Base line detector/descriptor

class limap.line2d.base_detector.BaseDetector(options=BaseDetectorOptions(set_gray=True, max_num_2d_segs=3000, do_merge_lines=False, visualize=False, weight_path=None))

Bases: object

Virtual class for line detector

detect(camview)

Virtual method (for detector) - detect 2D line segments

Parameters:

view (limap.base.CameraView) – The limap.base.CameraView instance corresponding to the image

Returns:

line detections. Each row corresponds to x1, y1, x2, y2 and score.

Return type:

np.array of shape (N, 5)

detect_all_images(output_folder, imagecols, skip_exists=False)

Perform line detection on all images and save the line segments

Parameters:

  • output_folder (str) – The output folder

  • imagecols (limap.base.ImageCollection) – The input image collection

  • skip_exists (bool) – Whether to skip already processed images

Returns:

The line detection for each image indexed by the image id. Each segment is with shape (N, 5). Each row corresponds to x1, y1, x2, y2 and score.

Return type:

dict[int -> np.array]

detect_and_extract(camview)

Virtual method (for dual-functional class that can perform both detection and extraction) - Detect and extract on a single image

Parameters:

view (limap.base.CameraView) – The limap.base.CameraView instance corresponding to the image

Returns:

of shape (N, 5), line detections. Each row corresponds to x1, y1, x2, y2 and score. Computed from the detect method. descinfo: The features extracted from the function extract

Return type:

segs (np.array)

detect_and_extract_all_images(output_folder, imagecols, skip_exists=False)

Perform line detection and description on all images and save the line segments and descriptors

Parameters:

  • output_folder (str) – The output folder

  • imagecols (limap.base.ImageCollection) – The input image collection

  • skip_exists (bool) – Whether to skip already processed images

Returns:

The line detection for each image indexed by the image id. Each segment is with shape (N, 5). Each row corresponds to x1, y1, x2, y2 and score. descinfo_folder (str): Path to the extracted descriptors.

Return type:

all_segs (dict[int -> np.array])

extract(camview, segs)

Virtual method (for extractor) - extract the features for the detected segments

Parameters:

  • view (limap.base.CameraView) – The limap.base.CameraView instance corresponding to the image

  • segs – np.array of shape (N, 5), line detections. Each row corresponds to x1, y1, x2, y2 and score. Computed from the detect method.

Returns:

The extracted feature

extract_all_images(output_folder, imagecols, all_2d_segs, skip_exists=False)

Perform line descriptor extraction on all images and save the descriptors.

Parameters:

  • output_folder (str) – The output folder.

  • imagecols (limap.base.ImageCollection) – The input image collection

  • all_2d_segs (dict[int -> np.array]) – The line detection for each image indexed by the image id. Each segment is with shape (N, 5). Each row corresponds to x1, y1, x2, y2 and score. Computed from detect_all_images

  • skip_exists (bool) – Whether to skip already processed images.

Returns:

The path to the saved descriptors.

Return type:

descinfo_folder (str)

get_descinfo_fname(descinfo_folder, img_id)

Virtual method (for extractor) - Get the target filename of the extracted feature

Parameters:

  • descinfo_folder (str) – The output folder

  • img_id (int) – The image id

Returns:

target filename

Return type:

str

get_descinfo_folder(output_folder)

Return the folder path to the extracted descriptors

Parameters:

output_folder (str) – The output folder

Returns:

The path to the saved descriptors

Return type:

path_to_descinfos (str)

get_module_name()

Virtual method (need to be implemented) - return the name of the module

get_segments_folder(output_folder)

Return the folder path to the detected segments

Parameters:

output_folder (str) – The output folder

Returns:

The path to the saved segments

Return type:

path_to_segments (str)

read_descinfo(descinfo_folder, img_id)

Virtual method (for extractor) - Read in the extracted feature. Dual function for save_descinfo.

Parameters:

  • descinfo_folder (str) – The output folder

  • img_id (int) – The image id

Returns:

The extracted feature

sample_descinfo_by_indexes(descinfo, indexes)

Virtual method (for dual-functional class that can perform both detection and extraction) - sample descriptors for a subset of images

Parameters:

  • descinfo – The features extracted from the function extract.

  • indexes (list[int]) – List of image ids for the subset.

save_descinfo(descinfo_folder, img_id, descinfo)

Virtual method (for extractor) - Save the extracted feature to the target folder

Parameters:

  • descinfo_folder (str) – The output folder

  • img_id (int) – The image id

  • descinfo – The features extracted from the function extract

namedtuple limap.line2d.base_detector.BaseDetectorOptions(set_gray: bool = True, max_num_2d_segs: int = 3000, do_merge_lines: bool = False, visualize: bool = False, weight_path: str | None = None)

Bases: NamedTuple

Base options for the line detector

Fields:

  1.  set_gray (bool) – whether to set the image to gray scale (sometimes depending on the detector)

  2.  max_num_2d_segs (int) – maximum number of detected line segments (default = 3000)

  3.  do_merge_lines (bool) – whether to merge close similar lines at post-processing (default = False)

  4.  visualize (bool) – whether to output visualizations into output folder along with the detections (default = False)

  5.  weight_path (str) – specify path to load weights (at default, weights will be downloaded to ~/.local)

static __new__(_cls, set_gray: bool = True, max_num_2d_segs: int = 3000, do_merge_lines: bool = False, visualize: bool = False, weight_path: str | None = None)

Create new instance of BaseDetectorOptions(set_gray, max_num_2d_segs, do_merge_lines, visualize, weight_path)