{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Random Translation Perturbations\n",
    "\n",
    "## Introduction\n",
    "This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.\n",
    "\n",
    "## Layout\n",
    "This notebook demonstrates how a particular augmentation (in this case, <b><i>Random Translation of the Image</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"
     ]
    }
   ],
   "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.generic.translation_perturber import RandomTranslationPerturber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e9bfa-fcea-4a1d-a8e0-9455b28777a2",
   "metadata": {},
   "source": [
    "# Setup: Source image\n",
    "\n",
    "In the next cell, we'll download and display a source image from the __[VisDrone](https://github.com/VisDrone/VisDrone-Dataset)__ dataset. The image will be cached in a local `data` subdirectory.\n",
    "\n",
    "### A note on image storage\n",
    "\n",
    "Typically in ML workflows, batches of images are processed as tensors of the color channels. Both our perturber (NRTK) and object detector (YOLO) accept numpy `ndarray` objects, and we will use [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) to view them. The complication is that although YOLO inferences on `ndarray`, [it expects the color channels to be in BGR](https://docs.ultralytics.com/modes/predict) order. If we naively view the same data YOLO inferences on, the colors will be wrong; if we naively inference on what we view, the detections will be wrong. (Our NRTK perturbation is agnostic to the channel order.)\n",
    "\n",
    "In this notebook, we'll convert the channel order to BGR when we load, and convert back whenever we explicitly call `imshow`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(540, 960, 3)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_dir = \"./data\"\n",
    "os.makedirs(data_dir, exist_ok=True)\n",
    "img_path = os.path.join(data_dir, \"visdrone_img.jpg\")\n",
    "if not os.path.isfile(img_path):\n",
    "    url = \"https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download\"\n",
    "    _ = urllib.request.urlretrieve(url, img_path)  # noqa: S310\n",
    "\n",
    "img_pil = Image.open(img_path)\n",
    "img_nd_bgr = np.asarray(img_pil)[\n",
    "    :,\n",
    "    :,\n",
    "    ::-1,\n",
    "]  # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb\n",
    "plt.figure()\n",
    "plt.axis(\"off\")\n",
    "print(img_nd_bgr.shape)\n",
    "_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
   "metadata": {},
   "source": [
    "# NRTK Random Translation perturbation: examples and guidance\n",
    "\n",
    "The [Random Translation perturbation](https://jatic.pages.jatic.net/kitware/nrtk/_implementations/nrtk.impls.perturb_image.generic.translation_perturber.html) is by a random sampling from 0 (no translation) to a `max_translation_limit` in both the x and y dimensions. For the purposes of this notebook we will keep the limit the same in both the x and y dimensions\n",
    "\n",
    "- `max_translation_limit == (0.0, 0.0)` (the minimum value): returns the original image. No pertubation in either direction\n",
    "- `0.0 < max_translation_limit <= (img_height, img_width)`: Allows translation of between 0 and max_translation_limit in either direction.\n",
    "- `max_translation_limit > (img_height, img_width)`: Throws an error as it is possible to completely remove the original image via translation\n",
    "  \n",
    "`max_translation_limit` is a metadata parameters included in the `perturb` call of the augmentation as opposed to an instance level parameter of the `RandomTranslationPerturber` object."
   ]
  },
  {
   "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, limit in enumerate([0, 50, 100, 200, 250, 300, 400, 500]):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    bp = RandomTranslationPerturber(seed=2, color_fill=(0, 0, 0))\n",
    "    ax[row, col].set_title(f\"max_translation_limit: {limit}\")\n",
    "    ax[row, col].imshow(bp(img_nd_bgr, additional_params={\"max_translation_limit\": (limit, limit)})[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, 329.1/914.7 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 66.7ms\n",
      "Speed: 3.7ms preprocess, 66.7ms inference, 1.3ms 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 the process, calling `evaluate` (and thus our wrapped objects) many times, once per loop on our single image perturbed to a known degree, and then observing how the metrics respond."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da908c7f-b499-4361-8e2b-cf7647b45de6",
   "metadata": {},
   "source": [
    "## Some helper classes\n",
    "\n",
    "The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:\n",
    "\n",
    "1. The `YOLODetectionTarget` helper class that stores the bounding boxes, label indices, and confidence scores for a single image's detections.\n",
    "\n",
    "2. The `MaiteYOLODetection` adapter class that conforms to the MAITE [Object Detection Dataset](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Dataset.html) protocol by providing the `__len__` and `__getitem__` methods. The returned item is a tuple of (image, `YOLODetectionTarget`, metadata-dictionary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "346f4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import torch\n",
    "from maite.protocols.object_detection import DatumMetadataType\n",
    "\n",
    "from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset\n",
    "\n",
    "##\n",
    "## Helper class for containing the boxes, label indices, and confidence scores.\n",
    "##\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class DetectionDatumMetadata(DatumMetadataType):\n",
    "    \"\"\"Dataclass for detection datum-level metadata\"\"\"\n",
    "\n",
    "    id: int | str\n",
    "    max_translation_limit: tuple[int, int]\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[DetectionDatumMetadata] = [{\"id\": 0, \"max_translation_limit\": (0, 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 an empty metadata dictionary\n",
    "# switch image to channel first\n",
    "single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id=\"visdrone_ex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7963f7fa-052c-476d-944e-5bbdae3181b5",
   "metadata": {},
   "source": [
    "## (3) Wrapping the perturbations as augmentations\n",
    "\n",
    "The `evaluate` function will perturb the image from the dataset using instances of the class defined below, one instance per perturbation value. Note that the object doesn't perform any augmentations until called by the `evaluate` workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "bp = RandomTranslationPerturber(seed=3)\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",
    "    \"\"\"\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 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, 23.52it/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 >= 0)\n",
    "- **sweep_high**: the maximum perturbation value\n",
    "- **sweep_count**: how many perturbations to generation\n",
    "\n",
    "You can also optionally select perturbations to visualize:\n",
    "- **visualization_indices**: a list of perturbation indices *p*, 0 <= *p* < sweep_count. These instances will be rendered along with their corresponding detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 30 perturbation datasets\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW = 0.0\n",
    "SWEEP_HIGH = 100\n",
    "SWEEP_COUNT = 30\n",
    "VISUALIZATION_INDICES = [3, 9, 21]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
    "augmentation_datasets = []\n",
    "for idx, p in enumerate(perturbation_values):\n",
    "    dset_metadata: list[DetectionDatumMetadata] = [{\"id\": 0, \"max_translation_limit\": (round(p), round(p))}]\n",
    "    augmentation_datasets.append(\n",
    "        JATICObjectDetectionDataset(imgs, dets, dset_metadata, dataset_id=f\"augment_{idx}\"),\n",
    "    )\n",
    "# create new pertruber for each dataset to preserve seed value\n",
    "print(f\"Generated {len(augmentation_datasets)} perturbation datasets\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549d0d4d-eabd-4f54-a487-7cfbc99916c9",
   "metadata": {},
   "source": [
    "## Calling evaluate on the augmented data\n",
    "\n",
    "We loop over all the augmentations, calling `evaluate` on each one and building up a list of resulting metrics for analysis.\n",
    "\n",
    "Any augmentation indices specified above will be rendered in this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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: translation perturbation value (10, 10)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #9: translation perturbation value (31, 31)\n"
     ]
    },
    {
     "name": "stderr",
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      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 24.05it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 24.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perturbation #21: translation perturbation value (72, 72)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.27it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.65it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.56it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.56it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.17it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 28.23it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.84it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 24.82it/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, d in enumerate(augmentation_datasets):\n",
    "    # reset the metric object for each dataset\n",
    "    mAP_metric.reset()\n",
    "    perturber = RandomTranslationPerturber(seed=3)\n",
    "    augmentation = JATICDetectionAugmentation(perturber, augment_id=str(idx))\n",
    "    result, _, _ = evaluate(model=yolo_model, dataset=d, augmentation=augmentation, metric=mAP_metric)\n",
    "    perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        value = d[0][-1][\"max_translation_limit\"]\n",
    "        print(f\"Perturbation #{idx}: translation perturbation value {value}\")\n",
    "        datum = d[0]\n",
    "        batch = ([datum[0]], [datum[1]], [datum[2]])\n",
    "        # Extract the image from the augmentation and switch it to channel last\n",
    "        aug = np.transpose(augmentation(batch)[0][0], (1, 2, 0))\n",
    "        # Plot image\n",
    "        ax_idx = VISUALIZATION_INDICES.index(idx)\n",
    "        ax[ax_idx].imshow(model(aug)[0].plot())\n",
    "        ax[ax_idx].set_title(f\"translation: {value}\")\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(\"Translation perturbation factor\")\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).) After translation is introduced, there is immediately a precipitous drop in performance. After a `max_translation_limit` of around 20, the mAP drops to 0. This is potentially due to the large amount of black space resulting from the newly translated image. (Note the relative mAP is guaranteed to be 1.0 when the perturbation is 0.0, 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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f8ID4O/6FXRP/AAAi/wDia6KigDnf+EB8Hf8AQq6J/wCAEX/xNRHwZ4HBwfDeg5/68ov8K6G6JFpMR12GnoiqigKAAPSgDmv+EM8Df9C3oP8A4BRf/E0f8IZ4G/6FvQf/AACi/wDia6favoPyo2r6D8qAOY/4QzwN/wBC3oP/AIBRf/E0f8IZ4G/6FvQf/AKL/wCJrp9q+g/KjavoPyoA5j/hDPA3/Qt6D/4BRf8AxNH/AAhngb/oW9B/8Aov/ia6favoPyo2r6D8qAOY/wCEM8Df9C3oP/gFF/8AE0f8IZ4G/wChb0H/AMAov/ia6favoPyo2r6CgDmP+EM8Df8AQt6D/wCAUX/xNH/CGeBv+hb0H/wCi/8Aia6favoPyo2r6D8qAOY/4QzwN/0Leg/+AUX/AMTR/wAIZ4G/6FvQf/AKL/4mun2r6D8qNq+g/KgDmP8AhDPA3/Qt6D/4BRf/ABNH/CGeBv8AoW9B/wDAKL/4mun2r6D8qNq+g/KgDmk8FeCJGCp4a0FmPYWMX/xNZupeGNB0bxN4TuNL0XTrGZtTkRpLa2SNiv2S4OCVA4yAfwrsLpQI0IAyJFwfxFYniX/kPeEf+wq//pHc0AdHRRRQAUUUUAFIzBVLEEgDOAMmlooA4Hw3rYHibxTctpOtpHdzR3NuZdLnj8xY7aNWALKAG3IwAJBParV5dzad4ttfEj6XqUtje6StuUgtWlmgkD+YFeNckZDkZ6Arziu0ooA57wPp91pvhK1hvYTBcyST3DwkgmLzZXkCHHcBwD7isnVdAs9N8V+FruCbUHkl1SUMtxqE8yDNpcHhHcqOnYDHSu3rnPEv/Ie8I/8AYVf/ANI7mgDo6KKKAPOdesNPufitZ29/AJYLqyG4FmX58sAcgj+6B+NdH/wgfhr/AKBg/wC/8n/xVcl8Q5ZLPxhpt7ECXithIP8AgEm6vTYpEmiSWM5R1DKfUGsYU4S5m0t/0T/U2xlKnKVNyineK6ebX6HJ6Fp1ro/jbUrKyi8q3a0jkCbi3Ofcmuvrmk+T4lSj+/pQb8pMV0tOkkk0u7OOgkk0u7CiiitTYKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAhu/+POb/cNUNXsdVvVg/szWP7O2A+Z/oyy784x16Y5/Or93/wAec3+4alX7o+lOMnF3QpRUlZnMf2H4q/6HD/ynR/40f2H4q/6HD/ynR/411FFae2l2X3L/ACMvYR7v73/mcv8A2H4q/wChw/8AKdH/AI10drHLFaQx3E3nzKgWSXaF3sBy2B0yecVLRUym5b2+5IuNNR2v97f5nDfE/wCy/wBmaF9u8z7J/bVv5/l5zs2vu+7zjGc47ZritQEJubweH5LaPwWb+389zC8tmr+VL5nyoy5i3+RuwQu7rkZFevappFvqzWLTvKpsrtLuPyyBl1BAByDx8x9PrWhUFnjj6faSeGxb22s2N9p9z4g08LHpUTwwW+ZEDCMl2xng/KcA56Zq54z07w9oOpLIiaHIlvZYi0K9jKbjvdy1uVziRySDhSSQvIr1eigDyXX5NFl1XXpPE8UkN09lC2ixy7vMQGLkQY/5aiXcDt+b7vatCz0GHXvGiQ+JrVbq4TwzZefFLyvnGSbc2Om4HOD2ycV6VRQBzXw+uZ7z4e6DPcytLM1lHukc5ZsDGSe54rpaKKACiiigCC7/ANSv++v8xWH4l/5D3hH/ALCr/wDpHc1uXf8AqV/31/mKw/Ev/Ie8I/8AYVf/ANI7mgDo6KKKACiiigAooooAKKKKACuc8S/8h7wj/wBhV/8A0jua6Ouc8S/8h7wj/wBhV/8A0juaAOjooooA878fW4uPFOiRMcLcRSwk+xwD/Ouk8E3jXnhOyEnEsCm3kB6godv8gK574jZj1nw7MvVWn/kprW8MsLLxJr+mDiN5VvYR6iQfNj6HArKg71KsO3K/zT/Q68arUsPU7qS/G6Jp/k+JFq39/TWX8nzXS1zWofJ8QNGb+/bTL+QzWprupro2h3l+2MwxkqD3Y8KPzIqqSbk4rv8A5Hmwajzt9H+iKVh4rsNQ8Q3mjJlZ7ckKxPEpH3gPcVvVxMHhCVvB9kInMWtQk3cc56iZuSp9jwD9BW/4d1tdc03zWTyruFvKuYDwY5B1GPT0rqqwjvDZaP8Az+YUqkvhqbvVf5fI16KKKwNwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIbv/jzm/3DTZr21tFT7Tcww7h8vmOFzj0zTrv/AI85v9w02aytbtU+020M20fL5iBsZ9M1M+bl9zfzKhy397Yi/tnS/wDoI2n/AH+X/Gj+2dL/AOgjaf8Af5f8aP7G0v8A6B1p/wB+V/wo/sbS/wDoHWn/AH5X/CsP9p/u/ibf7P8A3vwD+2dL/wCgjaf9/l/xq2jpLGskbBkYZVlOQR6iqn9jaX/0DrT/AL8r/hVtESKNY41CoowqqMAD0FaU/a3/AHlvlcip7K3uX+Zn63rlroVpFNcJNK88qwQQQJukmkOSFUcDOATyQAASTVN/EdytpHMPDOtNKzMrW4SHegXHJPmbcHPGGJODxxS+JdHu9TXTrvTpYU1DTboXMAnz5cnyMjIxHIBVzyAcHHBrB1rw1r2vXljeajY6HdLDHLG+mz3Er26lipWUHy/nYbWGCo4PBFamRqT+OdNjtNHngtr67OrM6W0UEI370BLKwYjaRtIOeAQckDmuitpWntYZngkgaRFYxS43xkjO1sEjI6HBI9zXE+HfBOoaPa+FYZprNv7IuL2SbydyqyzeZs2DHGN4yDjHOCa7ugAooooAKKKKACiiigCC7/1K/wC+v8xWH4l/5D3hH/sKv/6R3Nbl3/qV/wB9f5isPxL/AMh7wj/2FX/9I7mgDo6KKKACiiigAooooAKKKKACuc8S/wDIe8I/9hV//SO5ro65zxL/AMh7wj/2FX/9I7mgDo6KKKAOF+IiZn0R/SWVfzT/AOtUpk+y+J/DWpdEv7M2cp98Bl/X+VTeO497+H/9rU44v++gRVHVFeX4dWd/EMz6dKlwn1RyD+hzWGHdsdy/zRt+X+Z24xXy6Ev5X+rf6GxrXyeNfDT+v2lT/wB8CoPEn/E38RaRoK8xBvtt0P8AYT7oPsTx+VO12eOXWvC94jDy3mcq3syCk8Ig6ne6r4icZF3N5NtntCnAI+p/lXRh/dlOXb82keM/em4Lq/wsjq65LxBbTaDqg8T2EZaPATUYF/5aR/3wPVf89662kZVdCjKGVhggjIIqoT5XfodNSHOrdSO2uYby2iubeQSQyqHRx0INS1ynhNf7O1fXNDRm+z2kyS26Mc7UkG7A9ga6uipHllZBTnzxuwoooqCwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAhu/+POb/cNSr90fSorv/jzm/wBw1R1W91SzWD+ztJ+37gd/+kLFsxjHXrnn8qTdldilJRV2alFc1/bXif8A6FP/AMqEf+FH9teJ/wDoU/8AyoR/4VHtY+f3P/Iy9vHs/uf+R0tFc1/bXif/AKFP/wAqEf8AhXQWzyy2sUk8PkzMgLxbg2xscjI649acZqW35MuNRS2v9zX5ktFcz40v9TsoNHi0q7W1mvNUhtZJGiWTEbK+7g9+Afwrmb3UvFNna+LiviAv/wAI4nnwu1pFuuh5AmKS4XGByoKBTznNWWemUV5reeJNdn0bxB4ntdTW2t9ImZI9NMKMkyxqrMJHI37m3HG0jHy8GprFbux8ZeNNWGpXrRWiwztZbIgs3+j5Ck7Nwx0GCOnOaAPRKK4LSdU121u/C1xf6wuoQ6+pEluIERYGMLTBoio3FRt2ncW6g5FZuna34mbwdpOqz62Hu9Xuo7FA1tGIrYPKR5mAAWfauME7csOKAPT6K5LWJNY8MaRDqMutT39vbXkb3hngiVjbN8j/AHFA+UsJMjBwpBzWZc+JdYuIJ7myukit9R1pNK0+V4lZYI1yskw/vFnVwoJx933yAd3PdW9sYhPPFEZZBHH5jhd7nooz1PB49qlrh/ENleWKeG4rzVJ9Rc6/AVlmijRgNj8fu1UHnPbvWTb+JPF2qXM+oabZalLFHqEkCWqxWotWijmMbbnZxMHwpOcAA8bSOSAej3f+pX/fX+YrD8S/8h7wj/2FX/8ASO5rcu/9Sv8Avr/MVh+Jf+Q94R/7Cr/+kdzQB0dFFFABRRRQAUUUUAFFFFABXOeJf+Q94R/7Cr/+kdzXR1zniX/kPeEf+wq//pHc0AdHRRRQBynjv5bPR5f+eerW7/zqxo1ql5oGo6bJ9zzprc+wP/66r/EH5fDkUv8AzzvIX/8AHv8A69aGifu9V1qD0uBJ/wB9DP8ASuOb5cTCXqvwv+h6VubA2fn+a/zOCv57m98DaHBHn7dDdNY7c8hgCoH5ba9N0ywj0vS7axi+5BGEB9cDk/iea801fSr/AE7xss+x10z+0op42IwpkkKlsep+Q/Tj1r1au+o/3kktnZ/ejwMKnzyvurL7goooqDtOWH+ifE1h0S907P1dH/wrqa5bxH/o3irwzfDgefJbMfXeuB+ua6mtauqi/L8jGlo5Lz/PUKKKKyNgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAhu/+POb/cNSr90fSorv/jzm/wBw1Kv3R9KAFooooAKKKKAKt7ptpqJtTdxeYbWdbiH5iNsiggHg89TweKrzeH9LuE1ZJbXcurJ5d6PMYeavl+Xjr8vy8fLj86g8QeKNJ8MLZNqtzHAl5cC3Rnk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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(\"Translation perturbation factor\")\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",
    "- Person, car, and truck classes all follow the same general pattern with car being generally the most robust.\n",
    "- Truck is the most interesting category as it is the only category to have the mAP increase as well as decrease. This is potentially an attribute of the randomization in the perturber, the very few number of truck instances, and the location of the truck instances being somewhat in the corners.\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(\"relative mAP values per area\")\n",
    "plt.xlabel(\"Translation perturbation factor\")\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 the transformation perturbations than small ones. This is most likely due to the smaller objects being more likely to be translated out of the image than medium sized objects.\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",
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