{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Atmospheric Turbulence 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>atmospheric turbulence</i></b>), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
    "\n",
    "- **Traditional vs. relative mAP:**\n",
    "    - An overview of the nuances of what we'll be evaluating.\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 mean average precision metric relative to the unperturbed detections.\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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2e1964-46e2-4497-88ff-274eaca17960",
   "metadata": {},
   "source": [
    "# Evaluation guidance: traditional vs. relative mAP\n",
    "\n",
    "This notebook will be evaluating the perturbed images using mean average precision (mAP) **relative** to detections from the unperturbed image. Traditional mAP scores the computed detections to ground-truth annotations vetted by an analyst; the mAP metric indicates how well the detector does compared to that analyst and thus measures the detector's \"absolute\" performance (\"absolute\" in the sense that the assumption is no detector can do better than the analyst.)\n",
    "\n",
    "In contrast, in this notebook, we're not concerned with the **absolute** ability of the detector to find objects of interest. Rather, we're interested in how the **perturbations** affect the detector *relative to the unperturbed image*. It's expected that the detector won't find every target in the unperturbed image; instead, we're measuring the **change in the detections** (or classifications) caused by the perturbations.\n",
    "\n",
    "To support relative mAP, we'll be computing detections on the unperturbed image and using those as our \"ground truth\" dataset, and using the MAITE dataset class slightly differently than usual. For example, there's no on-disk json file of reference annotations with an associated data loader; instead, we'll be taking the computed detections and manually copying them over into the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9268412-0d13-4fed-ae78-f6ee02c94c9c",
   "metadata": {},
   "source": [
    "# Setup: Notebook initialization\n",
    "The next few cells import the python packages used in the rest of the notebook.\n",
    "\n",
    "**Note:** We are suppressing warnings within this notebook to reduce visual clutter for demonstration purposes. If any issues arise while executing this notebook, we recommend that the first cell is **not** executed so that any related warnings are shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "796743f8-2f1e-42aa-ad6b-f71e721602f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "# warning suppression\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2561179f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning package installation...\n",
      "Installing required packages...\n",
      "Doing a fresh install of opencv-python-headless...\n",
      "\u001b[33mWARNING: Skipping opencv-python as it is not installed.\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Skipping opencv-python-headless as it is not installed.\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import sys  # noqa: F401\n",
    "\n",
    "print(\"Beginning package installation...\")\n",
    "!{sys.executable} -m pip install -qU pip\n",
    "\n",
    "print(\"Installing required packages...\")\n",
    "!{sys.executable} -m pip install -q \"matplotlib\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchvision\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"torchmetrics\" --no-cache-dir\n",
    "!{sys.executable} -m pip install -q \"ultralytics\" --no-cache-dir\n",
    "\n",
    "# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\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\" --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
   "metadata": {},
   "outputs": [],
   "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.turbulence_aperture_otf_perturber import TurbulenceApertureOTFPerturber"
   ]
  },
  {
   "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": [
      "0.20.0\n"
     ]
    },
    {
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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 Turbulence Aperture perturbation: examples and guidance\n",
    "\n",
    "The [Turbulence Aperture perturbation](https://jatic.pages.jatic.net/kitware/nrtk/_implementations/nrtk.impls.perturb_image.pybsm.turbulence_aperture_otf_perturber.html) is set by the following parameters:\n",
    "\n",
    "- `mtf_wavelengths`: a sequence of wavelengths (m)\n",
    "- `mtf_weights`: a sequence of weights for each wavelength contribution (arb)\n",
    "- `altitude`: height of the aircraft above the ground (m)\n",
    "- `slant_range`: line-of-sight range between the aircraft and target (target is assumed to be on the ground) (m)\n",
    "- `D`: effective aperture diameter (m)\n",
    "- `ha_wind_speed`: the high altitude windspeed (m/s); used to calculate the turbulence profile\n",
    "- `cn2_at_1m`: the refractive index structure parameter \"near the ground\" (e.g. at h = 1 m); used to calculate the turbulence profile\n",
    "- `int_time`: dwell (i.e. integration) time (seconds)\n",
    "- `n_tdi`: the number of time-delay integration stages (relevant only when TDI cameras are used. For CMOS cameras, the value can be assumed to be 1.0)\n",
    "- `aircraft_speed`: apparent atmospheric velocity (m/s); this can just be the windspeed at the sensor position if the sensor is stationary\n",
    "\n",
    "The following are default values for these parameters when not supplied:\n",
    "\n",
    "- `mtf_wavelengths = [0.50e-6, 0.66e-6]`\n",
    "- `mtf_weights = [1.0, 1.0]`\n",
    "- `altitude = 250`\n",
    "- `slant_range = 250  # slant_range = altitude`\n",
    "- `D = 40e-3`\n",
    "- `ha_wind_speed = 0`\n",
    "- `cn2_at_1m = 1.7e-14`\n",
    "- `int_time = 30e-3`\n",
    "- `n_tdi = 1.0`\n",
    "- `aircraft_speed = 0`\n",
    "\n",
    "For the purpose of this example notebook, we will only change the `cn2_at_1m` value. All other parameters will use the values from the original image.\n",
    "\n",
    "Changing the value of `cn2_at_1m` will have the following affects: \n",
    "- `cn2_at_1m == 1.7e-14` (the original value): returns the original image unchanged.\n",
    "- `cn2_at_1m > 1.7e-14`: returns an image more turbulent than the original image. There is no upper bound, but values greater than ~1e-7 (for this image) can be less turbulent or blank.\n",
    "\n",
    "Please note for this example image, `cn2_at_1m < 1.7e-14` will produce the original image and should not be used.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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, cn2_at_1m in enumerate((1.7e-14, 9.35e-14, 1.7e-13, 9.35e-13, 1.7e-12, 9.35e-12, 1.7e-11, 9.35e-11)):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    perturber = TurbulenceApertureOTFPerturber(cn2_at_1m=cn2_at_1m)\n",
    "    ax[row, col].set_title(f\"cn2_at_1m: {cn2_at_1m}m\")\n",
    "    ax[row, col].imshow(perturber(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 our relative mAP evaluation later.\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 8.3.85 🚀 Python-3.10.12 torch-2.6.0+cu124 CPU (11th Gen Intel Core(TM) i9-11950H 2.60GHz)\n",
      "Setup complete ✅ (16 CPUs, 62.5 GB RAM, 328.9/914.7 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 52.9ms\n",
      "Speed: 3.9ms preprocess, 52.9ms inference, 1.5ms 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.workflows.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.workflows.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",
    "    \"\"\"\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",
    "    \"\"\"\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",
    "        \"\"\"\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",
    "        \"\"\"\n",
    "        Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`\n",
    "        instances.\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",
    "perturber = TurbulenceApertureOTFPerturber(cn2_at_1m=1.7e-14)\n",
    "identity_augmentation = JATICDetectionAugmentation(perturber, 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",
    "    \"\"\"\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",
    "        \"\"\"\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",
    "        \"\"\"\n",
    "        Converts an ObjectDetectionTarget into a dictionary format compatible with the Torchmetrics metric's\n",
    "        `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: od.TargetBatchType, targets: od.TargetBatchType) -> None:\n",
    "        \"\"\"\n",
    "        Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
    "\n",
    "        Args:\n",
    "            preds (od.TargetBatchType): A batch of predictions in the format expected by the Torchmetrics metric.\n",
    "            targets (od.TargetBatchType): 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",
    "        \"\"\"\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 cn2_at_1m value of 1.7e-14, 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,  2.27it/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.workflows 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 >= 1.7e-14)\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 = 1.7e-14\n",
    "SWEEP_HIGH = 1.7e-12\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(TurbulenceApertureOTFPerturber(cn2_at_1m=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": 18,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #3: cn2_at_1m perturbation value 1.911e-13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.52it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: cn2_at_1m perturbation value 5.3931e-13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.86it/s]\n",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.92it/s]\n",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.09it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.82it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: cn2_at_1m perturbation value 1.2357e-12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.45it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.66it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.75it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.51it/s]\n",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.64it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.64it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.73it/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",
    "        cn2_at_1m = a.augment.get_config()[\"cn2_at_1m\"]\n",
    "        print(f\"Perturbation #{idx}: cn2_at_1m perturbation value {cn2_at_1m: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\"cn2_at_1m: {cn2_at_1m: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 a **relative** mAP 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(\"relative mAP@50\")\n",
    "plt.xlabel(\"cn2_at_1m value\")\n",
    "plt.ylabel(\"relative 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",
    "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 the mAP value rapidly decrease as perturbation begins to increase but begins to smooth out as the perturbation reaches 1.6 where it is approximately 0. (Note the relative mAP is guaranteed to be 1.0 when the perturbation is 1.7e-14, 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[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(\"Relative mAP per class\")\n",
    "plt.xlabel(\"cn2_at_1m value\")\n",
    "plt.ylabel(\"relative 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": [
    "This plot shows several interesting results:\n",
    "\n",
    "- The person and car classes are much more robust to turbulence perturbations than the truck and motorcycle classes. Examination of the unperturbed and the references images suggest a few possibilities:\n",
    "\n",
    "    - One of the truck objects gets detected as a car object in more turbulent images. While trucks and cars have obvious differences, as the image becomes more turbulent, the line between car and truck becomes blurred and results in the false detections favored towards cars. \n",
    "    - The weakness of the motorcycle class can be attributed to the smaller size of the object. While the person class is also relatively small, most person objects are in the foreground of the image that result in a large enough size to be more robust than the motorcycle class. \n",
    "\n",
    "Any conclusions about classification accuracy should be considered in light of these caveats. In particular, the foreground positioning of the detected person 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(\"relative mAP values per area\")\n",
    "plt.xlabel(\"cn2_at_1m value\")\n",
    "plt.ylabel(\"relative mAP\")\n",
    "for k in (\"map\", \"map_small\", \"map_medium\"):\n",
    "    plt.plot(perturbation_values, [m[k].item() 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 more turbulent perturbations than small ones, which makes sense when observing how objects tend to get blurry in the perturbation examples shown in the [Examples and Guidance](#NRTK-Turbulence-Aperture-perturbation:-examples-and-guidance) section above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "# End of notebook"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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
}