asi_core.camera.obstacle_mask

This module provides functions to automatically generate camera masks for all-sky imagers.

Functions

save_mask(mask, mask_dir, mask_name)

Save a mask as both a .npy and a legacy .mat file.

adjust_gamma(image[, gamma])

Apply gamma correction to an image by building a lookup table mapping the pixel values [0, 255] to their adjusted

compute_mask(avg_img)

Create an image mask based on the input image. Input image should be a daily/longterm average image based

aggregate_images(img_list[, gray_scale, equalization, ...])

Process a list of images by applying optional grayscale conversion, histogram equalization,

create_mask(img_files[, num_images])

Create a mask of static objects from a set of image files.

detect_mask_cv(img, max_intensity, gaussian_kernel, ...)

Applies computer vision methods to automatically detect a mask of obstacles obscuring the sky in the ASI image.

Module Contents

asi_core.camera.obstacle_mask.save_mask(mask, mask_dir, mask_name)

Save a mask as both a .npy and a legacy .mat file.

Parameters:

  • mask (numpy.ndarray) – The mask array to be saved.

  • mask_dir (string) – The directory where the mask files will be saved.

  • mask_dir – The name of the mask files.

Returns:

None

asi_core.camera.obstacle_mask.adjust_gamma(image, gamma=1.0)

Apply gamma correction to an image by building a lookup table mapping the pixel values [0, 255] to their adjusted gamma values

asi_core.camera.obstacle_mask.compute_mask(avg_img)

Create an image mask based on the input image. Input image should be a daily/longterm average image based on equalized histogram to enhance contrast

asi_core.camera.obstacle_mask.aggregate_images(img_list, gray_scale=False, equalization=False, blur=False)

Process a list of images by applying optional grayscale conversion, histogram equalization, and blurring. Computes the average and standard deviation of the processed images.

Parameters:

  • img_list (list of numpy.ndarray) – List of input images as NumPy arrays.

  • gray_scale (bool, optional) – Whether to convert images to grayscale (default is False).

  • equalization (bool, optional) – Whether to apply histogram equalization to enhance contrast (default is False).

  • blur (bool, optional) – Whether to apply a blurring filter to reduce noise (default is False).

Returns:

A dictionary containing processed images, their average, and standard deviation.

Return type:

dict

asi_core.camera.obstacle_mask.create_mask(img_files, num_images=-1)

Create a mask of static objects from a set of image files.

This function aggregates a specified number of images from the provided list, computes an average image with optional grayscale conversion, histogram equalization, and blurring, and then generates a mask highlighting static objects present across the images.

Parameters:

  • img_files (list[str] or list[pathlib.Path]) – A list of image file paths to be used for mask generation.

  • num_images (int, optional) – The number of images to use for aggregation. If -1, use all provided images.

Returns:

The generated mask based on static objects detected in the aggregated image.

Return type:

numpy.ndarray

Raises:

ValueError – If img_files is empty or if num_images is 0.

asi_core.camera.obstacle_mask.detect_mask_cv(img, max_intensity, gaussian_kernel, adaptive_thres_block_size, adaptive_thres_mean_offset, erode_dilate_kernel, margin_horizon)

Applies computer vision methods to automatically detect a mask of obstacles obscuring the sky in the ASI image.

Parameters:

  • img – Image to be used for mask generation.

  • mask_intesity

  • gaussian_kernel

  • adaptive_thres_block_size

  • adaptive_thres_mean_offset

  • erode_dilate_kernel

  • margin_horizon

Returns:

automatically detected mask, dtype boolean, shape of greyscaled RGB input image