Python torch + opencv code to go from conventional stroboscopic video frames with low frame rate into realistic synthetic DVS event streams with much higher effective timing precision. v2e includes finite intensity-depenedent photoreceptor bandwidth, Gaussian pixel to pixel event threshold variation, and noise 'leak' events.
See the v2e home page for videos and further information.
Our paper (below) about v2e debunks incorrect assertions about event cameras that pervade the current computer vision literature.
See v2e-sample-input-data for sample input files.
Vote for new v2e features.
v2e development was supported by the Swiss National Competence Center for Robotics (NCCR Robotics).
See changelog for latest news
Yuhuang Hu (yuhuang.hu@ini.uzh.ch) Tobi Delbruck (tobi@ini.uzh.ch)
If you use v2e, we appreciate a citation to the paper below. See the v2e home page for futher background papers.
- Y. Hu, S-C. Liu, and T. Delbruck. v2e: From Video Frames to Realistic DVS Events. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), URL: https://arxiv.org/abs/2006.07722, 2021
To reproduce the experiments of the paper, please find this repository.
We recommend running v2e on a CUDA GPU or it will be very slow, particularly when using SuperSloMo upsampling. Even with a low-end GTX-1050, v2e runs about 50-200X slower than real time using 10X slowdown factor and 346x260 video.
Conversion speed depends linearly on the reciprocal of the desired DVS timestamp resolution. If you demand fine resolution of e.g. 100us, then expect many minutes of computing per second of source video. Running on Google colab with GPU, it took 500s per second of 12FPS source video, because of the very high upsampling ratio of over 800X and the 220k frames that needed to be produced for DVS modeling.
We advise using the --stop option for trial run before starting a long conversion.
If you don't want to install, try opening v2e in google colab.
There are 3 general steps
- Make a conda environment
- Install pytoch and other conda distributed packages to this environment
- Install the rest of the packages and v2e to the conda enviroment with pip.
pip is needed because some packages are not availble from the conda repositories. It is important to go in this order because conda in general is not aware of pip installs.
Install v2e to a separate Python environment
such as conda environment:
conda create -n v2e python=3.10 # create a new environment
conda activate v2e # activate the environmentSee https://pytorch.org/get-started/locally/ to generate the correct conda install command to enable GPU-accelerated CUDA. On windows 10, for example, this tool generated the following command. v2e does not use torchaudio so you can leave it out.
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
You may want to check this stackoverflow question: https://stackoverflow.com/questions/57238344/i-have-a-gpu-and-cuda-installed-in-windows-10-but-pytorchs-torch-cuda-is-availa
After installing pytorch to your CUDA environment, to install v2e in developer mode (so your edits to source take effect immediately), run the following command in your terminal inside the activated conda environment. The python -m pip install -e . command runs setup.py which installs more packages and adds a script to run v2e from the command line to the conda enviroment python path; see https://stackoverflow.com/questions/39023758/what-does-pip-install-dot-mean:
conda activate v2e # activate your env with pytoch already installed by conda
git clone https://github.com/SensorsINI/v2e
cd v2e
python -m pip install -e . # use pip to install requirements from setup.py in user mode (so your edits to source files in v2e take effect immediately-
If you want an additional Windows GUI interface, you will need to install Gooey package. This package works the best on Windows:
pip install Gooey
On Linux,
Gooeycan be hard to install.For a sample of conversion using the gooey GUI, see https://youtu.be/THJqRC_q2kY
We use the excellent Super SloMo framework to interpolate the APS frames. However, since APS frames only record light intensity, we retrained it on grayscale images.
Download our pre-trained model checkpoint from Google Drive SuperSloMo39.ckpt (151 MB) and save it to the input folder. The default value of --slomo_model argument is set to this location.
Special thanks to Zhe He for recovering the checkpoint file.
The sample input videos to try v2e with are in v2e-sample-input-data on google drive.
Download the tennis.mov video and put in the input folder to run the example below.
v2e serves multiple purposes. Please read to code if you would like to adapt it for your own application. Here, we only introduce the usage for generating DVS events from conventional video and from specific datasets.
v2e.py reads a standard video (e.g. in .avi, .mp4, .mov, or .wmv), or a folder of images, and generates emulated DVS events at upsampled timestamp resolution.
Don't be intimidated by the huge number of options. Running v2e.py with no arguments sets reasonable values and opens a file browser to let you select an input video. Inspect the logging output for hints.
Hint: Note the options [--dvs128 | --dvs240 | --dvs346 | --dvs640 | --dvs1024]; they set output size and width to popular DVS cameras.
On headless platforms, with no graphics output, use --no_preview option to suppress the OpenCV windows.
usage: v2e.py [-h] [-o OUTPUT_FOLDER] [--avi_frame_rate AVI_FRAME_RATE]
[--output_in_place [OUTPUT_IN_PLACE]] [--overwrite]
[--unique_output_folder [UNIQUE_OUTPUT_FOLDER]]
[--skip_video_output]
[--auto_timestamp_resolution [AUTO_TIMESTAMP_RESOLUTION]]
[--timestamp_resolution TIMESTAMP_RESOLUTION]
[--dvs_params DVS_PARAMS] [--pos_thres POS_THRES]
[--neg_thres NEG_THRES] [--sigma_thres SIGMA_THRES]
[--cutoff_hz CUTOFF_HZ] [--leak_rate_hz LEAK_RATE_HZ]
[--shot_noise_rate_hz SHOT_NOISE_RATE_HZ]
[--photoreceptor_noise]
[--leak_jitter_fraction LEAK_JITTER_FRACTION]
[--noise_rate_cov_decades NOISE_RATE_COV_DECADES]
[--refractory_period REFRACTORY_PERIOD]
[--dvs_emulator_seed DVS_EMULATOR_SEED]
[--show_dvs_model_state SHOW_DVS_MODEL_STATE [SHOW_DVS_MODEL_STATE ...]]
[--save_dvs_model_state]
[--record_single_pixel_states RECORD_SINGLE_PIXEL_STATES]
[--output_height OUTPUT_HEIGHT] [--output_width OUTPUT_WIDTH]
[--dvs128 | --dvs240 | --dvs346 | --dvs640 | --dvs1024]
[--disable_slomo] [--slomo_model SLOMO_MODEL]
[--batch_size BATCH_SIZE] [--vid_orig VID_ORIG]
[--vid_slomo VID_SLOMO] [--slomo_stats_plot] [-i INPUT]
[--input_frame_rate INPUT_FRAME_RATE]
[--input_slowmotion_factor INPUT_SLOWMOTION_FACTOR]
[--start_time START_TIME] [--stop_time STOP_TIME] [--crop CROP]
[--hdr] [--synthetic_input SYNTHETIC_INPUT]
[--dvs_exposure DVS_EXPOSURE [DVS_EXPOSURE ...]]
[--dvs_vid DVS_VID] [--dvs_vid_full_scale DVS_VID_FULL_SCALE]
[--no_preview] [--ddd_output] [--dvs_h5 DVS_H5]
[--dvs_aedat2 DVS_AEDAT2] [--dvs_text DVS_TEXT]
[--label_signal_noise] [--cs_lambda_pixels CS_LAMBDA_PIXELS]
[--cs_tau_p_ms CS_TAU_P_MS] [--scidvs]
v2e: generate simulated DVS events from video.
optional arguments:
-h, --help show this help message and exit
Output: General:
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
folder to store outputs.
--avi_frame_rate AVI_FRAME_RATE
frame rate of output AVI video files; only affects
playback rate.
--output_in_place [OUTPUT_IN_PLACE]
store output files in same folder as source video (in
same folder as frames if using folder of frames).
--overwrite overwrites files in existing folder (checks existence
of non-empty output_folder).
--unique_output_folder [UNIQUE_OUTPUT_FOLDER]
If specifying --output_folder, makes unique output
folder based on output_folder, e.g. output1 (if non-
empty output_folder already exists)
--skip_video_output Skip producing video outputs, including the original
video, SloMo video, and DVS video. This mode also
prevents showing preview of output (cf --no_preview).
DVS timestamp resolution:
--auto_timestamp_resolution [AUTO_TIMESTAMP_RESOLUTION]
(Ignored by --disable_slomo or --synthetic_input.) If
True (default), upsampling_factor is automatically
determined to limit maximum movement between frames to
1 pixel. If False, --timestamp_resolution sets the
upsampling factor for input video. Can be combined
with --timestamp_resolution to ensure DVS events have
at most some resolution.
--timestamp_resolution TIMESTAMP_RESOLUTION
(Ignored by --disable_slomo or --synthetic_input.)
Desired DVS timestamp resolution in seconds;
determines slow motion upsampling factor; the video
will be upsampled from source fps to achieve the at
least this timestamp resolution.I.e. slowdown_factor =
(1/fps)/timestamp_resolution; using a high resolution
e.g. of 1ms will result in slow rendering since it
will force high upsampling ratio. Can be combind with
--auto_timestamp_resolution to limit upsampling to a
maximum limit value.
DVS model:
--dvs_params DVS_PARAMS
Easy optional setting of parameters for DVS
model:None, 'clean', 'noisy'; 'clean' turns off noise,
sets unlimited bandwidth and makes threshold variation
small. 'noisy' sets limited bandwidth and adds leak
events and shot noise.This option by default will
disable user set DVS parameters. To use custom DVS
paramters, use None here.
--pos_thres POS_THRES
threshold in log_e intensity change to trigger a
positive event.
--neg_thres NEG_THRES
threshold in log_e intensity change to trigger a
negative event.
--sigma_thres SIGMA_THRES
1-std deviation threshold variation in log_e intensity
change.
--cutoff_hz CUTOFF_HZ
photoreceptor IIR lowpass filter cutoff-off 3dB
frequency in Hz - see
https://ieeexplore.ieee.org/document/4444573.CAUTION:
See interaction with timestamp_resolution and
auto_timestamp_resolution; check output logger
warnings. The input sample rate (frame rate) must be
fast enough to for accurate IIR lowpass filtering.
--leak_rate_hz LEAK_RATE_HZ
leak event rate per pixel in Hz - see
https://ieeexplore.ieee.org/abstract/document/7962235
--shot_noise_rate_hz SHOT_NOISE_RATE_HZ
Temporal noise rate of ON+OFF events in darkest parts
of scene; reduced in brightest parts.
--photoreceptor_noise
Create temporal noise by injecting Gaussian noise to
the log photoreceptor before lowpass filtering.This
way, more accurate statistics of temporal noise will
tend to result (in particular, alternating ON and OFF
noise events)but the noise rate will only approximate
the desired noise rate; the photoreceptor noise will
be computed to result in the --shot_noise_rate noise
value. Overrides the default shot noise mechanism
reported in 2020 v2e paper.
--leak_jitter_fraction LEAK_JITTER_FRACTION
Jitter of leak noise events relative to the (FPN)
interval, drawn from normal distribution
--noise_rate_cov_decades NOISE_RATE_COV_DECADES
Coefficient of Variation of noise rates (shot and
leak) in log normal distribution decades across pixel
arrayWARNING: currently only in leak events
--refractory_period REFRACTORY_PERIOD
Refractory period in seconds, default is 0.5ms.The new
event will be ignore if the previous event is
triggered less than refractory_period ago.Set to 0 to
disable this feature.
--dvs_emulator_seed DVS_EMULATOR_SEED
Set to a integer >0 to use a fixed random seed.default
is 0 which means the random seed is not fixed.
--show_dvs_model_state SHOW_DVS_MODEL_STATE [SHOW_DVS_MODEL_STATE ...]
One or more space separated list model states. Do not
use '='. E.g. '--show_dvs_model_state all'. Possible
models states are (without quotes) either 'all' or
chosen from dict_keys(['new_frame', 'log_new_frame',
'lp_log_frame', 'scidvs_highpass',
'photoreceptor_noise_arr', 'cs_surround_frame',
'c_minus_s_frame', 'base_log_frame', 'diff_frame'])
--save_dvs_model_state
save the model states that are shown (cf
--show_dvs_model_state) to avi files
--record_single_pixel_states RECORD_SINGLE_PIXEL_STATES
Record internal states of a single pixel specified by
(x,y) tuple to 'pixel-states.dat'.The file is a
pickled binary dict that has the state arrays over
time imcluding a time array.Pixel location can also be
specified as x,y without ()
DVS camera sizes (selecting --dvs346, --dvs640, etc. overrides --output_width and --output_height:
--output_height OUTPUT_HEIGHT
Height of output DVS data in pixels. If None, same as
input video. Use --output_height=260 for Davis346.
--output_width OUTPUT_WIDTH
Width of output DVS data in pixels. If None, same as
input video. Use --output_width=346 for Davis346.
--dvs128 Set size for 128x128 DVS (DVS128)
--dvs240 Set size for 240x180 DVS (DAVIS240)
--dvs346 Set size for 346x260 DVS (DAVIS346)
--dvs640 Set size for 640x480 DVS (DAVIS640)
--dvs1024 Set size for 1024x768 DVS (not supported for AEDAT-2.0
output since there is no jAER DVS1024 camera
SloMo upsampling (see also "DVS timestamp resolution" group):
--disable_slomo Disables slomo interpolation; the output DVS events
will have exactly the timestamp resolution of the
source video (which is perhaps modified by
--input_slowmotion_factor).
--slomo_model SLOMO_MODEL
path of slomo_model checkpoint.
--batch_size BATCH_SIZE
Batch size in frames for SuperSloMo. Batch size 8-16
is recommended if your GPU has sufficient memory.
--vid_orig VID_ORIG Output src video at same rate as slomo video (with
duplicated frames). Specify emtpy string or 'None' to
skip output.
--vid_slomo VID_SLOMO
Output slomo of src video slowed down by
slowdown_factor.Specify emtpy string or 'None' to skip
output.
--slomo_stats_plot show a plot of slomo statistics
Input file handling:
-i INPUT, --input INPUT
Input video file or a image folder; leave empty for
file chooser dialog.If the input is a folder, the
folder should contain a ordered list of image files.In
addition, the user has to set the frame rate manually.
--input_frame_rate INPUT_FRAME_RATE
Either override the video file metadata frame rate or
manually define the video frame rate when the video is
presented as a list of image files. Overrides the
stored (metadata) frame rate of input video. This
option overrides the --input_slowmotion_factor
argument in case the input is from a video file.
--input_slowmotion_factor INPUT_SLOWMOTION_FACTOR
(See --input_frame_rate argument too.) Sets the known slow-motion factor of the input video,
i.e. how much the video is slowed down, i.e.,
the ratio of shooting frame rate to playback frame rate.
input_slowmotion_factor<1 for sped-up video and
input_slowmotion_factor>1 for slowmotion video.
If an input video is shot at 120fps yet is presented as a 30fps video
(has specified playback frame rate of 30Hz,
according to file's FPS setting),
then set --input_slowdown_factor=4.
It means that each input frame represents (1/30)/4 s=(1/120)s.
If input is video with intended frame intervals of
1ms that is in AVI file
with default 30 FPS playback spec,
then use ((1/30)s)*(1000Hz)=33.33333.
--start_time START_TIME
Start at this time in seconds in video. Use None to
start at beginning of source video.
--stop_time STOP_TIME
Stop at this time in seconds in video. Use None to end
at end of source video.
--crop CROP Crop input video by (left, right, top, bottom) pixels.
E.g. CROP=(100,100,0,0) crops 100 pixels from left and
right of input frames. CROP can also be specified as
L,R,T,B without ()
--hdr Treat input video as high dynamic range (HDR)
logarithmic, i.e. skip the linlog conversion step. Use
--hdr for HDR input with floating point gray scale
input videos. Units of log input are based on white
255 pixels have values ln(255)=5.5441
Synthetic input:
--synthetic_input SYNTHETIC_INPUT
Input from class SYNTHETIC_INPUT that has methods
next_frame() and total_frames(). Disables file input
and SuperSloMo frame interpolation and the DVS
timestamp resolution is set by the times returned by
next_frame() method. SYNTHETIC_INPUT.next_frame()
should return a tuple (frame, time) with frame having
the correct resolution (see DVS model arguments) which
is array[y][x] with pixel [0][0] at upper left corner
and pixel values 0-255. The time is a float in
seconds. SYNTHETIC_INPUT must be resolvable from the
classpath. SYNTHETIC_INPUT is the module name without
.py suffix. See example moving_dot.py.
Output: DVS video:
--dvs_exposure DVS_EXPOSURE [DVS_EXPOSURE ...]
Mode to finish DVS frame event integration:
duration time: Use fixed accumulation time in seconds, e.g.
--dvs_exposure duration .005;
count n: Count n events per frame,e.g.
--dvs_exposure count 5000;
area_count M N: frame ends when any area of N x N pixels fills with M events, e.g.
-dvs_exposure area_count 500 64
source: each DVS frame is from one source frame (slomo or original, depending on if slomo is used)
--dvs_vid DVS_VID Output DVS events as AVI video at frame_rate. To
suppress, supply empty argument or 'None'.
--dvs_vid_full_scale DVS_VID_FULL_SCALE
Set full scale event count histogram count for DVS
videos to be this many ON or OFF events for full white
or black.
--no_preview disable preview in cv2 windows for faster processing.
Output: DVS events:
--ddd_output Save frames, frame timestamp and corresponding event
index in HDF5 format used for DDD17 and DDD20
datasets. Default is False.
--dvs_h5 DVS_H5 Output DVS events as hdf5 event database.
--dvs_aedat2 DVS_AEDAT2
Output DVS events as DAVIS346 camera AEDAT-2.0 event
file for jAER; one file for real and one file for v2e
events. To suppress, supply argument None.
--dvs_text DVS_TEXT Output DVS events as text file with one event per line
[timestamp (float s), x, y, polarity (0,1)].
--label_signal_noise append to the --dvs_text file a column,containing list
of signal and shot noise events. Each row of the CSV
appends a 1 for signal and 0 for noise.** Notes: 1:
requires activating --dvs_text option; 2: requires
disabling --photoreceptor_noise option (because when
noise arises from photoreceptor it is impossible to
tell if event was caused by signal or noise.3: Only
labels shot noise events (because leak noise events
arise from leak and cannot be distinguished from
photoreceptor input).
Center-Surround DVS:
--cs_lambda_pixels CS_LAMBDA_PIXELS
space constant of surround in pixels, None to disable.
This space constant lambda is sqrt(1/gR) where g is
the transverse conductance and R is the lateral
resistance.
--cs_tau_p_ms CS_TAU_P_MS
time constant of photoreceptor center of diffuser in
ms, or 0 to disable for instantaneous surround.
Defined as C/g where C is capacitance and g is the
transverse conductance from photoreceptor to
horizontal cell network. This time isthe time constant
for h cell diffuser network response time to global
input to photoreceptors. If set to zero, then the
simulation of diffuser runs until it converges, i.e.
until the maximum change between timesteps is smaller
than a threshold value
SCIDVS pixel:
--scidvs Simulate proposed SCIDVS pixel with nonlinear
adapatation and high gain
Run with no --input to open file dialog
You can put tennis.mov in the input folder to try it out with the command line below. Or leave out all options and just use the file chooser to select the movie.
From root of v2e, run the following
python v2e.py -i input/tennis.mov --overwrite --timestamp_resolution=.003 --auto_timestamp_resolution=False --dvs_exposure duration 0.005 --output_folder=output/tennis --overwrite --pos_thres=.15 --neg_thres=.15 --sigma_thres=0.03 --dvs_aedat2 tennis.aedat --output_width=346 --output_height=260 --stop_time=3 --cutoff_hz=15
Run the command above, and the following files will be created in a folder called output/tennis.
dvs-video.avi
dvs-video-frame_times.txt
tennis.aedat
v2e-args.txt
video_orig.avi
video_slomo.avi
- dvs-video.avi: DVS video (with playback rate 30Hz) but with frame rate (DVS timestamp resolution) set by source video frame rate times slowdown_factor.
- dvs-video-frame_times.txt: The times of the DVS frames. Useful when --dvs_exposure count or --dvs_exposure area-count methods are used.
- tennis.aedat: AEDAT-2.0 file for playback and algorithm experiments in jAER (use the AEChip Davis346Blue to play this file.)
- v2e-args.txt: All the parameters and logging output from the run.
- video_orig.avi: input video, but converted to luma and resized to output (width,height) and with repeated frames to allow comparison to slomo.avi.
- video_slomo.avi: slow motion video (with playback rate 30Hz) but slowed down by slowdown_factor.
The v2e site shows these videos.
See the scripts folder for various samples of shell and cmd scripts to run v2e from terminal.
There are also samples in the scripts folder of python modules to generate synthetic input to v2e, e.g. particles.py
You can specify particles as the class that generates input frames to generate DVS events from using the command line option
v2e --synthetic_input scripts.particles ...You synthetic input class should subclass base_synthetic_class.py. You should override the constructor and the next_frame() method.
- See base_synthetic_input.py for more information.
- You can pass command line arguments into your class; see particles.py for example.
The DVS ON and OFF threshold nominal values are set by pos_thres and neg_thres. The pixel to pixel variation is set by sigma_thres. The pixel cutoff frequency in Hz is set by cutoff_hz. The leak event rate is set by leak_rate_hz.
The -dvs_params argument sets reasonable DVS model parameters for high and low light conditions.
See our technical paper for futher information about these parameters.
The output DVS timestamps will be quantized to some value depending on options chosen.
- --disable_slomo will disable slomo interpolation and the DVS events will have exactly the times of the input video, perhaps modified by --input_slowmotion_factor
- --timestamp_resolution=X will upsample as needed to obtain this desired timestamp resolution X in seconds. If auto_timestamp_resolution is set, then timestamp_resolution will still set the minimum timestamp resolution, i.e. if automatic timestamping would result in 5ms timestamps but timestamp_resolution is 1ms, then 1ms will still be the timestamp resolution.
- --auto_timestamp_resolution will upsample in each --batch_size frames using the computed optical flow to limit motion per frame to at most 1 pixel. In this case, turning on --slomo_stats_plot will generate a plot like the following, which came from a driving video where the car sped up during part of the video:
This plot shows the actual timestamps of the interpolated frames (in orange) and the frame intervals for each batch of frames (in blue).
v2e includes an intensity-dependent 1st-order lowpass filtering of light intensity; see the paper for details. If you set a nonzero --cutofffreq_hz, then it is important that the sample rate be high enough to allow the IIR lowpass filters to update properly, i.e. the time constant tau of the lowpass filters must be at least 3 times larger than the frame interval. Check the console output for warnings about undersampling for lowpass filtering.
There are several different 'frame rates' in v2e. On opening the input video, v2e reads the frame rate of the video and assumes the video is shot in real time, except that you can specify a --input_slowmotion_factor slowdown_factor if the video is already a slow-motion video. The desired DVS timestamp resolution is combined with the source frame rate to compute the slow-motion upsampling factor. The output DVS AVI video is then generated using a --dvs-exposure method.
- --avi_frame_rate: Just sets the frame rate for playback of output AVI files
- --dvs-exposure: See next section
- --input_slowmotion_factor: Specifies by what factor the input video is slowed down.
Anytime a source (or upsampled source) video generates more than 1 event per frame, these events need to be distributed over the time between frames. v2e arbitrarily stacks them as shown in the example below, resulting in pyramids of events and periodic overall bursts of events at each frame. I.e. v2e first computes the maximum number of events by any pixel, then it subdivides the interframe interval by this number, then it puts all pixels with 1 event at the frame, then pixels with 2 events have thier events placed at the first sub-interval and so on. The reduce this effect, use a smaller timestamp resolution.
The DVS allows arbritrary frame rates. v2e provides 3 methods to 'expose' DVS video frames, which are selected by the --dvs_exposure argument:
- Constant-Duration: --dvs_exposure duration T: - Each frame has constant duration T.
- Constant-Count: --dvs_exposure_count N: - each frame has the same number N of DVS events, as first described in Delbruck, Tobi. 2008. “Frame-Free Dynamic Digital Vision.” In Proceedings of Intl. Symp. on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, 1:21–26. Tokyo, Japan: Tokyo. https://drive.google.com/open?id=0BzvXOhBHjRheTS1rSVlZN0l2MDg..
- Area-Event: --dvs_exposure area_event N M: - frames are accumulated until any block of MxM pixels fills up with N events, as first described in Liu, Min, and T. Delbruck. 2018. “Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors.” In Proceedings of British Machine Vision Conference (BMVC 2018). Newcastle upon Tyne, UK: Proceedings of BMVC 2018. https://doi.org/10.5167/uzh-168589.
- Source: --dvs_exposure source: DVS timestamps are based on source video timestamps. Extra events per frame are spaced between frames accoruding to the maximum number of events for that frame.
- Constant-Duration is like normal video, i.e. sampled at regular, ideally Nyquist rate.
- Constant-Count frames have the same number of pixel brightness change events per frame. But if the scene is very textured (i.e. busy) then frame can get very brief, while parts of the input with only a small object moving can have very long frames.
- Area-Event compensates for this effect to some extent by concluding exposure when any block of pixels fills with a constant count.
v2e can convert recordings from DDD20 and the original DDD17 which are the first public end-to-end training datasets of automotive driving using a DAVIS event + frame camera. It lets you compare the real DVS data with the conversion. This dataset is maintained by the Sensors Research Group of Institute of Neuroinformatics.
For your convenience, we offer via google drive one recording from DDD20 (our newer DDD dataset) of 800s of Los Angeles street driving. The file is aug04/rec1501902136.hdf5 [link] in Google Drive for you to try it with v2e (Warning: 2GB 7z compressed, 5.4 GB uncompressed).
mkdir -p input
mv rec1501902136.hdf5 ./inputNOTE you must run these scripts with the -m package.script.py notation, not by directly pointing to the .py file.
ddd_h5_extract_data.py extracts the DDD recording DVS events to jAER .aedat and video .avi files.
ddd_extract_data.py -h
usage: ddd_extract_data.py [-h] [-i INPUT] -o OUTPUT_FOLDER [--start_time START_TIME] [--stop_time STOP_TIME] [--rotate180] [--overwrite] [--dvs240] [--dvs346]
Extract data from DDD recording
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
input video file; leave empty for file chooser dialog (default: None)
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
folder to store outputs (default: None)
--start_time START_TIME
start at this time in seconds in video (default: None)
--stop_time STOP_TIME
stop point of video stream (default: None)
--rotate180 rotate output 180 deg (default: False)
--overwrite overwrites files in existing folder (checks existance of non-empty output_folder) (default: False)
--dvs240 Set size for 240x180 DVS (Davis240). Use for DDD17 recording. (default: False)
--dvs346 Set size for 346x260 DVS (Davis346). Use for DDD20 recording. (default: False)
Run with no --input to open file dialog
You can then synthesize events from the DDD APS video .avi file that is produced and compare with the real events extracted to the AEDAT-2.0 file that is also extracted.
DAVIS cameras like the one that recorded DDD17 and DDD20 are often used with jAER (although DDD recordings were made with custom python wrapper around caer). v2e will output a jAER-compatible .aedat file in AEDAT-2.0 format, which jAER uses.
To work with existing jAER DAVIS .aedat, you can export the DAVIS APS frames using the jAER EventFilter DavisFrameAVIWriter; see the jAER user guide, in particular, the section about using DavisFrameAVIWriter. In DavisFrameAVIWriter, don't forget to set the frameRate to the actual frame rate of the DAVIS frames (which you can see at the top of the jAER display). This will make the conversion have approximately the correct DVS event timing. (jAER can drop APS frames if there are too many DVS events, so don't count on this.) Once you have the AVI from jAER, you can generate v2e events from it with v2e.py and see how they compare with the original DVS events in jAER, by playing the exported v2e .aedat file in jAER.
An example of this conversion and comparison is on the v2e home page.
See the v2e home page.