    "            spine.set_color(\"#dddddd\")\n",
    "\n",
    "    # Create legend\n",
    "    if class_names_dict is None:\n",
    "        class_names_dict = {cid: f\"Class {cid}\" for cid in all_classes}\n",
    "\n",
    "    color_legend_patches = [\n",
    "        plt.Line2D([0], [0], marker='s', color='w', label=class_names_dict[cid],\n",
    "                   markerfacecolor=tuple(class_colors[cid][:3]), markersize=15)\n",
    "        for cid in all_classes\n",
    "    ]\n",
    "    ax[1].legend(handles=color_legend_patches, bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a41ccea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_case(pred, gt, stuff_cls_list=stuff_cls_list, thing_cls_list=thing_cls_list, part_cls_list=part_cls_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e362eb6",
   "metadata": {},
   "source": [
    "# Labelling the inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9163662",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_one_hot_cc = label(pred, 5)\n",
    "gt_one_hot_cc = label(gt, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3062d34",
   "metadata": {},
   "source": [
    "# Matching Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac978714",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "MATCHING RESULTS\n",
      "================================================================================\n",
      "\n",
      "CLASS 1\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "THING MATCHES (Class 1):\n",
      "  Pred 1 <-> GT 1 | Dice: 1.0000\n",
      "  Pred 2 <-> GT 2 | Dice: 0.9412\n",
      "\n",
      "RESPECTIVE PART SCORES: (Class 1):\n",
      "  (Pred 1 <-> GT 1) | Part Class 2 | Collective Score: 1.0000\n",
      "  (Pred 2 <-> GT 2) | Part Class 2 | Collective Score: 0.7576\n",
      "\n",
      "OPTIMAL ASSIGNMENTS (Class 1):\n",
      "  Optimal: Predicted 1 <-> Ground Truth 1 | Panoptic Score: 1.0000 | Part Scores: {2: 1.0}\n",
      "  Optimal: Predicted 2 <-> Ground Truth 2 | Panoptic Score: 0.9412 | Part Scores: {2: 0.7575757503509521}\n",
      "\n",
      "CLASS 3\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "THING MATCHES (Class 3):\n",
      "  Pred 1 <-> GT 1 | Dice: 1.0000\n",
      "\n",
      "RESPECTIVE PART SCORES: (Class 3):\n",
      "  No collective part scores found for this class.\n",
      "\n",
      "OPTIMAL ASSIGNMENTS (Class 3):\n",
      "  Optimal: Predicted 1 <-> Ground Truth 1 | Panoptic Score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# Forming the match dictionary\n",
    "match_dictionary = create_match_dict(\n",
    "    pred_one_hot_cc=pred_one_hot_cc,\n",
    "    gt_one_hot_cc=gt_one_hot_cc,\n",
    "    metric=dsc,\n",
    "    thing_cls_list=thing_cls_list,\n",
    "    part_cls_list=part_cls_list,\n",
    ")\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"MATCHING RESULTS\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "\"\"\"\n",
    "Only thing classes have to be matched. \n",
    "Only those part instances within a thing instance have to be accounted for, as they are important in the final calculation.\n",
    "\"\"\"\n",
    "\n",
    "# As we are following one-to-one matching, we can use the Hungarian algorithm to find the optimal assignment.\n",
    "optimal_matches_per_class = {}\n",
    "\n",
    "for class_index, matches_for_current_class in match_dictionary.items():\n",
    "    print(f\"\\nCLASS {class_index}\")\n",
    "    print(\"-\" * 80)\n",
    "\n",
    "    # Get all candidate panoptic matches and collective part scores for this class\n",
    "    candidate_panoptic_matches, candidate_part_matches = get_all_matches(matches_for_current_class)\n",
    "    \n",
    "    # THING MATCHES (including FPs and FNs)\n",
    "    print(f\"\\nTHING MATCHES (Class {class_index}):\")\n",
    "    if candidate_panoptic_matches:\n",
    "        processed_pairs = set() # Keep track of pairs to avoid double printing FP/FN messages if redundant\n",
    "        has_matches = False\n",
    "        for predicted_id, ground_truth_id, match_score in candidate_panoptic_matches:\n",
    "            pair = (predicted_id, ground_truth_id)\n",
    "            if pair in processed_pairs:\n",
    "                continue\n",
    "\n",
    "            if predicted_id is None:\n",
    "                print(f\"  MISSED: Ground Truth component {ground_truth_id} was not detected (False Negative)\")\n",
    "            elif ground_truth_id is None:\n",
    "                print(f\"  EXTRA: Predicted component {predicted_id} has no corresponding ground truth (False Positive)\")\n",
    "            else:\n",
    "                print(f\"  Pred {predicted_id} <-> GT {ground_truth_id} | Dice: {match_score:.4f}\")\n",
    "                has_matches = True\n",
    "            processed_pairs.add(pair)\n",
    "        if not has_matches and not any(p is None or g is None for p,g,_ in candidate_panoptic_matches):\n",
    "             print(\"  No candidate panoptic matches found, only potential FPs/FNs listed above (if any).\")\n",
    "    else:\n",
    "        # Check if there are unmatched components in GT or Pred based on the source dict\n",
    "        gt_ids_in_class = set(matches_for_current_class.get(\"gt_to_pred\", {}).keys())\n",
    "        pred_ids_in_class = set(matches_for_current_class.get(\"pred_to_gt\", {}).keys())\n",
    "        if not gt_ids_in_class and not pred_ids_in_class:\n",
    "            print(\"  No ground truth or predicted components found for this class.\")\n",
    "        else:\n",
    "            print(\"  No overlapping candidate matches found for this class.\")\n",
    "\n",
    "    # Corresponding PART MATCHES\n",
    "    print(f\"\\nRESPECTIVE PART SCORES: (Class {class_index}):\")\n",
    "    if candidate_part_matches:\n",
    "        for predicted_id, ground_truth_id, part_class_index, part_score in candidate_part_matches:\n",
    "            print(f\"  (Pred {predicted_id} <-> GT {ground_truth_id}) | Part Class {part_class_index} | Collective Score: {part_score:.4f}\")\n",
    "    else:\n",
    "        print(\"  No collective part scores found for this class.\")\n",
    "\n",
    "    # Calculate optimal one-to-one assignment using Hungarian algorithm\n",
    "    optimal_assignments_for_class = optimal_matching(candidate_panoptic_matches, candidate_part_matches)\n",
    "    optimal_matches_per_class[class_index] = optimal_assignments_for_class\n",
    "\n",
    "    print(f\"\\nOPTIMAL ASSIGNMENTS (Class {class_index}):\")\n",
    "    if optimal_assignments_for_class:\n",
    "        for assignment in optimal_assignments_for_class:\n",
    "            predicted_id = assignment[0]\n",
    "            ground_truth_id = assignment[1]\n",
    "            panoptic_score = assignment[2]\n",
    "            # Check if part scores are included (tuple length is 4)\n",
    "            if len(assignment) == 4:\n",
    "                part_scores_dict = assignment[3]\n",
    "                part_info = f\" | Part Scores: {part_scores_dict}\" # Or format more nicely\n",
    "            else:\n",
    "                part_info = \"\"\n",
    "            print(f\"  Optimal: Predicted {predicted_id} <-> Ground Truth {ground_truth_id} | Panoptic Score: {panoptic_score:.4f}{part_info}\")\n",
    "    else:\n",
    "        print(\"  No optimal matches found for this class (might be only FPs or FNs).\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5b67af",
   "metadata": {},
   "source": [
    "# THING + PART CLASS SCORES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "115ed8fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "FINAL THING CLS SCORES (ACCOUNTING FOR PARTS)\n",
      "================================================================================\n",
      "\n",
      "CLASS 1\n",
      "--------------------------------------------------------------------------------\n",
      "  Match 1: Predicted 1 <-> Ground Truth 1\n",
      "    - Thing Component Score: 1.0000\n",
      "    - Part Component Scores:\n",
      "      - Part Class 2: 1.0000\n",
      "    - Total Score: 1.0000 (Average of 2 scores)\n",
      "  Match 2: Predicted 2 <-> Ground Truth 2\n",
      "    - Thing Component Score: 0.9412\n",
      "    - Part Component Scores:\n",
      "      - Part Class 2: 0.7576\n",
      "    - Total Score: 0.8494 (Average of 2 scores)\n",
      "FINAL SCORE: 0.9247\n",
      "\n",
      "CLASS 3\n",
      "--------------------------------------------------------------------------------\n",
      "  Match 1: Predicted 1 <-> Ground Truth 1\n",
      "    - Thing Component Score: 1.0000\n",
      "    - No part component scores included in this match data.\n",
      "    - Total Score: 1.0000 (Based on panoptic score only)\n",
      "FINAL SCORE: 1.0000\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 80)\n",
    "print(\"FINAL THING CLS SCORES (ACCOUNTING FOR PARTS)\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "average_quality_per_class = {}\n",
    "\n",
    "# Iterate through the optimal matches we stored earlier\n",
    "for class_index, current_optimal_matches in optimal_matches_per_class.items():\n",
    "    # Only calculate quality contribution for panoptic classes\n",
    "    if class_index not in thing_cls_list:\n",
    "        continue\n",
    "\n",
    "    print(f\"\\nCLASS {class_index}\")\n",
    "    print(\"-\" * 80)\n",
    "    \n",
    "    average_match_quality_for_class, individual_match_qualities = thing_cls_scores(current_optimal_matches)\n",
    "\n",
    "    average_quality_per_class[class_index] = average_match_quality_for_class\n",
    "\n",
    "    if not current_optimal_matches:\n",
    "        print(f\"  No optimal matches found - Average Match Quality Contribution = 0.0\")\n",
    "        continue\n",
    "\n",
    "    # Print details for each optimally matched pair based on stored data\n",
    "    for i, current_match_tuple in enumerate(current_optimal_matches):\n",
    "        quality_score_for_this_match = individual_match_qualities[i]\n",
    "        predicted_component_id = current_match_tuple[0]\n",
    "        ground_truth_component_id = current_match_tuple[1]\n",
    "        current_panoptic_score = current_match_tuple[2]\n",
    "\n",
    "        print(f\"  Match {i+1}: Predicted {predicted_component_id} <-> Ground Truth {ground_truth_component_id}\")\n",
    "        print(f\"    - Thing Component Score: {current_panoptic_score:.4f}\")\n",
    "\n",
    "        # Check if part scores are included in the tuple\n",
    "        if len(current_match_tuple) == 4:\n",
    "            current_part_scores = current_match_tuple[3]\n",
    "            scores_for_quality_calculation = [current_panoptic_score] + list(current_part_scores.values())\n",
    "\n",
    "            if current_part_scores:\n",
    "                 print(f\"    - Part Component Scores:\")\n",
    "                 for part_class_index, part_component_score in current_part_scores.items():\n",
    "                     print(f\"      - Part Class {part_class_index}: {part_component_score:.4f}\")\n",
    "                 print(f\"    - Total Score: {quality_score_for_this_match:.4f} (Average of {len(scores_for_quality_calculation)} scores)\")\n",
    "            else:\n",
    "                 print(f\"    - No part scores found for this match pair, though parts were checked.\")\n",
    "                 print(f\"    - Total Score: {quality_score_for_this_match:.4f} (Based on panoptic score only)\")\n",
    "\n",
    "        else: # Match tuple only had panoptic info\n",
    "            print(f\"    - No part component scores included in this match data.\")\n",
    "            print(f\"    - Total Score: {quality_score_for_this_match:.4f} (Based on panoptic score only)\")\n",
    "\n",
    "    # Print the final score for the class\n",
    "    print(f\"FINAL SCORE: {average_match_quality_for_class:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a9bd7a",
   "metadata": {},
   "source": [
    "# STUFF CLASS SCORES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93d844a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "STUFF CLS SCORE\n",
      "================================================================================\n",
      "\n",
      "CLASS 4\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  SCORE: 0.9828\n",
      "\n",
      " TOTAL SCORE: 0.9828\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 80)\n",
    "print(\"STUFF CLS SCORE\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "stuff_cls_scores = 0.0\n",
    "# calculate the average metric score across all stuff classes\n",
    "if stuff_cls_list is not None:\n",
    "    for cls in stuff_cls_list:\n",
    "        print(f\"\\nCLASS {cls}\")\n",
    "        print(\"-\" * 80)\n",
    "        # calculate the simple metric score for each class\n",
    "        score = dsc(pred_one_hot_cc[cls], gt_one_hot_cc[cls])\n",
    "        fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "        ax[0].imshow(pred_one_hot_cc[cls].cpu().numpy(), cmap='gray', interpolation='nearest')\n",
    "        ax[0].set_title(f\"Predicted Class {cls}\")\n",
    "        ax[1].imshow(gt_one_hot_cc[cls].cpu().numpy(), cmap='gray', interpolation='nearest')\n",
    "        ax[1].set_title(f\"Ground Truth Class {cls}\")\n",
    "        plt.show()\n",
    "        print(f\"  SCORE: {score:.4f}\")\n",
    "\n",
    "        stuff_cls_scores += score\n",
    "\n",
    "    #! If both pred and gt are empty, this will be 1.0. If either are empty, it will be 0.0.\n",
    "    stuff_cls_scores /= len(stuff_cls_list)\n",
    "else:\n",
    "    print(\"No stuff classes provided for scoring.\")\n",
    "    \n",
    "print(f\"\\n TOTAL SCORE: {stuff_cls_scores:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a5eb14",
   "metadata": {},
   "source": [
    "# COUNTING METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea78bf5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "COUNTING METRICS\n",
      "================================================================================\n",
      "\n",
      "CLASS 1\n",
      "--------------------------------------------------------------------------------\n",
      "  True Positives (TP): 2\n",
      "  False Positives (FP): 0\n",
      "  False Negatives (FN): 0\n",
      "\n",
      "CLASS 3\n",
      "--------------------------------------------------------------------------------\n",
      "  True Positives (TP): 1\n",
      "  False Positives (FP): 0\n",
      "  False Negatives (FN): 0\n",
      "\n",
      "OVERALL COUNTS:\n",
      "Total True Positives: 3\n",
      "Total False Positives: 0\n",
      "Total False Negatives: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 80)\n",
    "print(\"COUNTING METRICS\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "\"\"\"\n",
    "Get the counting metrics based on the thing classes.\n",
    "\"\"\"\n",
    "\n",
    "thing_counts_per_class = thing_cls_counts(\n",
    "    pred_one_hot_cc=pred_one_hot_cc,\n",
    "    gt_one_hot_cc=gt_one_hot_cc,\n",
    "    metric='dsc', # Ensure metric matches\n",
    "    thing_cls_list=thing_cls_list,\n",
    "    part_cls_list=part_cls_list\n",
    ")\n",
    "\n",
    "total_true_positives = 0\n",
    "total_false_positives = 0\n",
    "total_false_negatives = 0\n",
    "\n",
    "if not thing_counts_per_class[0]:\n",
    "    print(\"  No panoptic classes found or processed for counts.\")\n",
    "else:\n",
    "    for class_index, (tp, fp, fn) in thing_counts_per_class[0].items():\n",
    "        print(f\"\\nCLASS {class_index}\")\n",
    "        print(\"-\" * 80)\n",
    "        print(f\"  True Positives (TP): {tp}\")\n",
    "        print(f\"  False Positives (FP): {fp}\")\n",
    "        print(f\"  False Negatives (FN): {fn}\")\n",
    "        total_true_positives += tp\n",
    "        total_false_positives += fp\n",
    "        total_false_negatives += fn\n",
    "\n",
    "print(\"\\nOVERALL COUNTS:\")\n",
    "print(f\"Total True Positives: {total_true_positives}\")\n",
    "print(f\"Total False Positives: {total_false_positives}\")\n",
    "print(f\"Total False Negatives: {total_false_negatives}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41d86749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "FINAL PART PQ CALCULATION\n",
      "================================================================================\n",
      "\n",
      "  Total Thing Class Score =  Total Thing Class Score: 1.9247 / Number of Classes: 2 = 0.9623\n",
      "  Mean Stuff Class Score: 0.9828\n",
      "\n",
      "  Total Count Metrics = TPs + FPs + FNs: 3 + 0 + 0 = 3\n",
      "\n",
      "  Final Part PQ Score = (Thing Class Score + Stuff Class Score) / (TPs + FPs + FNs): (0.9623 + 0.9828) / 3 = 0.6484\n",
      "\n",
      "FINAL PART PQ SCORE: 0.6484\n",
      "Direct PartPQ Calculation: 0.6484\n"
     ]
    }
   ],
   "source": [
    "print(\"=\" * 80)\n",
    "print(\"FINAL PART PQ CALCULATION\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Numerator: Sum of Thing (accounting for parts) Scores + Stuff Class Scores\n",
    "thing_numerator_score = (sum(average_quality_per_class.values()) / len(average_quality_per_class))\n",
    "print(f\"\\n  Total Thing Class Score =  Total Thing Class Score: {sum(average_quality_per_class.values()):.4f} / Number of Classes: {len(average_quality_per_class)} = {thing_numerator_score:.4f}\")\n",
    "stuff_numerator_score = stuff_cls_scores\n",
    "print(f\"  Mean Stuff Class Score: {stuff_numerator_score:.4f}\")\n",
    "\n",
    "# Denominator: Total TPs + FPs + FNs\n",
    "part_pq_denominator = total_true_positives + total_false_positives + total_false_negatives\n",
    "print(f\"\\n  Total Count Metrics = TPs + FPs + FNs: {total_true_positives} + {total_false_positives} + {total_false_negatives} = {part_pq_denominator}\")\n",
    "\n",
    "# Final Part PQ Calculation\n",
    "part_pq_score = (thing_numerator_score + stuff_numerator_score) / part_pq_denominator\n",
    "print(f\"\\n  Final Part PQ Score = (Thing Class Score + Stuff Class Score) / (TPs + FPs + FNs): ({thing_numerator_score:.4f} + {stuff_numerator_score:.4f}) / {part_pq_denominator} = {part_pq_score:.4f}\")\n",
    "print(f\"\\nFINAL PART PQ SCORE: {part_pq_score:.4f}\")\n",
    "\n",
    "# Comparing with complete function:\n",
    "direct_part_pq_verification = PartPQ(\n",
    "    pred_one_hot_cc=pred_one_hot_cc,\n",
    "    gt_one_hot_cc=gt_one_hot_cc,\n",
    "    metric='dsc', # Make sure this matches the metric used throughout\n",
    "    stuff_cls_list=stuff_cls_list,\n",
    "    thing_cls_list=thing_cls_list,\n",
    "    part_cls_list=part_cls_list\n",
    ")\n",
    "print(f\"Direct PartPQ Calculation: {direct_part_pq_verification:.4f}\")"
   ]
  }
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