bm_dataset.create_real_synth_dataset module

Script for generating synthetic datasets from a single image and landmarks. The output is set of geometrical deformed images with also change color space and related computed new landmarks.

Sample run:

python create_real_synth_dataset.py         -i ../data-images/images/Rat-Kidney_HE.jpg         -l ../data-images/landmarks/Rat-Kidney_HE.csv         -o ../output/synth_dataset  --visual

Copyright (C) 2016-2019 Jiri Borovec <jiri.borovec@fel.cvut.cz>

bm_dataset.create_real_synth_dataset.arg_parse_params()[source]

parse the input parameters :return dict: parameters

bm_dataset.create_real_synth_dataset.deform_image_landmarks(image, points, max_deform=50)[source]

deform the image by randomly generated deformation field and compute new positions for all landmarks

Parameters

  • image – np.array<height, width, 3>

  • points – np.array<nb_points, 2>

  • max_deform (float) – maximal deformation distance in any direction

Returns

np.array<height, width, 3>, np.array<nb_points, 2>

bm_dataset.create_real_synth_dataset.draw_image_landmarks(image, points)[source]

draw landmarks over the image and return the figure

Parameters

  • image – np.array<height, width, 3>

  • points – np.array<nb_points, 2>

Returns

object

bm_dataset.create_real_synth_dataset.export_image_landmarks(image, points, idx, path_out, name_img, visual=False)[source]

export the image, landmarks as csv file and if the ‘visual’ is set, draw also landmarks in the image (in separate image)

Parameters

  • image – np.array<height, width, 3>

  • points – np.array<nb_points, 2>

  • idx (int) –

  • path_out (str) – path to the output directory

  • name_img (str) – image file name

  • visual (bool) –

bm_dataset.create_real_synth_dataset.generate_deformation_field_gauss(shape, points, max_deform=50, deform_smooth=25)[source]

generate deformation field as combination of positive and negative Galatians densities scaled in range +/- max_deform

Parameters

  • shape (tuple(int,int)) – tuple of size 2

  • points – <nb_points, 2> list of landmarks

  • max_deform (float) – maximal deformation distance in any direction

  • deform_smooth (float) – smoothing the deformation by Gaussian filter

Returns

np.array<shape>

bm_dataset.create_real_synth_dataset.generate_deformation_field_rbf(shape, points, max_deform=50, nb_bound_points=25)[source]

generate deformation field as thin plate spline deformation in range +/- max_deform

Parameters

  • shape (tuple(int,int)) – tuple of size 2

  • points – np.array<nb_points, 2> list of landmarks

  • max_deform (float) – maximal deformation distance in any direction

  • nb_bound_points (int) – number of fix boundary points

Returns

np.array<shape>

bm_dataset.create_real_synth_dataset.get_name(path)[source]

parse the name without extension from complete path

Parameters

path (str) –

Return str

bm_dataset.create_real_synth_dataset.image_color_shift_hue(image, change_satur=True)[source]

take the original image and shift the colour space in HUE

Parameters

  • image – np.array<height, width, 3>

  • change_satur (bool) – whether change also the saturation

Returns

np.array<height, width, 3>

bm_dataset.create_real_synth_dataset.main(params)[source]

main entry point

Parameters

params (dict) – dict

bm_dataset.create_real_synth_dataset.perform_deform_export(idx, image, points, path_out, name_img, visual=False)[source]

perform complete image colour change, and deformation on image and landmarks and if required draw a visualisation

Parameters

  • idx (int) –

  • image – np.array<height, width, 3>

  • points – np.array<nb_points, 2>

  • path_out (str) –

  • name_img (str) –

  • visual (bool) –