├── LICENSE ├── README.md ├── base ├── __init__.py └── base_model.py ├── data └── .gitignore ├── image_reconstructor.py ├── model ├── __init__.py ├── model.py ├── submodules.py └── unet.py ├── options ├── __init__.py └── inference_options.py ├── pretrained └── .gitignore ├── run_reconstruction.py ├── scripts ├── embed_reconstructed_images_in_rosbag.py ├── extract_events_from_rosbag.py ├── image_folder_to_rosbag.py └── resample_reconstructions.py └── utils ├── __init__.py ├── event_readers.py ├── inference_utils.py ├── loading_utils.py ├── path_utils.py ├── timers.py └── util.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # High Speed and High Dynamic Range Video with an Event Camera 2 | 3 | [![High Speed and High Dynamic Range Video with an Event Camera](http://rpg.ifi.uzh.ch/E2VID/video_thumbnail.png)](https://youtu.be/eomALySSGVU) 4 | 5 | This is the code for the paper **High Speed and High Dynamic Range Video with an Event Camera** by [Henri Rebecq](http://henri.rebecq.fr), Rene Ranftl, [Vladlen Koltun](http://vladlen.info/) and [Davide Scaramuzza](http://rpg.ifi.uzh.ch/people_scaramuzza.html): 6 | 7 | You can find a pdf of the paper [here](http://rpg.ifi.uzh.ch/docs/TPAMI19_Rebecq.pdf). 8 | If you use any of this code, please cite the following publications: 9 | 10 | ```bibtex 11 | @Article{Rebecq19pami, 12 | author = {Henri Rebecq and Ren{\'{e}} Ranftl and Vladlen Koltun and Davide Scaramuzza}, 13 | title = {High Speed and High Dynamic Range Video with an Event Camera}, 14 | journal = {{IEEE} Trans. Pattern Anal. Mach. Intell. (T-PAMI)}, 15 | url = {http://rpg.ifi.uzh.ch/docs/TPAMI19_Rebecq.pdf}, 16 | year = 2019 17 | } 18 | ``` 19 | 20 | 21 | ```bibtex 22 | @Article{Rebecq19cvpr, 23 | author = {Henri Rebecq and Ren{\'{e}} Ranftl and Vladlen Koltun and Davide Scaramuzza}, 24 | title = {Events-to-Video: Bringing Modern Computer Vision to Event Cameras}, 25 | journal = {{IEEE} Conf. Comput. Vis. Pattern Recog. (CVPR)}, 26 | year = 2019 27 | } 28 | ``` 29 | 30 | ## Install 31 | 32 | Dependencies: 33 | 34 | - [PyTorch](https://pytorch.org/get-started/locally/) >= 1.0 35 | - [NumPy](https://www.numpy.org/) 36 | - [Pandas](https://pandas.pydata.org/) 37 | - [OpenCV](https://opencv.org/) 38 | 39 | ### Install with Anaconda 40 | 41 | The installation requires [Anaconda3](https://www.anaconda.com/distribution/). 42 | You can create a new Anaconda environment with the required dependencies as follows (make sure to adapt the CUDA toolkit version according to your setup): 43 | 44 | ```bash 45 | conda create -n E2VID 46 | conda activate E2VID 47 | conda install pytorch torchvision cudatoolkit=10.0 -c pytorch 48 | conda install pandas 49 | conda install -c conda-forge opencv 50 | ``` 51 | 52 | ## Run 53 | 54 | - Download the pretrained model: 55 | 56 | ```bash 57 | wget "http://rpg.ifi.uzh.ch/data/E2VID/models/E2VID_lightweight.pth.tar" -O pretrained/E2VID_lightweight.pth.tar 58 | ``` 59 | 60 | - Download an example file with event data: 61 | 62 | ```bash 63 | wget "http://rpg.ifi.uzh.ch/data/E2VID/datasets/ECD_IJRR17/dynamic_6dof.zip" -O data/dynamic_6dof.zip 64 | ``` 65 | 66 | Before running the reconstruction, make sure the conda environment is sourced: 67 | 68 | ```bash 69 | conda activate E2VID 70 | ``` 71 | 72 | - Run reconstruction: 73 | 74 | ```bash 75 | python run_reconstruction.py \ 76 | -c pretrained/E2VID_lightweight.pth.tar \ 77 | -i data/dynamic_6dof.zip \ 78 | --auto_hdr \ 79 | --display \ 80 | --show_events 81 | ``` 82 | 83 | ## Parameters 84 | 85 | Below is a description of the most important parameters: 86 | 87 | #### Main parameters 88 | 89 | - ``--window_size`` / ``-N`` (default: None) Number of events per window. This is the parameter that has the most influence of the image reconstruction quality. If set to None, this number will be automatically computed based on the sensor size, as N = width * height * num_events_per_pixel (see description of that parameter below). Ignored if `--fixed_duration` is set. 90 | - ``--fixed_duration`` (default: False) If True, will use windows of events with a fixed duration (i.e. a fixed output frame rate). 91 | - ``--window_duration`` / ``-T`` (default: 33 ms) Duration of each event window, in milliseconds. The value of this parameter has strong influence on the image reconstruction quality. Its value may need to be adapted to the dynamics of the scene. Ignored if `--fixed_duration` is not set. 92 | - ``--Imin`` (default: 0.0), `--Imax` (default: 1.0): linear tone mapping is performed by normalizing the output image as follows: `I = (I - Imin) / (Imax - Imin)`. If `--auto_hdr` is set to True, `--Imin` and `--Imax` will be automatically computed as the min (resp. max) intensity values. 93 | - ``--auto_hdr`` (default: False) Automatically compute `--Imin` and `--Imax`. Disabled when `--color` is set. 94 | - ``--color`` (default: False): if True, will perform color reconstruction as described in the paper. Only use this with a [color event camera](http://rpg.ifi.uzh.ch/CED.html) such as the Color DAVIS346. 95 | 96 | #### Output parameters 97 | 98 | - ``--output_folder``: path of the output folder. If not set, the image reconstructions will not be saved to disk. 99 | - ``--dataset_name``: name of the output folder directory (default: 'reconstruction'). 100 | 101 | #### Display parameters 102 | 103 | - ``--display`` (default: False): display the video reconstruction in real-time in an OpenCV window. 104 | - ``--show_events`` (default: False): show the input events side-by-side with the reconstruction. If ``--output_folder`` is set, the previews will also be saved to disk in ``/path/to/output/folder/events``. 105 | 106 | #### Additional parameters 107 | 108 | - ``--num_events_per_pixel`` (default: 0.35): Parameter used to automatically estimate the window size based on the sensor size. The value of 0.35 was chosen to correspond to ~ 15,000 events on a 240x180 sensor such as the DAVIS240C. 109 | - ``--no-normalize`` (default: False): Disable event tensor normalization: this will improve speed a bit, but might degrade the image quality a bit. 110 | - ``--no-recurrent`` (default: False): Disable the recurrent connection (i.e. do not maintain a state). For experimenting only, the results will be flickering a lot. 111 | - ``--hot_pixels_file`` (default: None): Path to a file specifying the locations of hot pixels (such a file can be obtained with [this tool](https://github.com/cedric-scheerlinck/dvs_tools/tree/master/dvs_hot_pixel_filter) for example). These pixels will be ignored (i.e. zeroed out in the event tensors). 112 | 113 | ## Example datasets 114 | 115 | We provide a list of example (publicly available) event datasets to get started with E2VID. 116 | 117 | - [High Speed (gun shooting!) and HDR Dataset](http://rpg.ifi.uzh.ch/E2VID.html) 118 | - [Event Camera Dataset](http://rpg.ifi.uzh.ch/data/E2VID/datasets/ECD_IJRR17/) 119 | - [Bardow et al., CVPR'16](http://rpg.ifi.uzh.ch/data/E2VID/datasets/SOFIE_CVPR16/) 120 | - [Scherlinck et al., ACCV'18](http://rpg.ifi.uzh.ch/data/E2VID/datasets/HF_ACCV18/) 121 | - [Color event sequences from the CED dataset Scheerlinck et al., CVPR'18](http://rpg.ifi.uzh.ch/data/E2VID/datasets/CED_CVPRW19/) 122 | 123 | ## Working with ROS 124 | 125 | Because PyTorch recommends Python 3 and ROS is only compatible with Python2, it is not straightforward to have the PyTorch reconstruction code and ROS code running in the same environment. 126 | To make things easy, the reconstruction code we provide has no dependency on ROS, and simply read events from a text file or ZIP file. 127 | We provide convenience functions to convert ROS bags (a popular format for event datasets) into event text files. 128 | In addition, we also provide scripts to convert a folder containing image reconstructions back to a rosbag (or to append image reconstructions to an existing rosbag). 129 | 130 | **Note**: it is **not** necessary to have a sourced conda environment to run the following scripts. However, [ROS](https://www.ros.org/) needs to be installed and sourced. 131 | 132 | ### rosbag -> events.txt 133 | 134 | To extract the events from a rosbag to a zip file containing the event data: 135 | 136 | ```bash 137 | python scripts/extract_events_from_rosbag.py /path/to/rosbag.bag \ 138 | --output_folder=/path/to/output/folder \ 139 | --event_topic=/dvs/events 140 | ``` 141 | 142 | ### image reconstruction folder -> rosbag 143 | 144 | ```bash 145 | python scripts/image_folder_to_rosbag.py \ 146 | --datasets dynamic_6dof \ 147 | --image_folder /path/to/image/folder \ 148 | --output_folder /path/to/output_folder \ 149 | --image_topic /dvs/image_reconstructed 150 | ``` 151 | 152 | ### Append image_reconstruction_folder to an existing rosbag 153 | 154 | ```bash 155 | cd scripts 156 | python embed_reconstructed_images_in_rosbag.py \ 157 | --rosbag_folder /path/to/rosbag/folder \ 158 | --datasets dynamic_6dof \ 159 | --image_folder /path/to/image/folder \ 160 | --output_folder /path/to/output_folder \ 161 | --image_topic /dvs/image_reconstructed 162 | ``` 163 | 164 | ### Generating a video reconstruction (with a fixed framerate) 165 | 166 | It can be convenient to convert an image folder to a video with a fixed framerate (for example for use in a video editing tool). 167 | You can proceed as follows: 168 | 169 | ```bash 170 | export FRAMERATE=30 171 | python resample_reconstructions.py -i /path/to/input_folder -o /tmp/resampled -r $FRAMERATE 172 | ffmpeg -framerate $FRAMERATE -i /tmp/resampled/frame_%010d.png video_"$FRAMERATE"Hz.mp4 173 | ``` 174 | 175 | ## Acknowledgements 176 | 177 | This code borrows from the following open source projects, whom we would like to thank: 178 | 179 | - [pytorch-template](https://github.com/victoresque/pytorch-template) 180 | -------------------------------------------------------------------------------- /base/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_model import * 2 | -------------------------------------------------------------------------------- /base/base_model.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import torch.nn as nn 3 | import numpy as np 4 | 5 | 6 | class BaseModel(nn.Module): 7 | """ 8 | Base class for all models 9 | """ 10 | def __init__(self, config): 11 | super(BaseModel, self).__init__() 12 | self.config = config 13 | self.logger = logging.getLogger(self.__class__.__name__) 14 | 15 | def forward(self, *input): 16 | """ 17 | Forward pass logic 18 | 19 | :return: Model output 20 | """ 21 | raise NotImplementedError 22 | 23 | def summary(self): 24 | """ 25 | Model summary 26 | """ 27 | model_parameters = filter(lambda p: p.requires_grad, self.parameters()) 28 | params = sum([np.prod(p.size()) for p in model_parameters]) 29 | self.logger.info('Trainable parameters: {}'.format(params)) 30 | self.logger.info(self) 31 | -------------------------------------------------------------------------------- /data/.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedric-scheerlinck/rpg_e2vid/d0a7c005f460f2422f2a4bf605f70820ea7a1e5f/data/.gitignore -------------------------------------------------------------------------------- /image_reconstructor.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import cv2 3 | import numpy as np 4 | from model.model import * 5 | from utils.inference_utils import CropParameters, EventPreprocessor, IntensityRescaler, ImageFilter, ImageDisplay, ImageWriter, UnsharpMaskFilter 6 | from utils.inference_utils import upsample_color_image, merge_channels_into_color_image # for color reconstruction 7 | from utils.util import robust_min, robust_max 8 | from utils.timers import CudaTimer, cuda_timers 9 | from os.path import join 10 | from collections import deque 11 | import torch.nn.functional as F 12 | 13 | 14 | class ImageReconstructor: 15 | def __init__(self, model, height, width, num_bins, options): 16 | 17 | self.model = model 18 | self.use_gpu = options.use_gpu 19 | self.device = torch.device('cuda:0') if self.use_gpu else torch.device('cpu') 20 | self.height = height 21 | self.width = width 22 | self.num_bins = num_bins 23 | 24 | self.initialize(self.height, self.width, options) 25 | 26 | def initialize(self, height, width, options): 27 | print('== Image reconstruction == ') 28 | print('Image size: {}x{}'.format(self.height, self.width)) 29 | 30 | self.no_recurrent = options.no_recurrent 31 | if self.no_recurrent: 32 | print('!!Recurrent connection disabled!!') 33 | 34 | self.perform_color_reconstruction = options.color # whether to perform color reconstruction (only use this with the DAVIS346color) 35 | if self.perform_color_reconstruction: 36 | if options.auto_hdr: 37 | print('!!Warning: disabling auto HDR for color reconstruction!!') 38 | options.auto_hdr = False # disable auto_hdr for color reconstruction (otherwise, each channel will be normalized independently) 39 | 40 | self.crop = CropParameters(self.width, self.height, self.model.num_encoders) 41 | 42 | self.last_states_for_each_channel = {'grayscale': None} 43 | 44 | if self.perform_color_reconstruction: 45 | self.crop_halfres = CropParameters(int(width / 2), int(height / 2), 46 | self.model.num_encoders) 47 | for channel in ['R', 'G', 'B', 'W']: 48 | self.last_states_for_each_channel[channel] = None 49 | 50 | self.event_preprocessor = EventPreprocessor(options) 51 | self.intensity_rescaler = IntensityRescaler(options) 52 | self.image_filter = ImageFilter(options) 53 | self.unsharp_mask_filter = UnsharpMaskFilter(options, device=self.device) 54 | self.image_writer = ImageWriter(options) 55 | self.image_display = ImageDisplay(options) 56 | 57 | def update_reconstruction(self, event_tensor, event_tensor_id, stamp=None): 58 | with torch.no_grad(): 59 | 60 | with CudaTimer('Reconstruction'): 61 | 62 | with CudaTimer('NumPy (CPU) -> Tensor (GPU)'): 63 | events = event_tensor.unsqueeze(dim=0) 64 | events = events.to(self.device) 65 | 66 | events = self.event_preprocessor(events) 67 | 68 | # Resize tensor to [1 x C x crop_size x crop_size] by applying zero padding 69 | events_for_each_channel = {'grayscale': self.crop.pad(events)} 70 | reconstructions_for_each_channel = {} 71 | if self.perform_color_reconstruction: 72 | events_for_each_channel['R'] = self.crop_halfres.pad(events[:, :, 0::2, 0::2]) 73 | events_for_each_channel['G'] = self.crop_halfres.pad(events[:, :, 0::2, 1::2]) 74 | events_for_each_channel['W'] = self.crop_halfres.pad(events[:, :, 1::2, 0::2]) 75 | events_for_each_channel['B'] = self.crop_halfres.pad(events[:, :, 1::2, 1::2]) 76 | 77 | # Reconstruct new intensity image for each channel (grayscale + RGBW if color reconstruction is enabled) 78 | for channel in events_for_each_channel.keys(): 79 | with CudaTimer('Inference'): 80 | new_predicted_frame, states = self.model(events_for_each_channel[channel], 81 | self.last_states_for_each_channel[channel]) 82 | 83 | if self.no_recurrent: 84 | self.last_states_for_each_channel[channel] = None 85 | else: 86 | self.last_states_for_each_channel[channel] = states 87 | 88 | # Output reconstructed image 89 | crop = self.crop if channel == 'grayscale' else self.crop_halfres 90 | 91 | # Unsharp mask (on GPU) 92 | new_predicted_frame = self.unsharp_mask_filter(new_predicted_frame) 93 | 94 | # Intensity rescaler (on GPU) 95 | new_predicted_frame = self.intensity_rescaler(new_predicted_frame) 96 | 97 | with CudaTimer('Tensor (GPU) -> NumPy (CPU)'): 98 | reconstructions_for_each_channel[channel] = new_predicted_frame[0, 0, crop.iy0:crop.iy1, 99 | crop.ix0:crop.ix1].cpu().numpy() 100 | 101 | if self.perform_color_reconstruction: 102 | out = merge_channels_into_color_image(reconstructions_for_each_channel) 103 | else: 104 | out = reconstructions_for_each_channel['grayscale'] 105 | 106 | # Post-processing, e.g bilateral filter (on CPU) 107 | out = self.image_filter(out) 108 | 109 | self.image_writer(out, event_tensor_id, stamp, events=events) 110 | self.image_display(out, events) 111 | -------------------------------------------------------------------------------- /model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedric-scheerlinck/rpg_e2vid/d0a7c005f460f2422f2a4bf605f70820ea7a1e5f/model/__init__.py -------------------------------------------------------------------------------- /model/model.py: -------------------------------------------------------------------------------- 1 | from base import BaseModel 2 | import torch.nn as nn 3 | import torch 4 | from model.unet import UNet, UNetRecurrent 5 | from os.path import join 6 | from model.submodules import ConvLSTM, ResidualBlock, ConvLayer, UpsampleConvLayer, TransposedConvLayer 7 | 8 | 9 | class BaseE2VID(BaseModel): 10 | def __init__(self, config): 11 | super().__init__(config) 12 | 13 | assert('num_bins' in config) 14 | self.num_bins = int(config['num_bins']) # number of bins in the voxel grid event tensor 15 | 16 | try: 17 | self.skip_type = str(config['skip_type']) 18 | except KeyError: 19 | self.skip_type = 'sum' 20 | 21 | try: 22 | self.num_encoders = int(config['num_encoders']) 23 | except KeyError: 24 | self.num_encoders = 4 25 | 26 | try: 27 | self.base_num_channels = int(config['base_num_channels']) 28 | except KeyError: 29 | self.base_num_channels = 32 30 | 31 | try: 32 | self.num_residual_blocks = int(config['num_residual_blocks']) 33 | except KeyError: 34 | self.num_residual_blocks = 2 35 | 36 | try: 37 | self.norm = str(config['norm']) 38 | except KeyError: 39 | self.norm = None 40 | 41 | try: 42 | self.use_upsample_conv = bool(config['use_upsample_conv']) 43 | except KeyError: 44 | self.use_upsample_conv = True 45 | 46 | 47 | class E2VID(BaseE2VID): 48 | def __init__(self, config): 49 | super(E2VID, self).__init__(config) 50 | 51 | self.unet = UNet(num_input_channels=self.num_bins, 52 | num_output_channels=1, 53 | skip_type=self.skip_type, 54 | activation='sigmoid', 55 | num_encoders=self.num_encoders, 56 | base_num_channels=self.base_num_channels, 57 | num_residual_blocks=self.num_residual_blocks, 58 | norm=self.norm, 59 | use_upsample_conv=self.use_upsample_conv) 60 | 61 | def forward(self, event_tensor, prev_states=None): 62 | """ 63 | :param event_tensor: N x num_bins x H x W 64 | :return: a predicted image of size N x 1 x H x W, taking values in [0,1]. 65 | """ 66 | return self.unet.forward(event_tensor), None 67 | 68 | 69 | class E2VIDRecurrent(BaseE2VID): 70 | """ 71 | Recurrent, UNet-like architecture where each encoder is followed by a ConvLSTM or ConvGRU. 72 | """ 73 | 74 | def __init__(self, config): 75 | super(E2VIDRecurrent, self).__init__(config) 76 | 77 | try: 78 | self.recurrent_block_type = str(config['recurrent_block_type']) 79 | except KeyError: 80 | self.recurrent_block_type = 'convlstm' # or 'convgru' 81 | 82 | self.unetrecurrent = UNetRecurrent(num_input_channels=self.num_bins, 83 | num_output_channels=1, 84 | skip_type=self.skip_type, 85 | recurrent_block_type=self.recurrent_block_type, 86 | activation='sigmoid', 87 | num_encoders=self.num_encoders, 88 | base_num_channels=self.base_num_channels, 89 | num_residual_blocks=self.num_residual_blocks, 90 | norm=self.norm, 91 | use_upsample_conv=self.use_upsample_conv) 92 | 93 | def forward(self, event_tensor, prev_states): 94 | """ 95 | :param event_tensor: N x num_bins x H x W 96 | :param prev_states: previous ConvLSTM state for each encoder module 97 | :return: reconstructed image, taking values in [0,1]. 98 | """ 99 | img_pred, states = self.unetrecurrent.forward(event_tensor, prev_states) 100 | return img_pred, states 101 | -------------------------------------------------------------------------------- /model/submodules.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as f 4 | from torch.nn import init 5 | 6 | 7 | class ConvLayer(nn.Module): 8 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, activation='relu', norm=None): 9 | super(ConvLayer, self).__init__() 10 | 11 | bias = False if norm == 'BN' else True 12 | self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias) 13 | if activation is not None: 14 | self.activation = getattr(torch, activation, 'relu') 15 | else: 16 | self.activation = None 17 | 18 | self.norm = norm 19 | if norm == 'BN': 20 | self.norm_layer = nn.BatchNorm2d(out_channels) 21 | elif norm == 'IN': 22 | self.norm_layer = nn.InstanceNorm2d(out_channels, track_running_stats=True) 23 | 24 | def forward(self, x): 25 | out = self.conv2d(x) 26 | 27 | if self.norm in ['BN', 'IN']: 28 | out = self.norm_layer(out) 29 | 30 | if self.activation is not None: 31 | out = self.activation(out) 32 | 33 | return out 34 | 35 | 36 | class TransposedConvLayer(nn.Module): 37 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, activation='relu', norm=None): 38 | super(TransposedConvLayer, self).__init__() 39 | 40 | bias = False if norm == 'BN' else True 41 | self.transposed_conv2d = nn.ConvTranspose2d( 42 | in_channels, out_channels, kernel_size, stride=2, padding=padding, output_padding=1, bias=bias) 43 | 44 | if activation is not None: 45 | self.activation = getattr(torch, activation, 'relu') 46 | else: 47 | self.activation = None 48 | 49 | self.norm = norm 50 | if norm == 'BN': 51 | self.norm_layer = nn.BatchNorm2d(out_channels) 52 | elif norm == 'IN': 53 | self.norm_layer = nn.InstanceNorm2d(out_channels, track_running_stats=True) 54 | 55 | def forward(self, x): 56 | out = self.transposed_conv2d(x) 57 | 58 | if self.norm in ['BN', 'IN']: 59 | out = self.norm_layer(out) 60 | 61 | if self.activation is not None: 62 | out = self.activation(out) 63 | 64 | return out 65 | 66 | 67 | class UpsampleConvLayer(nn.Module): 68 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, activation='relu', norm=None): 69 | super(UpsampleConvLayer, self).__init__() 70 | 71 | bias = False if norm == 'BN' else True 72 | self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias) 73 | 74 | if activation is not None: 75 | self.activation = getattr(torch, activation, 'relu') 76 | else: 77 | self.activation = None 78 | 79 | self.norm = norm 80 | if norm == 'BN': 81 | self.norm_layer = nn.BatchNorm2d(out_channels) 82 | elif norm == 'IN': 83 | self.norm_layer = nn.InstanceNorm2d(out_channels, track_running_stats=True) 84 | 85 | def forward(self, x): 86 | x_upsampled = f.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) 87 | out = self.conv2d(x_upsampled) 88 | 89 | if self.norm in ['BN', 'IN']: 90 | out = self.norm_layer(out) 91 | 92 | if self.activation is not None: 93 | out = self.activation(out) 94 | 95 | return out 96 | 97 | 98 | class RecurrentConvLayer(nn.Module): 99 | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, 100 | recurrent_block_type='convlstm', activation='relu', norm=None): 101 | super(RecurrentConvLayer, self).__init__() 102 | 103 | assert(recurrent_block_type in ['convlstm', 'convgru']) 104 | self.recurrent_block_type = recurrent_block_type 105 | if self.recurrent_block_type == 'convlstm': 106 | RecurrentBlock = ConvLSTM 107 | else: 108 | RecurrentBlock = ConvGRU 109 | self.conv = ConvLayer(in_channels, out_channels, kernel_size, stride, padding, activation, norm) 110 | self.recurrent_block = RecurrentBlock(input_size=out_channels, hidden_size=out_channels, kernel_size=3) 111 | 112 | def forward(self, x, prev_state): 113 | x = self.conv(x) 114 | state = self.recurrent_block(x, prev_state) 115 | x = state[0] if self.recurrent_block_type == 'convlstm' else state 116 | return x, state 117 | 118 | 119 | class DownsampleRecurrentConvLayer(nn.Module): 120 | def __init__(self, in_channels, out_channels, kernel_size=3, recurrent_block_type='convlstm', padding=0, activation='relu'): 121 | super(DownsampleRecurrentConvLayer, self).__init__() 122 | 123 | self.activation = getattr(torch, activation, 'relu') 124 | 125 | assert(recurrent_block_type in ['convlstm', 'convgru']) 126 | self.recurrent_block_type = recurrent_block_type 127 | if self.recurrent_block_type == 'convlstm': 128 | RecurrentBlock = ConvLSTM 129 | else: 130 | RecurrentBlock = ConvGRU 131 | self.recurrent_block = RecurrentBlock(input_size=in_channels, hidden_size=out_channels, kernel_size=kernel_size) 132 | 133 | def forward(self, x, prev_state): 134 | state = self.recurrent_block(x, prev_state) 135 | x = state[0] if self.recurrent_block_type == 'convlstm' else state 136 | x = f.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False) 137 | return self.activation(x), state 138 | 139 | 140 | # Residual block 141 | class ResidualBlock(nn.Module): 142 | def __init__(self, in_channels, out_channels, stride=1, downsample=None, norm=None): 143 | super(ResidualBlock, self).__init__() 144 | bias = False if norm == 'BN' else True 145 | self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=bias) 146 | self.norm = norm 147 | if norm == 'BN': 148 | self.bn1 = nn.BatchNorm2d(out_channels) 149 | self.bn2 = nn.BatchNorm2d(out_channels) 150 | elif norm == 'IN': 151 | self.bn1 = nn.InstanceNorm2d(out_channels) 152 | self.bn2 = nn.InstanceNorm2d(out_channels) 153 | 154 | self.relu = nn.ReLU(inplace=True) 155 | self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias) 156 | self.downsample = downsample 157 | 158 | def forward(self, x): 159 | residual = x 160 | out = self.conv1(x) 161 | if self.norm in ['BN', 'IN']: 162 | out = self.bn1(out) 163 | out = self.relu(out) 164 | out = self.conv2(out) 165 | if self.norm in ['BN', 'IN']: 166 | out = self.bn2(out) 167 | 168 | if self.downsample: 169 | residual = self.downsample(x) 170 | 171 | out += residual 172 | out = self.relu(out) 173 | return out 174 | 175 | 176 | class ConvLSTM(nn.Module): 177 | """Adapted from: https://github.com/Atcold/pytorch-CortexNet/blob/master/model/ConvLSTMCell.py """ 178 | 179 | def __init__(self, input_size, hidden_size, kernel_size): 180 | super(ConvLSTM, self).__init__() 181 | 182 | self.input_size = input_size 183 | self.hidden_size = hidden_size 184 | pad = kernel_size // 2 185 | 186 | # cache a tensor filled with zeros to avoid reallocating memory at each inference step if --no-recurrent is enabled 187 | self.zero_tensors = {} 188 | 189 | self.Gates = nn.Conv2d(input_size + hidden_size, 4 * hidden_size, kernel_size, padding=pad) 190 | 191 | def forward(self, input_, prev_state=None): 192 | 193 | # get batch and spatial sizes 194 | batch_size = input_.data.size()[0] 195 | spatial_size = input_.data.size()[2:] 196 | 197 | # generate empty prev_state, if None is provided 198 | if prev_state is None: 199 | 200 | # create the zero tensor if it has not been created already 201 | state_size = tuple([batch_size, self.hidden_size] + list(spatial_size)) 202 | if state_size not in self.zero_tensors: 203 | # allocate a tensor with size `spatial_size`, filled with zero (if it has not been allocated already) 204 | self.zero_tensors[state_size] = ( 205 | torch.zeros(state_size).to(input_.device), 206 | torch.zeros(state_size).to(input_.device) 207 | ) 208 | 209 | prev_state = self.zero_tensors[tuple(state_size)] 210 | 211 | prev_hidden, prev_cell = prev_state 212 | 213 | # data size is [batch, channel, height, width] 214 | stacked_inputs = torch.cat((input_, prev_hidden), 1) 215 | gates = self.Gates(stacked_inputs) 216 | 217 | # chunk across channel dimension 218 | in_gate, remember_gate, out_gate, cell_gate = gates.chunk(4, 1) 219 | 220 | # apply sigmoid non linearity 221 | in_gate = torch.sigmoid(in_gate) 222 | remember_gate = torch.sigmoid(remember_gate) 223 | out_gate = torch.sigmoid(out_gate) 224 | 225 | # apply tanh non linearity 226 | cell_gate = torch.tanh(cell_gate) 227 | 228 | # compute current cell and hidden state 229 | cell = (remember_gate * prev_cell) + (in_gate * cell_gate) 230 | hidden = out_gate * torch.tanh(cell) 231 | 232 | return hidden, cell 233 | 234 | 235 | class ConvGRU(nn.Module): 236 | """ 237 | Generate a convolutional GRU cell 238 | Adapted from: https://github.com/jacobkimmel/pytorch_convgru/blob/master/convgru.py 239 | """ 240 | 241 | def __init__(self, input_size, hidden_size, kernel_size): 242 | super().__init__() 243 | padding = kernel_size // 2 244 | self.input_size = input_size 245 | self.hidden_size = hidden_size 246 | self.reset_gate = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size, padding=padding) 247 | self.update_gate = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size, padding=padding) 248 | self.out_gate = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size, padding=padding) 249 | 250 | init.orthogonal_(self.reset_gate.weight) 251 | init.orthogonal_(self.update_gate.weight) 252 | init.orthogonal_(self.out_gate.weight) 253 | init.constant_(self.reset_gate.bias, 0.) 254 | init.constant_(self.update_gate.bias, 0.) 255 | init.constant_(self.out_gate.bias, 0.) 256 | 257 | def forward(self, input_, prev_state): 258 | 259 | # get batch and spatial sizes 260 | batch_size = input_.data.size()[0] 261 | spatial_size = input_.data.size()[2:] 262 | 263 | # generate empty prev_state, if None is provided 264 | if prev_state is None: 265 | state_size = [batch_size, self.hidden_size] + list(spatial_size) 266 | prev_state = torch.zeros(state_size).to(input_.device) 267 | 268 | # data size is [batch, channel, height, width] 269 | stacked_inputs = torch.cat([input_, prev_state], dim=1) 270 | update = torch.sigmoid(self.update_gate(stacked_inputs)) 271 | reset = torch.sigmoid(self.reset_gate(stacked_inputs)) 272 | out_inputs = torch.tanh(self.out_gate(torch.cat([input_, prev_state * reset], dim=1))) 273 | new_state = prev_state * (1 - update) + out_inputs * update 274 | 275 | return new_state 276 | -------------------------------------------------------------------------------- /model/unet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as f 4 | from torch.nn import init 5 | from .submodules import ConvLayer, UpsampleConvLayer, TransposedConvLayer, RecurrentConvLayer, ResidualBlock, ConvLSTM, ConvGRU 6 | 7 | 8 | def skip_concat(x1, x2): 9 | return torch.cat([x1, x2], dim=1) 10 | 11 | 12 | def skip_sum(x1, x2): 13 | return x1 + x2 14 | 15 | 16 | class BaseUNet(nn.Module): 17 | def __init__(self, num_input_channels, num_output_channels=1, skip_type='sum', activation='sigmoid', 18 | num_encoders=4, base_num_channels=32, num_residual_blocks=2, norm=None, use_upsample_conv=True): 19 | super(BaseUNet, self).__init__() 20 | 21 | self.num_input_channels = num_input_channels 22 | self.num_output_channels = num_output_channels 23 | self.skip_type = skip_type 24 | self.apply_skip_connection = skip_sum if self.skip_type == 'sum' else skip_concat 25 | self.activation = activation 26 | self.norm = norm 27 | 28 | if use_upsample_conv: 29 | print('Using UpsampleConvLayer (slow, but no checkerboard artefacts)') 30 | self.UpsampleLayer = UpsampleConvLayer 31 | else: 32 | print('Using TransposedConvLayer (fast, with checkerboard artefacts)') 33 | self.UpsampleLayer = TransposedConvLayer 34 | 35 | self.num_encoders = num_encoders 36 | self.base_num_channels = base_num_channels 37 | self.num_residual_blocks = num_residual_blocks 38 | self.max_num_channels = self.base_num_channels * pow(2, self.num_encoders) 39 | 40 | assert(self.num_input_channels > 0) 41 | assert(self.num_output_channels > 0) 42 | 43 | self.encoder_input_sizes = [] 44 | for i in range(self.num_encoders): 45 | self.encoder_input_sizes.append(self.base_num_channels * pow(2, i)) 46 | 47 | self.encoder_output_sizes = [self.base_num_channels * pow(2, i + 1) for i in range(self.num_encoders)] 48 | 49 | self.activation = getattr(torch, self.activation, 'sigmoid') 50 | 51 | def build_resblocks(self): 52 | self.resblocks = nn.ModuleList() 53 | for i in range(self.num_residual_blocks): 54 | self.resblocks.append(ResidualBlock(self.max_num_channels, self.max_num_channels, norm=self.norm)) 55 | 56 | def build_decoders(self): 57 | decoder_input_sizes = list(reversed([self.base_num_channels * pow(2, i + 1) for i in range(self.num_encoders)])) 58 | 59 | self.decoders = nn.ModuleList() 60 | for input_size in decoder_input_sizes: 61 | self.decoders.append(self.UpsampleLayer(input_size if self.skip_type == 'sum' else 2 * input_size, 62 | input_size // 2, 63 | kernel_size=5, padding=2, norm=self.norm)) 64 | 65 | def build_prediction_layer(self): 66 | self.pred = ConvLayer(self.base_num_channels if self.skip_type == 'sum' else 2 * self.base_num_channels, 67 | self.num_output_channels, 1, activation=None, norm=self.norm) 68 | 69 | 70 | class UNet(BaseUNet): 71 | def __init__(self, num_input_channels, num_output_channels=1, skip_type='sum', activation='sigmoid', 72 | num_encoders=4, base_num_channels=32, num_residual_blocks=2, norm=None, use_upsample_conv=True): 73 | super(UNet, self).__init__(num_input_channels, num_output_channels, skip_type, activation, 74 | num_encoders, base_num_channels, num_residual_blocks, norm, use_upsample_conv) 75 | 76 | self.head = ConvLayer(self.num_input_channels, self.base_num_channels, 77 | kernel_size=5, stride=1, padding=2) # N x C x H x W -> N x 32 x H x W 78 | 79 | self.encoders = nn.ModuleList() 80 | for input_size, output_size in zip(self.encoder_input_sizes, self.encoder_output_sizes): 81 | self.encoders.append(ConvLayer(input_size, output_size, kernel_size=5, 82 | stride=2, padding=2, norm=self.norm)) 83 | 84 | self.build_resblocks() 85 | self.build_decoders() 86 | self.build_prediction_layer() 87 | 88 | def forward(self, x): 89 | """ 90 | :param x: N x num_input_channels x H x W 91 | :return: N x num_output_channels x H x W 92 | """ 93 | 94 | # head 95 | x = self.head(x) 96 | head = x 97 | 98 | # encoder 99 | blocks = [] 100 | for i, encoder in enumerate(self.encoders): 101 | x = encoder(x) 102 | blocks.append(x) 103 | 104 | # residual blocks 105 | for resblock in self.resblocks: 106 | x = resblock(x) 107 | 108 | # decoder 109 | for i, decoder in enumerate(self.decoders): 110 | x = decoder(self.apply_skip_connection(x, blocks[self.num_encoders - i - 1])) 111 | 112 | img = self.activation(self.pred(self.apply_skip_connection(x, head))) 113 | 114 | return img 115 | 116 | 117 | class UNetRecurrent(BaseUNet): 118 | """ 119 | Recurrent UNet architecture where every encoder is followed by a recurrent convolutional block, 120 | such as a ConvLSTM or a ConvGRU. 121 | Symmetric, skip connections on every encoding layer. 122 | """ 123 | 124 | def __init__(self, num_input_channels, num_output_channels=1, skip_type='sum', 125 | recurrent_block_type='convlstm', activation='sigmoid', num_encoders=4, base_num_channels=32, 126 | num_residual_blocks=2, norm=None, use_upsample_conv=True): 127 | super(UNetRecurrent, self).__init__(num_input_channels, num_output_channels, skip_type, activation, 128 | num_encoders, base_num_channels, num_residual_blocks, norm, 129 | use_upsample_conv) 130 | 131 | self.head = ConvLayer(self.num_input_channels, self.base_num_channels, 132 | kernel_size=5, stride=1, padding=2) # N x C x H x W -> N x 32 x H x W 133 | 134 | self.encoders = nn.ModuleList() 135 | for input_size, output_size in zip(self.encoder_input_sizes, self.encoder_output_sizes): 136 | self.encoders.append(RecurrentConvLayer(input_size, output_size, 137 | kernel_size=5, stride=2, padding=2, 138 | recurrent_block_type=recurrent_block_type, 139 | norm=self.norm)) 140 | 141 | self.build_resblocks() 142 | self.build_decoders() 143 | self.build_prediction_layer() 144 | 145 | def forward(self, x, prev_states): 146 | """ 147 | :param x: N x num_input_channels x H x W 148 | :param prev_states: previous LSTM states for every encoder layer 149 | :return: N x num_output_channels x H x W 150 | """ 151 | 152 | # head 153 | x = self.head(x) 154 | head = x 155 | 156 | if prev_states is None: 157 | prev_states = [None] * self.num_encoders 158 | 159 | # encoder 160 | blocks = [] 161 | states = [] 162 | for i, encoder in enumerate(self.encoders): 163 | x, state = encoder(x, prev_states[i]) 164 | blocks.append(x) 165 | states.append(state) 166 | 167 | # residual blocks 168 | for resblock in self.resblocks: 169 | x = resblock(x) 170 | 171 | # decoder 172 | for i, decoder in enumerate(self.decoders): 173 | x = decoder(self.apply_skip_connection(x, blocks[self.num_encoders - i - 1])) 174 | 175 | # tail 176 | img = self.activation(self.pred(self.apply_skip_connection(x, head))) 177 | 178 | return img, states 179 | -------------------------------------------------------------------------------- /options/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedric-scheerlinck/rpg_e2vid/d0a7c005f460f2422f2a4bf605f70820ea7a1e5f/options/__init__.py -------------------------------------------------------------------------------- /options/inference_options.py: -------------------------------------------------------------------------------- 1 | def set_inference_options(parser): 2 | 3 | parser.add_argument('-o', '--output_folder', default=None, type=str) # if None, will not write the images to disk 4 | parser.add_argument('--dataset_name', default='reconstruction', type=str) 5 | 6 | parser.add_argument('--use_gpu', dest='use_gpu', action='store_true') 7 | parser.set_defaults(use_gpu=True) 8 | 9 | """ Display """ 10 | parser.add_argument('--display', dest='display', action='store_true') 11 | parser.set_defaults(display=False) 12 | 13 | parser.add_argument('--show_events', dest='show_events', action='store_true') 14 | parser.set_defaults(show_events=False) 15 | 16 | parser.add_argument('--event_display_mode', default='red-blue', type=str, 17 | help="Event display mode ('red-blue' or 'grayscale')") 18 | 19 | parser.add_argument('--num_bins_to_show', default=-1, type=int, 20 | help="Number of bins of the voxel grid to show when displaying events (-1 means show all the bins).") 21 | 22 | parser.add_argument('--display_border_crop', default=0, type=int, 23 | help="Remove the outer border of size display_border_crop before displaying image.") 24 | 25 | parser.add_argument('--display_wait_time', default=1, type=int, 26 | help="Time to wait after each call to cv2.imshow, in milliseconds (default: 1)") 27 | 28 | """ Post-processing / filtering """ 29 | 30 | # (optional) path to a text file containing the locations of hot pixels to ignore 31 | parser.add_argument('--hot_pixels_file', default=None, type=str) 32 | 33 | # (optional) unsharp mask 34 | parser.add_argument('--unsharp_mask_amount', default=0.3, type=float) 35 | parser.add_argument('--unsharp_mask_sigma', default=1.0, type=float) 36 | 37 | # (optional) bilateral filter 38 | parser.add_argument('--bilateral_filter_sigma', default=0.0, type=float) 39 | 40 | # (optional) flip the event tensors vertically 41 | parser.add_argument('--flip', dest='flip', action='store_true') 42 | parser.set_defaults(flip=False) 43 | 44 | """ Tone mapping (i.e. rescaling of the image intensities)""" 45 | parser.add_argument('--Imin', default=0.0, type=float, 46 | help="Min intensity for intensity rescaling (linear tone mapping).") 47 | parser.add_argument('--Imax', default=1.0, type=float, 48 | help="Max intensity value for intensity rescaling (linear tone mapping).") 49 | parser.add_argument('--auto_hdr', dest='auto_hdr', action='store_true', 50 | help="If True, will compute Imin and Imax automatically.") 51 | parser.set_defaults(auto_hdr=False) 52 | parser.add_argument('--auto_hdr_median_filter_size', default=10, type=int, 53 | help="Size of the median filter window used to smooth temporally Imin and Imax") 54 | 55 | """ Perform color reconstruction? (only use this flag with the DAVIS346color) """ 56 | parser.add_argument('--color', dest='color', action='store_true') 57 | parser.set_defaults(color=False) 58 | 59 | """ Advanced parameters """ 60 | # disable normalization of input event tensors (saves a bit of time, but may produce slightly worse results) 61 | parser.add_argument('--no-normalize', dest='no_normalize', action='store_true') 62 | parser.set_defaults(no_normalize=False) 63 | 64 | # disable recurrent connection (will severely degrade the results; for testing purposes only) 65 | parser.add_argument('--no-recurrent', dest='no_recurrent', action='store_true') 66 | parser.set_defaults(no_recurrent=False) 67 | -------------------------------------------------------------------------------- /pretrained/.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedric-scheerlinck/rpg_e2vid/d0a7c005f460f2422f2a4bf605f70820ea7a1e5f/pretrained/.gitignore -------------------------------------------------------------------------------- /run_reconstruction.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from utils.loading_utils import load_model, get_device 3 | import numpy as np 4 | import argparse 5 | import pandas as pd 6 | from utils.event_readers import FixedSizeEventReader, FixedDurationEventReader 7 | from utils.inference_utils import events_to_voxel_grid, events_to_voxel_grid_pytorch 8 | from utils.timers import Timer 9 | import time 10 | from image_reconstructor import ImageReconstructor 11 | from options.inference_options import set_inference_options 12 | 13 | 14 | if __name__ == "__main__": 15 | 16 | parser = argparse.ArgumentParser( 17 | description='Evaluating a trained network') 18 | parser.add_argument('-c', '--path_to_model', required=True, type=str, 19 | help='path to model weights') 20 | parser.add_argument('-i', '--input_file', required=True, type=str) 21 | parser.add_argument('--fixed_duration', dest='fixed_duration', action='store_true') 22 | parser.set_defaults(fixed_duration=False) 23 | parser.add_argument('-N', '--window_size', default=None, type=int, 24 | help="Size of each event window, in number of events. Ignored if --fixed_duration=True") 25 | parser.add_argument('-T', '--window_duration', default=33.33, type=float, 26 | help="Duration of each event window, in milliseconds. Ignored if --fixed_duration=False") 27 | parser.add_argument('--num_events_per_pixel', default=0.35, type=float, 28 | help='in case N (window size) is not specified, it will be \ 29 | automatically computed as N = width * height * num_events_per_pixel') 30 | parser.add_argument('--skipevents', default=0, type=int) 31 | parser.add_argument('--suboffset', default=0, type=int) 32 | parser.add_argument('--compute_voxel_grid_on_cpu', dest='compute_voxel_grid_on_cpu', action='store_true') 33 | parser.set_defaults(compute_voxel_grid_on_cpu=False) 34 | 35 | set_inference_options(parser) 36 | 37 | args = parser.parse_args() 38 | 39 | # Read sensor size from the first first line of the event file 40 | path_to_events = args.input_file 41 | 42 | header = pd.read_csv(path_to_events, delim_whitespace=True, header=None, names=['width', 'height'], 43 | dtype={'width': np.int, 'height': np.int}, 44 | nrows=1) 45 | width, height = header.values[0] 46 | print('Sensor size: {} x {}'.format(width, height)) 47 | 48 | # Load model 49 | model = load_model(args.path_to_model) 50 | device = get_device(args.use_gpu) 51 | 52 | model = model.to(device) 53 | model.eval() 54 | 55 | reconstructor = ImageReconstructor(model, height, width, model.num_bins, args) 56 | 57 | """ Read chunks of events using Pandas """ 58 | 59 | # Loop through the events and reconstruct images 60 | N = args.window_size 61 | if not args.fixed_duration: 62 | if N is None: 63 | N = int(width * height * args.num_events_per_pixel) 64 | print('Will use {} events per tensor (automatically estimated with num_events_per_pixel={:0.2f}).'.format( 65 | N, args.num_events_per_pixel)) 66 | else: 67 | print('Will use {} events per tensor (user-specified)'.format(N)) 68 | mean_num_events_per_pixel = float(N) / float(width * height) 69 | if mean_num_events_per_pixel < 0.1: 70 | print('!!Warning!! the number of events used ({}) seems to be low compared to the sensor size. \ 71 | The reconstruction results might be suboptimal.'.format(N)) 72 | elif mean_num_events_per_pixel > 1.5: 73 | print('!!Warning!! the number of events used ({}) seems to be high compared to the sensor size. \ 74 | The reconstruction results might be suboptimal.'.format(N)) 75 | 76 | initial_offset = args.skipevents 77 | sub_offset = args.suboffset 78 | start_index = initial_offset + sub_offset 79 | 80 | if args.compute_voxel_grid_on_cpu: 81 | print('Will compute voxel grid on CPU.') 82 | 83 | if args.fixed_duration: 84 | event_window_iterator = FixedDurationEventReader(path_to_events, 85 | duration_ms=args.window_duration, 86 | start_index=start_index) 87 | else: 88 | event_window_iterator = FixedSizeEventReader(path_to_events, num_events=N, start_index=start_index) 89 | 90 | with Timer('Processing entire dataset'): 91 | for event_window in event_window_iterator: 92 | 93 | last_timestamp = event_window[-1, 0] 94 | 95 | with Timer('Building event tensor'): 96 | if args.compute_voxel_grid_on_cpu: 97 | event_tensor = events_to_voxel_grid(event_window, 98 | num_bins=model.num_bins, 99 | width=width, 100 | height=height) 101 | event_tensor = torch.from_numpy(event_tensor) 102 | else: 103 | event_tensor = events_to_voxel_grid_pytorch(event_window, 104 | num_bins=model.num_bins, 105 | width=width, 106 | height=height, 107 | device=device) 108 | 109 | num_events_in_window = event_window.shape[0] 110 | reconstructor.update_reconstruction(event_tensor, start_index + num_events_in_window, last_timestamp) 111 | 112 | start_index += num_events_in_window 113 | -------------------------------------------------------------------------------- /scripts/embed_reconstructed_images_in_rosbag.py: -------------------------------------------------------------------------------- 1 | import rosbag 2 | import cv2 3 | import numpy as np 4 | from cv_bridge import CvBridge, CvBridgeError 5 | from os.path import join 6 | import rospy 7 | import argparse 8 | import shutil 9 | import os 10 | import glob 11 | 12 | 13 | if __name__ == "__main__": 14 | 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument("--datasets", default='dynamic_6dof', type=lambda s: [str(item) for item in s.split(',')], 17 | help="Delimited list of datasets") 18 | parser.add_argument("--rosbag_folder", required=True, 19 | type=str, help="Path to the base folder containing the rosbags") 20 | parser.add_argument("--image_folder", required=True, 21 | type=str, help="Path to the base folder containing the image reconstructions") 22 | parser.add_argument("--output_folder", default='.', 23 | type=str, help="Path to the output folder") 24 | parser.add_argument("--image_topic", required=True, type=str, 25 | help="Name of the topic which will contain the reconstructed images") 26 | parser.add_argument('--overwrite', dest='overwrite', action='store_true', 27 | help="Whether to overwrite existing rosbags (default: false)") 28 | parser.set_defaults(feature=False) 29 | 30 | args = parser.parse_args() 31 | 32 | print('Datasets to process: {}'.format(args.datasets)) 33 | 34 | for dataset in args.datasets: 35 | original_bag_filename = join( 36 | args.rosbag_folder, '{}.bag'.format(dataset)) 37 | reconstructed_images_folder = join( 38 | args.image_folder, dataset) 39 | 40 | bridge = CvBridge() 41 | continue_processing = True 42 | 43 | if not os.path.exists(args.output_folder): 44 | os.makedirs(args.output_folder) 45 | 46 | input_bag_filename = join( 47 | args.output_folder, '{}.bag'.format(dataset)) 48 | 49 | if os.path.exists(input_bag_filename): 50 | print('Detected existing rosbag: {}.'.format(input_bag_filename)) 51 | if args.overwrite: 52 | print('Will overwrite the existing bag.') 53 | else: 54 | print('Will not overwrite. If you want to overwrite, use option --overwrite') 55 | continue_processing = False 56 | 57 | if continue_processing: 58 | # Copy the source bag to the output folder 59 | print('Copying bag: {} to {}'.format( 60 | original_bag_filename, input_bag_filename)) 61 | shutil.copyfile(original_bag_filename, input_bag_filename) 62 | 63 | # Now append the reconstructed images to the copied rosbag 64 | stamps = np.loadtxt( 65 | join(reconstructed_images_folder, 'timestamps.txt')) 66 | if len(stamps.shape) == 2: 67 | stamps = stamps[:, 1] 68 | 69 | # list all images in the folder 70 | images = [f for f in glob.glob(join(reconstructed_images_folder, "*.png"))] 71 | images = sorted(images) 72 | print('Found {} images'.format(len(images))) 73 | 74 | with rosbag.Bag(input_bag_filename, 'a') as outbag: 75 | 76 | for i, image_path in enumerate(images): 77 | 78 | stamp = stamps[i] 79 | img = cv2.imread(join(reconstructed_images_folder, image_path), 0) 80 | try: 81 | img_msg = bridge.cv2_to_imgmsg(img, encoding='mono8') 82 | stamp_ros = rospy.Time(stamp) 83 | print(img.shape, stamp_ros) 84 | img_msg.header.stamp = stamp_ros 85 | img_msg.header.seq = i 86 | outbag.write(args.image_topic, img_msg, 87 | img_msg.header.stamp) 88 | 89 | except CvBridgeError, e: 90 | print e 91 | -------------------------------------------------------------------------------- /scripts/extract_events_from_rosbag.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import argparse 4 | import rosbag 5 | import rospy 6 | import os 7 | import zipfile 8 | import shutil 9 | import sys 10 | from os.path import basename 11 | 12 | 13 | # Function borrowed from: https://stackoverflow.com/a/3041990 14 | def query_yes_no(question, default="yes"): 15 | """Ask a yes/no question via raw_input() and return their answer. 16 | 17 | "question" is a string that is presented to the user. 18 | "default" is the presumed answer if the user just hits . 19 | It must be "yes" (the default), "no" or None (meaning 20 | an answer is required of the user). 21 | 22 | The "answer" return value is True for "yes" or False for "no". 23 | """ 24 | valid = {"yes": True, "y": True, "ye": True, 25 | "no": False, "n": False} 26 | if default is None: 27 | prompt = " [y/n] " 28 | elif default == "yes": 29 | prompt = " [Y/n] " 30 | elif default == "no": 31 | prompt = " [y/N] " 32 | else: 33 | raise ValueError("invalid default answer: '%s'" % default) 34 | 35 | while True: 36 | sys.stdout.write(question + prompt) 37 | choice = raw_input().lower() 38 | if default is not None and choice == '': 39 | return valid[default] 40 | elif choice in valid: 41 | return valid[choice] 42 | else: 43 | sys.stdout.write("Please respond with 'yes' or 'no' " 44 | "(or 'y' or 'n').\n") 45 | 46 | 47 | def timestamp_str(ts): 48 | t = ts.secs + ts.nsecs / float(1e9) 49 | return '{:.12f}'.format(t) 50 | 51 | 52 | if __name__ == "__main__": 53 | 54 | # arguments 55 | parser = argparse.ArgumentParser() 56 | parser.add_argument("bag", help="ROS bag file to extract") 57 | parser.add_argument("--output_folder", default="extracted_data", help="Folder where to extract the data") 58 | parser.add_argument("--event_topic", default="/dvs/events", help="Event topic") 59 | parser.add_argument('--no-zip', dest='no_zip', action='store_true') 60 | parser.set_defaults(no_zip=False) 61 | args = parser.parse_args() 62 | 63 | print('Data will be extracted in folder: {}'.format(args.output_folder)) 64 | 65 | if not os.path.exists(args.output_folder): 66 | os.makedirs(args.output_folder) 67 | 68 | width, height = None, None 69 | event_sum = 0 70 | event_msg_sum = 0 71 | num_msgs_between_logs = 25 72 | output_name = os.path.basename(args.bag).split('.')[0] # /path/to/mybag.bag -> mybag 73 | path_to_events_file = os.path.join(args.output_folder, '{}.txt'.format(output_name)) 74 | 75 | with open(path_to_events_file, 'w') as events_file: 76 | 77 | with rosbag.Bag(args.bag, 'r') as bag: 78 | 79 | # Look for the topics that are available and save the total number of messages for each topic (useful for the progress bar) 80 | total_num_event_msgs = 0 81 | topics = bag.get_type_and_topic_info().topics 82 | for topic_name, topic_info in topics.iteritems(): 83 | if topic_name == args.event_topic: 84 | total_num_event_msgs = topic_info.message_count 85 | print('Found events topic: {} with {} messages'.format(topic_name, topic_info.message_count)) 86 | 87 | # Extract events to text file 88 | for topic, msg, t in bag.read_messages(): 89 | if topic == args.event_topic: 90 | 91 | if width is None: 92 | width = msg.width 93 | height = msg.height 94 | print('Found sensor size: {} x {}'.format(width, height)) 95 | events_file.write("{} {}\n".format(width, height)) 96 | 97 | if event_msg_sum % num_msgs_between_logs == 0 or event_msg_sum >= total_num_event_msgs - 1: 98 | print('Event messages: {} / {}'.format(event_msg_sum + 1, total_num_event_msgs)) 99 | event_msg_sum += 1 100 | 101 | for e in msg.events: 102 | events_file.write(timestamp_str(e.ts) + " ") 103 | events_file.write(str(e.x) + " ") 104 | events_file.write(str(e.y) + " ") 105 | events_file.write(("1" if e.polarity else "0") + "\n") 106 | event_sum += 1 107 | 108 | # statistics 109 | print('All events extracted!') 110 | print('Events:', event_sum) 111 | 112 | # Zip text file 113 | if not args.no_zip: 114 | print('Compressing text file...') 115 | path_to_events_zipfile = os.path.join(args.output_folder, '{}.zip'.format(output_name)) 116 | with zipfile.ZipFile(path_to_events_zipfile, 'w') as zip_file: 117 | zip_file.write(path_to_events_file, basename(path_to_events_file), compress_type=zipfile.ZIP_DEFLATED) 118 | print('Finished!') 119 | 120 | # Remove events.txt 121 | if query_yes_no('Remove text file {}?'.format(path_to_events_file)): 122 | if os.path.exists(path_to_events_file): 123 | os.remove(path_to_events_file) 124 | print('Removed {}.'.format(path_to_events_file)) 125 | 126 | print('Done extracting events!') 127 | -------------------------------------------------------------------------------- /scripts/image_folder_to_rosbag.py: -------------------------------------------------------------------------------- 1 | import rosbag 2 | import cv2 3 | import numpy as np 4 | from cv_bridge import CvBridge, CvBridgeError 5 | from os.path import join 6 | import rospy 7 | import argparse 8 | import shutil 9 | import os 10 | import glob 11 | 12 | if __name__ == "__main__": 13 | 14 | parser = argparse.ArgumentParser() 15 | parser.add_argument("--datasets", default='dynamic_6dof', type=lambda s: [str(item) for item in s.split(',')], 16 | help="Delimited list of datasets") 17 | parser.add_argument("--image_folder", required=True, 18 | type=str, help="Path to the base folder containing the image reconstructions") 19 | parser.add_argument("--output_folder", default='.', 20 | type=str, help="Path to the output folder") 21 | parser.add_argument("--image_topic", required=True, type=str, 22 | help="Name of the topic which will contain the reconstructed images") 23 | parser.add_argument('--overwrite', dest='overwrite', action='store_true', 24 | help="Whether to overwrite existing rosbags (default: false)") 25 | parser.set_defaults(feature=False) 26 | 27 | args = parser.parse_args() 28 | 29 | print('Datasets to process: {}'.format(args.datasets)) 30 | 31 | for dataset in args.datasets: 32 | reconstructed_images_folder = join( 33 | args.image_folder, dataset) 34 | 35 | bridge = CvBridge() 36 | continue_processing = True 37 | 38 | if not os.path.exists(args.output_folder): 39 | os.makedirs(args.output_folder) 40 | 41 | output_bag_filename = join( 42 | args.output_folder, '{}.bag'.format(dataset)) 43 | 44 | if continue_processing: 45 | # Write the images to a rosbag 46 | stamps = np.loadtxt( 47 | join(reconstructed_images_folder, 'timestamps.txt')) 48 | if len(stamps.shape) == 2: 49 | stamps = stamps[:, 1] 50 | 51 | # list all images in the folder 52 | images = [f for f in glob.glob(join(reconstructed_images_folder, "*.png"))] 53 | images = sorted(images) 54 | print('Found {} images'.format(len(images))) 55 | 56 | with rosbag.Bag(output_bag_filename, 'w') as outbag: 57 | 58 | for i, image_path in enumerate(images): 59 | 60 | stamp = stamps[i] 61 | img = cv2.imread(join(reconstructed_images_folder, image_path), 0) 62 | 63 | try: 64 | img_msg = bridge.cv2_to_imgmsg(img, encoding='mono8') 65 | stamp_ros = rospy.Time(stamp) 66 | print(img.shape, stamp_ros) 67 | img_msg.header.stamp = stamp_ros 68 | img_msg.header.seq = i 69 | outbag.write(args.image_topic, img_msg, 70 | img_msg.header.stamp) 71 | 72 | except CvBridgeError, e: 73 | print e 74 | -------------------------------------------------------------------------------- /scripts/resample_reconstructions.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from matplotlib import pyplot as plt 3 | import argparse 4 | import glob 5 | from os.path import basename, join, exists 6 | from os import makedirs 7 | import math 8 | import shutil 9 | 10 | 11 | def find_nearest(array, value): 12 | idx = np.searchsorted(array, value, side="left") 13 | if idx > 0 and (idx == len(array) or math.fabs(value - array[idx - 1]) < math.fabs(value - array[idx])): 14 | return (idx - 1), array[idx - 1] 15 | else: 16 | return idx, array[idx] 17 | 18 | 19 | if __name__ == "__main__": 20 | 21 | parser = argparse.ArgumentParser( 22 | description='Pick images in a folder containing timestamped images so that the resulting video has a fixed frame rate (used defined)') 23 | 24 | parser.add_argument('-i', '--input_folder', required=True, type=str) 25 | parser.add_argument('-o', '--output_folder', required=True, type=str) 26 | parser.add_argument('-r', '--framerate', default=1000.0, type=float) 27 | args = parser.parse_args() 28 | 29 | output_folder = args.output_folder 30 | if not exists(output_folder): 31 | makedirs(output_folder) 32 | 33 | # list all images in the folder 34 | images = [f for f in glob.glob(join(args.input_folder, "*.png"), recursive=False)] 35 | images = sorted(images) 36 | print('Found {} images'.format(len(images))) 37 | 38 | # read timestamps (and check there is one timestamp per image...) 39 | stamps = np.loadtxt(join(args.input_folder, 'timestamps.txt')) 40 | stamps = np.sort(stamps) 41 | np.savetxt(join(args.input_folder, 'timestamps_sorted.txt'), stamps) 42 | assert(len(stamps) == len(images)) 43 | 44 | # find the closest image to each element in [t0, t0 + dt, t0 + 2 * dt, t0 + 3 * dt, ...] 45 | # where t0 = stamps[0] 46 | dt = 1.0 / args.framerate 47 | t = stamps[0] 48 | img_index, _ = find_nearest(stamps, t) 49 | 50 | i = 0 51 | while t <= stamps[-1]: 52 | t += dt 53 | img_index, _ = find_nearest(stamps, t) 54 | path_to_img = images[img_index] 55 | shutil.copyfile(path_to_img, join(output_folder, 'frame_{:010d}.png'.format(i))) 56 | i += 1 57 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cedric-scheerlinck/rpg_e2vid/d0a7c005f460f2422f2a4bf605f70820ea7a1e5f/utils/__init__.py -------------------------------------------------------------------------------- /utils/event_readers.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import zipfile 3 | from os.path import splitext 4 | import numpy as np 5 | from .timers import Timer 6 | 7 | 8 | class FixedSizeEventReader: 9 | """ 10 | Reads events from a '.txt' or '.zip' file, and packages the events into 11 | non-overlapping event windows, each containing a fixed number of events. 12 | """ 13 | 14 | def __init__(self, path_to_event_file, num_events=10000, start_index=0): 15 | print('Will use fixed size event windows with {} events'.format(num_events)) 16 | print('Output frame rate: variable') 17 | self.iterator = pd.read_csv(path_to_event_file, delim_whitespace=True, header=None, 18 | names=['t', 'x', 'y', 'pol'], 19 | dtype={'t': np.float64, 'x': np.int16, 'y': np.int16, 'pol': np.int16}, 20 | engine='c', 21 | skiprows=start_index + 1, chunksize=num_events, nrows=None, memory_map=True) 22 | 23 | def __iter__(self): 24 | return self 25 | 26 | def __next__(self): 27 | with Timer('Reading event window from file'): 28 | event_window = self.iterator.__next__().values 29 | return event_window 30 | 31 | 32 | class FixedDurationEventReader: 33 | """ 34 | Reads events from a '.txt' or '.zip' file, and packages the events into 35 | non-overlapping event windows, each of a fixed duration. 36 | 37 | **Note**: This reader is much slower than the FixedSizeEventReader. 38 | The reason is that the latter can use Pandas' very efficient cunk-based reading scheme implemented in C. 39 | """ 40 | 41 | def __init__(self, path_to_event_file, duration_ms=50.0, start_index=0): 42 | print('Will use fixed duration event windows of size {:.2f} ms'.format(duration_ms)) 43 | print('Output frame rate: {:.1f} Hz'.format(1000.0 / duration_ms)) 44 | file_extension = splitext(path_to_event_file)[1] 45 | assert(file_extension in ['.txt', '.zip']) 46 | self.is_zip_file = (file_extension == '.zip') 47 | 48 | if self.is_zip_file: # '.zip' 49 | self.zip_file = zipfile.ZipFile(path_to_event_file) 50 | files_in_archive = self.zip_file.namelist() 51 | assert(len(files_in_archive) == 1) # make sure there is only one text file in the archive 52 | self.event_file = self.zip_file.open(files_in_archive[0], 'r') 53 | else: 54 | self.event_file = open(path_to_event_file, 'r') 55 | 56 | # ignore header + the first start_index lines 57 | for i in range(1 + start_index): 58 | self.event_file.readline() 59 | 60 | self.last_stamp = None 61 | self.duration_s = duration_ms / 1000.0 62 | 63 | def __iter__(self): 64 | return self 65 | 66 | def __del__(self): 67 | if self.is_zip_file: 68 | self.zip_file.close() 69 | 70 | self.event_file.close() 71 | 72 | def __next__(self): 73 | with Timer('Reading event window from file'): 74 | event_list = [] 75 | for line in self.event_file: 76 | if self.is_zip_file: 77 | line = line.decode("utf-8") 78 | t, x, y, pol = line.split(' ') 79 | t, x, y, pol = float(t), int(x), int(y), int(pol) 80 | event_list.append([t, x, y, pol]) 81 | if self.last_stamp is None: 82 | self.last_stamp = t 83 | if t > self.last_stamp + self.duration_s: 84 | self.last_stamp = t 85 | event_window = np.array(event_list) 86 | return event_window 87 | 88 | raise StopIteration 89 | -------------------------------------------------------------------------------- /utils/inference_utils.py: -------------------------------------------------------------------------------- 1 | from .util import robust_min, robust_max 2 | from .path_utils import ensure_dir 3 | from .timers import Timer, CudaTimer 4 | from .loading_utils import get_device 5 | from os.path import join 6 | from math import ceil, floor 7 | from torch.nn import ReflectionPad2d 8 | import numpy as np 9 | import torch 10 | import cv2 11 | from collections import deque 12 | import atexit 13 | import scipy.stats as st 14 | import torch.nn.functional as F 15 | from math import sqrt 16 | 17 | 18 | def make_event_preview(events, mode='red-blue', num_bins_to_show=-1): 19 | # events: [1 x C x H x W] event tensor 20 | # mode: 'red-blue' or 'grayscale' 21 | # num_bins_to_show: number of bins of the voxel grid to show. -1 means show all bins. 22 | assert(mode in ['red-blue', 'grayscale']) 23 | if num_bins_to_show < 0: 24 | sum_events = torch.sum(events[0, :, :, :], dim=0).detach().cpu().numpy() 25 | else: 26 | sum_events = torch.sum(events[0, -num_bins_to_show:, :, :], dim=0).detach().cpu().numpy() 27 | 28 | if mode == 'red-blue': 29 | # Red-blue mode 30 | # positive events: blue, negative events: red 31 | event_preview = np.zeros((sum_events.shape[0], sum_events.shape[1], 3), dtype=np.uint8) 32 | b = event_preview[:, :, 0] 33 | r = event_preview[:, :, 2] 34 | b[sum_events > 0] = 255 35 | r[sum_events < 0] = 255 36 | else: 37 | # Grayscale mode 38 | # normalize event image to [0, 255] for display 39 | m, M = -10.0, 10.0 40 | event_preview = np.clip((255.0 * (sum_events - m) / (M - m)).astype(np.uint8), 0, 255) 41 | 42 | return event_preview 43 | 44 | 45 | def gkern(kernlen=5, nsig=1.0): 46 | """Returns a 2D Gaussian kernel array.""" 47 | """https://stackoverflow.com/a/29731818""" 48 | interval = (2 * nsig + 1.) / (kernlen) 49 | x = np.linspace(-nsig - interval / 2., nsig + interval / 2., kernlen + 1) 50 | kern1d = np.diff(st.norm.cdf(x)) 51 | kernel_raw = np.sqrt(np.outer(kern1d, kern1d)) 52 | kernel = kernel_raw / kernel_raw.sum() 53 | return torch.from_numpy(kernel).float() 54 | 55 | 56 | class EventPreprocessor: 57 | """ 58 | Utility class to preprocess event tensors. 59 | Can perform operations such as hot pixel removing, event tensor normalization, 60 | or flipping the event tensor. 61 | """ 62 | 63 | def __init__(self, options): 64 | 65 | print('== Event preprocessing ==') 66 | self.no_normalize = options.no_normalize 67 | if self.no_normalize: 68 | print('!!Will not normalize event tensors!!') 69 | else: 70 | print('Will normalize event tensors.') 71 | 72 | self.hot_pixel_locations = [] 73 | if options.hot_pixels_file: 74 | try: 75 | self.hot_pixel_locations = np.loadtxt(options.hot_pixels_file, delimiter=',').astype(np.int) 76 | print('Will remove {} hot pixels'.format(self.hot_pixel_locations.shape[0])) 77 | except IOError: 78 | print('WARNING: could not load hot pixels file: {}'.format(options.hot_pixels_file)) 79 | 80 | self.flip = options.flip 81 | if self.flip: 82 | print('Will flip event tensors.') 83 | 84 | def __call__(self, events): 85 | 86 | # Remove (i.e. zero out) the hot pixels 87 | for x, y in self.hot_pixel_locations: 88 | events[:, :, y, x] = 0 89 | 90 | # Flip tensor vertically and horizontally 91 | if self.flip: 92 | events = torch.flip(events, dims=[2, 3]) 93 | 94 | # Normalize the event tensor (voxel grid) so that 95 | # the mean and stddev of the nonzero values in the tensor are equal to (0.0, 1.0) 96 | if not self.no_normalize: 97 | with CudaTimer('Normalization'): 98 | nonzero_ev = (events != 0) 99 | num_nonzeros = nonzero_ev.sum() 100 | if num_nonzeros > 0: 101 | # compute mean and stddev of the **nonzero** elements of the event tensor 102 | # we do not use PyTorch's default mean() and std() functions since it's faster 103 | # to compute it by hand than applying those funcs to a masked array 104 | mean = events.sum() / num_nonzeros 105 | stddev = torch.sqrt((events ** 2).sum() / num_nonzeros - mean ** 2) 106 | mask = nonzero_ev.float() 107 | events = mask * (events - mean) / stddev 108 | 109 | return events 110 | 111 | 112 | class IntensityRescaler: 113 | """ 114 | Utility class to rescale image intensities to the range [0, 1], 115 | using (robust) min/max normalization. 116 | Optionally, the min/max bounds can be smoothed over a sliding window to avoid jitter. 117 | """ 118 | 119 | def __init__(self, options): 120 | self.auto_hdr = options.auto_hdr 121 | self.intensity_bounds = deque() 122 | self.auto_hdr_median_filter_size = options.auto_hdr_median_filter_size 123 | self.Imin = options.Imin 124 | self.Imax = options.Imax 125 | 126 | def __call__(self, img): 127 | """ 128 | param img: [1 x 1 x H x W] Tensor taking values in [0, 1] 129 | """ 130 | if self.auto_hdr: 131 | with CudaTimer('Compute Imin/Imax (auto HDR)'): 132 | Imin = torch.min(img).item() 133 | Imax = torch.max(img).item() 134 | 135 | # ensure that the range is at least 0.1 136 | Imin = np.clip(Imin, 0.0, 0.45) 137 | Imax = np.clip(Imax, 0.55, 1.0) 138 | 139 | # adjust image dynamic range (i.e. its contrast) 140 | if len(self.intensity_bounds) > self.auto_hdr_median_filter_size: 141 | self.intensity_bounds.popleft() 142 | 143 | self.intensity_bounds.append((Imin, Imax)) 144 | self.Imin = np.median([rmin for rmin, rmax in self.intensity_bounds]) 145 | self.Imax = np.median([rmax for rmin, rmax in self.intensity_bounds]) 146 | 147 | with CudaTimer('Intensity rescaling'): 148 | img = 255.0 * (img - self.Imin) / (self.Imax - self.Imin) 149 | img.clamp_(0.0, 255.0) 150 | img = img.byte() # convert to 8-bit tensor 151 | 152 | return img 153 | 154 | 155 | class ImageWriter: 156 | """ 157 | Utility class to write images to disk. 158 | Also writes the image timestamps into a text file. 159 | """ 160 | 161 | def __init__(self, options): 162 | 163 | self.output_folder = options.output_folder 164 | self.dataset_name = options.dataset_name 165 | self.save_events = options.show_events 166 | self.event_display_mode = options.event_display_mode 167 | self.num_bins_to_show = options.num_bins_to_show 168 | print('== Image Writer ==') 169 | if self.output_folder: 170 | ensure_dir(self.output_folder) 171 | ensure_dir(join(self.output_folder, self.dataset_name)) 172 | print('Will write images to: {}'.format(join(self.output_folder, self.dataset_name))) 173 | self.timestamps_file = open(join(self.output_folder, self.dataset_name, 'timestamps.txt'), 'a') 174 | 175 | if self.save_events: 176 | self.event_previews_folder = join(self.output_folder, self.dataset_name, 'events') 177 | ensure_dir(self.event_previews_folder) 178 | print('Will write event previews to: {}'.format(self.event_previews_folder)) 179 | 180 | atexit.register(self.__cleanup__) 181 | else: 182 | print('Will not write images to disk.') 183 | 184 | def __call__(self, img, event_tensor_id, stamp=None, events=None): 185 | if not self.output_folder: 186 | return 187 | 188 | if self.save_events and events is not None: 189 | event_preview = make_event_preview(events, mode=self.event_display_mode, 190 | num_bins_to_show=self.num_bins_to_show) 191 | cv2.imwrite(join(self.event_previews_folder, 192 | 'events_{:010d}.png'.format(event_tensor_id)), event_preview) 193 | 194 | cv2.imwrite(join(self.output_folder, self.dataset_name, 195 | 'frame_{:010d}.png'.format(event_tensor_id)), img) 196 | if stamp is not None: 197 | self.timestamps_file.write('{:.18f}\n'.format(stamp)) 198 | 199 | def __cleanup__(self): 200 | if self.output_folder: 201 | self.timestamps_file.close() 202 | 203 | 204 | class ImageDisplay: 205 | """ 206 | Utility class to display image reconstructions 207 | """ 208 | 209 | def __init__(self, options): 210 | self.display = options.display 211 | self.show_events = options.show_events 212 | self.color = options.color 213 | self.event_display_mode = options.event_display_mode 214 | self.num_bins_to_show = options.num_bins_to_show 215 | 216 | self.window_name = 'Reconstruction' 217 | if self.show_events: 218 | self.window_name = 'Events | ' + self.window_name 219 | 220 | if self.display: 221 | cv2.namedWindow(self.window_name, cv2.WINDOW_NORMAL) 222 | 223 | self.border = options.display_border_crop 224 | self.wait_time = options.display_wait_time 225 | 226 | def crop_outer_border(self, img, border): 227 | if self.border == 0: 228 | return img 229 | else: 230 | return img[border:-border, border:-border] 231 | 232 | def __call__(self, img, events=None): 233 | 234 | if not self.display: 235 | return 236 | 237 | img = self.crop_outer_border(img, self.border) 238 | 239 | if self.show_events: 240 | assert(events is not None) 241 | event_preview = make_event_preview(events, mode=self.event_display_mode, 242 | num_bins_to_show=self.num_bins_to_show) 243 | event_preview = self.crop_outer_border(event_preview, self.border) 244 | 245 | if self.show_events: 246 | img_is_color = (len(img.shape) == 3) 247 | preview_is_color = (len(event_preview.shape) == 3) 248 | 249 | if(preview_is_color and not img_is_color): 250 | img = np.dstack([img] * 3) 251 | elif(img_is_color and not preview_is_color): 252 | event_preview = np.dstack([event_preview] * 3) 253 | 254 | img = np.hstack([event_preview, img]) 255 | 256 | cv2.imshow(self.window_name, img) 257 | cv2.waitKey(self.wait_time) 258 | 259 | 260 | class UnsharpMaskFilter: 261 | """ 262 | Utility class to perform unsharp mask filtering on reconstructed images. 263 | """ 264 | 265 | def __init__(self, options, device): 266 | self.unsharp_mask_amount = options.unsharp_mask_amount 267 | self.unsharp_mask_sigma = options.unsharp_mask_sigma 268 | self.gaussian_kernel_size = 5 269 | self.gaussian_kernel = gkern(self.gaussian_kernel_size, 270 | self.unsharp_mask_sigma).unsqueeze(0).unsqueeze(0).to(device) 271 | 272 | def __call__(self, img): 273 | if self.unsharp_mask_amount > 0: 274 | with CudaTimer('Unsharp mask'): 275 | blurred = F.conv2d(img, self.gaussian_kernel, 276 | padding=self.gaussian_kernel_size // 2) 277 | img = (1 + self.unsharp_mask_amount) * img - self.unsharp_mask_amount * blurred 278 | return img 279 | 280 | 281 | class ImageFilter: 282 | """ 283 | Utility class to perform some basic filtering on reconstructed images. 284 | """ 285 | 286 | def __init__(self, options): 287 | self.bilateral_filter_sigma = options.bilateral_filter_sigma 288 | 289 | def __call__(self, img): 290 | 291 | if self.bilateral_filter_sigma: 292 | with Timer('Bilateral filter (sigma={:.2f})'.format(self.bilateral_filter_sigma)): 293 | filtered_img = np.zeros_like(img) 294 | filtered_img = cv2.bilateralFilter( 295 | img, 5, 25.0 * self.bilateral_filter_sigma, 25.0 * self.bilateral_filter_sigma) 296 | img = filtered_img 297 | 298 | return img 299 | 300 | 301 | def optimal_crop_size(max_size, max_subsample_factor): 302 | """ Find the optimal crop size for a given max_size and subsample_factor. 303 | The optimal crop size is the smallest integer which is greater or equal than max_size, 304 | while being divisible by 2^max_subsample_factor. 305 | """ 306 | crop_size = int(pow(2, max_subsample_factor) * ceil(max_size / pow(2, max_subsample_factor))) 307 | return crop_size 308 | 309 | 310 | class CropParameters: 311 | """ Helper class to compute and store useful parameters for pre-processing and post-processing 312 | of images in and out of E2VID. 313 | Pre-processing: finding the best image size for the network, and padding the input image with zeros 314 | Post-processing: Crop the output image back to the original image size 315 | """ 316 | 317 | def __init__(self, width, height, num_encoders): 318 | 319 | self.height = height 320 | self.width = width 321 | self.num_encoders = num_encoders 322 | self.width_crop_size = optimal_crop_size(self.width, num_encoders) 323 | self.height_crop_size = optimal_crop_size(self.height, num_encoders) 324 | 325 | self.padding_top = ceil(0.5 * (self.height_crop_size - self.height)) 326 | self.padding_bottom = floor(0.5 * (self.height_crop_size - self.height)) 327 | self.padding_left = ceil(0.5 * (self.width_crop_size - self.width)) 328 | self.padding_right = floor(0.5 * (self.width_crop_size - self.width)) 329 | self.pad = ReflectionPad2d((self.padding_left, self.padding_right, self.padding_top, self.padding_bottom)) 330 | 331 | self.cx = floor(self.width_crop_size / 2) 332 | self.cy = floor(self.height_crop_size / 2) 333 | 334 | self.ix0 = self.cx - floor(self.width / 2) 335 | self.ix1 = self.cx + ceil(self.width / 2) 336 | self.iy0 = self.cy - floor(self.height / 2) 337 | self.iy1 = self.cy + ceil(self.height / 2) 338 | 339 | 340 | def shift_image(X, dx, dy): 341 | X = np.roll(X, dy, axis=0) 342 | X = np.roll(X, dx, axis=1) 343 | if dy > 0: 344 | X[:dy, :] = np.expand_dims(X[dy, :], axis=0) 345 | elif dy < 0: 346 | X[dy:, :] = np.expand_dims(X[dy, :], axis=0) 347 | if dx > 0: 348 | X[:, :dx] = np.expand_dims(X[:, dx], axis=1) 349 | elif dx < 0: 350 | X[:, dx:] = np.expand_dims(X[:, dx], axis=1) 351 | return X 352 | 353 | 354 | def upsample_color_image(grayscale_highres, color_lowres_bgr, colorspace='LAB'): 355 | """ 356 | Generate a high res color image from a high res grayscale image, and a low res color image, 357 | using the trick described in: 358 | http://www.planetary.org/blogs/emily-lakdawalla/2013/04231204-image-processing-colorizing-images.html 359 | """ 360 | assert(len(grayscale_highres.shape) == 2) 361 | assert(len(color_lowres_bgr.shape) == 3 and color_lowres_bgr.shape[2] == 3) 362 | 363 | if colorspace == 'LAB': 364 | # convert color image to LAB space 365 | lab = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2LAB) 366 | # replace lightness channel with the highres image 367 | lab[:, :, 0] = grayscale_highres 368 | # convert back to BGR 369 | color_highres_bgr = cv2.cvtColor(src=lab, code=cv2.COLOR_LAB2BGR) 370 | elif colorspace == 'HSV': 371 | # convert color image to HSV space 372 | hsv = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HSV) 373 | # replace value channel with the highres image 374 | hsv[:, :, 2] = grayscale_highres 375 | # convert back to BGR 376 | color_highres_bgr = cv2.cvtColor(src=hsv, code=cv2.COLOR_HSV2BGR) 377 | elif colorspace == 'HLS': 378 | # convert color image to HLS space 379 | hls = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HLS) 380 | # replace lightness channel with the highres image 381 | hls[:, :, 1] = grayscale_highres 382 | # convert back to BGR 383 | color_highres_bgr = cv2.cvtColor(src=hls, code=cv2.COLOR_HLS2BGR) 384 | 385 | return color_highres_bgr 386 | 387 | 388 | def merge_channels_into_color_image(channels): 389 | """ 390 | Combine a full resolution grayscale reconstruction and four color channels at half resolution 391 | into a color image at full resolution. 392 | 393 | :param channels: dictionary containing the four color reconstructions (at quarter resolution), 394 | and the full resolution grayscale reconstruction. 395 | :return a color image at full resolution 396 | """ 397 | 398 | with Timer('Merge color channels'): 399 | 400 | assert('R' in channels) 401 | assert('G' in channels) 402 | assert('W' in channels) 403 | assert('B' in channels) 404 | assert('grayscale' in channels) 405 | 406 | # upsample each channel independently 407 | for channel in ['R', 'G', 'W', 'B']: 408 | channels[channel] = cv2.resize(channels[channel], dsize=None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR) 409 | 410 | # Shift the channels so that they all have the same origin 411 | channels['B'] = shift_image(channels['B'], dx=1, dy=1) 412 | channels['G'] = shift_image(channels['G'], dx=1, dy=0) 413 | channels['W'] = shift_image(channels['W'], dx=0, dy=1) 414 | 415 | # reconstruct the color image at half the resolution using the reconstructed channels RGBW 416 | reconstruction_bgr = np.dstack([channels['B'], 417 | cv2.addWeighted(src1=channels['G'], alpha=0.5, 418 | src2=channels['W'], beta=0.5, 419 | gamma=0.0, dtype=cv2.CV_8U), 420 | channels['R']]) 421 | 422 | reconstruction_grayscale = channels['grayscale'] 423 | 424 | # combine the full res grayscale resolution with the low res to get a full res color image 425 | upsampled_img = upsample_color_image(reconstruction_grayscale, reconstruction_bgr) 426 | return upsampled_img 427 | 428 | return upsampled_img 429 | 430 | 431 | def events_to_voxel_grid(events, num_bins, width, height): 432 | """ 433 | Build a voxel grid with bilinear interpolation in the time domain from a set of events. 434 | 435 | :param events: a [N x 4] NumPy array containing one event per row in the form: [timestamp, x, y, polarity] 436 | :param num_bins: number of bins in the temporal axis of the voxel grid 437 | :param width, height: dimensions of the voxel grid 438 | """ 439 | 440 | assert(events.shape[1] == 4) 441 | assert(num_bins > 0) 442 | assert(width > 0) 443 | assert(height > 0) 444 | 445 | voxel_grid = np.zeros((num_bins, height, width), np.float32).ravel() 446 | 447 | # normalize the event timestamps so that they lie between 0 and num_bins 448 | last_stamp = events[-1, 0] 449 | first_stamp = events[0, 0] 450 | deltaT = last_stamp - first_stamp 451 | 452 | if deltaT == 0: 453 | deltaT = 1.0 454 | 455 | events[:, 0] = (num_bins - 1) * (events[:, 0] - first_stamp) / deltaT 456 | ts = events[:, 0] 457 | xs = events[:, 1].astype(np.int) 458 | ys = events[:, 2].astype(np.int) 459 | pols = events[:, 3] 460 | pols[pols == 0] = -1 # polarity should be +1 / -1 461 | 462 | tis = ts.astype(np.int) 463 | dts = ts - tis 464 | vals_left = pols * (1.0 - dts) 465 | vals_right = pols * dts 466 | 467 | valid_indices = tis < num_bins 468 | np.add.at(voxel_grid, xs[valid_indices] + ys[valid_indices] * width 469 | + tis[valid_indices] * width * height, vals_left[valid_indices]) 470 | 471 | valid_indices = (tis + 1) < num_bins 472 | np.add.at(voxel_grid, xs[valid_indices] + ys[valid_indices] * width 473 | + (tis[valid_indices] + 1) * width * height, vals_right[valid_indices]) 474 | 475 | voxel_grid = np.reshape(voxel_grid, (num_bins, height, width)) 476 | 477 | return voxel_grid 478 | 479 | 480 | def events_to_voxel_grid_pytorch(events, num_bins, width, height, device): 481 | """ 482 | Build a voxel grid with bilinear interpolation in the time domain from a set of events. 483 | 484 | :param events: a [N x 4] NumPy array containing one event per row in the form: [timestamp, x, y, polarity] 485 | :param num_bins: number of bins in the temporal axis of the voxel grid 486 | :param width, height: dimensions of the voxel grid 487 | :param device: device to use to perform computations 488 | :return voxel_grid: PyTorch event tensor (on the device specified) 489 | """ 490 | 491 | DeviceTimer = CudaTimer if device.type == 'cuda' else Timer 492 | 493 | assert(events.shape[1] == 4) 494 | assert(num_bins > 0) 495 | assert(width > 0) 496 | assert(height > 0) 497 | 498 | with torch.no_grad(): 499 | 500 | events_torch = torch.from_numpy(events) 501 | with DeviceTimer('Events -> Device (voxel grid)'): 502 | events_torch = events_torch.to(device) 503 | 504 | with DeviceTimer('Voxel grid voting'): 505 | voxel_grid = torch.zeros(num_bins, height, width, dtype=torch.float32, device=device).flatten() 506 | 507 | # normalize the event timestamps so that they lie between 0 and num_bins 508 | last_stamp = events_torch[-1, 0] 509 | first_stamp = events_torch[0, 0] 510 | deltaT = last_stamp - first_stamp 511 | 512 | if deltaT == 0: 513 | deltaT = 1.0 514 | 515 | events_torch[:, 0] = (num_bins - 1) * (events_torch[:, 0] - first_stamp) / deltaT 516 | ts = events_torch[:, 0] 517 | xs = events_torch[:, 1].long() 518 | ys = events_torch[:, 2].long() 519 | pols = events_torch[:, 3].float() 520 | pols[pols == 0] = -1 # polarity should be +1 / -1 521 | 522 | tis = torch.floor(ts) 523 | tis_long = tis.long() 524 | dts = ts - tis 525 | vals_left = pols * (1.0 - dts.float()) 526 | vals_right = pols * dts.float() 527 | 528 | valid_indices = tis < num_bins 529 | valid_indices &= tis >= 0 530 | voxel_grid.index_add_(dim=0, 531 | index=xs[valid_indices] + ys[valid_indices] 532 | * width + tis_long[valid_indices] * width * height, 533 | source=vals_left[valid_indices]) 534 | 535 | valid_indices = (tis + 1) < num_bins 536 | valid_indices &= tis >= 0 537 | 538 | voxel_grid.index_add_(dim=0, 539 | index=xs[valid_indices] + ys[valid_indices] * width 540 | + (tis_long[valid_indices] + 1) * width * height, 541 | source=vals_right[valid_indices]) 542 | 543 | voxel_grid = voxel_grid.view(num_bins, height, width) 544 | 545 | return voxel_grid 546 | -------------------------------------------------------------------------------- /utils/loading_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from model.model import * 3 | 4 | 5 | def load_model(path_to_model): 6 | print('Loading model {}...'.format(path_to_model)) 7 | raw_model = torch.load(path_to_model) 8 | arch = raw_model['arch'] 9 | 10 | try: 11 | model_type = raw_model['model'] 12 | except KeyError: 13 | model_type = raw_model['config']['model'] 14 | 15 | # instantiate model 16 | model = eval(arch)(model_type) 17 | 18 | # load model weights 19 | model.load_state_dict(raw_model['state_dict']) 20 | 21 | return model 22 | 23 | 24 | def get_device(use_gpu): 25 | if use_gpu and torch.cuda.is_available(): 26 | device = torch.device('cuda:0') 27 | else: 28 | device = torch.device('cpu') 29 | print('Device:', device) 30 | 31 | return device 32 | -------------------------------------------------------------------------------- /utils/path_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | def ensure_dir(path): 5 | if not os.path.exists(path): 6 | os.makedirs(path) 7 | -------------------------------------------------------------------------------- /utils/timers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import time 3 | import numpy as np 4 | import atexit 5 | 6 | cuda_timers = {} 7 | timers = {} 8 | 9 | 10 | class CudaTimer: 11 | def __init__(self, timer_name=''): 12 | self.timer_name = timer_name 13 | if self.timer_name not in cuda_timers: 14 | cuda_timers[self.timer_name] = [] 15 | 16 | self.start = torch.cuda.Event(enable_timing=True) 17 | self.end = torch.cuda.Event(enable_timing=True) 18 | 19 | def __enter__(self): 20 | self.start.record() 21 | return self 22 | 23 | def __exit__(self, *args): 24 | self.end.record() 25 | torch.cuda.synchronize() 26 | cuda_timers[self.timer_name].append(self.start.elapsed_time(self.end)) 27 | 28 | 29 | class Timer: 30 | def __init__(self, timer_name=''): 31 | self.timer_name = timer_name 32 | if self.timer_name not in timers: 33 | timers[self.timer_name] = [] 34 | 35 | def __enter__(self): 36 | self.start = time.time() 37 | return self 38 | 39 | def __exit__(self, *args): 40 | self.end = time.time() 41 | self.interval = self.end - self.start # measured in seconds 42 | self.interval *= 1000.0 # convert to milliseconds 43 | timers[self.timer_name].append(self.interval) 44 | 45 | 46 | def print_timing_info(): 47 | print('== Timing statistics ==') 48 | for timer_name, timing_values in [*cuda_timers.items(), *timers.items()]: 49 | timing_value = np.mean(np.array(timing_values)) 50 | if timing_value < 1000.0: 51 | print('{}: {:.2f} ms'.format(timer_name, timing_value)) 52 | else: 53 | print('{}: {:.2f} s'.format(timer_name, timing_value / 1000.0)) 54 | 55 | 56 | # this will print all the timer values upon termination of any program that imported this file 57 | atexit.register(print_timing_info) 58 | -------------------------------------------------------------------------------- /utils/util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from math import fabs 3 | 4 | 5 | def robust_min(img, p=5): 6 | return np.percentile(img.ravel(), p) 7 | 8 | 9 | def robust_max(img, p=95): 10 | return np.percentile(img.ravel(), p) 11 | 12 | 13 | def normalize(img, m=10, M=90): 14 | return np.clip((img - robust_min(img, m)) / (robust_max(img, M) - robust_min(img, m)), 0.0, 1.0) 15 | 16 | 17 | def first_element_greater_than(values, req_value): 18 | """Returns the pair (i, values[i]) such that i is the minimum value that satisfies values[i] >= req_value. 19 | Returns (-1, None) if there is no such i. 20 | Note: this function assumes that values is a sorted array!""" 21 | i = np.searchsorted(values, req_value) 22 | val = values[i] if i < len(values) else None 23 | return (i, val) 24 | 25 | 26 | def last_element_less_than(values, req_value): 27 | """Returns the pair (i, values[i]) such that i is the maximum value that satisfies values[i] <= req_value. 28 | Returns (-1, None) if there is no such i. 29 | Note: this function assumes that values is a sorted array!""" 30 | i = np.searchsorted(values, req_value, side='right') - 1 31 | val = values[i] if i >= 0 else None 32 | return (i, val) 33 | 34 | 35 | def closest_element_to(values, req_value): 36 | """Returns the tuple (i, values[i], diff) such that i is the closest value to req_value, 37 | and diff = |values(i) - req_value| 38 | Note: this function assumes that values is a sorted array!""" 39 | assert(len(values) > 0) 40 | 41 | i = np.searchsorted(values, req_value, side='left') 42 | if i > 0 and (i == len(values) or fabs(req_value - values[i - 1]) < fabs(req_value - values[i])): 43 | idx = i - 1 44 | val = values[i - 1] 45 | else: 46 | idx = i 47 | val = values[i] 48 | 49 | diff = fabs(val - req_value) 50 | return (idx, val, diff) 51 | --------------------------------------------------------------------------------