Demonstrating Lens Flare Perturbations

Introduction

This notebook is part of the NRTK demonstration suite, demonstrating how perturbations can be applied and their impact measured via MAITE evaluation workflows.

Layout

This notebook demonstrates how a particular condition (in this case, a lens flare caused by a bright light source), can affect an object detection model, and how that impact can be measured. The overall structure is:

• Traditional vs. relative mAP:

  • An overview of the nuances of what we’ll be evaluating.

• Setup:

  • 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.

  • Loading the source image, which will be used throughout the notebook.

• Image perturbation examples:

  • The NRTK perturbation is demonstrated on the source image.

• Baseline detections:

  • The object detection model is loaded and run on the unperturbed image. These will serve as “ground truth” for comparisons against the perturbed images.

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:

• Wrapping the detection model

• Wrapping the reference image as a dataset

• Wrapping the perturbation as augmentation objects

• Wrapping the metrics

After the evaluation elements have been wrapped, we can run the evaluation:

• Preparing the augmentations:

  • We specify the range of perturbation values to evaluate and optionally specify which ones we’d like to visualize.

• Evaluation of augmented data:

  • Each augmentation is run through MAITE’s evaluation workflow, computing the mean average precision metric relative to the unperturbed detections.

• Evaluation analysis:

  • We plot and discuss the mAP@50 metric from each of the perturbed images, as well as per-class and per-area results.

Evaluation guidance: traditional vs. relative mAP

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.)

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.

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.

Setup: Notebook initialization

The next few cells import the python packages used in the rest of the notebook.

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.

from __future__ import annotations

# warning suppression
import warnings

warnings.filterwarnings("ignore")

import sys  # noqa: F401

print("Beginning package installation...")
!{sys.executable} -m pip install -qU pip

print("Installing required packages...")
!{sys.executable} -m pip install -q "matplotlib" --no-cache-dir
!{sys.executable} -m pip install -q "torchvision" --no-cache-dir
!{sys.executable} -m pip install -q "torchmetrics" --no-cache-dir
!{sys.executable} -m pip install -q "ultralytics" --no-cache-dir
!{sys.executable} -m pip install -q "nrtk[maite, albumentations]" --no-cache-dir

# OpenCV must be uninstalled and reinstalled last due to other packages installing OpenCV
print("Doing a fresh install of opencv-python-headless...")
!{sys.executable} -m pip uninstall -qy "opencv-python" "opencv-python-headless"
!{sys.executable} -m pip install -q "opencv-python-headless" --no-cache-dir

Beginning package installation...
Installing required packages...
WARNING: nrtk 0.20.0 does not provide the extra 'albumentations'
Doing a fresh install of opencv-python-headless...

import os
import urllib.request
from collections.abc import Sequence
from typing import Any

import numpy as np

# some initial imports
%matplotlib inline
%config InlineBackend.figure_format = "jpeg"  # Use JPEG format for inline visualizations

from matplotlib import pyplot as plt  # type: ignore
from PIL import Image

from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber

Setup: Source image

In the next cell, we’ll download and display a source image from the VisDrone dataset. The image will be cached in a local data subdirectory.

A note on image storage

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 to view them. The complication is that although YOLO inferences on ndarray, it expects the color channels to be in BGR 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.)

In this notebook, we’ll convert the channel order to BGR when we load, and convert back whenever we explicitly call imshow.

import nrtk

print(nrtk.__version__)
data_dir = "./data"
os.makedirs(data_dir, exist_ok=True)
img_path = os.path.join(data_dir, "visdrone_img.jpg")
if not os.path.isfile(img_path):
    url = "https://data.kitware.com/api/v1/item/623880f14acac99f429fe3ca/download"
    _ = urllib.request.urlretrieve(url, img_path)  # noqa: S310

img_pil = Image.open(img_path)
img_nd_bgr = np.asarray(img_pil)[
    :,
    :,
    ::-1,
]  # tip o' the hat to https://stackoverflow.com/questions/4661557/pil-rotate-image-colors-bgr-rgb
plt.figure()
plt.axis("off")

_ = plt.imshow(img_nd_bgr[:, :, ::-1])  # explicitly changing BGR to RGB for imshow

0.20.0

NRTK Lens Flare: examples and guidance

Lens flare (i.e. “Sun flare”) is created by unwanted scattering of light from bright sources within the optics of the camera. These flares are present in most outdoor images with the sun present, and since they are not actually a part of the image, they should be ignored in an object detection model. However, because the flare is typically very bright and extend across many parts of the image, they can easily obscure objects and confuse a model which is not trained to ignore them.

While the flare is a complex phenomenon which depends on the camera’s lens, it has two main components: The first is a bright glow or haze near the bright object which reduces the image contrast. This can happen even if the bright object is just out of frame. The second component is a series of circles or geometrical shapes which represent the internal reflections of the light in the optical components. Most often, these shapes are colocated along a line emanating from the bright source.

The AlbumentationsPerturber serves as an interface from the perturbations available from Albumentations. We can simulate a lens flare on our input image by using the RandomSunFlare perturber from Albumentations with the following parameters:

• flare_center: An (x, y) tuple of floats representing the location in the image to draw the flare

• src_radius: The radius of the primary sun flare

• src_color: A tuple of integers representing the RGB color of the flare

• circles: A list of additional circle overlays. Each includes an alpha value, center, radius and color.

• p: The probability of applying a perturbation to the image

If p is 0, the output image will be unchanged.

These parameters are all optional. For any parameter that isn’t provided, Albumentations will select some random default value.

_, ax = plt.subplots(2, 4, figsize=(10, 4))

params = {
    "p": 1.0,
}

for idx in range(0, 8):
    (row, col) = (int(idx / 4), idx % 4)
    factor = (idx - 1) / 2
    radius = (idx + 1) * 100
    params["src_radius"] = radius
    perturber = AlbumentationsPerturber(perturber="RandomSunFlare", seed=idx, parameters=params)
    ax[row, col].set_title(f"Lens Flare Radius: {radius}")
    ax[row, col].imshow(perturber(img_nd_bgr)[0][:, :, ::-1])
    _ = ax[row, col].axis("off")

plt.tight_layout()

Baseline detections

In the next cell, we’ll download a YOLOv11 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.

Note that here, we’re using YOLO’s built-in visualization tool, which automatically adjusts for BGR / RGB order.

# Import YOLO support
import ultralytics

ultralytics.checks()
print("Downloading model...")
model = ultralytics.YOLO("yolo11n.pt")
print("Computing baseline...")
baseline = model(img_nd_bgr)
baseline[0].show()

Ultralytics 8.3.85 🚀 Python-3.10.12 torch-2.6.0+cu124 CUDA:0 (NVIDIA RTX A2000 Laptop GPU, 3782MiB)
Setup complete ✅ (16 CPUs, 62.5 GB RAM, 269.2/914.7 GB disk)
Downloading model...
Computing baseline...

0: 384x640 5 persons, 15 cars, 1 motorcycle, 2 trucks, 47.2ms
Speed: 4.4ms preprocess, 47.2ms inference, 81.1ms postprocess per image at shape (1, 3, 384, 640)

MAITE Evaluation workflow preparation

We’ll use the MAITE Evaluation workflow 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:

• We’ll wrap the model to make predictions on input data when called.

• 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…

• …the augmentation object, which applies the perturbation to the image inside the evaluation.

• Finally, the metric object will define our precise scoring methodology.

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.

Some helper classes

The following cell adds two classes to allow us to use YOLO detections with the MAITE evaluation workflow:

1. The YOLODetectionTarget helper class that stores the bounding boxes, label indices, and confidence scores for a single image’s detections.

2. The MaiteYOLODetection adapter class that conforms to the MAITE Object Detection Dataset protocol by providing the __len__ and __getitem__ methods. The returned item is a tuple of (image, YOLODetectionTarget, metadata-dictionary).

from dataclasses import dataclass

import torch
from maite.protocols.object_detection import DatumMetadataType

from nrtk.interop.maite.interop.object_detection.dataset import JATICObjectDetectionDataset

##
## Helper class for containing the boxes, label indices, and confidence scores.
##


@dataclass
class YOLODetectionTarget:
    """
    A helper class to represent object detection results in the format expected by YOLO-based models.

    Attributes:
        boxes (torch.Tensor): A tensor containing the bounding boxes for detected objects in
            [x_min, y_min, x_max, y_max] format.
        labels (torch.Tensor): A tensor containing the class labels for the detected objects.
            These may be floats for compatibility with specific datasets or tools.
        scores (torch.Tensor): A tensor containing the confidence scores for the detected objects.
    """

    boxes: torch.Tensor
    labels: torch.Tensor
    scores: torch.Tensor


##
## Prepare results for ingestion into maite dataset by puttin them into detection object
## Images must be channel first (c, h, w) in maite dataset objects
##
imgs = [np.transpose(img_nd_bgr, (2, 0, 1))]
dets = []
metadata: list[DatumMetadataType] = [{"id": 0}]
for _detection in baseline:
    boxes = baseline[0].boxes.xyxy.cpu()
    labels = baseline[0].boxes.cls.cpu()  # note, these are floats, not ints
    scores = baseline[0].boxes.conf.cpu()

    dets.append(YOLODetectionTarget(boxes, labels, scores))

(1) Wrapping the detection model

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 protocol. The __call__ method runs the model on images in the batch and is called by the MAITE evaluation workflow later in the notebook.

import maite.protocols.object_detection as od
import ultralytics.models
from maite.protocols import ArrayLike, ModelMetadata


class MaiteYOLODetector:
    """
    A wrapper class for a YOLO model to simplify its usage with input batches and object detection targets.

    This class takes a YOLO model instance, processes input image batches, and converts predictions into
    `YOLODetectionTarget` instances.

    Attributes:
        _model (ultralytics.models.yolo.model.YOLO): The YOLO model instance used for predictions.

    Methods:
        __call__(batch):
            Processes a batch of images through the YOLO model and returns the predictions as
            `YOLODetectionTarget` instances.
    """

    def __init__(self, model: ultralytics.models.yolo.model.YOLO) -> None:
        """
        Initializes the MaiteYOLODetector with a YOLO model instance.

        Args:
            model (ultralytics.models.yolo.model.YOLO): The YOLO model to use for predictions.
        """
        self._model = model
        # Dummy model metadata type to pass type checking
        self.metadata = ModelMetadata(id="0")

    def __call__(self, batch: Sequence[ArrayLike]) -> Sequence[YOLODetectionTarget]:
        """
        Processes a batch of images using the YOLO model and converts the predictions to `YOLODetectionTarget`
        instances.

        Args:
            batch (Sequence[ArrayLike]): A batch of images in (c, h, w) format (channel-first).

        Returns:
            Sequence[YOLODetectionTarget]: A list of YOLODetectionTarget instances containing the predictions for each
            image in the batch.
        """
        # Convert images to channel-last format (h, w, c) for YOLO model
        batch_transposed = [np.transpose(batch[i], (1, 2, 0)) for i in range(len(batch))]

        yolo_predictions = self._model(batch_transposed, verbose=False)
        return [
            YOLODetectionTarget(
                p.boxes.xyxy.cpu(),  # Bounding boxes in (x_min, y_min, x_max, y_max) format
                p.boxes.cls.cpu(),  # Class indices for the detected objects
                p.boxes.conf.cpu(),  # Confidence scores for the detections
            )
            for p in yolo_predictions
        ]


# create the wrapped model object
yolo_model: od.Model = MaiteYOLODetector(model)

(2) Wrapping the dataset

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.)

# our single image, its baseline detections, and metadata dictionary
# switch image to channel first
single_image_dataset: od.Dataset = JATICObjectDetectionDataset(imgs, dets, metadata, dataset_id="visdrone_ex")

(3) Wrapping the perturbations as augmentations

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.

from nrtk.impls.perturb_image.generic.albumentations_perturber import AlbumentationsPerturber
from nrtk.interop.maite.interop.object_detection.augmentation import JATICDetectionAugmentation

perturber = AlbumentationsPerturber(perturber="RandomSunFlare", parameters={"p": 0.0}, seed=1)
identity_augmentation = JATICDetectionAugmentation(perturber, augment_id="identity")

(4) Wrapping the metrics

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 protocol-compatible class, and then creates an instance of this class, which will be called by evaluate.

This code is copied directly from the MAITE object detection tutorial (with the exception of setting class_metrics=True.)

from maite.protocols import MetricMetadata
from torchmetrics import Metric as TorchMetric
from torchmetrics.detection.mean_ap import MeanAveragePrecision

##
## Create an instance of the MAP metric object
##

tm_metric = MeanAveragePrecision(
    box_format="xyxy",
    iou_type="bbox",
    iou_thresholds=[0.5],
    rec_thresholds=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
    max_detection_thresholds=[1, 10, 100],
    class_metrics=True,
    extended_summary=False,
    average="macro",
)

##
## This wrapper associates the MAP metric object with methods called by the evaluate
## workflow to accumulate detection data and compute the metrics.
##


class WrappedTorchmetricsMetric:
    """
    A wrapper class for a Torchmetrics metric designed to simplify its usage for object detection tasks.

    This class facilitates the conversion of object detection targets and predictions into the format
    expected by Torchmetrics metrics, allowing for easier integration with existing pipelines.

    Attributes:
        _tm_metric (Callable): The Torchmetrics metric to be wrapped, which takes lists of dictionaries
            containing torch.Tensor objects representing predictions and targets.

    Methods:
        to_tensor_dict(target):
            Converts an `ObjectDetectionTarget` into a dictionary format compatible with the Torchmetrics
            metric's `update` method.

        update(preds, targets):
            Updates the wrapped Torchmetrics metric with batches of predictions and targets in their native format.

        compute():
            Computes the final metric values using the wrapped Torchmetrics metric.

        reset():
            Resets the state of the wrapped Torchmetrics metric.
    """

    def __init__(
        self,
        tm_metric: TorchMetric,
    ) -> None:
        """
        Initializes the WrappedTorchmetricsMetric with the given Torchmetrics metric.

        Args:
            tm_metric (Callable): A Torchmetrics metric instance that expects predictions and targets as lists of
                dictionaries containing torch.Tensor objects.
        """
        self._tm_metric = tm_metric
        # Dummy metric metadata type to pass type checking
        self.metadata = MetricMetadata(id="0")

    @staticmethod
    def to_tensor_dict(target: od.ObjectDetectionTarget) -> dict[str, torch.Tensor]:
        """
        Converts an ObjectDetectionTarget into a dictionary format compatible with the Torchmetrics metric's
        `update` method.

        Args:
            target (od.ObjectDetectionTarget): An object detection target instance containing boxes, labels, and scores.

        Returns:
            dict[str, torch.Tensor]: A dictionary with keys `boxes`, `scores`, and `labels`, each mapping to a tensor.
        """
        return {
            "boxes": torch.as_tensor(target.boxes),
            "scores": torch.as_tensor(target.scores),
            "labels": torch.as_tensor(target.labels).type(torch.int64),
        }

    def update(self, preds: od.TargetBatchType, targets: od.TargetBatchType) -> None:
        """
        Updates the wrapped Torchmetrics metric with the given predictions and targets.

        Args:
            preds (od.TargetBatchType): A batch of predictions in the format expected by the Torchmetrics metric.
            targets (od.TargetBatchType): A batch of targets in the format expected by the Torchmetrics metric.
        """
        preds_tm = [self.to_tensor_dict(pred) for pred in preds]
        targets_tm = [self.to_tensor_dict(tgt) for tgt in targets]
        self._tm_metric.update(preds_tm, targets_tm)

    def compute(self) -> dict[str, Any]:
        """
        Computes and returns the final metric values using the wrapped Torchmetrics metric.

        Returns:
            dict[str, Any]: A dictionary containing the computed metric values.
        """
        return self._tm_metric.compute()

    def reset(self) -> None:
        """Resets the state of the wrapped Torchmetrics metric, clearing any accumulated data."""
        self._tm_metric.reset()


##
## This is our instance variable that can compute the MAP metrics.
##

mAP_metric: od.Metric = WrappedTorchmetricsMetric(tm_metric)  # noqa: N816

Running the evaluation

We now have all the wrappings required to evaluate our range of perturbations:

• The yolo_model object, wrapping the YOLO model

• The single_image_dataset object, providing our source image and its baseline detections

• The augmentation object, which when instantiated, applies a single perturbation value to its input

• The mAP_metrics object, defining the metrics to compute at each perturbation value

Evaluation sanity check: ground truth against itself

Here we quickly check the evaluation workflow by creating an identity augmentation (with a cn2_at_1m value of 1.7e-14, leaving the image unchanged) and scoring it. The detections should also be unchanged from the baseline and thus give an mAP of 1.0.

from maite.workflows import evaluate

# call the model for each image in the dataset (in this case, just the source image),
# scoring the resulting detections against those from the dataset
sanity_check_results, _, _ = evaluate(
    model=yolo_model,
    dataset=single_image_dataset,
    augmentation=identity_augmentation,
    metric=mAP_metric,
)

print("Sanity check: overall mAP (should be 1.0):", sanity_check_results["map"].item())

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Sanity check: overall mAP (should be 1.0): 1.0

Preparing the data

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:

• sweep_low: the minimum perturbation value (0.0 is no haze)

• sweep_high: the maximum perturbation value (we use something high to see where accuracy will drop to zero)

• sweep_count: how many perturbations to generation

You can also optionally select perturbations to visualize:

• visualization_indices: a list of perturbation indices p, 0 <= p < sweep_count. These instances will be rendered along with their corresponding detections.

SWEEP_LOW = 100
SWEEP_HIGH = 800
SWEEP_COUNT = 10
VISUALIZATION_INDICES = [0, 25, 50, 75]

##
## end user-settable parameters
##

perturbation_values = np.linspace(SWEEP_LOW, SWEEP_HIGH, SWEEP_COUNT, endpoint=True)
augmentations = [
    JATICDetectionAugmentation(
        AlbumentationsPerturber(perturber="RandomSunFlare", parameters={"p": 1, "src_radius": int(p)}, seed=seed),
        augment_id=str(p),
    )
    for _, p in enumerate(perturbation_values)
    for seed in range(10)
]

print(f"Generated {len(augmentations)} perturbation augmentations")

Generated 100 perturbation augmentations

Calling evaluate on the augmented data

We loop over all the augmentations, calling evaluate on each one and building up a list of resulting metrics for analysis.

The location of the sun-flare will have a high impact on our results. If the area of the image obscured by the flare contains no objects, it would not interefere with detection. Because we select the locations randomly, this results in a high variation in relative mAP depending on the random seed used to place the flare. To get more meaningful results, we will repeat the evaluation for 10 different random seeds.

Any augmentation indices specified above will be rendered in this step.

perturbed_metrics = dict()
for idx, a in enumerate(augmentations):
    # reset the metric object for each dataset
    mAP_metric.reset()
    src_radius = a.augment.get_config()["parameters"]["src_radius"]
    result, _, _ = evaluate(model=yolo_model, dataset=single_image_dataset, augmentation=a, metric=mAP_metric)
    if src_radius not in perturbed_metrics:
        perturbed_metrics[src_radius] = list()
    perturbed_metrics[src_radius].append(result)

    if idx in VISUALIZATION_INDICES:
        # quickest way is to re-evaluate
        print(f"Perturbation #{idx}: Radius {src_radius}")
        datum = single_image_dataset[0]
        batch = ([datum[0]], [datum[1]], [datum[2]])
        # Extract the image from the augmentation and switch it to channel last
        aug = np.transpose(a(batch)[0][0], (1, 2, 0))
        _ = model(aug)[0].show()

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Perturbation #0: Radius 100

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Perturbation #25: Radius 255

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100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 

Perturbation #50: Radius 488

100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|█| 1/1 [00:00<00:00, 
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.00it/s]

Perturbation #75: Radius 644

100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.99it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.14it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.41it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.93it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.47it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.30it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.93it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  6.75it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.22it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  6.84it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.48it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.36it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.98it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.26it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.22it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.02it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.98it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  6.94it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.66it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.76it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  7.80it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.10it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.15it/s]
100%|██████████████████████████████████| 1/1 [00:00<00:00,  8.01it/s]

Evaluation analysis

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.

Because we repeated each perturbation 10 times with different random seeds, we will plot the average, worst-case, and best-case relative mAP for each flare radius.

map50s_dict = dict()
for radius, metrics in perturbed_metrics.items():
    for m in metrics:
        if radius not in map50s_dict:
            map50s_dict[radius] = list()
        map50s_dict[radius].append(m["map_50"].item())

map50_avg = [np.average(map50s_dict[m]) for m in map50s_dict]
map50_best = [np.max(map50s_dict[m]) for m in map50s_dict]
map50_worst = [np.min(map50s_dict[m]) for m in map50s_dict]

plt.title("relative mAP@50")
plt.xlabel("Flare Radius")
plt.ylabel("relative mAP @ 50% IoU")
_ = plt.plot(perturbation_values, map50_avg, label="Average")
_ = plt.plot(perturbation_values, map50_worst, label="Worst")
_ = plt.plot(perturbation_values, map50_best, label="Best")
plt.legend()

<matplotlib.legend.Legend at 0x7f41e86fba30>

Evaluation interpretation

The general trend is that mAP decreases as the flare radius increases, as the flare is able to obscure a greater number of objects. However, the most important factor in effected mAP will be the location of the flare and whether it could obscure any of the objects in the image. It is possible even with a smaller lens flare for it to significantly impact mAP, if the location of that flare happens to obscure multiple objects. As the size of the flare increases, the probability of each object being obscured also increases.

End of notebook