A re-implemented project which focus on dash-cam images according to the paper (Deep Outdoor Illumination Estimation [Hold-Geoffroy et al. CVPR 2017]) (https://arxiv.org/abs/1611.06403). This project is an end-to-end system that outputs corresponding sun position and physcial sky, camera parameters by inputing single dash-cam image.
You can download the weights from here and our dataset from here.
If you want to test your own image, run this command:
python inference.py --img_path <image-path> --pre-trained <weight-path>
You can generate the dataset and list by using generate_data.py
and
the data (360 panorama images seperated into test and train) which followed the format in GS_skymodel.csv
.
After generating dataset, run command below for training:
python train.py
The trained weights will be stored as weights.pth
.
Evaluate the trained model on the test dataset (data/test_list.csv
), it will output the average error of each predictions.
python eval.py --pre-trained <weight-path>
- numpy
- skimage
- pytorch
- opencv-python
- progressbar