{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Radial Distortion Perturbations\n",
    "\n",
    "## Introduction\n",
    "This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.\n",
    "\n",
    "## Layout\n",
    "This notebook demonstrates how a particular augmentation (in this case, <b><i>Radial Distortion of the Image</i></b>), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
    "\n",
    "- **Evaluation Guidance: Computing Mean Average Precision (mAP):**\n",
    "    - An overview of the evaluation strategy.\n",
    "- **Setup:**\n",
    "    - Notebook initialization, loading the supporting python code. Depending on if this is the first time you've run this notebook, this may take some time.\n",
    "    - Loading the source image, which will be used throughout the notebook.\n",
    "- **Image Perturbation Examples:**\n",
    "    - The NRTK perturbation is demonstrated on the source image.\n",
    "- **Baseline Detections:**\n",
    "    - The object detection model is loaded and run on the unperturbed image. These will serve as \\\"ground truth\\\" for comparisons against the perturbed images.\n",
    " \n",
    "At this point, we have the fundamental elements of our evaluation: the model, our reference image, and a mechanism for creating the perturbed test images. Next we adapt these elements to be used with the MAITE evaluation workflow:\n",
    "\n",
    "- **Wrapping the Detection Model**\n",
    "- **Wrapping the Reference Image as a Dataset**\n",
    "- **Wrapping the Perturbation as Augmentation Objects**\n",
    "- **Wrapping the Metrics**\n",
    "\n",
    "After the evaluation elements have been wrapped, we can run the evaluation:\n",
    "\n",
    "- **Preparing the Augmentations:**\n",
    "    - We specify the range of perturbation values to evaluate and optionally specify which ones we'd like to visualize.\n",
    "- **Evaluation of Augmented Data:**\n",
    "    - Each augmentation is run through MAITE's evaluation workflow, computing the absolute mAP metric of the detector on the perturbed datasets.\n",
    "- **Evaluation Analysis:**\n",
    "    - We plot and discuss the mAP@50 metric from each of the perturbed images, as well as per-class and per-area results.\n",
    "\n",
    "To run this notebook in Colab, use the link below:\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Kitware/nrtk/blob/main/docs/examples/maite/nrtk_radial_distortion_perturber_demo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2e1964-46e2-4497-88ff-274eaca17960",
   "metadata": {},
   "source": [
    "## Evaluation Guidance: Computing Mean Average Precision (mAP)\n",
    "\n",
    "This notebook evaluates the robustness of an Object Detector when exposed to NRTK (Natural, Physics-based) perturbations, using mean Average Precision (mAP) as the performance metric. The baseline case, where no perturbations are applied (identity augmentation), typically yields an mAP close to 1.0 and serves as the reference point. Unlike standard evaluations that focus on whether the detector can correctly identify and localize objects, our goal here is to measure how well the detector maintains its performance under realistic perturbations. \n",
    "\n",
    "By comparing the absolute mAP scores of the baseline against perturbed datasets, we can assess the detector's sensitivity to environmental variations and quantify its robustness in generating reliable predictions beyond controlled conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9268412-0d13-4fed-ae78-f6ee02c94c9c",
   "metadata": {},
   "source": [
    "## Setup: Notebook Initialization\n",
    "The next few cells import the python packages used in the rest of the notebook.\n",
    "\n",
    "**Note:** We are suppressing warnings within this notebook to reduce visual clutter for demonstration purposes. If any issues arise while executing this notebook, we recommend that the first cell is **not** executed so that any related warnings are shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "796743f8-2f1e-42aa-ad6b-f71e721602f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "# warning suppression\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2561179f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning package installation...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing nrtk's extras...\n"
     ]
    }
   ],
   "source": [
    "import sys  # noqa: F401\n",
    "\n",
    "print(\"Beginning package installation...\")\n",
    "!{sys.executable} -m pip install -qU pip\n",
    "\n",
    "print(\"Installing nrtk's extras...\")\n",
    "!{sys.executable} -c \"import nrtk\" || !{sys.executable} -m pip install -q \\\n",
    "  \"nrtk[maite,albumentations]>=0.25.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57946f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing required packages...\n"
     ]
    }
   ],
   "source": [
    "print(\"Installing required packages...\")\n",
    "# Add temporary numpy<2.0 constraint to avoid dependency conflicts\n",
    "!{sys.executable} -m pip install -q \"matplotlib\" \"torchvision\" \"torchmetrics\" \"ultralytics\" \"numpy<2.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "982435c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing a fresh install of opencv-python-headless...\n"
     ]
    }
   ],
   "source": [
    "# opencv-python depends on libGL, which is unnecessary for running the notebook\n",
    "# and can cause failures in some environments (e.g., GitLab CI). To avoid this,\n",
    "# we uninstall all opencv variants and install opencv-python-headless instead.\n",
    "\n",
    "# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\n",
    "# Add temporary numpy<2.0 constraint to avoid dependency conflicts\n",
    "print(\"Doing a fresh install of opencv-python-headless...\")\n",
    "!{sys.executable} -m pip uninstall -qy \"opencv-python\" \"opencv-python-headless\"\n",
    "!{sys.executable} -m pip install -q \"opencv-python-headless\" \"numpy<2.0\" --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected status of NRTK extras and their dependencies:\n",
      "\n",
      "[Pillow]\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "\n",
      "[albumentations]\n",
      "  - albumentations            ✓ 2.0.5\n",
      "\n",
      "[diffusion]\n",
      "  - torch                     ✓ 2.8.0+cu128\n",
      "  - diffusers                 ✓ 0.34.0\n",
      "  - accelerate                ✓ 1.9.0\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "  - transformers              ✓ 4.53.1\n",
      "  - protobuf                  ✗ missing\n",
      "\n",
      "[graphics]\n",
      "  - opencv-python             ✗ missing\n",
      "\n",
      "[headless]\n",
      "  - opencv-python-headless    ✓ 4.11.0\n",
      "\n",
      "[maite]\n",
      "  - maite                     ✓ 0.8.2\n",
      "  - fastapi                   ✓ 0.116.1\n",
      "  - pydantic                  ✓ 2.10.4\n",
      "  - pydantic_settings         ✓ 2.7.0\n",
      "  - uvicorn                   ✓ 0.34.0\n",
      "\n",
      "[notebook-testing]\n",
      "  - datasets                  ✓ 4.0.0\n",
      "  - matplotlib                ✓ 3.10.7\n",
      "  - numba                     ✓ 0.60.0\n",
      "  - tabulate                  ✓ 0.9.0\n",
      "  - torch                     ✓ 2.8.0+cu128\n",
      "  - torchmetrics              ✓ 1.6.2\n",
      "  - torchvision               ✓ 0.23.0+cu128\n",
      "  - transformers              ✓ 4.53.1\n",
      "  - ultralytics               ✓ 8.3.85\n",
      "  - jupytext                  ✓ 1.16.7\n",
      "  - albumentations            ✓ 2.0.5\n",
      "\n",
      "[pybsm]\n",
      "  - pybsm                     ✓ 0.12.0\n",
      "\n",
      "[scikit-image]\n",
      "  - scikit-image              ✓ 0.24.0\n",
      "\n",
      "[tools]\n",
      "  - kwcoco                    ✓ 0.8.7\n",
      "  - Pillow                    ✓ 10.4.0\n",
      "\n",
      "[waterdroplet]\n",
      "  - scipy                     ✓ 1.13.1\n",
      "  - shapely                   ✓ 2.0.7\n",
      "  - geopandas                 ✓ 0.14.0\n",
      "\n",
      "\n",
      "For details about installing NRTK extras, please visit:\n",
      "    https://nrtk.readthedocs.io/en/v0.25.0/installation.html#extras\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "from collections.abc import Sequence\n",
    "from typing import Any\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# some initial imports\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"jpeg\"  # Use JPEG format for inline visualizations\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from nrtk.impls.perturb_image.generic.radial_distortion_perturber import RadialDistortionPerturber\n",
    "from nrtk.utils._extras import print_extras_status\n",
    "\n",
    "print_extras_status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e9bfa-fcea-4a1d-a8e0-9455b28777a2",
   "metadata": {},
   "source": [
    "## Setup: Source Image\n",
    "\n",
    "In the next cell, we'll download and display a source image from the __[VisDrone](https://github.com/VisDrone/VisDrone-Dataset)__ dataset. The image will be cached in a local `data` subdirectory.\n",
    "\n",
    "### A Note on Image Storage\n",
    "\n",
    "Typically in ML workflows, batches of images are processed as tensors of the color channels. Both our perturber (NRTK) and object detector (YOLO) accept numpy `ndarray` objects, and we will use [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) to view them. The complication is that although YOLO inferences on `ndarray`, [it expects the color channels to be in BGR](https://docs.ultralytics.com/modes/predict/) order. If we naively view the same data YOLO inferences on, the colors will be wrong; if we naively inference on what we view, the detections will be wrong. (Our NRTK perturbation is agnostic to the channel order.)\n",
    "\n",
    "In this notebook, we'll convert the channel order to BGR when we load, and convert back whenever we explicitly call `imshow`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(540, 960, 3)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_dir = \"./data\"\n",
    "os.makedirs(data_dir, exist_ok=True)\n",
    "img_path = os.path.join(data_dir, \"visdrone_img.jpg\")\n",
    "if not os.path.isfile(img_path):\n",
    "    url = \"https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download\"\n",
    "    _ = urllib.request.urlretrieve(url, img_path)  # noqa: S310\n",
    "\n",
    "img_pil = Image.open(img_path)\n",
    "img_nd_bgr = np.asarray(img_pil)[\n",
    "    :,\n",
    "    :,\n",
    "    ::-1,\n",
    "]  # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb\n",
    "plt.figure()\n",
    "plt.axis(\"off\")\n",
    "print(img_nd_bgr.shape)\n",
    "_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
   "metadata": {},
   "source": [
    "## NRTK Radial Distortion Perturbation: Examples and Guidance\n",
    "\n",
    "The [Radial Distortion perturbation](<project:../../_implementations/nrtk.impls.perturb_image.generic.radial_distortion_perturber.rst#module-nrtk.impls.perturb_image.generic.radial_distortion_perturber>) simulates lens distortion effects, such as\n",
    "barrel or pincushion distortion, and is useful for augmenting datasets to improve\n",
    "robustness to camera nonlinearities.\n",
    "\n",
    "The distortion uses the following equations:\n",
    "\n",
    "- `x1 = x0 * (1 + k1*r^2 + k2*r^4 + k3*r^6)`\n",
    "- `y1 = y0 * (1 + k1*r^2 + k2*r^4 + k3*r^6)`\n",
    "    \n",
    "Where:\n",
    "- `x0` and `y0` are normalized coordinates between `[-1, 1]`.\n",
    "- `r` is the distance from the image center.\n",
    "- `k1`, `k2`, and `k3` are distortion coefficients.\n",
    "\n",
    "\n",
    "The `RadialDistortionPerturber` takes the following inputs in order to perform the distortion:\n",
    "- `color_fill`: A list of 3 values between `0` and `255` representing the background color to fill portions of the output image which do not have a corresponding value in the original image.\n",
    "- `k`: A list of 3 values representing `k1`, `k2`, and `k3` in the above equations. These coefficients are typically very small negative or positive values. Negative coefficients represent a pinhole distortion while positive coefficients represent a barrel/fisheye distortion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a370d2a-2489-4c48-b29a-ea8ad3ef98fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
    "for idx, k in enumerate([-0.2, -0.1, 0, 0.02, 0.04, 0.06, 0.08, 0.1]):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    bp = RadialDistortionPerturber(color_fill=(0, 0, 0), k=[k, 0, 0])\n",
    "    ax[row, col].set_title(f\"k1: {k}\")\n",
    "    ax[row, col].imshow(bp(img_nd_bgr)[0][:, :, ::-1])\n",
    "    _ = ax[row, col].axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30eb653b-6be9-4d58-8d69-db4b64c3d81c",
   "metadata": {},
   "source": [
    "## Baseline Detections\n",
    "\n",
    "In the next cell, we'll download a [YOLOv11](https://docs.ultralytics.com/models/yolo11/) model, compute object detections on the source image, and display the results. As discussed above, these detections will serve as the \"ground truth\" for comparisons against the perturbed images.\n",
    "\n",
    "*Note that here, we're using YOLO's built-in visualization tool, which automatically adjusts for BGR / RGB order.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55600de0-089f-4dd6-ac4d-7896b8df5494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics 8.3.170 🚀 Python-3.10.12 torch-2.7.1+cu126 CUDA:0 (Quadro T2000, 3718MiB)\n",
      "Setup complete ✅ (16 CPUs, 62.4 GB RAM, 179.8/913.8 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 39.9ms\n",
      "Speed: 4.0ms preprocess, 39.9ms inference, 121.2ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "# Import YOLO support\n",
    "import ultralytics\n",
    "\n",
    "ultralytics.checks()\n",
    "print(\"Downloading model...\")\n",
    "model = ultralytics.YOLO(\"yolo11n.pt\")\n",
    "print(\"Computing baseline...\")\n",
    "baseline = model(img_nd_bgr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39050929-bf48-44b1-9247-5cac7f44cd8c",
   "metadata": {},
   "source": [
    "## MAITE Evaluation Workflow Preparation\n",
    "\n",
    "We'll use the [MAITE Evaluation workflow](https://jatic.pages.jatic.net/cdao/maite/generated/maite.tasks.evaluate.html) to evaluate the performance of the perturbed data against our baseline detections. We'll need to \"wrap\" our model, data, and perturbations into callable objects to pass to the `maite.tasks.evaluate` function:\n",
    "\n",
    "- We'll wrap the **model** to make predictions on input data when called.\n",
    "\n",
    "- The wrapped **dataset** will return our test image when called. Note that this will be the original, unperturbed image; we'll apply our perturbations via...\n",
    "\n",
    "- ...the **augmentation** object, which applies the perturbation to the image inside the evaluation.\n",
    "\n",
    "- Finally, the **metric** object will define our precise scoring methodology.\n",
    "\n",
    "The evaluation workflow in this notebook is slightly unusual. Typical ML workflows apply many different augmentations / perturbations to much larger datasets, and only call `evaluate` once to get a statistical view of performance. But since the goal of this notebook is to drill down into how perturbation affects performance, we've essentially flipped the process, calling `evaluate` (and thus our wrapped objects) many times, once per loop on our single image perturbed to a known degree, and then observing how the metrics respond."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da908c7f-b499-4361-8e2b-cf7647b45de6",
   "metadata": {},
   "source": [
    "### Some Helper Classes\n",
    "\n",
    "The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:\n",
    "\n",
    "1. The `YOLODetectionTarget` helper class that stores the bounding boxes, label indices, and confidence scores for a single image's detections.\n",
    "\n",
    "2. The `MaiteYOLODetection` adapter class that conforms to the MAITE [Object Detection Dataset](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Dataset.html) protocol by providing the `__len__` and `__getitem__` methods. The returned item is a tuple of (image, `YOLODetectionTarget`, metadata-dictionary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "346f4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import torch\n",
    "from maite.protocols.object_detection import DatumMetadataType\n",
    "\n",
    "from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset\n",
    "\n",
    "##\n",
    "## Helper class for containing the boxes, label indices, and confidence scores.\n",
    "##\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class DetectionDatumMetadata(DatumMetadataType):\n",
    "    \"\"\"Dataclass for detection datum-level metadata.\"\"\"\n",
    "\n",
    "    id: int | str\n",
    "    k: tuple[float, float, float]\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class YOLODetectionTarget:\n",
    "    \"\"\"A helper class to represent object detection results in the format expected by YOLO-based models.\n",
    "\n",
    "    Attributes:\n",
    "        boxes (torch.Tensor): A tensor containing the bounding boxes for detected objects in\n",
    "            [x_min, y_min, x_max, y_max] format.\n",
    "        labels (torch.Tensor): A tensor containing the class labels for the detected objects.\n",
    "            These may be floats for compatibility with specific datasets or tools.\n",
    "        scores (torch.Tensor): A tensor containing the confidence scores for the detected objects.\n",
    "    \"\"\"\n",
    "\n",
    "    boxes: torch.Tensor\n",
    "    labels: torch.Tensor\n",
    "    scores: torch.Tensor\n",
    "\n",
    "\n",
    "##\n",
    "## Prepare results for ingestion into maite dataset by puttin them into detection object\n",
    "## Images must be channel first (c, h, w) in maite dataset objects\n",
    "##\n",
    "imgs = [np.transpose(img_nd_bgr, (2, 0, 1))]\n",
    "dets = []\n",
    "metadata: list[DatumMetadataType] = [{\"id\": 0}]\n",
    "for _detection in baseline:\n",
    "    boxes = baseline[0].boxes.xyxy.cpu()\n",
    "    labels = baseline[0].boxes.cls.cpu()  # note, these are floats, not ints\n",
    "    scores = baseline[0].boxes.conf.cpu()\n",
    "\n",
    "    dets.append(YOLODetectionTarget(boxes, labels, scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cd8fb3-2ea6-492c-ab84-f3432b9d3440",
   "metadata": {},
   "source": [
    "### (1) Wrapping the Detection Model\n",
    "\n",
    "The first object we'll wrap will be the detection model. The cell below defines a class adapting YOLO for the [MAITE Object Detection Model](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Model.html) protocol. The `__call__` method runs the model on images in the batch and is called by the MAITE evaluation workflow later in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1131aa7-3716-479f-838b-0464b74106e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import maite.protocols.object_detection as od\n",
    "import ultralytics.models\n",
    "from maite.protocols import ArrayLike, ModelMetadata\n",
    "\n",
    "\n",
    "class MaiteYOLODetector:\n",
    "    \"\"\"A wrapper class for a YOLO model to simplify its usage with input batches and object detection targets.\n",
    "\n",
    "    This class takes a YOLO model instance, processes input image batches, and converts predictions into\n",
    "    `YOLODetectionTarget` instances.\n",
    "\n",
    "    Attributes:\n",
    "        _model (ultralytics.models.yolo.model.YOLO): The YOLO model instance used for predictions.\n",
    "\n",
    "    Methods:\n",
    "        __call__(batch):\n",
    "            Processes a batch of images through the YOLO model and returns the predictions as\n",
    "            `YOLODetectionTarget` instances.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model: ultralytics.models.yolo.model.YOLO) -> None:\n",
    "        \"\"\"Initializes the MaiteYOLODetector with a YOLO model instance.\n",
    "\n",
    "        Args:\n",
    "            model (ultralytics.models.yolo.model.YOLO): The YOLO model to use for predictions.\n",
    "        \"\"\"\n",
    "        self._model = model\n",
    "        # Dummy model metadata type to pass type checking\n",
    "        self.metadata = ModelMetadata(id=\"0\")\n",
    "\n",
    "    def __call__(self, batch: Sequence[ArrayLike]) -> Sequence[YOLODetectionTarget]:\n",
    "        \"\"\"Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`s.\n",
    "\n",
    "        Args:\n",
    "            batch (Sequence[ArrayLike]): A batch of images in (c, h, w) format (channel-first).\n",
    "\n",
    "        Returns:\n",
    "            Sequence[YOLODetectionTarget]: A list of YOLODetectionTarget instances containing the predictions for each\n",
    "            image in the batch.\n",
    "        \"\"\"\n",
    "        # Convert images to channel-last format (h, w, c) for YOLO model\n",
    "        batch_transposed = [np.transpose(batch[i], (1, 2, 0)) for i in range(len(batch))]\n",
    "\n",
    "        yolo_predictions = self._model(batch_transposed, verbose=False)\n",
    "        return [\n",
    "            YOLODetectionTarget(\n",
    "                p.boxes.xyxy.cpu(),  # Bounding boxes in (x_min, y_min, x_max, y_max) format\n",
    "                p.boxes.cls.cpu(),  # Class indices for the detected objects\n",
    "                p.boxes.conf.cpu(),  # Confidence scores for the detections\n",
    "            )\n",
    "            for p in yolo_predictions\n",
    "        ]\n",
    "\n",
    "\n",
    "# create the wrapped model object\n",
    "yolo_model: od.Model = MaiteYOLODetector(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cef0788-137f-41c1-a9e4-80e7d08b47d1",
   "metadata": {},
   "source": [
    "### (2) Wrapping the Dataset\n",
    "\n",
    "MAITE pairs images and their reference detections (aka targets, ground truth) into **datasets**. Typical ML workflows have many images per dataset; when these do not all fit in memory simultaneously, a *dataloader* object is used which can page images and annotations in from disk. For this notebook, however, each invocation of `evaluate` will use the same single-image dataset (our reference image with its baseline detections.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1dcbb5d0-b899-4355-9922-9d52fb866b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our single image, its baseline detections, and an empty metadata dictionary\n",
    "# switch image to channel first\n",
    "single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id=\"visdrone_ex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7963f7fa-052c-476d-944e-5bbdae3181b5",
   "metadata": {},
   "source": [
    "### (3) Wrapping the Perturbations as Augmentations\n",
    "\n",
    "The `evaluate` function will perturb the image from the dataset using instances of the class defined below, one instance per perturbation value. Note that the object doesn't perform any augmentations until called by the `evaluate` workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "bp = RadialDistortionPerturber(k=[0, 0, 0])\n",
    "identity_augmentation = JATICDetectionAugmentation(bp, augment_id=\"identity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81020be7-76ba-43e3-b231-7d2bd7c30387",
   "metadata": {},
   "source": [
    "### (4) Wrapping the Metrics\n",
    "\n",
    "We'll compare the detections in each perturbed image to the unperturbed detections using the Mean Average Precision (mAP) metric from the `torchmetrics` package. The following cell creates a mAP metrics object, wraps it in a MAITE [MAITE Object Detection Metric](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Metric.html) protocol-compatible class, and then creates an instance of this class, which will be called by `evaluate`.\n",
    "\n",
    "This code is copied directly from the [MAITE object detection tutorial](https://jatic.pages.jatic.net/cdao/maite/tutorials/torchvision_object_detection.html#metrics) (with the exception of setting `class_metrics=True`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e33407c5-f1d7-4c7a-9a31-2d500f97edcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from maite.protocols import MetricMetadata\n",
    "from torchmetrics import Metric as TorchMetric\n",
    "from torchmetrics.detection.mean_ap import MeanAveragePrecision\n",
    "\n",
    "##\n",
    "## Create an instance of the MAP metric object\n",
    "##\n",
    "\n",
    "tm_metric = MeanAveragePrecision(\n",
    "    box_format=\"xyxy\",\n",
    "    iou_type=\"bbox\",\n",
    "    iou_thresholds=[0.5],\n",
    "    rec_thresholds=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    max_detection_thresholds=[1, 10, 100],\n",
    "    class_metrics=True,\n",
    "    extended_summary=False,\n",
    "    average=\"macro\",\n",
    ")\n",
    "\n",
    "##\n",
    "## This wrapper associates the MAP metric object with methods called by the evaluate\n",
    "## workflow to accumulate detection data and compute the metrics.\n",
    "##\n",
    "\n",
    "\n",
    "class WrappedTorchmetricsMetric:\n",
    "    \"\"\"A wrapper class for a Torchmetrics metric designed to simplify its usage for object detection tasks.\n",
    "\n",
    "    This class facilitates the conversion of object detection targets and predictions into the format\n",
    "    expected by Torchmetrics metrics, allowing for easier integration with existing pipelines.\n",
    "\n",
    "    Attributes:\n",
    "        _tm_metric (Callable): The Torchmetrics metric to be wrapped, which takes lists of dictionaries\n",
    "            containing torch.Tensor objects representing predictions and targets.\n",
    "\n",
    "    Methods:\n",
    "        to_tensor_dict(target):\n",
    "            Converts an `ObjectDetectionTarget` into a dictionary format compatible with the Torchmetrics\n",
    "            metric's `update` method.\n",
    "\n",
    "        update(preds, targets):\n",
    "            Updates the wrapped Torchmetrics metric with batches of predictions and targets in their native format.\n",
    "\n",
    "        compute():\n",
    "            Computes the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        reset():\n",
    "            Resets the state of the wrapped Torchmetrics metric.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        tm_metric: TorchMetric,\n",
    "    ) -> None:\n",
    "        \"\"\"Initializes the WrappedTorchmetricsMetric with the given Torchmetrics metric.\n",
    "\n",
    "        Args:\n",
    "            tm_metric (Callable): A Torchmetrics metric instance that expects predictions and targets as lists of\n",
    "                dictionaries containing torch.Tensor objects.\n",
    "        \"\"\"\n",
    "        self._tm_metric = tm_metric\n",
    "        # Dummy metric metadata type to pass type checking\n",
    "        self.metadata = MetricMetadata(id=\"0\")\n",
    "\n",
    "    @staticmethod\n",
    "    def to_tensor_dict(target: od.ObjectDetectionTarget) -> dict[str, torch.Tensor]:\n",
    "        \"\"\"Converts an ObjectDetectionTarget into a dictionary format compatible with the Torch's `update` method.\n",
    "\n",
    "        Args:\n",
    "            target (od.ObjectDetectionTarget): An object detection target instance containing boxes, labels, and scores.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, torch.Tensor]: A dictionary with keys `boxes`, `scores`, and `labels`, each mapping to a tensor.\n",
    "        \"\"\"\n",
    "        return {\n",
    "            \"boxes\": torch.as_tensor(target.boxes),\n",
    "            \"scores\": torch.as_tensor(target.scores),\n",
    "            \"labels\": torch.as_tensor(target.labels).type(torch.int64),\n",
    "        }\n",
    "\n",
    "    def update(self, preds: Sequence[od.TargetType], targets: Sequence[od.TargetType]) -> None:\n",
    "        \"\"\"Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
    "\n",
    "        Args:\n",
    "            preds (Sequence[od.TargetType]): A batch of predictions in the format expected by the Torchmetrics metric.\n",
    "            targets (Sequence[od.TargetType]): A batch of targets in the format expected by the Torchmetrics metric.\n",
    "        \"\"\"\n",
    "        preds_tm = [self.to_tensor_dict(pred) for pred in preds]\n",
    "        targets_tm = [self.to_tensor_dict(tgt) for tgt in targets]\n",
    "        self._tm_metric.update(preds_tm, targets_tm)\n",
    "\n",
    "    def compute(self) -> dict[str, Any]:\n",
    "        \"\"\"Computes and returns the final metric values using the wrapped Torchmetrics metric.\n",
    "\n",
    "        Returns:\n",
    "            dict[str, Any]: A dictionary containing the computed metric values.\n",
    "        \"\"\"\n",
    "        return self._tm_metric.compute()\n",
    "\n",
    "    def reset(self) -> None:\n",
    "        \"\"\"Resets the state of the wrapped Torchmetrics metric, clearing any accumulated data.\"\"\"\n",
    "        self._tm_metric.reset()\n",
    "\n",
    "\n",
    "##\n",
    "## This is our instance variable that can compute the MAP metrics.\n",
    "##\n",
    "\n",
    "mAP_metric: od.Metric = WrappedTorchmetricsMetric(tm_metric)  # noqa: N816"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0102dfde-cb9b-46a7-8e4a-d12ccd948864",
   "metadata": {},
   "source": [
    "## Running the Evaluation\n",
    "\n",
    "We now have all the wrappings required to evaluate our range of perturbations:\n",
    "- The `yolo_model` object, wrapping the YOLO model\n",
    "- The `single_image_dataset` object, providing our source image and its baseline detections\n",
    "- The `augmentation` object, which when instantiated, applies a single perturbation value to its input\n",
    "- The `mAP_metrics` object, defining the metrics to compute at each perturbation value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a7fef1-a3cc-4e63-8a2b-6133274e4b1c",
   "metadata": {},
   "source": [
    "### Evaluation Sanity Check: Ground Truth Against Itself\n",
    "\n",
    "Here we quickly check the evaluation workflow by creating an *identity augmentation* (with radial distortion coefficients of [0, 0, 0], leaving the image unchanged) and scoring it. The detections should also be unchanged from the baseline and thus give an mAP of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0af9f36-c69b-400f-82ec-f1fd7e9d98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 19.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check: overall mAP (should be 1.0): 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from maite.tasks import evaluate\n",
    "\n",
    "# call the model for each image in the dataset (in this case, just the source image),\n",
    "# scoring the resulting detections against those from the dataset\n",
    "sanity_check_results, _, _ = evaluate(\n",
    "    model=yolo_model,\n",
    "    dataset=single_image_dataset,\n",
    "    augmentation=identity_augmentation,\n",
    "    metric=mAP_metric,\n",
    ")\n",
    "\n",
    "print(\"Sanity check: overall mAP (should be 1.0):\", sanity_check_results[\"map\"].item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1a72bb-dbf1-431e-892e-132771cd7b92",
   "metadata": {},
   "source": [
    "### Preparing the Data\n",
    "\n",
    "Now we'll prepare the augmentation instances for the evaluation. In the cell below, you can set three parameters for sweeping the set of perturbation values:\n",
    "- **sweep_low**: the minimum perturbation value (must be >= 0)\n",
    "- **sweep_high**: the maximum perturbation value\n",
    "- **sweep_count**: how many perturbations to generation\n",
    "\n",
    "You can also optionally select perturbations to visualize:\n",
    "- **visualization_indices**: a list of perturbation indices *p*, 0 <= *p* < sweep_count. These instances will be rendered along with their corresponding detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 30 perturbation datasets\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 0.001\n",
    "SWEEP_HIGH = 0.2\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [3, 9, 21]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentations = [\n",
    "    JATICDetectionAugmentation(RadialDistortionPerturber(k=[k1, 0, 0]), augment_id=str(idx))\n",
    "    for idx, k1 in enumerate(perturbation_values)\n",
    "]\n",
    "# create new pertruber for each dataset to preserve seed value\n",
    "print(f\"Generated {len(augmentations)} perturbation datasets\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549d0d4d-eabd-4f54-a487-7cfbc99916c9",
   "metadata": {},
   "source": [
    "### Calling Evaluate on the Augmented Data\n",
    "\n",
    "We loop over all the augmentations, calling `evaluate` on each one and building up a list of resulting metrics for analysis.\n",
    "\n",
    "Any augmentation indices specified above will be rendered in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #3: k1 0.021586206896551725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: k1 0.06275862068965517\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: k1 0.14510344827586208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 3000x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perturbed_metrics = list()\n",
    "_, ax = plt.subplots(len(VISUALIZATION_INDICES), figsize=(30, 12))\n",
    "for idx, a in enumerate(augmentations):  # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    k1: float = a.augment.get_config()[\"k\"][0]\n",
    "    perturber = RadialDistortionPerturber(k=[k1, 0, 0])\n",
    "    augmentation = JATICDetectionAugmentation(perturber, augment_id=str(idx))\n",
    "    result, _, _ = evaluate(\n",
    "        model=yolo_model,\n",
    "        dataset=single_image_dataset,\n",
    "        augmentation=augmentation,\n",
    "        metric=mAP_metric,\n",
    "    )\n",
    "    perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: k1 {k1}\")\n",
    "        datum = single_image_dataset[0]\n",
    "        batch = ([datum[0]], [datum[1]], [datum[2]])\n",
    "        # Extract the image from the augmentation and switch it to channel last\n",
    "        aug = np.transpose(augmentation(batch)[0][0], (1, 2, 0))\n",
    "        # Plot image\n",
    "        ax_idx = VISUALIZATION_INDICES.index(idx)\n",
    "        ax[ax_idx].imshow(model(aug)[0].plot())\n",
    "        ax[ax_idx].set_title(f\"k1: {k1}\")\n",
    "        _ = ax[ax_idx].axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0303b6a-f587-43c8-ae9f-0057a7884241",
   "metadata": {},
   "source": [
    "## Evaluation Analysis\n",
    "\n",
    "Now we can plot how the metrics (for example, mAP @ IoU=50) vary with perturbation level, keeping in mind this is the mAP scores compared against the detections in the unperturbed image.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09b6804b-c98c-46fb-af98-ab5598892ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map50_list = [m[\"map_50\"].item() for m in perturbed_metrics]\n",
    "plt.title(\"mAP@50 on radial distortion perturbed dataset\")\n",
    "plt.xlabel(\"Radial Distortion Coefficient (k)\")\n",
    "plt.ylabel(\"mAP @ 50% IoU\")\n",
    "_ = plt.plot(perturbation_values, map50_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8720fd-32dc-493d-ab5c-81c313615504",
   "metadata": {},
   "source": [
    "### Evaluation Interpretation\n",
    "\n",
    "**General things to know about mAP calculation**:\n",
    "- The mAP metric calculation does not take into account the images that do not have any detection or any ground truth. In such cases, it returns the default initialization value, -1. It means it couldn't compute the metric. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1184#issuecomment-1589210748)). Hence, in this notebook example, we clip values to a range of `[0, 1]`.\n",
    "- When having images with no ground truth, the order of those images in the batch can change the mAP calculation. To be more precise, if empty images are at the end of the batch, they will be ignored in the computation. But the empty images that are placed before the last non-empty image are taken into account. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1774#issuecomment-1590681234)).\n",
    "\n",
    "The metric shown, mAP@50, is the average precision of detections across all classes when the bounding box IoU is at least 0.5 (for more details, [see here](https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html).) As the value of the distortion coefficient increases, the mAP@50 decreases almost linearly. However, worth noting that the trend isn't strictly downwards, and occasionally an increase in the distortion coefficient results in a small improvement to the accuracy of detection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0f9b97-ce54-4d21-be91-160f4a5d3054",
   "metadata": {},
   "source": [
    "### Additional Plots\n",
    "\n",
    "For further insight, we can plot the mAP per class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65d28ff5-838e-467a-a703-81432a408b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Each instance of the metrics object has, potentially, a different set of observed classes.\n",
    "# Loop through them to accumulate a unified set of classes to ensure consistent plotting across\n",
    "# all thresholds.\n",
    "#\n",
    "\n",
    "unified_classes = set()\n",
    "for m in perturbed_metrics:\n",
    "    for class_idx in m[\"classes\"].tolist():\n",
    "        unified_classes.add(class_idx)\n",
    "\n",
    "#\n",
    "# dictionary of class_idx -> list of per-class mAP, or 0 if not present at that threshold\n",
    "#\n",
    "\n",
    "class_mAP = {class_idx: list() for class_idx in unified_classes}  # noqa: N816\n",
    "\n",
    "#\n",
    "# populate the lists across the perturbation values\n",
    "#\n",
    "\n",
    "for m in perturbed_metrics:\n",
    "    this_perturbation_classes = m[\"classes\"].tolist()\n",
    "    for class_idx in unified_classes:\n",
    "        if class_idx in this_perturbation_classes:\n",
    "            # the index of the class in this individual metric instance\n",
    "            this_class_idx = this_perturbation_classes.index(class_idx)\n",
    "            class_map_value = m[\"map_per_class\"][this_class_idx].item()\n",
    "            # Clip value to 0 if negative\n",
    "            if class_map_value < 0:\n",
    "                class_mAP[class_idx].append(0)\n",
    "            else:\n",
    "                class_mAP[class_idx].append(m[\"map_per_class\"][this_class_idx].item())\n",
    "        else:\n",
    "            class_mAP[class_idx].append(0)\n",
    "\n",
    "#\n",
    "# plot\n",
    "#\n",
    "\n",
    "plt.title(\"mAP per class for radial distortion perturbed dataset\")\n",
    "plt.xlabel(\"Radial Distortion Coefficient (k)\")\n",
    "plt.ylabel(\"mAP @ 50% IoU\")\n",
    "for class_idx, class_mAP_list in class_mAP.items():  # noqa: N816\n",
    "    plt.plot(perturbation_values, class_mAP_list, label=baseline[0].names[class_idx])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aef482b-1a2b-4b2e-b522-7032776320d2",
   "metadata": {},
   "source": [
    "The above plots shows several interesting results:\n",
    "\n",
    "- The trends are not strictly downwards across all classes. Occasionally, an increase in the distortion coefficients can improve object detection, which may occur when the resulting skew better aligns the orientation of the object with the training data.\n",
    "- The person and motorcycle classes appear to be affected the least by radial distortion perturbations.\n",
    "- Radial distortion skews objects, so smaller objects would likely be less impacted since they have less total distortion between the minimum and maximum vertices of their bounding boxes.\n",
    "- The variation in performance across perturbation levels may be influenced by the limited dataset size, where individual detections can significantly impact the overall mAP scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23ec21e7-e682-4d08-ad1d-6c42d850402f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"mAP values per area for radial distortion perturbed dataset\")\n",
    "plt.xlabel(\"Radial Distortion Coefficient (k)\")\n",
    "plt.ylabel(\"mAP\")\n",
    "for k in (\"map\", \"map_small\", \"map_medium\"):\n",
    "    plt.plot(perturbation_values, [m[k].item() if m[k].item() >= 0 else 0 for m in perturbed_metrics], label=k)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e276bd-2eaf-4a4c-a3cd-9bb8c98fa30c",
   "metadata": {},
   "source": [
    "The `map` line covers all sizes; `map_small` and `map_medium` are the mean average precision for objects (smaller than 32^2 pixels, between 32^2 and 96^2 pixels) in area, respectively. (There are no detections in the `map_large` category.) (Here, the mAP value is averaged over a **range** of IoU thresholds, between 0.5 and 0.95.)\n",
    "\n",
    "These results further reinforce our previous conclusion that smaller objects would be less impacted by the radial distortion. It also demonstrates how additional data points make the downward trend more evident and result in less dramatic spikes in precision.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
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