{
"cells": [
{
"cell_type": "markdown",
"id": "71b1eb19-3148-46ca-8a9a-fdf18bd2b18d",
"metadata": {},
"source": [
"# Demonstrating Fog/Haze Perturbations\n",
"\n",
"## Introduction\n",
"This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.\n",
"\n",
"## Layout\n",
"This notebook demonstrates how a particular condition (in this case, fog or haze), can affect an object detection model, and how that impact can be measured. The overall structure is:\n",
"\n",
"- **Evaluation Guidance: Computing Mean Average Precision (mAP):**\n",
" - An overview of the evaluation strategy.\n",
"- **Setup:**\n",
" - Notebook initialization, loading the supporting python code. Depending on if this is the first time you've run this notebook, this may take some time.\n",
" - Loading the source image, which will be used throughout the notebook.\n",
"- **Image Perturbation Examples:**\n",
" - The NRTK perturbation is demonstrated on the source image.\n",
"- **Baseline Detections:**\n",
" - The object detection model is loaded and run on the unperturbed image. These will serve as \\\"ground truth\\\" for comparisons against the perturbed images.\n",
" \n",
"At this point, we have the fundamental elements of our evaluation: the model, our reference image, and a mechanism for creating the perturbed test images. Next we adapt these elements to be used with the MAITE evaluation workflow:\n",
"\n",
"- **Wrapping the Detection Model**\n",
"- **Wrapping the Reference Image as a Dataset**\n",
"- **Wrapping the Perturbation as Augmentation Objects**\n",
"- **Wrapping the Metrics**\n",
"\n",
"After the evaluation elements have been wrapped, we can run the evaluation:\n",
"\n",
"- **Preparing the Augmentations:**\n",
" - We specify the range of perturbation values to evaluate and optionally specify which ones we'd like to visualize.\n",
"- **Evaluation of Augmented Data:**\n",
" - Each augmentation is run through MAITE's evaluation workflow, computing the absolute mAP metric of the detector on the perturbed datasets.\n",
"- **Evaluation Analysis:**\n",
" - We plot and discuss the mAP@50 metric from each of the perturbed images, as well as per-class and per-area results.\n",
"\n",
"To run this notebook in Colab, use the link below:\n",
"\n",
"[](https://colab.research.google.com/github/Kitware/nrtk/blob/main/docs/examples/maite/nrtk_haze_perturber_demo.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "4b2e1964-46e2-4497-88ff-274eaca17960",
"metadata": {},
"source": [
"## Evaluation Guidance: Computing Mean Average Precision (mAP)\n",
"\n",
"This notebook evaluates the robustness of an Object Detector when exposed to NRTK (Natural, Physics-based) perturbations, using mean Average Precision (mAP) as the performance metric. The baseline case, where no perturbations are applied (identity augmentation), typically yields an mAP close to 1.0 and serves as the reference point. Unlike standard evaluations that focus on whether the detector can correctly identify and localize objects, our goal here is to measure how well the detector maintains its performance under realistic perturbations. \n",
"\n",
"By comparing the absolute mAP scores of the baseline against perturbed datasets, we can assess the detector's sensitivity to environmental variations and quantify its robustness in generating reliable predictions beyond controlled conditions."
]
},
{
"cell_type": "markdown",
"id": "f9268412-0d13-4fed-ae78-f6ee02c94c9c",
"metadata": {},
"source": [
"## Setup: Notebook Initialization\n",
"The next few cells import the python packages used in the rest of the notebook.\n",
"\n",
"**Note:** We are suppressing warnings within this notebook to reduce visual clutter for demonstration purposes. If any issues arise while executing this notebook, we recommend that the first cell is **not** executed so that any related warnings are shown.\n",
"\n",
"**Note for Colab users**: After setting up the environment, you may need to \"Restart Runtime\" in order to resolve package version conflicts (see the [README](https://github.com/Kitware/nrtk/blob/main/docs/examples/README.md#run-the-notebooks-from-colab) for more info)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "796743f8-2f1e-42aa-ad6b-f71e721602f8",
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"\n",
"# warning suppression\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2561179f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beginning package installation...\n",
"Installing nrtk's extras...\n"
]
}
],
"source": [
"import sys # noqa: F401\n",
"\n",
"print(\"Beginning package installation...\")\n",
"!{sys.executable} -m pip install -qU pip\n",
"\n",
"print(\"Installing nrtk's extras...\")\n",
"!{sys.executable} -c \"import nrtk\" || !{sys.executable} -m pip install -q \\\n",
" \"nrtk[maite,albumentations]>=0.25.0\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5a4da9c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installing required packages...\n"
]
}
],
"source": [
"print(\"Installing required packages...\")\n",
"# Add temporary numpy<2.0 constraint to avoid dependency conflicts\n",
"!{sys.executable} -m pip install -q \"matplotlib\" \"torchvision\" \"torchmetrics\" \"ultralytics\" \"numpy<2.0\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9a00ce08",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Doing a fresh install of opencv-python-headless...\n"
]
}
],
"source": [
"# opencv-python depends on libGL, which is unnecessary for running the notebook\n",
"# and can cause failures in some environments (e.g., GitLab CI). To avoid this,\n",
"# we uninstall all opencv variants and install opencv-python-headless instead.\n",
"\n",
"# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV\n",
"# Add temporary numpy<2.0 constraint to avoid dependency conflicts\n",
"print(\"Doing a fresh install of opencv-python-headless...\")\n",
"!{sys.executable} -m pip uninstall -qy \"opencv-python\" \"opencv-python-headless\"\n",
"!{sys.executable} -m pip install -q \"opencv-python-headless\" \"numpy<2.0\" --no-cache-dir"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c18e6bd1-1c56-447a-b1e4-16df26269ed8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Detected status of NRTK extras and their dependencies:\n",
"\n",
"[Pillow]\n",
" - Pillow ✓ 10.4.0\n",
"\n",
"[albumentations]\n",
" - albumentations ✓ 2.0.5\n",
"\n",
"[diffusion]\n",
" - torch ✓ 2.8.0+cu128\n",
" - diffusers ✓ 0.34.0\n",
" - accelerate ✓ 1.9.0\n",
" - Pillow ✓ 10.4.0\n",
" - transformers ✓ 4.53.1\n",
" - protobuf ✗ missing\n",
"\n",
"[graphics]\n",
" - opencv-python ✗ missing\n",
"\n",
"[headless]\n",
" - opencv-python-headless ✓ 4.11.0\n",
"\n",
"[maite]\n",
" - maite ✓ 0.8.2\n",
" - fastapi ✓ 0.116.1\n",
" - pydantic ✓ 2.10.4\n",
" - pydantic_settings ✓ 2.7.0\n",
" - uvicorn ✓ 0.34.0\n",
"\n",
"[notebook-testing]\n",
" - datasets ✓ 4.0.0\n",
" - matplotlib ✓ 3.10.7\n",
" - numba ✓ 0.60.0\n",
" - tabulate ✓ 0.9.0\n",
" - torch ✓ 2.8.0+cu128\n",
" - torchmetrics ✓ 1.6.2\n",
" - torchvision ✓ 0.23.0+cu128\n",
" - transformers ✓ 4.53.1\n",
" - ultralytics ✓ 8.3.85\n",
" - jupytext ✓ 1.16.7\n",
" - albumentations ✓ 2.0.5\n",
"\n",
"[pybsm]\n",
" - pybsm ✓ 0.12.0\n",
"\n",
"[scikit-image]\n",
" - scikit-image ✓ 0.24.0\n",
"\n",
"[tools]\n",
" - kwcoco ✓ 0.8.7\n",
" - Pillow ✓ 10.4.0\n",
"\n",
"[waterdroplet]\n",
" - scipy ✓ 1.13.1\n",
" - shapely ✓ 2.0.7\n",
" - geopandas ✓ 0.14.0\n",
"\n",
"\n",
"For details about installing NRTK extras, please visit:\n",
" https://nrtk.readthedocs.io/en/v0.25.0/installation.html#extras\n",
"\n"
]
}
],
"source": [
"import os\n",
"import urllib.request\n",
"from collections.abc import Sequence\n",
"from typing import Any\n",
"\n",
"import numpy as np\n",
"\n",
"# some initial imports\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = \"jpeg\" # Use JPEG format for inline visualizations\n",
"\n",
"from matplotlib import pyplot as plt # type: ignore\n",
"from PIL import Image\n",
"\n",
"from nrtk.impls.perturb_image.generic.haze_perturber import HazePerturber\n",
"from nrtk.utils._extras import print_extras_status\n",
"\n",
"print_extras_status()"
]
},
{
"cell_type": "markdown",
"id": "242e9bfa-fcea-4a1d-a8e0-9455b28777a2",
"metadata": {},
"source": [
"## Setup: Source Image\n",
"\n",
"In the next cell, we'll download and display a source image from the __[VisDrone](https://github.com/VisDrone/VisDrone-Dataset)__ dataset. The image will be cached in a local `data` subdirectory.\n",
"\n",
"### A Note on Image Storage\n",
"\n",
"Typically in ML workflows, batches of images are processed as tensors of the color channels. Both our perturber (NRTK) and object detector (YOLO) accept numpy `ndarray` objects, and we will use [matplotlib.imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) to view them. The complication is that although YOLO inferences on `ndarray`, [it expects the color channels to be in BGR](https://docs.ultralytics.com/modes/predict/) order. If we naively view the same data YOLO inferences on, the colors will be wrong; if we naively inference on what we view, the detections will be wrong. (Our NRTK perturbation is agnostic to the channel order.)\n",
"\n",
"In this notebook, we'll convert the channel order to BGR when we load, and convert back whenever we explicitly call `imshow`.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "57facb8b-4cb6-476b-a321-f48d70e773ba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.24.0\n"
]
},
{
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import nrtk\n",
"\n",
"print(nrtk.__version__)\n",
"data_dir = \"./data\"\n",
"os.makedirs(data_dir, exist_ok=True)\n",
"img_path = os.path.join(data_dir, \"visdrone_img.jpg\")\n",
"if not os.path.isfile(img_path):\n",
" url = \"https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download\"\n",
" _ = urllib.request.urlretrieve(url, img_path) # noqa: S310\n",
"\n",
"img_pil = Image.open(img_path)\n",
"img_nd_bgr = np.asarray(img_pil)[\n",
" :,\n",
" :,\n",
" ::-1,\n",
"] # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb\n",
"plt.figure()\n",
"plt.axis(\"off\")\n",
"\n",
"_ = plt.imshow(img_nd_bgr[:, :, ::-1]) # explicitly changing BGR to RGB for imshow"
]
},
{
"cell_type": "markdown",
"id": "efe2e2a3-a3a2-4101-9872-bae618effab0",
"metadata": {},
"source": [
"## NRTK Haze Perturbation: Examples and Guidance\n",
"\n",
"The [Haze perturber]() is set by the following parameters:\n",
"\n",
"- `factor`: strength of haze applied to the image\n",
"- `sky_color`: optional sky color to use for weathering\n",
"- `depth_map`: optional depth map for adding haze\n",
"\n",
"When a `depth_map` is not provided, the image is assumed to be planar and and a default depth map of all ones, consistent with the input image dimensions, is used. When the `sky_color` is not provided, the average pixel color of the input image is used. When neither of these parameters are provided, the resulting image is effectively a lower contrast version of the input.\n",
"\n",
"The ranges of these parameters are as follows:\n",
"\n",
"- `factor >= 0`\n",
"- `0 <= sky_color[n] <= 255`\n",
"- `0.0 <= depth_map[x][y] <= 1.0`\n",
"\n",
"For the purpose of this example notebook, we will only change the `factor` value. All other parameters will use the values from the original image. Note that a haze factor of `0.0` represents no change to the image.\n",
"\n",
"We calculate a sample `depth_map` by generating a linear gradient with `1.0` at the top of the image and `0.2` at the bottom. Decreasing the `depth_map` from the top down indicates a horizon near the top of the image.\n",
"\n",
"We obtain a `sky_color` by sampling the original image.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5a370d2a-2489-4c48-b29a-ea8ad3ef98fa",
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, ax = plt.subplots(2, 4, figsize=(10, 4))\n",
"\n",
"sky_color = [236, 240, 239] # Taken from the visible sky at the top of the image\n",
"depth_map = np.zeros_like(img_nd_bgr, dtype=\"float\") # Depth map size = image size\n",
"depth_map[...] = np.linspace(1.0, 0.2, depth_map.shape[0])[..., None, None] # Decreasing linspace\n",
"params = {\n",
" \"sky_color\": sky_color,\n",
" \"depth_map\": depth_map,\n",
"}\n",
"\n",
"ax[0, 0].set_title(\"Depth Map\")\n",
"ax[0, 0].imshow(depth_map)\n",
"ax[0, 0].axes.get_xaxis().set_visible(False)\n",
"ax[0, 0].axes.get_yaxis().set_visible(False)\n",
"\n",
"for idx in range(1, 8):\n",
" (row, col) = (int(idx / 4), idx % 4)\n",
" factor = (idx - 1) / 2\n",
" perturber = HazePerturber(factor=factor)\n",
" ax[row, col].set_title(f\"Haze factor: {factor}\")\n",
" ax[row, col].imshow(perturber(img_nd_bgr, **params)[0][:, :, ::-1])\n",
" _ = ax[row, col].axis(\"off\")\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "30eb653b-6be9-4d58-8d69-db4b64c3d81c",
"metadata": {},
"source": [
"## Baseline Detections\n",
"\n",
"In the next cell, we'll download a [YOLOv11](https://docs.ultralytics.com/models/yolo11/) model, compute object detections on the source image, and display the results. As discussed above, these detections will serve as the \"ground truth\" for comparisons against the perturbed images.\n",
"\n",
"*Note that here, we're using YOLO's built-in visualization tool, which automatically adjusts for BGR / RGB order.*"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "55600de0-089f-4dd6-ac4d-7896b8df5494",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics 8.3.170 🚀 Python-3.10.12 torch-2.7.1+cu126 CUDA:0 (Quadro T2000, 3718MiB)\n",
"Setup complete ✅ (16 CPUs, 62.4 GB RAM, 179.4/913.8 GB disk)\n",
"Downloading model...\n",
"Computing baseline...\n",
"\n",
"0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 41.8ms\n",
"Speed: 4.7ms preprocess, 41.8ms inference, 123.0ms postprocess per image at shape (1, 3, 384, 640)\n"
]
}
],
"source": [
"# Import YOLO support\n",
"import ultralytics\n",
"\n",
"ultralytics.checks()\n",
"print(\"Downloading model...\")\n",
"model = ultralytics.YOLO(\"yolo11n.pt\")\n",
"print(\"Computing baseline...\")\n",
"baseline = model(img_nd_bgr)"
]
},
{
"cell_type": "markdown",
"id": "39050929-bf48-44b1-9247-5cac7f44cd8c",
"metadata": {},
"source": [
"## MAITE Evaluation Workflow Preparation\n",
"\n",
"We'll use the [MAITE Evaluation workflow](https://jatic.pages.jatic.net/cdao/maite/generated/maite.tasks.evaluate.html) to evaluate the performance of the perturbed data against our baseline detections. We'll need to \"wrap\" our model, data, and perturbations into callable objects to pass to the `maite.tasks.evaluate` function:\n",
"\n",
"- We'll wrap the **model** to make predictions on input data when called.\n",
"\n",
"- The wrapped **dataset** will return our test image when called. Note that this will be the original, unperturbed image; we'll apply our perturbations via...\n",
"\n",
"- ...the **augmentation** object, which applies the perturbation to the image inside the evaluation.\n",
"\n",
"- Finally, the **metric** object will define our precise scoring methodology.\n",
"\n",
"The evaluation workflow in this notebook is slightly unusual. Typical ML workflows apply many different augmentations / perturbations to much larger datasets, and only call `evaluate` once to get a statistical view of performance. But since the goal of this notebook is to drill down into how perturbation affects performance, we've essentially flipped process, calling `evaluate` (and thus our wrapped objects) many times, once per loop on our single image perturbed to a known degree, and then observing how the metrics respond."
]
},
{
"cell_type": "markdown",
"id": "da908c7f-b499-4361-8e2b-cf7647b45de6",
"metadata": {},
"source": [
"### Some Helper Classes\n",
"\n",
"The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:\n",
"\n",
"1. The `YOLODetectionTarget` helper class that stores the bounding boxes, label indices, and confidence scores for a single image's detections.\n",
"\n",
"2. The `MaiteYOLODetection` adapter class that conforms to the MAITE [Object Detection Dataset](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Dataset.html) protocol by providing the `__len__` and `__getitem__` methods. The returned item is a tuple of (image, `YOLODetectionTarget`, metadata-dictionary)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "346f4207",
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"\n",
"import torch\n",
"from maite.protocols.object_detection import DatumMetadataType\n",
"\n",
"from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset\n",
"\n",
"##\n",
"## Helper class for containing the boxes, label indices, and confidence scores.\n",
"##\n",
"\n",
"\n",
"@dataclass\n",
"class YOLODetectionTarget:\n",
" \"\"\"A helper class to represent object detection results in the format expected by YOLO-based models.\n",
"\n",
" Attributes:\n",
" boxes (torch.Tensor): A tensor containing the bounding boxes for detected objects in\n",
" [x_min, y_min, x_max, y_max] format.\n",
" labels (torch.Tensor): A tensor containing the class labels for the detected objects.\n",
" These may be floats for compatibility with specific datasets or tools.\n",
" scores (torch.Tensor): A tensor containing the confidence scores for the detected objects.\n",
" \"\"\"\n",
"\n",
" boxes: torch.Tensor\n",
" labels: torch.Tensor\n",
" scores: torch.Tensor\n",
"\n",
"\n",
"##\n",
"## Prepare results for ingestion into maite dataset by puttin them into detection object\n",
"## Images must be channel first (c, h, w) in maite dataset objects\n",
"##\n",
"imgs = [np.transpose(img_nd_bgr, (2, 0, 1))]\n",
"dets = []\n",
"metadata: list[DatumMetadataType] = [{\"id\": 0}]\n",
"for _detection in baseline:\n",
" boxes = baseline[0].boxes.xyxy.cpu()\n",
" labels = baseline[0].boxes.cls.cpu() # note, these are floats, not ints\n",
" scores = baseline[0].boxes.conf.cpu()\n",
"\n",
" dets.append(YOLODetectionTarget(boxes, labels, scores))"
]
},
{
"cell_type": "markdown",
"id": "45cd8fb3-2ea6-492c-ab84-f3432b9d3440",
"metadata": {},
"source": [
"### (1) Wrapping the Detection Model\n",
"\n",
"The first object we'll wrap will be the detection model. The cell below defines a class adapting YOLO for the [MAITE Object Detection Model](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Model.html) protocol. The `__call__` method runs the model on images in the batch and is called by the MAITE evaluation workflow later in the notebook."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e1131aa7-3716-479f-838b-0464b74106e9",
"metadata": {},
"outputs": [],
"source": [
"import maite.protocols.object_detection as od\n",
"import ultralytics.models\n",
"from maite.protocols import ArrayLike, ModelMetadata\n",
"\n",
"\n",
"class MaiteYOLODetector:\n",
" \"\"\"A wrapper class for a YOLO model to simplify its usage with input batches and object detection targets.\n",
"\n",
" This class takes a YOLO model instance, processes input image batches, and converts predictions into\n",
" `YOLODetectionTarget` instances.\n",
"\n",
" Attributes:\n",
" _model (ultralytics.models.yolo.model.YOLO): The YOLO model instance used for predictions.\n",
"\n",
" Methods:\n",
" __call__(batch):\n",
" Processes a batch of images through the YOLO model and returns the predictions as\n",
" `YOLODetectionTarget` instances.\n",
" \"\"\"\n",
"\n",
" def __init__(self, model: ultralytics.models.yolo.model.YOLO) -> None:\n",
" \"\"\"Initializes the MaiteYOLODetector with a YOLO model instance.\n",
"\n",
" Args:\n",
" model (ultralytics.models.yolo.model.YOLO): The YOLO model to use for predictions.\n",
" \"\"\"\n",
" self._model = model\n",
" # Dummy model metadata type to pass type checking\n",
" self.metadata = ModelMetadata(id=\"0\")\n",
"\n",
" def __call__(self, batch: Sequence[ArrayLike]) -> Sequence[YOLODetectionTarget]:\n",
" \"\"\"Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`s.\n",
"\n",
" Args:\n",
" batch (Sequence[ArrayLike]): A batch of images in (c, h, w) format (channel-first).\n",
"\n",
" Returns:\n",
" Sequence[YOLODetectionTarget]: A list of YOLODetectionTarget instances containing the predictions for each\n",
" image in the batch.\n",
" \"\"\"\n",
" # Convert images to channel-last format (h, w, c) for YOLO model\n",
" batch_transposed = [np.transpose(batch[i], (1, 2, 0)) for i in range(len(batch))]\n",
"\n",
" yolo_predictions = self._model(batch_transposed, verbose=False)\n",
" return [\n",
" YOLODetectionTarget(\n",
" p.boxes.xyxy.cpu(), # Bounding boxes in (x_min, y_min, x_max, y_max) format\n",
" p.boxes.cls.cpu(), # Class indices for the detected objects\n",
" p.boxes.conf.cpu(), # Confidence scores for the detections\n",
" )\n",
" for p in yolo_predictions\n",
" ]\n",
"\n",
"\n",
"# create the wrapped model object\n",
"yolo_model: od.Model = MaiteYOLODetector(model)"
]
},
{
"cell_type": "markdown",
"id": "3cef0788-137f-41c1-a9e4-80e7d08b47d1",
"metadata": {},
"source": [
"### (2) Wrapping the Dataset\n",
"\n",
"MAITE pairs images and their reference detections (aka targets, ground truth) into **datasets**. Typical ML workflows have many images per dataset; when these do not all fit in memory simultaneously, a *dataloader* object is used which can page images and annotations in from disk. For this notebook, however, each invocation of `evaluate` will use the same single-image dataset (our reference image with its baseline detections.)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1dcbb5d0-b899-4355-9922-9d52fb866b4b",
"metadata": {},
"outputs": [],
"source": [
"# our single image, its baseline detections, and metadata dictionary\n",
"# switch image to channel first\n",
"single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id=\"visdrone_ex\")"
]
},
{
"cell_type": "markdown",
"id": "7963f7fa-052c-476d-944e-5bbdae3181b5",
"metadata": {},
"source": [
"### (3) Wrapping the Perturbations as Augmentations\n",
"\n",
"The `evaluate` function will perturb the image from the dataset using instances of the class defined below, one instance per perturbation value. Note that the object doesn't perform any augmentations until called by the `evaluate` workflow."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "aa7a398f",
"metadata": {},
"outputs": [],
"source": [
"from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation\n",
"\n",
"perturber = HazePerturber(factor=0.0)\n",
"identity_augmentation = JATICDetectionAugmentation(perturber, augment_id=\"identity\")"
]
},
{
"cell_type": "markdown",
"id": "81020be7-76ba-43e3-b231-7d2bd7c30387",
"metadata": {},
"source": [
"### (4) Wrapping the Metrics\n",
"\n",
"We'll compare the detections in each perturbed image to the unperturbed detections using the Mean Average Precision (mAP) metric from the `torchmetrics` package. The following cell creates a mAP metrics object, wraps it in a MAITE [MAITE Object Detection Metric](https://jatic.pages.jatic.net/cdao/maite/generated/maite.protocols.object_detection.Metric.html) protocol-compatible class, and then creates an instance of this class, which will be called by `evaluate`.\n",
"\n",
"This code is copied directly from the [MAITE object detection tutorial](https://jatic.pages.jatic.net/cdao/maite/tutorials/torchvision_object_detection.html#metrics) (with the exception of setting `class_metrics=True`.)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e33407c5-f1d7-4c7a-9a31-2d500f97edcc",
"metadata": {},
"outputs": [],
"source": [
"from maite.protocols import MetricMetadata\n",
"from torchmetrics import Metric as TorchMetric\n",
"from torchmetrics.detection.mean_ap import MeanAveragePrecision\n",
"\n",
"##\n",
"## Create an instance of the MAP metric object\n",
"##\n",
"\n",
"tm_metric = MeanAveragePrecision(\n",
" box_format=\"xyxy\",\n",
" iou_type=\"bbox\",\n",
" iou_thresholds=[0.5],\n",
" rec_thresholds=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
" max_detection_thresholds=[1, 10, 100],\n",
" class_metrics=True,\n",
" extended_summary=False,\n",
" average=\"macro\",\n",
")\n",
"\n",
"##\n",
"## This wrapper associates the MAP metric object with methods called by the evaluate\n",
"## workflow to accumulate detection data and compute the metrics.\n",
"##\n",
"\n",
"\n",
"class WrappedTorchmetricsMetric:\n",
" \"\"\"A wrapper class for a Torchmetrics metric designed to simplify its usage for object detection tasks.\n",
"\n",
" This class facilitates the conversion of object detection targets and predictions into the format\n",
" expected by Torchmetrics metrics, allowing for easier integration with existing pipelines.\n",
"\n",
" Attributes:\n",
" _tm_metric (Callable): The Torchmetrics metric to be wrapped, which takes lists of dictionaries\n",
" containing torch.Tensor objects representing predictions and targets.\n",
"\n",
" Methods:\n",
" to_tensor_dict(target):\n",
" Converts an `ObjectDetectionTarget` into a dictionary format compatible with the Torchmetrics\n",
" metric's `update` method.\n",
"\n",
" update(preds, targets):\n",
" Updates the wrapped Torchmetrics metric with batches of predictions and targets in their native format.\n",
"\n",
" compute():\n",
" Computes the final metric values using the wrapped Torchmetrics metric.\n",
"\n",
" reset():\n",
" Resets the state of the wrapped Torchmetrics metric.\n",
" \"\"\"\n",
"\n",
" def __init__(\n",
" self,\n",
" tm_metric: TorchMetric,\n",
" ) -> None:\n",
" \"\"\"Initializes the WrappedTorchmetricsMetric with the given Torchmetrics metric.\n",
"\n",
" Args:\n",
" tm_metric (Callable): A Torchmetrics metric instance that expects predictions and targets as lists of\n",
" dictionaries containing torch.Tensor objects.\n",
" \"\"\"\n",
" self._tm_metric = tm_metric\n",
" # Dummy metric metadata type to pass type checking\n",
" self.metadata = MetricMetadata(id=\"0\")\n",
"\n",
" @staticmethod\n",
" def to_tensor_dict(target: od.ObjectDetectionTarget) -> dict[str, torch.Tensor]:\n",
" \"\"\"Converts an ObjectDetectionTarget into a dictionary format compatible with the Torch's `update` method.\n",
"\n",
" Args:\n",
" target (od.ObjectDetectionTarget): An object detection target instance containing boxes, labels, and scores.\n",
"\n",
" Returns:\n",
" dict[str, torch.Tensor]: A dictionary with keys `boxes`, `scores`, and `labels`, each mapping to a tensor.\n",
" \"\"\"\n",
" return {\n",
" \"boxes\": torch.as_tensor(target.boxes),\n",
" \"scores\": torch.as_tensor(target.scores),\n",
" \"labels\": torch.as_tensor(target.labels).type(torch.int64),\n",
" }\n",
"\n",
" def update(self, preds: Sequence[od.TargetType], targets: Sequence[od.TargetType]) -> None:\n",
" \"\"\"Updates the wrapped Torchmetrics metric with the given predictions and targets.\n",
"\n",
" Args:\n",
" preds (Sequence[od.TargetType]): A batch of predictions in the format expected by the Torchmetrics metric.\n",
" targets (Sequence[od.TargetType]): A batch of targets in the format expected by the Torchmetrics metric.\n",
" \"\"\"\n",
" preds_tm = [self.to_tensor_dict(pred) for pred in preds]\n",
" targets_tm = [self.to_tensor_dict(tgt) for tgt in targets]\n",
" self._tm_metric.update(preds_tm, targets_tm)\n",
"\n",
" def compute(self) -> dict[str, Any]:\n",
" \"\"\"Computes and returns the final metric values using the wrapped Torchmetrics metric.\n",
"\n",
" Returns:\n",
" dict[str, Any]: A dictionary containing the computed metric values.\n",
" \"\"\"\n",
" return self._tm_metric.compute()\n",
"\n",
" def reset(self) -> None:\n",
" \"\"\"Resets the state of the wrapped Torchmetrics metric, clearing any accumulated data.\"\"\"\n",
" self._tm_metric.reset()\n",
"\n",
"\n",
"##\n",
"## This is our instance variable that can compute the MAP metrics.\n",
"##\n",
"\n",
"mAP_metric: od.Metric = WrappedTorchmetricsMetric(tm_metric) # noqa: N816"
]
},
{
"cell_type": "markdown",
"id": "0102dfde-cb9b-46a7-8e4a-d12ccd948864",
"metadata": {},
"source": [
"## Running the Evaluation\n",
"\n",
"We now have all the wrappings required to evaluate our range of perturbations:\n",
"- The `yolo_model` object, wrapping the YOLO model\n",
"- The `single_image_dataset` object, providing our source image and its baseline detections\n",
"- The `augmentation` object, which when instantiated, applies a single perturbation value to its input\n",
"- The `mAP_metrics` object, defining the metrics to compute at each perturbation value"
]
},
{
"cell_type": "markdown",
"id": "38a7fef1-a3cc-4e63-8a2b-6133274e4b1c",
"metadata": {},
"source": [
"### Evaluation Sanity Check: Ground Truth Against Itself\n",
"\n",
"Here we quickly check the evaluation workflow by creating an *identity augmentation* (with a haze factor of 0.0, 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, 18.58it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sanity check: overall mAP (should be 1.0): 1.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from maite.tasks import evaluate\n",
"\n",
"# call the model for each image in the dataset (in this case, just the source image),\n",
"# scoring the resulting detections against those from the dataset\n",
"sanity_check_results, _, _ = evaluate(\n",
" model=yolo_model,\n",
" dataset=single_image_dataset,\n",
" augmentation=identity_augmentation,\n",
" metric=mAP_metric,\n",
")\n",
"\n",
"print(\"Sanity check: overall mAP (should be 1.0):\", sanity_check_results[\"map\"].item())"
]
},
{
"cell_type": "markdown",
"id": "2a1a72bb-dbf1-431e-892e-132771cd7b92",
"metadata": {},
"source": [
"### Preparing the Data\n",
"\n",
"Now we'll prepare the augmentation instances for the evaluation. In the cell below, you can set three parameters for sweeping the set of perturbation values:\n",
"- **sweep_low**: the minimum perturbation value (`0.0` is no haze)\n",
"- **sweep_high**: the maximum perturbation value (we use something high to see where accuracy will drop to zero)\n",
"- **sweep_count**: how many perturbations to generation\n",
"\n",
"You can also optionally select perturbations to visualize:\n",
"- **visualization_indices**: a list of perturbation indices *p*, 0 <= *p* < sweep_count. These instances will be rendered along with their corresponding detections."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "48b5e334-d8eb-415c-b263-4c4bd3618b43",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated 30 perturbation augmentations\n"
]
}
],
"source": [
"SWEEP_LOW = 0.0\n",
"SWEEP_HIGH = 4.0\n",
"SWEEP_COUNT = 30\n",
"VISUALIZATION_INDICES = [0, 5, 9]\n",
"\n",
"##\n",
"## end user-settable parameters\n",
"##\n",
"\n",
"perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)\n",
"augmentations = [\n",
" JATICDetectionAugmentation(HazePerturber(factor=p), augment_id=str(idx))\n",
" for idx, p in enumerate(perturbation_values)\n",
"]\n",
"\n",
"print(f\"Generated {len(augmentations)} perturbation augmentations\")"
]
},
{
"cell_type": "markdown",
"id": "549d0d4d-eabd-4f54-a487-7cfbc99916c9",
"metadata": {},
"source": [
"### Calling Evaluate on the Augmented Data\n",
"\n",
"We loop over all the augmentations, calling `evaluate` on each one and building up a list of resulting metrics for analysis.\n",
"\n",
"Any augmentation indices specified above will be rendered in this step."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a4951dfc-c2cc-434a-8fd2-1786ba3acf96",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 19.87it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Perturbation #0: haze factor 0.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 18.13it/s]\n",
"100%|██████████| 1/1 [00:00<00:00, 17.74it/s]\n",
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"100%|██████████| 1/1 [00:00<00:00, 19.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Perturbation #5: haze factor 0.6896551724137931\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 18.56it/s]\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Perturbation #9: haze factor 1.2413793103448276\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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]
},
{
"data": {
"image/jpeg": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"perturbed_metrics = list()\n",
"_, ax = plt.subplots(len(VISUALIZATION_INDICES), figsize=(30, 12))\n",
"for idx, a in enumerate(augmentations):\n",
" # reset the metric object for each dataset\n",
" mAP_metric.reset()\n",
" result, _, _ = evaluate(model=yolo_model, dataset=single_image_dataset, augmentation=a, metric=mAP_metric)\n",
" perturbed_metrics.append(result)\n",
"\n",
" if idx in VISUALIZATION_INDICES:\n",
" # quickest way is to re-evaluate\n",
" factor = a.augment.get_config()[\"factor\"]\n",
" print(f\"Perturbation #{idx}: haze factor {factor}\")\n",
" datum = single_image_dataset[0]\n",
" batch = ([datum[0]], [datum[1]], [datum[2]])\n",
" # Extract the image from the augmentation and switch it to channel last\n",
" aug = np.transpose(a(batch)[0][0], (1, 2, 0))\n",
" # Plot image\n",
" ax_idx = VISUALIZATION_INDICES.index(idx)\n",
" ax[ax_idx].imshow(model(aug)[0].plot())\n",
" ax[ax_idx].set_title(f\"Factor: {factor:0.5}\")\n",
" _ = ax[ax_idx].axis(\"off\")\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "d0303b6a-f587-43c8-ae9f-0057a7884241",
"metadata": {},
"source": [
"## Evaluation Analysis\n",
"\n",
"Now we can plot how the metrics (for example, mAP @ IoU=50) vary with perturbation level, keeping in mind this is the mAP scores compared against the detections in the unperturbed image.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "09b6804b-c98c-46fb-af98-ab5598892ba7",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"map50_list = [m[\"map_50\"].item() for m in perturbed_metrics]\n",
"plt.title(\"mAP@50 on haze perturbed dataset\")\n",
"plt.xlabel(\"Haze factor\")\n",
"plt.ylabel(\"mAP @ 50% IoU\")\n",
"_ = plt.plot(perturbation_values, map50_list)"
]
},
{
"cell_type": "markdown",
"id": "3a8720fd-32dc-493d-ab5c-81c313615504",
"metadata": {},
"source": [
"### Evaluation Interpretation\n",
"\n",
"**General things to know about mAP calculation**:\n",
"- The mAP metric calculation does not take into account the images that do not have any detection or any ground truth. In such cases, it returns the default initialization value, -1. It means it couldn't compute the metric. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1184#issuecomment-1589210748)). Hence, in this notebook example, we clip values to a range of `[0, 1]`.\n",
"- When having images with no ground truth, the order of those images in the batch can change the mAP calculation. To be more precise, if empty images are at the end of the batch, they will be ignored in the computation. But the empty images that are placed before the last non-empty image are taken into account. (Source: [link](https://github.com/Lightning-AI/torchmetrics/issues/1774#issuecomment-1590681234)).\n",
"\n",
"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).) The mAP value appears to decrease almost linearly as the haze factor increases. It reaches zero when the haze factor is around 2.5.\n",
"\n",
"Viewing the object detection boundaries in the perturbed image highlights the fact that objects closer to the foreground are more easily detected when using the haze perturber. With our approximated depth map input, this means any object closer to the bottom of the image."
]
},
{
"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": {
"scrolled": true
},
"outputs": [
{
"data": {
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P+EH8O/8APgf+/wBJ/wDFV0NFHtqn8z+8OeXc57/hB/Dv/Pgf+/0n/wAVR/wg/h3/AJ8D/wB/pP8A4quhoo9tU/mf3hzy7nPf8IP4d/58D/3+k/8AiqP+EH8O/wDPgf8Av9J/8VXQ0Ue2qfzP7w55dznv+EH8O/8APgf+/wBJ/wDFUf8ACD+Hf+fA/wDf6T/4quhoo9tU/mf3hzy7nPf8IP4d/wCfA/8Af6T/AOKo/wCEH8O/8+B/7/Sf/FV0NFHtqn8z+8OeXc57/hB/Dv8Az4H/AL/Sf/FUf8IP4d/58D/3+k/+KroaKPbVP5n94c8u5z3/AAg/h3/nwP8A3+k/+Ko/4Qfw7/z4H/v9J/8AFV0NFHtqn8z+8OeXc57/AIQfw7/z4H/v9J/8VR/wg/h3/nwP/f6T/wCKroaKPbVP5n94c8u5z3/CD+Hf+fA/9/pP/iqP+EH8O/8APgf+/wBJ/wDFV0NFHtqn8z+8OeXc57/hB/Dv/Pgf+/0n/wAVR/wg/h3/AJ8D/wB/pP8A4quhoo9tU/mf3hzy7nPf8IP4d/58D/3+k/8AiqP+EH8O/wDPgf8Av9J/8VXQ0Ue2qfzP7w55dznv+EH8O/8APgf+/wBJ/wDFUf8ACD+Hf+fA/wDf6T/4quhoo9tU/mf3hzy7nPf8IP4d/wCfA/8Af6T/AOKo/wCEH8O/8+B/7/Sf/FV0NFHtqn8z+8OeXc57/hB/Dv8Az4H/AL/Sf/FUf8IP4d/58D/3+k/+KroaKPbVP5n94c8u5z3/AAg/h3/nwP8A3+k/+Ko/4Qfw7/z4H/v9J/8AFV0NFHtqn8z+8OeXc57/AIQfw7/z4H/v9J/8VR/wg/h3/nwP/f6T/wCKroaKPbVP5n94c8u5z3/CD+Hf+fA/9/pP/iqP+EH8O/8APgf+/wBJ/wDFV0NFHtqn8z+8OeXc57/hB/Dv/Pgf+/0n/wAVR/wg/h3/AJ8D/wB/pP8A4quhoo9tU/mf3hzy7nPf8IP4d/58D/3+k/8AiqP+EH8O/wDPgf8Av9J/8VXQ0Ue2qfzP7w55dznv+EH8O/8APgf+/wBJ/wDFUf8ACD+Hf+fA/wDf6T/4quhoo9tU/mf3hzy7nPf8IP4d/wCfA/8Af6T/AOKo/wCEH8O/8+B/7/Sf/FV0NFHtqn8z+8OeXc57/hB/Dv8Az4H/AL/Sf/FUf8IP4d/58D/3+k/+KroaKPbVP5n94c8u5z3/AAg/h3/nwP8A3+k/+Ko/4Qfw7/z4H/v9J/8AFV0NFHtqn8z+8OeXc57/AIQfw7/z4H/v9J/8VR/wg/h3/nwP/f6T/wCKroaKPbVP5n94c8u5z3/CD+Hf+fA/9/pP/iq6GiiplOUvidxNt7hXOeJf+Q94R/7Cr/8ApHc10dc54l/5D3hH/sKv/wCkdzUiOjooooAKKKKACiiigAooooAKyNd0Ia2LFl1C7sJ7K4+0QzWojLBjG8ZGHVgRtkbtWvRQBzn/AAjWq/8AQ665/wB+bP8A+MUf8I1qv/Q665/35s//AIxXR1iax4mg0m/jsI7C/wBRvXhM5gso1ZkiBxvbcyjGeAM5ODgHFAGHPpWvx+KLDTk8X62bSezuJ5Jfs9plXR4Qoz5GBkSPx7exrU/4RrVf+h11z/vzZ/8AxitWz1iwv9Fi1iC4U2EkPniU8AJjJJz0x39Ky9H8Y2er3tva/YdQszdwtPZvdxBFuoxjJTDEjgg4YKcHOKAE/wCEa1X/AKHXXP8AvzZ//GKP+Ea1X/oddc/782f/AMYro6KAOc/4RrVf+h11z/vzZ/8Axij/AIRrVf8Aoddc/wC/Nn/8Yro6KAOc/wCEa1X/AKHXXP8AvzZ//GKy59K1+PxRYacni/WzaT2dxPJL9ntMq6PCFGfIwMiR+Pb2NbmseJoNJv47COwv9RvXhM5gso1ZkiBxvbcyjGeAM5ODgHFXLXWLO+0OPWLWQzWckHnoyjkrjPQ9D7HvQBl/8I1qv/Q665/35s//AIxR/wAI1qv/AEOuuf8Afmz/APjFTeHvEj+IYYblND1OytJ7dbiG5ujBskVsFQAkjMCQc8gdD3rdoA5z/hGtV/6HXXP+/Nn/APGKP+Ea1X/oddc/782f/wAYro6KAOc/4RrVf+h11z/vzZ//ABij/hGtV/6HXXP+/Nn/APGK6OsPxB4mh8Om3a40+/nhlkjjae3jUpEXdY13FmH8TDpk0AYmqaVr9nqOiwQeL9beO8vGgnY29odiCCVwRiDj5kUZPr9K1P8AhGtV/wCh11z/AL82f/xiuglljgheWVwkaKWZmOAAOSTXP6R4zstXvbW2FlqFoL2JprKW6iCJdIMElMMSOCDhgpxzigA/4RrVf+h11z/vzZ//ABij/hGtV/6HXXP+/Nn/APGK6OigDnP+Ea1X/oddc/782f8A8Yo/4RrVf+h11z/vzZ//ABiujooA5z/hGtV/6HXXP+/Nn/8AGKy9U0rX7PUdFgg8X628d5eNBOxt7Q7EEErgjEHHzIoyfX6Vt+IPE0Ph027XGn388MskcbT28alIi7rGu4sw/iYdMmtmaRYYZJWBKopY464AoA5//hGtV/6HXXP+/Nn/APGKP+Ea1X/oddc/782f/wAYqXw74mfxHBBcxaHqdnZ3EC3ENzdGDY6sAVACSswJBzyB0reoA5z/AIRrVf8Aoddc/wC/Nn/8Yo/4RrVf+h11z/vzZ/8AxiujooA5z/hGtV/6HXXP+/Nn/wDGKP8AhGtV/wCh11z/AL82f/xiujrH1bXpNKn8pNF1W/Aj815LOJCqDJ4yzrk8fdXJ6cc0AYGqaVr9nqOiwQeL9beO8vGgnY29odiCCVwRiDj5kUZPr9K1P+Ea1X/oddc/782f/wAYra07ULbVdNttQspRLa3MSyxOBjcrDIOO1YVp44068v7eBbW+S1up2trbUHiUW88q5yqndu52tglQDjgmgB//AAjWq/8AQ665/wB+bP8A+MUf8I1qv/Q665/35s//AIxXR0UAc5/wjWq/9Drrn/fmz/8AjFH/AAjWq/8AQ665/wB+bP8A+MV0dFAHOf8ACNar/wBDrrn/AH5s/wD4xWXr+la/punRT2ni/W5ZGvLaAq1vaNhJJ0RzxB2VmOe2Oa6XVtWk0wwrFpOoag8u75bNEO0DH3i7Ko68c5PPpT9G1i113TUvrTzBGzPGySpteN1YqyMOxDAg/SgDL/4RrVf+h11z/vzZ/wDxij/hGtV/6HXXP+/Nn/8AGKdb+KzeaxdafaaFqk8dpdi0nu08gRI+FYn5pQ5ADgnCn2BroaAOc/4RrVf+h11z/vzZ/wDxij/hGtV/6HXXP+/Nn/8AGK6OigDnP+Ea1X/oddc/782f/wAYo/4RrVf+h11z/vzZ/wDxiujrP1XU5tNSMw6VfagzkjZaCPK47ku6gfnQBzGv6Vr+m6dFPaeL9blka8toCrW9o2EknRHPEHZWY57Y5rU/4RrVf+h11z/vzZ//ABitPRdZttd04XlqsqASPFJFMm14pEYqyMOxBBFZNp44068v7eBbW+S1up2trbUHiUW88q5yqndu52tglQDjgmgB/wDwjWq/9Drrn/fmz/8AjFH/AAjWq/8AQ665/wB+bP8A+MV0dFAHOf8ACNar/wBDrrn/AH5s/wD4xR/wjWq/9Drrn/fmz/8AjFdHRQBzn/CNar/0Ouuf9+bP/wCMVl6/pWv6bp0U9p4v1uWRry2gKtb2jYSSdEc8QdlZjntjmun1XU5tNSMw6VfagzkjZaCPK47ku6gfnRous22u6cLy1WVAJHikimTa8UiMVZGHYggigDM/4RrVf+h11z/vzZ//ABij/hGtV/6HXXP+/Nn/APGKdovis67Mv2bQtUSzaWWIX0vkCLMbMp4EpfG5SB8vp25roaAOc/4RrVf+h11z/vzZ/wDxij/hGtV/6HXXP+/Nn/8AGK6OigDnP+Ea1X/oddc/782f/wAYo/4RrVf+h11z/vzZ/wDxiujqO4uIbS2lubiVYoIkLySOcBVAyST6YoA43xFpOvaV4Z1XULPxfrc11a2ks0MbQWjB3VCQCBBk5I7VpL4b1YqCfGmuZI/542f/AMYq74c8RWvibT5b20guoEjneBkuYwj7l6nGTgc98H1AqhdeONOtb+eBrW+e0t7hbW41BIlNvDK2AFY7t3VlBIUgE8kUAP8A+Ea1X/oddc/782f/AMYpIvCk/wDamn3174k1W/8AsMzTRQzpbqm8xvHk+XEp+67d66SigAooooAKKKKACiiigAooooAKKKKACuO1OafQfHU2syadf3lleabHbBrK3adkljkkbaVXkBhJwemVOSK7GigDg9K0q+Pw9m8JT2lxBf3WlXDmUrmGJ5i+I9443KX6egzTdNkvNc1zwsf7Jv7EaPDK9411AY1WQxeUI0J4flicrkYUc8131FABRRRQAUUUUAcdqc0+g+OptZk06/vLK802O2DWVu07JLHJI20qvIDCTg9MqckUvhRJ9H8LW+jajpl4Z0spLuZY4t8fzuzGEMDguN2NorsKKAPPtBsLSPxhYz+GdEvdJ02O2lTUPOtHtY5SdvlqEcDc4IY7gOBkZ5r0GiigAooooAK4z4kXbL4eSxhsdRup5bq1lUWllLOAsdxE7ZKKQPlUkA9ccV2dFAHPahd/8JFo97pVtaX8LX+mymOe4tXiRC2UCtuAKvk52kdOa5/S2vdZ1TwlEdIv7I6KjyXr3MBjRX8gwiNGPEmS5OVyML15r0GigAooooAKKKKAOM+JF2y+HksYbHUbqeW6tZVFpZSzgLHcRO2SikD5VJAPXHFbia3DeKUXTdTMb2jXGZLR4wQDt8vDYIkPUKQOOa16KAPPNC0+zj8X6dN4Y0O90nT4oJV1Ey2klrHKCB5a7HA3uG53AcDPPNeh0UUAFFFFABXG+NNcvYLqDRLW21SKG6jL3WpWmnzXHkx5I2R+WjfvTg8nhRzycCuyooAx9MubOxtNH07T9PvY7OSApBm2dFgRFGBJuwyEjgZGSQa4fTLPUW0Tw14UbS76O60rUIZLm5eBhB5UDlg6yfdbfhQACT8xyBivS7m6t7K2kubqeKCCMbnllcKqj1JPApLW7tr61jurS4iuLeUbo5YXDo49QRwRQBNRRRQAUUUUAcz4y1+/0e1trfTbG8lubxin2qGyluUtFGMu6xqSTz8q9z1IANO0Gax0jw/p8FhZ6tLDLcGJnmtJEm8x2ZnmlVwrAFsktjHPHFdJRQB5v4jsbG71GQ6F4e1CDxKb6Nl1BbOSJBh13O0xAVkKA/Lk5BxivSKKKACiiigArD8TXdrFZJa3kOstFcZ/eaVHOXQqQeWh+dc5+hwc1uUUAcd4Ja60rSrXTbnTb6OO4ublrV5IRujhDblNwR0dsnk8nvzmsDTLPUW0Tw14UbS76O60rUIZLm5eBhB5UDlg6yfdbfhQACT8xyBivUKKACiiigAooooAw/E13axWSWt5DrLRXGf3mlRzl0KkHlofnXOfocHNZXglrrStKtdNudNvo47i5uWtXkhG6OENuU3BHR2yeTye/Oa7GigDzeOxsZfEmkT+GfD2oaZdpeGTULmSzktozAQ29XLACUsxGMbsHnIxXpFFFABRRRQAVg+KdG1DW7O1gsb22gWO4WaaO5gaWOcLyFYK6nG7a3Xnbg8ZFb1FAHCeDDrWipqg1i3eb7ZrcqxfZLF02liS0rbnP7o4GD29TnjK1Gz1FdE8ReE10u+ku9T1KaS3uUgYweVNKHLtL91dgLAgnPyjAOa9QooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5T4llh8PdWKAFtseATgE+YveurqrqWm2mr6fLY30Xm20uN6biucEEcgg9QKAOK1S512fxj4PXVdKsbSIX8xV7e/aclvss3BBiTA98mqa+LtSTXtMura51O70e/1L7GHubW3jtiGLAeUQRLkFerAggHpxXoN1ptpe3dldXEW+aykaW3bcRsYoyE4BwflZhznrWPH4F8ORXkV0mnkSQ3H2mEfaJSkUmd25E3bVyTkgAA96AMkPrV345FnpniO+msrOXzdSWWC3MMYPK26ERByxBBJ3ZVcZySKydE8SeLtZex1a2stSktbm7CyW7RWq2qW/mFWIbf529V5yepBG0ZrrrLwVomnXpu7NL+GQztcMi6lc+W0jHcSY/M2HJ6gjFSJ4P0OPUvt8VpJHMZvPKR3MqxGTOd5iDbCc8529aAMj4m2st14cs4472a2U6pZK4jVG37riMDO9T0JDD3AzkZFZfirXdV0lb6PSdR1e6n0ezWS48uztTAGCF8zM+w/MBkiPGAeB2rvdQ0201S3SC9i82NJo51XcVw6MHQ8EdGUGsvVPBugazdz3N/YmSS4jEc4WeRFmUDA3qrBWIzwSCR2oAyI9T1bxLr/ANhstTfSLaDTba9YwwxySzNMXwMyKwCLs5wMknqKr2Fher8UNcnOs3mIdPsXkijih2zjM42nKEgZUngg5Y84wB0V74S0W/Fr51rIr2kXkQyQXMsMix8fIXRgxXjoSRUz+G9LbU7bURDLHd28axJJFcSR7kUkqrhWAcDJ4YHqaAOP0rXde+w+GNfutVW5t9duEik08QIqQLKjMvlsBvJTAB3E556VreCZ9a1W3n1TUtXeaIXd3bxWqwRqgRJ3RSxC7iwC44IGMZBPNadl4P0HTtTXULWw2XCM7xgyu0cTPncUjLFEJyclQOprS07TbTSrQ2tlF5UJkkl27i3zO5djkknlmJ/GgC1RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/Z",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#\n",
"# Each instance of the metrics object has, potentially, a different set of observed classes.\n",
"# Loop through them to accumulate a unified set of classes to ensure consistent plotting across\n",
"# all thresholds.\n",
"#\n",
"\n",
"unified_classes = set()\n",
"for m in perturbed_metrics:\n",
" for class_idx in m[\"classes\"].tolist():\n",
" unified_classes.add(class_idx)\n",
"\n",
"#\n",
"# dictionary of class_idx -> list of per-class mAP, or 0 if not present at that threshold\n",
"#\n",
"\n",
"class_mAP = {class_idx: list() for class_idx in unified_classes} # noqa: N816\n",
"\n",
"#\n",
"# populate the lists across the perturbation values\n",
"#\n",
"\n",
"for m in perturbed_metrics:\n",
" this_perturbation_classes = m[\"classes\"].tolist()\n",
" for class_idx in unified_classes:\n",
" if class_idx in this_perturbation_classes:\n",
" # the index of the class in this individual metric instance\n",
" this_class_idx = this_perturbation_classes.index(class_idx)\n",
" class_map_value = m[\"map_per_class\"][this_class_idx].item()\n",
" # Clip value to 0 if negative\n",
" if class_map_value < 0:\n",
" class_mAP[class_idx].append(0)\n",
" else:\n",
" class_mAP[class_idx].append(m[\"map_per_class\"][this_class_idx].item())\n",
" else:\n",
" class_mAP[class_idx].append(0)\n",
"\n",
"#\n",
"# plot\n",
"#\n",
"\n",
"plt.title(\"mAP per class for haze perturbed dataset\")\n",
"plt.xlabel(\"Haze factor\")\n",
"plt.ylabel(\"mAP @ 50% IoU\")\n",
"for class_idx, class_mAP_list in class_mAP.items(): # noqa: N816\n",
" plt.plot(perturbation_values, class_mAP_list, label=baseline[0].names[class_idx])\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3aef482b-1a2b-4b2e-b522-7032776320d2",
"metadata": {},
"source": [
"The above plots shows several interesting results:\n",
"\n",
"- Each class experiences a significant decrease in performance as the haze factor increases, though at different rates.\n",
"- The motorcycle class shows initial robustness to low haze factors but experiences a rapid performance drop around a haze factor of 0.5.\n",
"- The person class demonstrates the most robustness to haze perturbations across the tested range.\n",
"- The car class shows a more gradual decline in detection accuracy, which can be attributed to the positioning of several cars near the bottom of the image (foreground) where they are less affected by the depth-based haze simulation.\n",
"- All classes eventually reach near-zero performance at higher haze factors, demonstrating the significant impact of atmospheric conditions on object detection.\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "23ec21e7-e682-4d08-ad1d-6c42d850402f",
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"mAP values per area for haze perturbed dataset\")\n",
"plt.xlabel(\"Haze factor\")\n",
"plt.ylabel(\"mAP\")\n",
"for k in (\"map\", \"map_small\", \"map_medium\"):\n",
" plt.plot(perturbation_values, [m[k].item() if m[k].item() >= 0 else 0 for m in perturbed_metrics], label=k)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f6e276bd-2eaf-4a4c-a3cd-9bb8c98fa30c",
"metadata": {},
"source": [
"The `map` line covers all sizes; `map_small` and `map_medium` are the mean average precision for objects (smaller than 32^2 pixels, between 32^2 and 96^2 pixels) in area, respectively. (There are no detections in the `map_large` category.) (Here, the mAP value is averaged over a **range** of IoU thresholds, between 0.5 and 0.95.) We see that medium objects, regardless of class, are generally much more robust to more haze perturbations than small ones.\n"
]
},
{
"cell_type": "markdown",
"id": "b28b9f2a-7217-4427-9c4f-f84656a4dcf2",
"metadata": {},
"source": [
"## End of Notebook"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "nrtk-QgDWFgV4-py3.10",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}