{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Visual Focus 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 sensor condition (in this case, <b><i>Visual Focus</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_focus_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.\n",
    "\n",
    "**Note for Colab users**: After setting up the environment, you may need to \"Restart Runtime\" in order to resolve package version conflicts (see the [README](https://github.com/Kitware/nrtk/blob/main/docs/examples/README.md#run-the-notebooks-from-colab) for more info)."
   ]
  },
  {
   "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": "414bc5fc",
   "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": "129fee9f",
   "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.pybsm.defocus_otf_perturber import DefocusOTFPerturber\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",
    "print(img_nd_bgr.shape)\n",
    "plt.figure()\n",
    "plt.axis(\"off\")\n",
    "\n",
    "_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
   "metadata": {},
   "source": [
    "## NRTK Defocus OTF Perturbation: Examples and Guidance\n",
    "\n",
    "The [Defocus OTF](<project:../../_implementations/nrtk.impls.perturb_image.pybsm.defocus_otf_perturber.rst#module-nrtk.impls.perturb_image.pybsm.defocus_otf_perturber>) applies the effects of optics defocus to the image, modeled as a gaussian blur. This function sets the width of the Gaussian blur in the spatial domain in terms of ‘angular extent’, which is given by the instantaneous field-of-view (IFOV) for each pixel (see image below for calculation). The angle for each pixel is approximately pixel_size/focal_length (radians) --- wx and wy scale the blur size in units of IFOV. For example, we can blur the image with an approximately 5 pixel blur kernel by setting `wx` and `wy` = `5 * pixel_size/focal_length = 5 * IFOV`.\n",
    "\n",
    "The Defocus OTF perturbation is set by two parameters `w_x` and `w_y`, which work to define the blur spot used to simulate the sensor defocus:\n",
    "\n",
    "- `w_x`: the 1/e blur spot radii in the x direction\n",
    "- `w_y`: the 1/e blur spot radii in the y direction\n",
    "\n",
    "For the purpose of this notebook we will keep the values of these two parameters equal.\n",
    "\n",
    "- `w_x == 0.0`: The perturber is undefined at exactly 0 due to the size 0 blur it creates.\n",
    "- `0.0 < w_x`: Creates a blur spot of size `w_x*w_y` that is used to defocus the image\n",
    "\n",
    "A normal IFOV value is on the order of 1e-6, and since `w_x` and `w_y` are in the same units as IFOV, a value of 10e3 larger than the normal IFOV value results in a blur kernel that extends to a high proportion of the size of the image in the frequency domain resulting in a largely blurred/defocused image.\n",
    "\n",
    "For additional information on how these parameters affect image formation, see this [pyBSM explanation](https://pybsm.readthedocs.io/en/latest/explanation.html).\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, w_x in enumerate((0.0001, 0.0005, 0.0007, 0.001, 0.005, 0.007, 0.01, 0.05)):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    bp = DefocusOTFPerturber(w_x=w_x, w_y=w_x)\n",
    "    ax[row, col].set_title(f\"w_x: {w_x}\")\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, 179.2/913.8 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 40.8ms\n",
      "Speed: 6.1ms preprocess, 40.8ms inference, 130.5ms 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 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 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 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 = DefocusOTFPerturber(w_x=0.00001, w_y=0.000001)\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 minimal defocus, leaving the image essentially 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,  3.14it/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 augmentations\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 0.0001\n",
    "SWEEP_HIGH = 0.003\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [0, 9, 21]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentations = [\n",
    "    JATICDetectionAugmentation(DefocusOTFPerturber(w_x=p, w_y=p), augment_id=str(idx))\n",
    "    for idx, p in enumerate(perturbation_values)\n",
    "]\n",
    "\n",
    "print(f\"Generated {len(augmentations)} perturbation augmentations\")"
   ]
  },
  {
   "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": [
      "100%|██████████| 1/1 [00:00<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #0: w_x value 0.0001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  3.18it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: w_x value 0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  3.20it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: w_x value 0.0022\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):\n",
    "    # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    result, _, _ = evaluate(model=yolo_model, dataset=single_image_dataset, augmentation=a, metric=mAP_metric)\n",
    "    perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        w_x = a.augment.get_config()[\"w_x\"]\n",
    "        print(f\"Perturbation #{idx}: w_x value {w_x:0.5}\")\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(a(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\"w_x: {w_x:0.5}\")\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 defocus perturbed dataset\")\n",
    "plt.xlabel(\"Defocus perturbation factor\")\n",
    "plt.ylabel(\"mAP @ 50% IoU\")\n",
    "_ = plt.plot(perturbation_values, map50_list)"
   ]
  },
  {
   "attachments": {},
   "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",
    "Note that as plotted, the minimum y-axis value is 0.0. 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).) In general, we observe a perturbation value range between around 0.0 and 0.0002 where the score is about 0.95 or higher, an initial fall off to a mAP@50 of around 0.2 between pertubation values 0.0005 and 0.0012, and then another sharp falloff to an mAP@50 of 0.0. (Note the mAP is guaranteed to be 1.0 when the perturbation is 0.0001, i.e. when the image is unchanged, the two detection sets are identical.)"
   ]
  },
  {
   "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 defocus perturbed dataset\")\n",
    "plt.xlabel(\"Defocus perturbation factor\")\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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3aef482b-1a2b-4b2e-b522-7032776320d2",
   "metadata": {},
   "source": [
    "The above plots shows several interesting results:\n",
    "\n",
    "- The truck and motorcycle classes show initial robustness to defocus but experience more dramatic performance drops compared to car and person classes.\n",
    "- The car class demonstrates a more gradual mAP degradation across the defocus range, which could be attributed to the variety of car sizes and the higher number of car instances (15 cars vs. fewer instances of other classes).\n",
    "- The person class maintains reasonable performance longer than other classes under defocus perturbations, potentially due to their typically larger size and distinct visual features that remain detectable even when slightly blurred.\n",
    "- All classes eventually experience significant performance degradation as defocus increases, which is expected as objects become increasingly blurred and harder to detect.\n",
    "\n",
    "Any conclusions about classification accuracy should be considered in light of these caveats. In particular, the foreground positioning of the detected non-car objects suggests that instead of looking at per-class results, we drill down by bounding box area. Fortunately, the metrics class supports this:\n"
   ]
  },
  {
   "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 defocus perturbed dataset\")\n",
    "plt.xlabel(\"Defocus perturbation factor\")\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()"
   ]
  },
  {
   "attachments": {},
   "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.) We see that medium objects, regardless of class, are generally much more robust to defocus  perturbations than small ones.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
 ],
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