Welcome to the world of event-based vision! In this guide, we’ll walk you through how to evaluate the Automotive Datasets provided by Prophesee using a set of helpful Python scripts. Buckle up as we navigate through the setup, data acquisition, visualization, and evaluation processes. Don’t worry; we will also troubleshoot some common issues along the way!

Requirements

Before you hit the road, make sure you have the following requirements met. You can run the scripts with either Python 2.x or Python 3.x. Here are the essential packages you’ll need:

• NumPy: This is necessary for input/output operations.
• OpenCV: This is required for visualization, and you’ll need the Python bindings for it.

You can install these dependencies easily using pip:

pip install numpy
pip install opencv-python

Downloading the Data

Now, let’s get our hands on some data! You’ll be working with two primary datasets:

1 Megapixel Automotive Detection Dataset

Head to the dataset presentation page to download it. The dataset comes divided into three folders: train, test, and validation. Each file consists of 60-second recordings cut from longer sessions, with bounding box annotations for 7 classes including pedestrians, two-wheelers, cars, trucks, buses, traffic signs, and traffic lights. More details can be found in the associated NeurIPS paper.

GEN1 Automotive Detection Dataset

For the GEN1 dataset, go to the dataset presentation page and download this larger dataset (200GB compressed, 750GB uncompressed). This dataset is organized differently, with manual annotations only for pedestrians and cars.

Visualizing Data

To visualize the dataset, you can use the following command:

python3 dataset_visualization.py file_1_td.dat file_2_td.dat ... file_n_dat

This command will display the events in a grid format. Just remember, using too many files might slow down the display!

Understanding the Scripts: An Analogy

Think of the Prophesee Automotive Dataset Toolbox as a sophisticated automotive navigation system. You’ve got several components working in unison:

• Data Preparation: Just as you would collect maps and GPS details before a journey, here you gather datasets pertinent to your evaluation.
• Visualization: Like scanning the road ahead, the visualization script helps you interpret the data through graphical representation, ensuring you’re on the right track.
• Evaluation: Just as you would assess your journey based on the quality of the roads, speed, and conditions, you use evaluation scripts to measure performance against standard metrics.

This analogy highlights how each part of the toolbox plays a crucial role in navigating the world of event-based automotive detection.

Running a Baseline

If you’re keen to run a baseline, you can reference prior results in event-based literature or leverage the e2vid project for frame-based detection algorithms.

Evaluation Using the COCO API

To evaluate your results using the COCO API, you need to be aware of the recent changes in the dataset format. Here’s a sample code snippet for loading your results:

import numpy as np
from src.metrics.coco_eval import evaluate_detection
from src.io.box_loading import reformat_boxes

RESULT_FILE_PATHS = [file1_results_bbox.npy, file2_results_bbox.npy]
GT_FILE_PATHS = [file1_bbox.npy, file2_bbox.npy]
result_boxes_list = [np.load(p) for p in RESULT_FILE_PATHS]
gt_boxes_list = [np.load(p) for p in GT_FILE_PATHS]

# For backward-compatibility
result_boxes_list = [reformat_boxes(p) for p in result_boxes_list]
gt_boxes_list = [reformat_boxes(p) for p in gt_boxes_list]

# For fair comparison with paper results
gt_boxes_list = map(filter_boxes, gt_boxes_list)
result_boxes_list = map(filter_boxes, result_boxes_list)

evaluate_detection(gt_boxes_list, result_boxes_list)

This code will help you in filtering the boxes to compare your detection results effectively.

Troubleshooting Common Issues

If you encounter issues while using the toolbox, consider the following tips:

• Ensure all required packages are correctly installed. Use pip install commands mentioned in the requirements section.
• Verify that you have the correct dataset structure for both 1 Megapixel and GEN1 datasets.
• For visualization lag, try reducing the number of files you’re displaying at once.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.