{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Extreme Illumination 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 condition (in this case, <b><i>extreme illumination conditions</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_brightness_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": "0f36d221",
   "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": "0030c7f0",
   "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.PIL.enhance import BrightnessPerturber\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": [
      "0.24.0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nrtk\n",
    "\n",
    "print(nrtk.__version__)\n",
    "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",
    "\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 Brightness Perturbation: Examples and Guidance\n",
    "\n",
    "The  [Brightness perturber](<project:../../_implementations/nrtk.impls.perturb_image.generic.PIL.enhance.rst#module-nrtk.impls.perturb_image.generic.PIL.enhance>) perturbation is set by a single floating point value `f`:\n",
    "\n",
    "- `f == 0.0` (the minimum value): returns a black image.\n",
    "- `0.0 < f < 1.0`: returns an image dimmer than the original.\n",
    "- `f == 1.0`: returns the original image unchanged.\n",
    "- `f > 1.0`: returns an image brighter than the original. There is no upper bound, but values greater than 2 or 3 start to wash out objects in a typical image, as seen below.\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, f in enumerate((0.0, 0.25, 0.6, 1.0, 1.5, 2, 3, 6)):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    bp = BrightnessPerturber(factor=f)\n",
    "    ax[row, col].set_title(f\"factor: {f}\")\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, 179.0/913.8 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 41.1ms\n",
      "Speed: 4.5ms preprocess, 41.1ms inference, 158.7ms 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 a 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 = BrightnessPerturber(factor=1.0)\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 a brightness perturbation factor of 1.0, 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, 45.36it/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 = 0.2\n",
    "SWEEP_HIGH = 2.0\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [3, 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(BrightnessPerturber(p), augment_id=str(idx)) 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, 43.61it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 45.84it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.75it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #3: brightness perturbation value 0.38621\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 51.49it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.46it/s]\n",
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      "100%|██████████| 1/1 [00:00<00:00, 52.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: brightness perturbation value 0.75862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 52.52it/s]\n",
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      "100%|██████████| 1/1 [00:00<00:00, 42.70it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 37.42it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 41.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: brightness perturbation value 1.5034\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 41.73it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 46.40it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 42.88it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.81it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 39.10it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 43.47it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.31it/s]\n",
      "100%|██████████| 1/1 [00:00<00:00, 48.64it/s]\n"
     ]
    },
    {
     "data": {
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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",
    "        factor = a.augment.get_config()[\"factor\"]\n",
    "        print(f\"Perturbation #{idx}: brightness perturbation value {factor: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\"Factor: {factor:0.5}\")\n",
    "        _ = ax[ax_idx].axis(\"off\")\n",
    "\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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cZq3ijQPEOreBY9HUu9vqse/ELL9lHlOPKbI+Vjj7v+wa9W3p/eX86N6f3l/OgDxfxh4ju76x8XWGoeILmyuIftMFtolvYoxnhCnbIzMjMVYfMWBAUZ9K2pdVtNE8R+C9Y1e5W2sX0KSA3UudgkIhYAt2JAPXrivTt6f3l/Ojen95fzoA8p8SeJbqfWNe0688Q3GlxxIEsNPtbFJXv0aIHcGdG3ZYlcLgDHPrUFpPpNv4K8EX954kl0LVYNLC2t2YhJGyMqBkcMpU/dXjIPHFeu70/vL+dG9P7y/nQB4/quoa1rnh7w1r9/PcWttYapP599ZWmSYtrpFdCGQNhTnnIOA2R7aGnW2nawfEN/pvii88Q6hLo0locwIqbTuKjKRqC2SeOTya9Q3p/eX86N6f3l/OgDlfh3rmmar4O0u1sryOa4sbG3huolPzQuIwNrDscq35V1lN3p/eX86N6f3l/OgB1FN3p/eX86N6f3l/OgB1FN3p/eX86N6f3l/OgB1FN3p/eX86N6f3l/OgB1FN3p/eX86N6kgBhk+9ADqKKKACiiigAoopsj+XGz7WbaCdqjJPsKAHUVjaB4jh1+S/iSxvbKaxlWKaK8RVbLIrggKx42sOuDSax4kh0m9gsUsb3UL6aNphbWSKzrGpALncygDJA65J6A0AbVcn4zsLPU9S8K2d/aQXVtJqr74Z4w6Ni0uCMg8HkA/hXQaVqlrrWl2+o2Tl7a4TehKkEeoIPQg5BHYisjxL/wAh7wj/ANhV/wD0juaAF/4QHwd/0Kuif+AEX/xNH/CA+Dv+hV0T/wAAIv8A4muiooA53/hAfB3/AEKuif8AgBF/8TR/wgPg7/oVdE/8AIv/AImuiooA53/hAfB3/Qq6J/4ARf8AxNH/AAgPg7/oVdE/8AIv/ia6KigDnf8AhAfB3/Qq6J/4ARf/ABNH/CA+Dv8AoVdE/wDACL/4muiooA53/hAfB3/Qq6J/4ARf/E0f8ID4O/6FXRP/AAAi/wDia6KigDnf+EB8Hf8AQq6J/wCAEX/xNH/CA+Dv+hV0T/wAi/8Aia6KigDnf+EB8Hf9Cron/gBF/wDE0f8ACA+Dv+hV0T/wAi/+JroqKAOd/wCEB8Hf9Cron/gBF/8AE0f8ID4O/wChV0T/AMAIv/ia6KigDnf+EB8Hf9Cron/gBF/8TR/wgPg7/oVdE/8AACL/AOJroqKAOd/4QHwd/wBCron/AIARf/E0f8ID4O/6FXRP/ACL/wCJroqKAOd/4QHwd/0Kuif+AEX/AMTR/wAID4O/6FXRP/ACL/4muiooA53/AIQHwd/0Kuif+AEX/wATR/wgPg7/AKFXRP8AwAi/+JroqCQBknAoA53/AIQHwd/0Kuif+AEX/wATR/wgPg7/AKFXRP8AwAi/+JroPMj/AL6/nR5kf99fzoA5/wD4QHwd/wBCron/AIARf/E0f8ID4O/6FXRP/ACL/wCJroPMj/vr+dHmR/31/OgDn/8AhAfB3/Qq6J/4ARf/ABNH/CA+Dv8AoVdE/wDACL/4mug8yP8Avr+dHmR/31/OgDn/APhAfB3/AEKuif8AgBF/8TR/wgPg7/oVdE/8AIv/AImug8yP++v50B1Y4DAn2NAHP/8ACA+Dv+hV0T/wAi/+Jo/4QHwd/wBCron/AIARf/E10VFAHO/8ID4O/wChV0T/AMAIv/iaP+EB8Hf9Cron/gBF/wDE10VFAHO/8ID4O/6FXRP/AAAi/wDiaP8AhAfB3/Qq6J/4ARf/ABNdFRQBzv8AwgPg7/oVdE/8AIv/AImj/hAfB3/Qq6J/4ARf/E10VFAHO/8ACA+Dv+hV0T/wAi/+Jo/4QHwd/wBCron/AIARf/E10VFAHO/8ID4O/wChV0T/AMAIv/iaP+EB8Hf9Cron/gBF/wDE10VFAHO/8ID4O/6FXRP/AAAi/wDiaP8AhAfB3/Qq6J/4ARf/ABNdFRQBzv8AwgPg7/oVdE/8AIv/AImj/hAfB3/Qq6J/4ARf/E10VFAHO/8ACA+Dv+hV0T/wAi/+Jo/4QHwd/wBCron/AIARf/E10VFAHO/8ID4O/wChV0T/AMAIv/iaP+EB8Hf9Cron/gBF/wDE10VFAHO/8ID4O/6FXRP/AAAi/wDiaP8AhAfB3/Qq6J/4ARf/ABNdFRQBzv8AwgPg7/oVdE/8AIv/AImj/hAfB3/Qq6J/4ARf/E10VFAHO/8ACA+Dv+hV0T/wAi/+Jo/4QHwd/wBCron/AIARf/E10VFAHO/8ID4O/wChV0T/AMAIv/iaP+EB8Hf9Cron/gBF/wDE10VFAHO/8ID4O/6FXRP/AAAi/wDiaydS8MaDo3ibwncaXounWMzanIjSW1skbFfslwcEqBxkA/hXcVzniX/kPeEf+wq//pHc0AdHRRRQAUUUUAFIzBVLEEgDOAMmlooA4Hw3rYHibxTctpOtpHdzR3NuZdLnj8xY7aNWALKAG3IwAJBParV5dzad4ttfEj6XqUtje6StuUgtWlmgkD+YFeNckZDkZ6Arziu0ooA57wPp91pvhK1hvYTBcyST3DwkgmLzZXkCHHcBwD7isnVdAs9N8V+FruCbUHkl1SUMtxqE8yDNpcHhHcqOnYDHSu3rnPEv/Ie8I/8AYVf/ANI7mgDo6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorP1fWbLQ7I3V7LtXoiLy0jeijuaaTbshNqKuzQorlh4xnIyPDGu4/69v/r0v/CYz/8AQr67/wCA3/1609hPsZe3p9/zOoorl/8AhMZ/+hX13/wG/wDr0jeMbnadnhbXC2OAbfAJ+uaPYT7fkHt6ff8AM3tS1Oz0iye8vZlihTuepPoB3PtXLx2F/wCNJFudVSSz0QHdDY5KvP6NIR0Ht/8ArNnTfD93qd6mseJCslwvNvZDmK3Hv/eb3/8ArY6qq5lS0jq+/b0/z+4XK6ustI9u/r/l95zP/CvvC3/QL/8AI8v/AMVR/wAK+8Lf9Av/AMjy/wDxVdNRU+3q/wAz+8r2FL+Vfcjmf+FfeFv+gX/5Hl/+Ko/4V94W/wCgX/5Hl/8Aiq6aij29X+Z/eHsKX8q+5HM/8K+8Lf8AQL/8jy//ABVH/CvvC3/QL/8AI8v/AMVXTUUe3q/zP7w9hS/lX3I5n/hX3hb/AKBf/keX/wCKqG48AaRHH5ukLJp19Gd0VxHK7bT6EEnIrrKKPb1f5mJ4el/KvuOa0fxHML0aPr0S2uqD7jj/AFdyP7yH19v/ANQ6Ws/WNFstcsja3se4dUdeHjb1U9jXPwanr3h1jY6jp95q8Kj9xeWke52X0cdj7/zp8samsdH2/wAv8hKUqek9V3/z/wAzsKK5f/hMZ/8AoV9d/wDAb/69T6b4sivtVj06fTb+wnlRni+1xBA+OoHPXFJ0ZpXsUq8G7XOhooorI1CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5zxL/yHvCP/YVf/wBI7mujrnPEv/Ie8I/9hV//AEjuaAOjooooAKKKKACiiigAooooAK5zxL/yHvCP/YVf/wBI7mujrnPEv/Ie8I/9hV//AEjuaAOjooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK5rWPEc73p0bQI1utTP8ArJD/AKq2Hq59fb/9RqEHN2RE5qCuy1rviODR/Ltoo2u9Sn4gtI/vMfU+g96q6P4cna9Gs69It1qZ/wBWg/1dsPRB6+//AOs29C8OQaP5lzLI13qU/M93J95j6D0HtW1Wjmorlh9/9dCFBzfNU+S/rqFFFFYmwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcz42tpV0yDV7Zc3WlzC4XH8Sfxr9Mcn6V01MljSaJ4pFDI6lWU9CD1FXCXLJSIqQ54uIy1uYry0huoW3RTIHQ+oIyKmrlvBkj2aX/AIfnYmXTZisZPVoW+ZD/AD/SupoqR5ZNBTnzxTCiiioLCiorm5is7WW5ncJDEpd2PYDrXE2niPxDBEdevLTztDuWJWGNR5ttH/C/uCOT+fFaQpSmroznVjBpM7uioLK9ttRtI7q0mSaCQZV1PB/+v7VPWbVtGWmnqgorI1zxFaaHGiOGnvJeILWIZkkP07D3rLji8bXkYle602x38+SIi7IPQnoTWkaTau3ZeZnKqk+VK78jq6K5b+xvFkn+s8VRxj0jsEP86l8L6hetc6lpGqXHn3tjLxKVC+ZEwyrYH+eRTdLRtNO3r/kCq+8k4tX9P8zpKKKKyNQooooAKKKKACiiigAooooAKKKKACuc8S/8h7wj/wBhV/8A0jua6Ouc8S/8h7wj/wBhV/8A0juaAOjooooAKKKKACiiigAooooAK5zxL/yHvCP/AGFX/wDSO5ro65zxL/yHvCP/AGFX/wDSO5oA6OiiigAooooAKKKKACiiigAooooAKOlR3FxDa27z3EqRRRjczucACuQaa/8AG7mK1aWx8Pg4efG2S79Qvovv/wDqGkKfNq9F3M51FHRavsbT+LPD6OyNq9oGU4P7wUn/AAl/h7/oMWn/AH8qxB4e0e3gSGPS7TYgwN0KsfxJGTUn9i6V/wBAyy/78L/hT/def4E/vvL8TnbnWb7xRcPp/h6Qw2Snbc6mRwPVY/U+/wD9Y10Gj6NZaHZC1sotq9Xc8tI3qx7mrsMMVvEsUMSRxr0RFAA/AU+lOpdcsdF/W44U7Pmlq/62CiiiszUKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5PXf+JN4s0vWx8sFz/oN0e3PKMfx7+1dZWZ4g0pda0K7sDjdIn7sns45U/mBUPhXVW1fw9bXEuftCDypweokXg5+vX8a2l71NS7af5GMfdqOPfX/M2aKKy/EGsR6Fo814w3yfchj7ySH7q/57A1lGLk7I1lJRV2YviB28Ra5D4agY/ZYsT6i6n+E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RrVf+h11z/vzZ/wDxij/hGtV/6HXXP+/Nn/8AGKsaB4ik1+KKddD1OytZoFninujBtkVsEABJGYEg55ArboA5z/hGtV/6HXXP+/Nn/wDGKP8AhGtV/wCh11z/AL82f/xiujooA5z/AIRrVf8Aoddc/wC/Nn/8Yo/4RrVf+h11z/vzZ/8AxiujrnrXxdDNqNpZ3Wk6pp/2xmS1lvIVVJWCltvDEqdoJAYLnBoAyvEWk69pXhnVdQs/F+tzXVraSzQxtBaMHdUJAIEGTkjtWkvhvVioJ8aa5kj/AJ42f/xitPWtZttCsBdXKyyb5FhihhXdJLIxwqKO5P4DqTxWdH4z03+ytRvruG7sn05wl1azxjzkZsbAApIbduXbgkEmgBP+Ea1X/oddc/782f8A8YpIvCk/9qaffXviTVb/AOwzNNFDOluqbzG8eT5cSn7rt3q3o3iODV7u5snsrywvrdEke1vEVX2Nna42sykEqw4PBGDitmgAooooAKKKKACiiigAooooAKKKKACiiigDjtTmn0Hx1NrMmnX95ZXmmx2wayt2nZJY5JG2lV5AYScHplTkineEnufD/hi102/069+0wWUl9L5MJkQFpGYwqR96QZxtHWuvooA4TwrqDa54gOsarZapb6iYXjtLWfTbiKKzhJBIMjoFaRtqljnsAOBk0rKwsX8TaNP4X8PahpTxzu+ozS2clrG0BjbKPuAEjFyhGN2MZyK9IooAKKKKACiiigDz3VDfaRf+L7ddIv71taVZLF7aAyIzmBYSjsOI8FM5bAw1bMQtrbwrLoep6Xe3kGn2lvbXAW2Z1ugUUHywOXA74rqaKAPLx4fk1S38UWfh7TLrS9IvtJNvHDcwtbpJdnf8yRvgqNpUMcAHjrjNbFjPdeIvFWg3a6Tf2MWl2s5uWvLdogJJFVRGmfv4wxLLleBzzXcUUAFFFFABSMNykZIyMZFLRQB5hpVpqMujeFPCz6VfQ3Wj3kL3dxJAywCODdhlk+6+/wCXAUk/Mc4xXW6xrN9J4KudQ0rTr5dQmjMdtbywFZUkZtisy84AJ3E+gzXRVFJdW8M0MMs8Ucs7FYUZwGkIBYhR3IAJ47CgDmbzRtD0bwhpmj6hpU+p2Fr5cYjis3uTuVT+8ZFBJyc5ODy1J4GtJ7WLVilpc2WkyXm/TrW6BV449ihsKeUUuHIU4xnoM11lRQXVvc+b9nnil8qQxyeW4bY46qcdCM9KAJaKKKACobu5SztJbl0ldIlLFYY2kcgeiqCSfYCpqKAOB8H62bHSNZaXR9cEg1K7u1ibTJkaSOW4Ypt3qAThgSM5Az6U7UZbzQtf8TP/AGTf3y6vBE9m1pbtKrSLEYzG5HCchTlsDDHniu8ooA87020vPBWo6dNd2F9fQf2Ba6ez2Nu07JNAWyCq8gNv4PT5eSK6TwTpt1pPg7TrS9j8q6CtJJHnPll3Z9ufbdj8K6CigAooooAKqajqEWmWT3U0NzKikApa27zucnHCICT+VTXN1b2VtJc3U8UEEa7nllcKqD1JPAFS0AcL4A1R7HwJoVhPpOrx3MCQWc0clhJGY2I+8d4GUGOWGQKh1jVzrPiY6ZqGnazBo1jOjYj0u4kF9MpBUl1QqIlYA9fmI7Ac+gVFPdW9qqNcTxQq8ixoZHChnY4VRnqSeAO9AHBeNrPSryTUkg8OalP4jaEJZ31vZyDEm392y3A+VApIzlh0PBrvLRZ1s4FuWV7gRqJWXoWxyR+NTUUAFFFVLDVNP1WKSXTr61vI43MbvbzLIFcYJUlScHkce9AFuuY+IFwY/A+sWsdpe3U97ZT20MdpayTsXaNguQgO0Z7niunooAwdG1tLiz0uFdP1RDMpiLTWUkQiKICd+8AqD0BxgmuVNjYzeI9Lm8NeHtQ06/W/82/uns5LZDCdxkDswCy7ieAN3ODxivSKKAPL72z1EaFrXhFdLvnu77VZZYbpYGMHkyz+aZGl+6CoJBBO7K8DmvUKKKACiiigAriPHGpeTrPhyNNO1W5+x6kt3O9rp00yLH5MyZ3IpBO5l468129FAFKz1Jby5uIVtLyIQpG/mTwFFkDjOFzySOhGODxXO+GrWU6Preu6pY3H2jVpZZ3tWiIlECjZFFtODnYucermuvooA8/8P2FrH4xtZ/Dei3ulaWlpKl/51q9rHMxK+UBG4BZhhzuA6HGTmvQKKKACiiigArz/AEnVz4k8VwX2qadrNolrI66bZzaXcIqEgqZ5ZCmwMVJCjOFB7k8egUUAcf4mkudRt7DULXTb5xo2srLLAYSJJo1V42eNf4x+83DHXbxzWHqGnalra+IddtNNu1R7nTpbW1niMUtwtrIJHOxsEbssAGwTtHrXplFAHI6M02s+N7rXUsby0sY9OSzQ3kDQvK/mM7EIwDYUbRkjkk46V11FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVxXxMt/tWk6Pb/Yre98zWLZfs1y22OX73ysdrYB+h+ldrWfrGiWGvWkdtqEcjxxyrMhineJldejBkIYEZ9aAPNori78KQ+N44LO30S9i0hb+zsrJhNbxhVkHmqSoG4sMFdgHyr97NdKLnWtO1jwtBd6u93/aVxL9pUwRoqgWzuFTC5ChgDySfU44rXh8G6FDZahaizeRNRi8m7ea5llllTBG0yMxfGCeh4zWjNpVlcXVhcyw7prBma2bcRsLIUPGeflYjnNAHnsGveI5dI8OeIDrYEWsapBDJYfZo9kUTuRsRsbtwAwSSe+MYqut1rGj6f4r1yz1PyobLXJW+xeQjJOu6MPvYjcCQeNpGMd61j4Cmn8TW121np1nY22oG+DQXU8ryEEsoETARxEkgsVznB9a6uXw3pM2n39hJaZtr+Zp7lPMf945IJOc5H3RwMDigDktNtb638ZeNr6PWLsLBJG/2fy4djk2qlcnZu+XIxgjO0ZzzlNA1fxBGfBl3qWrm9TXrf8AfwfZo0WNvs5mDIVAOfkIIJIOeAOlddL4b0ubVpNUaGVbuWPy5THcSIkq7SvzorBWIBIBIJHGOgp8WgaZDHpKR22F0kbbIeY37oeWY/X5vkJHzZ9evNAHCeHPEfi7WjpOrx2WpSWl9MpmheK0W1igYkZRg/m7lGD82c4Pyjto6VquuRa59l1u9vbe/uHnW1s5LWL7FMQGZBHKgLZ2gMQzZ4PFdBB4P0O21AXsFpJFIJTMI0uZRCHJyW8oNsznnO3rSW/g7Qra7+1JaSvKFdU866llWMOCG2KzEJkEj5QODQBj+ENU1N9RWx1+/wBSXVXtTK9jd2kMcWQVDtDJGMMoJAwWJwwJqXWbrWLvx9aaHY6w2nWsmmSXUhjgjeQssiqNu9SB97nIIx2zyNjS/C+kaPdtd2cEv2gx+UJZ7mWdlTIJVTIzbRkDgYHArP1fwhDrfi6DU70BrSKwe3URzyRTLIZFbKsmCBtDA4bvjGKAOYbxhr91aaNp0IuZL2ea+iuLrToYTJILWTy90azMEG7IY9cYOB3FmTVvF/2TQ7O4kl027u9Ze0M08ELSS23kSOGKqWRX+XscZXOMHFdbN4U0OfSrTTWsFS1szm2ELtG8J5GVdSGBOTkg5OTmnweGdJtks1S3kP2O4NzC0k8jsJShQsWZiW+ViOSaAOX0PS75PiZrrPr99IIbSwMimKAC4GJhhsR5HIJ+Xbyx7YAbpvibVZ/CngO8lvAbrVbqKO8by0HmqYZWYYxgfMq9MdK66bQNOn1qPV2ilS+RVTzIriSMOqkkB1VgrgbjgMD1qhB4H8O2t9BeQ6eVmt5zcQfv5CsLnOSiFtqg7jkAAHjjgUAcNrVxrWqfDXxdqWoau8kET39tDaLBGqbEmZFLMF3FhtxwQMYyCea6Uapqega/Bbatra31neadcXjyG3RPsrQ7CxXYMmMhzw2T8o5Oa6B/DekyaLd6O1pmwvHleeLzH+cyMXfnORliTweO1RWHhLQ9NkuJLawG64h8h/NkeUCL/nmockKn+yuB7UAcloniHWZvElhp09zq5s9Ws55ILi/tbaJ0ZQpDxCPJxhuki/3evIqh4Yl1PRfht4ZuItXuJheXtjB5UsUO2GNptrquEB5B6sSRjgiu303wXoGk31ve2dk63NsjRwySXMshjQjBUb2OFx0HQdsU638HaHax+VFayiEXMd0sJupTHHJG5dSiFtqDcc4UAHuDQByt/wCINeXTtc8SQ6oI7bStQe2TTPIQpLHHIEbcxG/e3JGCAMrwaXWNV8Ri58b3dprX2e30ELLa2wto2EmLVJWRyRnaST0IIyecYFdVceD9ButVOpTWG65aVZnHmuI3kXG12jDbGYYGCQTwKtS6Bpk0erJJbZXVhtvR5jfvR5Yj9fl+QAfLj1680AVdeeS+8EX80M8lq8tg0gkjCll+Tdj5gR7dO/Y1yFhcXeleEPDOmWmq6xNfXVktxHFY2lq0vlrHHkZdVRUUsBk/Md3U16I9lbyae1i0ebZovJKbj9zGMZ69KzLzwnot9bWEE1q4Wwj8q2aK4kieNMAFd6sGIIUZBJzgZoA5LTfFWt65pXh6yW5Fjfahd3dvc3nkozqLcuPlXLIHbaP7yj5sZ4qXXtI1I+LfCNu3iO+E2bwfaUgtw/8Aq85wYyuccdMY7A810v8Awhvh8aNHpCacqWUU7XEUccjqYpGYsWRgdyHLH7pGAcDinTeEtGuLC2s5IbgpayNJDILyYTIzAgkSh9/IJB+agDldU1vXvsPifW7XV1t4NAmeOOxaCNluBFGrt5jEbgX3EDaVxx1rUsLrWtX8catbjVZLXTbBbSRLZIIyzl0LMrMykhTjtzzwRitS88HaDf6gb66sTJOxQyfvpAkpTG0yIG2yEYGCwPStODTbS21C7voYttzebPPfcTv2DC8E4GAe1AFqiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//Z",
      "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 brightness perturbed dataset\")\n",
    "plt.xlabel(\"Brightness perturbation factor\")\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",
    "Note that as plotted, the minimum y-axis value is 0.4. 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 0.6 and 1.6 where the score is about 0.95 or higher, and sharp falloffs when the perturbation factor is below roughly 0.4 or above 1.75. (Note the mAP is guaranteed to be 1.0 when the perturbation is 1.0, 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 brightness perturbed dataset\")\n",
    "plt.xlabel(\"Brightness 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aef482b-1a2b-4b2e-b522-7032776320d2",
   "metadata": {},
   "source": [
    "The above plots shows several interesting results:\n",
    "\n",
    "- Person, truck, and motorcycle classes are generally more robust to brightness perturbations than the car class.\n",
    "- The truck and motorcycle classes show more consistent performance across the perturbation range, while cars show more variation.\n",
    "- At extreme brightness perturbations (very dim or very bright), all classes experience degraded performance, which is expected as objects become harder to detect under poor lighting conditions.\n",
    "- The car class shows more sensitivity to perturbations, which may be due to having more instances (15 cars vs. fewer instances of other classes) and their varied positions throughout the image.\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 brightness perturbed dataset\")\n",
    "plt.xlabel(\"Brightness 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()"
   ]
  },
  {
   "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 brighter illumination perturbations than small ones, which makes sense when observing how objects tend to get \"washed out\" in the perturbation examples shown in the [Examples and Guidance](#NRTK-Brightness-perturbation:-examples-and-guidance) section above. For dimming perturbations, the situation is reversed (although not as dramatically): small objects tend to be more robust than medium, but both fall off dramatically around 0.3.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nrtk-QgDWFgV4-py3.10",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}