first-release

.gitignore

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README.md

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+# MVSEC-NIGHTL21
+
+https://user-images.githubusercontent.com/939553/122247023-57b18e80-cec7-11eb-8ac9-ed6bb88c1095.mp4
+
+## Usage
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1qL8LoCZ-mm_O8K3aMDo22M3KSUtaK0cp?usp=sharing)
+
+### Data Description
+
+### MVSEC at night condition
+
+We used `outdoor_night1_data.hdf5` of the MVSEC dataset.
+The dataset is recorded with dual camera, we use the `left` camera.
+In the HDF5 archive, the relevant dataset can be accessed as following:
+
+```python
+mvsec_data = h5py.File(mvsec_data_path, "r")
+
+# raw frame
+frame_data = mvsec_data["davis"]["left"]["image_raw"]
+# frame timestamps
+frame_ts = mvsec_data["davis"]["left"]["image_raw_ts"]
+
+# raw events 
+events_data = mvsec_data["davis"]["left"]["events"]
+
+# event indices that corresponds to the frame
+frame_event_inds = mvsec_data["davis"]["left"]["image_raw_event_inds"]
+```
+
+For visualization in this repository, we only used the raw frames.
+
+### MVSEC-NIGHTL21 Labels
+
+In the validation set, there are 400 frames. The list of the frame indices is in [`frame_list.txt`](./frame_list.txt).
+
+Among these 400 frames, 368 frames are labeled. The frames that don't have labels are listed in [`frames_that_dont_have_labels.txt`](./frames_that_dont_have_labels.txt).
+We labelled `car` in these frames.
+
+The labelled groundtruths are stored in `.txt` files and can be found in [`mvsec_nightl21_labels`](./mvsec_nightl21_labels).
+
+Each labeled car is in the format `car x_min y_min x_max y_max`. For example:
+
+```
+car 48 112 143 170
+```
+
+means `x_min=48, y_min=112`, and `x_max=143, y_max=170`.
+
+
+### Visualization
+
+1. Install dependency
+    ```
+    pip install h5py
+    pip install matplotlib
+    pip install opencv-python
+    ```
+
+2. Clone this repository
+    ```
+    git clone https://github.com/SensorsINI/MVSEC-NIGHTL21
+    cd MVSEC-NIGHTL21
+    ```
+
+3. Download the `outdoor_night1_data.hdf5` from MVSEC dataset, available [here](https://drive.google.com/drive/folders/1rwyRk26wtWeRgrAx_fgPc-ubUzTFThkV)
+
+4. Run The Visualization
+    ```
+     python visualize_mvsec_nightl21.py --mvsec_data /path/to/outdoor_night1_data.hdf5 --gt_root ./mvsec_nightl21_labels
+    ```
+
+    If everything works, you should see a video that annotates the cars.
+
+## Citation
+
+When use this dataset, please cite:
+
+```bibtex
+@InProceedings{Hu_2021_CVPR,
+    author    = {Hu, Yuhuang and Liu, Shih-Chii and Delbruck, Tobi},
+    title     = {v2e: From Video Frames to Realistic DVS Events},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    month     = {June},
+    year      = {2021},
+    pages     = {1312-1321}
+}
+```
+
+## Contact
+
+Yuhuang Hu  
+[email protected]