suncompass 0.0.1

Suncompass

This repository contains code for estimating the direction of the sun from a single outdoor image.

Repository Contents

1. pretrained_resnet.py: Defines a regression model based on a pre-trained ResNet model and includes functions for training and loading the model.
2. 3_224_224_resnet.pth: A pre-trained ResNet model for estimating the direction of the sun from a single outdoor image.
3. suncompass.py: Class for interacting with the pre-trained ResNet model.
4. demo.py: Demonstrates how to use the pre-trained ResNet model to estimate the direction of the sun from a single outdoor image.

Requirements

• Python 3.7+
• PyTorch
• torchvision
• pandas
• numpy
• matplotlib
• tqdm
• click
• PIL
• tensorboard
• suncalc

Installation

1. Clone the repository:

   git clone https://github.com/hugohadfield/suncompass.git
   cd suncompass
   

2. Install the package:

   pip install .
   

Usage

add_sun_vector.py

This script calculates the sun vector in the ENU frame and the relative frame based on heading.

Example usage:

python add_sun_vector.py

extract_motion.py

This script extracts motion parameters such as speed, heading, and curvature from a given dataset.

Example usage:

python extract_motion.py

pretrained_resnet.py

This script defines and trains a regression model based on a pre-trained ResNet model.

Example usage:

python pretrained_resnet.py

run_model_on_images.py

This script runs the trained ResNet model on a set of images and saves the results to a CSV file.

Example usage:

python run_model_on_images.py --model_name 3_224_224_resnet.pth --image_dir /path/to/images --gt_csv /path/to/ground_truth.csv

Function Details

add_sun_vector.py

• calculate_sun_vector_ENU(time: datetime, lat_degs: float, long_degs: float) -> np.ndarray: Calculates the sun vector in the ENU frame.
• calculate_sun_vector_relative(time_unixus: int, lat_degs: float, long_degs: float, heading_rads_from_east: Union[float, np.complex128]) -> np.ndarray: Calculates the sun vector in the relative frame.
• calculate_relative_azimuth_angle(time_unixus: int, lat_degs: float, long_degs: float, heading_rads_from_east: float) -> float: Calculates the azimuth angle of the sun vector in the relative frame.

extract_motion.py

• haversine(lat1, lon1, lat2, lon2) -> float: Calculates the great circle distance between two points on the Earth.
• calculate_bearing_clockwise_from_north(lat1, lon1, lat2, lon2) -> float: Calculates the bearing between two points.
• calculate_heading_anticlockwise_from_east(lat1, lon1, lat2, lon2) -> float: Calculates the heading anti-clockwise from east.
• extract_motion(data: pd.DataFrame) -> pd.DataFrame: Extracts speed, heading, and curvature from the given data.

pretrained_resnet.py

• ResNetRegression: Defines a regression model based on a pre-trained ResNet model.
• train_network(device, n_epochs: int = 10, image_size: Tuple[int, int] = (128, 128)): Trains the network for a set number of epochs.

run_model_on_images.py

• run_model_on_images(model_name: Path, image_dir: Path, image_size: Tuple[int, int, int] = (1, 100, 100)): Runs the model on the images and saves the results to a CSV file.
• plot_results_csv(gt_csv: Optional[str] = None): Plots the results from the CSV file.

License

This project is licensed under the MIT License - see the licence.txt file for details.