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How To Get RADDET Dataset

In this tutorial you will learn how to download and convert the RADDet dataset that can be processed by Intel® Metro AI Suite Sensor Fusion for Traffic Management.

RADDet Dataset

Download

Download dataset from website: https://github.com/ZhangAoCanada/RADDet

Refer to how to use in the website above to download the dataset.

Then from BaiduPan to download the corresponding ADC data.

Attention: Please keep the same directory tree as shown in OneDrive

Download the dataset and arrange it as the following directory tree:

|-- raddet_adc
	|-- ADC
		|-- ******.npy
		|-- ******.npy
|-- sensors_para
	|-- registration_matrix
		|-- registration_matrix.npy
	|-- stereo_para
		|-- left_maps.npy
		|-- ProjLeft.npy
		|-- ProjRight.npy
		|-- Q.npy
		|-- right_maps.npy
		|-- roiL.npy
		|-- roiR.npy
|-- test
	|-- RAD
		|-- part1
			|-- ******.npy
			|-- ******.npy
		|-- part2
			|-- ******.npy
			|-- ******.npy
	|-- gt
		|-- part1
			|-- ******.pickle
			|-- ******.pickle
		|-- part2
			|-- ******.pickle
			|-- ******.pickle
	|-- stereo_image
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg
|-- train
	|-- RAD
		|-- part1
			|-- ******.npy
			|-- ******.npy
		|-- part2
			|-- ******.npy
			|-- ******.npy
	|-- gt
		|-- part1
			|-- ******.pickle
			|-- ******.pickle
		|-- part2
			|-- ******.pickle
			|-- ******.pickle
	|-- stereo_image
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg

Dataset description

The details of the data capture is shown below.

"designed_frequency":			76.8 Hz,
"config_frequency":			77 Hz,
"range_size":				256,
"maximum_range":			50 m,
"doppler_size":				64,
"azimuth_size":				256,
"range_resolution":			0.1953125 m/bin,
"angular_resolution":			0.006135923 radian/bin,
"velocity_resolution":			0.41968030701528203 (m/s)/bin

The dataset has totally 6 categories, different input formats and ground truth formats. All the information that stored in the dataset can be concluded as follow.

RAD:		3D-FFT radar data with size (256, 256, 64)
stereo_image:	2 rectified stereo images
gt:		ground truth with {"classes", "boxes", "cart_boxes"}
sensors_para: 	"stereo_para" for stereo depth estimation, and "registration_matrix" for cross-sensor registration

Note: for the classes, they are ["person", "bicycle", "car", "motorcycle", "bus", "truck" ].

Also Note: for the boxes, the format is [x_center, y_center, z_center, w, h, d].

Also Note: for the cart_box, the format is [x_center, y_center, w, h].

Also Note: for the stereo_para, left_maps.npy and right_maps.npy are derived from cv2.initUndistortRectifyMap(...); all other matrices are derived from cv2.stereoRectify(...).

License: MIT License

Generate left image and radar data, then convert to bin file as multi sensor input

cd $PROJ_DIR/deployments/raddet_tools

# If you already source python virtual environment during `edgesoftware install`, you can skip this
python3 -m venv raddet-test
source raddet-test/bin/activate

python3 -m pip install --upgrade pip
pip install -r requirements.txt

export RADDET_DATASET_ROOT=/path/to/datasets/RADDet
# dataset org:

cd scripts
bash process_dataset.sh $RADDET_DATASET_ROOT

Upon success, the radar data and the corresponding left image will be extracted, the directory tree should be as follows:

|-- bin_files_v1.0
	|-- bgr
		|-- ******.bin
		|-- ******.bin
	|-- radar
		|-- ******.bin
		|-- ******.bin
|-- raddet_adc
	|-- ADC
		|-- ******.npy
		|-- ******.npy
|-- sensors_para
	|-- registration_matrix
		|-- registration_matrix.npy
		|-- registration_matrix.bin
	|-- stereo_para
		|-- left_maps.npy
		|-- left_maps.bin
		|-- ProjLeft.npy
		|-- ProjLeft.bin
		|-- ProjRight.npy
		|-- ProjRigh.bin
		|-- Q.npy
		|-- Q.bin
		|-- right_maps.npy
		|-- right_maps.bin
		|-- roiL.npy
		|-- roiL.bin
		|-- roiR.npy
		|-- roiR.bin
|-- test
	|-- RAD
		|-- part1
			|-- ******.npy
			|-- ******.npy
		|-- part2
			|-- ******.npy
			|-- ******.npy
	|-- gt
		|-- part1
			|-- ******.pickle
			|-- ******.pickle
		|-- part2
			|-- ******.pickle
			|-- ******.pickle
	|-- stereo_image
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg
	|-- left
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg
|-- train
	|-- RAD
		|-- part1
			|-- ******.npy
			|-- ******.npy
		|-- part2
			|-- ******.npy
			|-- ******.npy
	|-- gt
		|-- part1
			|-- ******.pickle
			|-- ******.pickle
		|-- part2
			|-- ******.pickle
			|-- ******.pickle
	|-- stereo_image
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg
	|-- left
		|-- part1
			|-- ******.jpg
			|-- ******.jpg
		|-- part2
			|-- ******.jpg
			|-- ******.jpg

Where bin_files_v1.0 stores all the bin files as multi sensor input.