{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
   "metadata": {},
   "source": [
    "# Demonstrating Resolution and Noise Transformations\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 particular sensor transformations (in this case, transformations affecting <b><i>Resolution</i></b> and <b><i>Noise</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 \"nrtk[pybsm]\" --no-cache-dir\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.perturber import PybsmPerturber\n",
    "from nrtk.impls.perturb_image.pybsm.scenario import PybsmScenario\n",
    "from nrtk.impls.perturb_image.pybsm.sensor import PybsmSensor"
   ]
  },
  {
   "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": [
    {
     "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",
    "\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 Sensor transformation perturbation: examples and guidance\n",
    "\n",
    "This notebook used the `PybsmPerturber` to perform a sensor transformation perturbation. The `PybsmPerturber` takes in a `PybsmSensor` object which contains all of the relevant sensor parameters needed to simulate an image captured using that sensor. It also takes in a `PybsmScenario` object, but that will not be explored in this notebook.\n",
    "\n",
    "## Setup\n",
    "\n",
    "We begin the setup for these sensor transformation perturbations by creating `PybsmSensor` and `PybsmScenario` objects. The original objects are meant to represent the parameters of the sensor used to capture the original image and the conditions that were present in order to faithfully create an identity transformation. Since we do not know the exact parameters of the sensor that we are trying to model, we use the `estimate_capture_parameters` function to approximate reasonable values for the sensor parameters based on the estimated `gsd` and `altitude` of the image.\n",
    "\n",
    "We can estimate the gsd (meters/pixel) by taking the average height of people (~1.7 meters) and divide it by the average height of the person detections in the image. We then estimate the altitude of the drone used to capture the visdrone images, and use that value as input to the `estimate_capture_parameter` function.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1a7b31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pybsm.simulation.ref_image import RefImage\n",
    "\n",
    "gsd = 1.7 / 58  # average height of person in meters divided by average height of person detection in pixels\n",
    "\n",
    "ref_img = RefImage(img_nd_bgr, gsd)\n",
    "sensor, scenario = ref_img.estimate_capture_parameters(altitude=75)\n",
    "\n",
    "pybsm_sensor = PybsmSensor(\n",
    "    # required\n",
    "    name=\"ideal_sensor\",\n",
    "    D=sensor.D,  # Telescope diameter (m)\n",
    "    f=sensor.f,  # telescope focal length (m)\n",
    "    p_x=sensor.p_x,  # detector pitch (m)\n",
    "    opt_trans_wavelengths=sensor.opt_trans_wavelengths,  # Optical system transmission, red  band first (m)\n",
    "    # optional\n",
    "    optics_transmission=sensor.optics_transmission,  # guess at the full system optical transmission\n",
    "    # (excluding obscuration)\n",
    "    eta=sensor.eta,  # guess\n",
    "    w_x=sensor.p_x,  # detector width is assumed to be equal to the pitch\n",
    "    w_y=sensor.p_x,  # detector width is assumed to be equal to the pitch\n",
    "    int_time=sensor.int_time,  # integration time (s) - this is a maximum, the actual integration time will be,\n",
    "    # determined by the well fill percentage\n",
    "    dark_current=sensor.dark_current,  # dark current density of 1 nA/cm2 guess, guess mid\n",
    "    # range for a silicon camera\n",
    "    read_noise=sensor.read_noise,  # rms read noise (rms electrons)\n",
    "    max_n=sensor.max_n,  # maximum ADC level (electrons)\n",
    "    bit_depth=sensor.bit_depth,  # bit depth\n",
    "    max_well_fill=sensor.max_well_fill,  # maximum allowable well fill (see the paper for the logic behind this)\n",
    "    s_x=sensor.s_x,  # jitter (radians) - The Olson paper says its \"good\" so we'll guess 1/4 ifov rms\n",
    "    s_y=sensor.s_y,  # jitter (radians) - The Olson paper says its \"good\" so we'll guess 1/4 ifov rms\n",
    "    da_x=sensor.da_x,  # drift (radians/s) - again, we'll guess that it's really good\n",
    "    da_y=sensor.da_y,  # drift (radians/s) - again, we'll guess that it's really good\n",
    "    qe_wavelengths=sensor.qe_wavelengths,\n",
    "    qe=sensor.qe,\n",
    ")\n",
    "\n",
    "pybsm_scenario = PybsmScenario(\n",
    "    name=\"niceday\",\n",
    "    ihaze=scenario.ihaze,  # weather model\n",
    "    altitude=scenario.altitude,  # sensor altitude\n",
    "    ground_range=scenario.ground_range,  # range to target\n",
    "    aircraft_speed=scenario.aircraft_speed,\n",
    ")\n",
    "\n",
    "sensor_config = pybsm_sensor.get_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4a28b9",
   "metadata": {},
   "source": [
    "## Resolution Transformation\n",
    "\n",
    " The first is a change in resolution of the image. We will change the resolution by modifying the pixel width parameters `p_x` and `p_y`. For the purpose of this example notebook we will keep the pixels as squares by ensuring `p_x` and `p_y` are the same.\n",
    "\n",
    "Changing the value of `p_x` and `p_y` will have the following affects:\n",
    "\n",
    "- `p_x != 0.0`: This would result in an infinitely small pixel. The value of 0 is out of bounds\n",
    "- `0.0 < p_x < min(height, width)`: allowable pixel heights and widths. As the pixel size gets larger, it will result in a lower resolution as each pixel is responsible for representing a greater area \n",
    "\n",
    "In reality the minimum pixel size is the one used to capture the original image as pybsm and nrtk only support image degradation, not image super resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "656ceb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:The simulated image has oversampled the reference image!  This result should not be trusted!!\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
    "for idx, f in enumerate((1e-06, 3e-05, 4e-05, 5e-05, 6e-05, 7e-05, 8e-05, 1e-04)):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    pybsm_sensor.p_x = f\n",
    "    pybsm_sensor.p_y = f\n",
    "    bp = PybsmPerturber(pybsm_sensor, pybsm_scenario)\n",
    "    img = bp(img_nd_bgr, additional_params={\"img_gsd\": gsd})[0][:, :, ::-1]\n",
    "    ax[row, col].set_title(f\"pixel side length: {f}\")\n",
    "    ax[row, col].imshow(img)\n",
    "    # _ = ax[row, col].axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96aa913c",
   "metadata": {},
   "source": [
    "## Noise Transformation\n",
    "\n",
    "The second tranformation that we will be exploring is a change in the noise in the image. The parameter that we will be modifying to accomplish this is the `bit_depth` parameter. This parameter represents the number of bits available to represent each pixels value.\n",
    "\n",
    "An example of where this use case might come into practice is if a model was trained on imagery captured from a model with a bit depth of 16 bits, but needs to be deployed on a low power sensor that only has 4 bits available. \n",
    "\n",
    "There is no official upper limit to the bit_depth, but it would be very rare to encounter a value higher that 16\n",
    "\n",
    "- `1 <= bit_depth < ~16`\n",
    "\n",
    "It is important to note that we re-instantiate the `PybsmSensor` object to reset the changes to the focal length from the previous code section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7444b975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pybsm_sensor = PybsmSensor.from_config(sensor_config)\n",
    "_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
    "for idx, bd in enumerate((1, 2, 4, 8, 10, 12, 14, 16)):\n",
    "    (row, col) = (int(idx / 4), idx % 4)\n",
    "    pybsm_sensor.bit_depth = bd\n",
    "    bp = PybsmPerturber(pybsm_sensor, pybsm_scenario)\n",
    "    img = bp(img_nd_bgr, additional_params={\"img_gsd\": gsd})[0][:, :, ::-1]\n",
    "    ax[row, col].set_title(f\"bit depth: {bd}\")\n",
    "    ax[row, col].imshow(img)\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": 8,
   "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 CUDA:0 (Quadro T2000, 3719MiB)\n",
      "Setup complete ✅ (16 CPUs, 62.4 GB RAM, 397.1/914.7 GB disk)\n",
      "Downloading model...\n",
      "Computing baseline...\n",
      "\n",
      "0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 37.2ms\n",
      "Speed: 4.3ms preprocess, 37.2ms inference, 79.7ms postprocess per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "# Import YOLO support\n",
    "import torch\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)\n",
    "baseline[0].show()"
   ]
  },
  {
   "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": 9,
   "id": "346f4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\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",
    "    img_gsd: float\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, \"img_gsd\": gsd}]\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": 10,
   "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": 11,
   "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(\n",
    "    imgs,\n",
    "    dets,\n",
    "    metadata,\n",
    "    dataset_id=\"vidsrone_ex\",\n",
    ")"
   ]
  },
  {
   "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": 12,
   "id": "aa7a398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
    "\n",
    "pybsm_sensor = PybsmSensor.from_config(sensor_config)\n",
    "bp = PybsmPerturber(pybsm_sensor, pybsm_scenario)\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": 13,
   "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": 14,
   "id": "a0af9f36-c69b-400f-82ec-f1fd7e9d98ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:17<00:00, 17.05s/it]"
     ]
    },
    {
     "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.\n",
    "\n",
    "We set up perturbation values for both the resolution and noise perturbations ensuring that the changes for one do no affect the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 30 resolution perturbation augmentations\n",
      "Generated 16 noise perturbation augmentations\n"
     ]
    }
   ],
   "source": [
    "SWEEP_LOW_RES = 2e-05\n",
    "SWEEP_HIGH_RES = 2e-04\n",
    "SWEEP_LOW_NOISE = 1\n",
    "SWEEP_HIGH_NOISE = 16\n",
    "SWEEP_RES_COUNT = 30\n",
    "SWEEP_NOISE_COUNT = 16\n",
    "VISUALIZATION_INDICES = [3, 9, 21]\n",
    "\n",
    "##\n",
    "## end user-settable parameters\n",
    "##\n",
    "\n",
    "resolution_perturbation_values = np.linspace(SWEEP_LOW_RES, SWEEP_HIGH_RES, SWEEP_RES_COUNT, endpoint=True)\n",
    "pybsm_sensor = PybsmSensor.from_config(sensor_config)\n",
    "resolution_augmentations = []\n",
    "for p in resolution_perturbation_values:\n",
    "    pybsm_sensor.p_x = p\n",
    "    pybsm_sensor.p_y = p\n",
    "    bp = PybsmPerturber(pybsm_sensor, pybsm_scenario)\n",
    "    resolution_augmentations.append(JATICDetectionAugmentation(bp, augment_id=\"resolution\"))\n",
    "\n",
    "print(f\"Generated {len(resolution_augmentations)} resolution perturbation augmentations\")\n",
    "\n",
    "noise_perturbation_values = np.linspace(SWEEP_LOW_NOISE, SWEEP_HIGH_NOISE, SWEEP_NOISE_COUNT, endpoint=True)\n",
    "pybsm_sensor = PybsmSensor.from_config(sensor_config)\n",
    "noise_augmentations = []\n",
    "for p in noise_perturbation_values:\n",
    "    pybsm_sensor.bit_depth = int(p)\n",
    "    bp = PybsmPerturber(pybsm_sensor, pybsm_scenario)\n",
    "    noise_augmentations.append(JATICDetectionAugmentation(bp, augment_id=\"noise\"))\n",
    "\n",
    "print(f\"Generated {len(noise_augmentations)} noise 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 resolution and noise augmentations separately, 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": null,
   "id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:18<00:00, 18.04s/it]\n",
      "100%|█████████████████████████████████████████████| 1/1 [00:18<00:00, 18.25s/it]\n",
      "100%|█████████████████████████████████████████████| 1/1 [00:17<00:00, 17.40s/it]\n",
      "  0%|                                                     | 0/1 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "# Resolution Augmentation Evaluation\n",
    "\n",
    "res_perturbed_metrics = list()\n",
    "for idx, a in enumerate(resolution_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",
    "    res_perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: p_x value {a.augment.sensor.p_x:0.5}\")\n",
    "        datum = single_image_dataset[0]\n",
    "        batch = ([datum[0]], [datum[1]], [datum[2]])\n",
    "        # Extract the image from the augmentation and switch it to channel last\n",
    "        aug = np.transpose(a(batch)[0][0], (1, 2, 0))\n",
    "        _ = model(aug)[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96767bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Noise Augmentation Perturbation\n",
    "\n",
    "noise_perturbed_metrics = list()\n",
    "for idx, a in enumerate(noise_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",
    "    noise_perturbed_metrics.append(result)\n",
    "\n",
    "    if idx in VISUALIZATION_INDICES:\n",
    "        # quickest way is to re-evaluate\n",
    "        print(f\"Perturbation #{idx}: bit depth value {a.augment.sensor.bit_depth}\")\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",
    "        _ = model(aug)[0].show()"
   ]
  },
  {
   "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",
    "\n",
    "## Resolution focused sensor transformation analysis\n",
    "\n",
    "We will start by analysing the resolution focused sensor transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09b6804b-c98c-46fb-af98-ab5598892ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "map50_list = [m[\"map_50\"].item() for m in res_perturbed_metrics]\n",
    "print(len(resolution_perturbation_values))\n",
    "plt.title(\"relative mAP@50\")\n",
    "plt.xlabel(\"pixel width perturbation factor\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "plt.xticks(\n",
    "    [j for i, j in enumerate(resolution_perturbation_values) if not i % 6] + [resolution_perturbation_values[-1]],\n",
    ")\n",
    "_ = plt.plot(resolution_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 see a decline in mAP as the pixel size increases. As seen in the example section, the resolution has a reasonably steady decline as the pixel size increases which explains the steady decline in mAP from the original pixel size value and the maximum"
   ]
  },
  {
   "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": null,
   "id": "65d28ff5-838e-467a-a703-81432a408b35",
   "metadata": {},
   "outputs": [],
   "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 res_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 res_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(\"pixel width perturbation factor\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "plt.xticks(\n",
    "    [j for i, j in enumerate(resolution_perturbation_values) if not i % 6] + [resolution_perturbation_values[-1]],\n",
    ")\n",
    "for class_idx, class_mAP_list in class_mAP.items():  # noqa: N816\n",
    "    plt.plot(resolution_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 only \"false alarm\" **class** (with a mAP value of -1) is the appearance of bus and traffic light detections that occur when the pixel width size has gotten much larger than the original value. There are no buses or traffic light detections in the \"ground truth\" unperturbed detections (but again, since this a relative mAP, this does not necessarily mean there are no true buses or traffic lights in the image.)\n",
    "\n",
    "- motorcycles are slightly more robust to small changes in the pixel size, but quickly fall off entirely\n",
    "\n",
    "- person and car detections are relatively robust with person detections maintaining an mAP of ~0.5 almost all the way through the perturbation range. This could be for a number of reasons:\n",
    "\n",
    "    - The non-car classes detected in the unperturbed image are all in the foreground, and are thus larger. Thus when the resolution decreases, they remain visible the longest.\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": null,
   "id": "23ec21e7-e682-4d08-ad1d-6c42d850402f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.title(\"relative mAP values per area\")\n",
    "plt.xlabel(\"pixel width perturbation factor\")\n",
    "plt.ylabel(\"relative mAP\")\n",
    "plt.xticks(\n",
    "    [j for i, j in enumerate(resolution_perturbation_values) if not i % 6] + [resolution_perturbation_values[-1]],\n",
    ")\n",
    "for k in (\"map\", \"map_small\", \"map_medium\"):\n",
    "    plt.plot(resolution_perturbation_values, [m[k].item() for m in res_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, quickly go from accurate to false positive detections while the small targets have a steady decline in mAP. The false positive values for the medium targets could be due to the fact that the medium targets become the size of small targets and get confused with the original small target distribution while the small targets do not move into any other categories original distribution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa67a919",
   "metadata": {},
   "source": [
    "## Noise focused sensor tranformation analysis\n",
    "\n",
    "Now we will analyse the noise focused sensor transformation that perturbes the bit depth parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d04630",
   "metadata": {},
   "outputs": [],
   "source": [
    "map50_list = [m[\"map_50\"].item() for m in noise_perturbed_metrics]\n",
    "plt.title(\"relative mAP@50\")\n",
    "plt.xlabel(\"bit depth perturbation factor\")\n",
    "plt.ylabel(\"relative mAP @ 50% IoU\")\n",
    "_ = plt.plot(noise_perturbation_values, map50_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c80ff811",
   "metadata": {},
   "source": [
    "### Evaluation interpretation\n",
    "\n",
    "When The bit depth is extremely low with less than 9-10 bits, there is significant performance drop, but after 9-10 bits, the performance holds steady at one. This is due sto the fact that once the necessary bit depth is reached, all higher bit depth values are guaranteed to be able to adequately represent the values of the pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135a54be",
   "metadata": {},
   "outputs": [],
   "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 noise_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 noise_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(\"bit depth 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(noise_perturbation_values, class_mAP_list, label=baseline[0].names[class_idx])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8649321b",
   "metadata": {},
   "source": [
    "We see here that the car class is the least resilient to change in the bit depth as it takes the highest value to achieve perfect mAP.\n",
    "\n",
    "Interestingly when the bit depth is ~3, the model incorrectly attributes one of the detections to the boat class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5e32cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.title(\"relative mAP values per area\")\n",
    "plt.xlabel(\"bit depth perturbation factor\")\n",
    "plt.ylabel(\"relative mAP\")\n",
    "for k in (\"map\", \"map_small\", \"map_medium\"):\n",
    "    plt.plot(noise_perturbation_values, [m[k].item() for m in noise_perturbed_metrics], label=k)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb27b941602401d91542211134fc71a",
   "metadata": {},
   "source": [
    "This final comparison shows that medium and small detections respond similarly to the perturbations in bit depth. There are no large detections in the original set of detections, so it is not unexpected that there are no large objects in the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
   "metadata": {},
   "source": [
    "# End of notebook"
   ]
  }
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