config.yaml

# RUN CONFIG
# # # # # # # # # # # # # # # # # #
image_size: 1280 960  # Input image size
edge_detection_type: "2"  # Choose type of run, i.e. type of edge detection
# ''=DexiNed standard, '1'=DexiNed fine, '2'=DexiNed coarse, '3'=DexiNed very coarse
# 'canny'=Canny, 'canny_adap'= adap Canny


# ALGORITHM CONFIG
# # # # # # # # # # # # # # # # # #
clustering_eps: 0.02
clustering_min_sample: 10
mask_size: 51
n_random_points: 10
kernel_harris_corner_detector: 50


# DIRECTIONS ON MACHINE
# # # # # # # # # # # # # # # # # #
path_output: "./output"
path_summary: "./output/summary"
dir_result_details: details
file_name_input_image: image.png
file_name_plane_mask_suf: _plane_masks_0.npy
file_name_planercnn_image_suf: _segmentation_0_final.png


# VISUALIZATIONS
# # # # # # # # # # # # # # # # # #
visualization_dict:  # specifies which visualizations should be saved
  no_result_folder: 0  # necessary to be 0 for all but summary
  edges: 1
  keep_edges: 1
  widened_contour: 1
  lines: 1
  initial_mask: 1
  refined_mask: 1
  mask_folder: 1
  hough_lines: 1
  hough_summary: 1
  summary: 1  # additional summary for which result check in separate folder