Add distance transform functionality by harshitpant1 · Pull Request #594 · boostorg/gil

harshitpant1 · 2021-04-03T05:01:02Z

Description

Adds Distance Transform functionality to GIL, using algorithms which run in linear time in the worst case.

References

Precise Euclidean distance transform -
http://www.theoryofcomputing.org/articles/v008a019/v008a019.pdf

Manhattan and chessboard distance transforms -
Principles of Digital Image Processing - core Techniques by Wilhelm Burger, Mark J.Burge.

Approximate Euclidean distance transform -
http://www.cmm.mines-paristech.fr/~marcoteg/cv/publi_pdf/MM_refs/1986_Borgefors_distance.pdf

Tasklist

Add test case(s)
Ensure all CI builds pass
Review and approve

harshitpant1 · 2021-04-03T05:08:47Z

Aside from the first wikipedia test, all other test have been verified with openCV.

import numpy as np
import cv2 as cv

################################################################################################
# test_chessboard_uint16_t_input_uint8_t_output_distance_from_on_pixels

img2 = np.array([[255,   0, 255, 255, 255, 255, 255],
                 [255, 255, 255, 255, 255, 255, 255],
                 [255, 255, 255, 255, 255, 255, 255],
                 [255, 255, 255, 255, 255, 255, 255],
                 [255, 255, 255,   0, 255, 255, 255],
                 [255, 255, 255, 255, 255, 255, 255],
                 [255, 255, 255, 255, 255, 255, 255]], dtype = np.uint8)

#/ 8 bit (16 bit tested in gil) image used which is inverted to test distance from on pixels

test2_chessboard_three = cv.distanceTransform(img2, cv.DIST_C,cv.DIST_MASK_3)
print("\n test2_chessboard mask size 3: ")
print(test2_chessboard_three)

################################################################################################
# test_euclidean_approx_and_manhattan_uint8_t_input_float32_t_output_distance_from_off_pixels

img3 = np.array([
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255,   0,   0,   0,   0,   0, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255,   0, 255],
 [255,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]], dtype = np.uint8)

test3_euclidean_five = cv.distanceTransform(img3, cv.DIST_L2,cv.DIST_MASK_5)
print("\n test3_euclidean approx mask size 5: ")
print(test3_euclidean_five)

test3_manhattan_five = cv.distanceTransform(img3, cv.DIST_L1,cv.DIST_MASK_5)
print("\n test3_manhattan approx mask size 5: ")
print(test3_manhattan_five)

################################################################################################
# test_all_uint8_t_input_float32_t_ouptut_distance_from_off_pixels
img4 = np.array([
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255],
 [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0]], dtype = np.uint8)


test4_precise_euclidean = cv.distanceTransform(img4, cv.DIST_L2,cv.DIST_MASK_PRECISE)
print("\n test4_ Precise Euclidean: ")
print(test4_precise_euclidean)

test4_euclidean_three = cv.distanceTransform(img4, cv.DIST_L2,cv.DIST_MASK_3)
print("\n test4_ Euclidean Approximation mask size 3 \n")
print(test4_euclidean_three)

test4_euclidean_five = cv.distanceTransform(img4, cv.DIST_L2,cv.DIST_MASK_5)
print("\n test4_ Euclidean Approximation mask size 5: ")
print(test4_euclidean_five)

test4_manhattan_three = cv.distanceTransform(img4, cv.DIST_L1,cv.DIST_MASK_3)
print("\n test4_ manhattan mask size 3: ")
print(test4_manhattan_three)

test4_manhattan_five = cv.distanceTransform(img4, cv.DIST_L1,cv.DIST_MASK_5)
print("\n test4_ manhattan mask size 5: ")
print(test4_manhattan_five)

test4_chessboard_three = cv.distanceTransform(img4, cv.DIST_C,cv.DIST_MASK_3)
print("\n test4_chessboard mask size 3: ")
print(test4_chessboard_three)

test4_chessboard_five = cv.distanceTransform(img4, cv.DIST_C,cv.DIST_MASK_5)
print("\n test4_chessboard mask size 3: ")
print(test4_chessboard_five)

codecov · 2021-04-03T05:55:37Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 79.43%. Comparing base (bbdce36) to head (bd907af).
⚠️ Report is 175 commits behind head on develop.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop     #594      +/-   ##
===========================================
+ Coverage    78.72%   79.43%   +0.71%     
===========================================
  Files          118      119       +1     
  Lines         5034     5208     +174     
===========================================
+ Hits          3963     4137     +174     
  Misses        1071     1071

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

Use constexpr wherever preferred; Replace static const bool by inheriting std::true_type or std::false_type in metafunctions.

Moves 2 metafunctions: check_mask_size and check_distance_type to detail namespace and prefixes them with 'dt' (distance_transform) to avoid naming conflicts.

Uses the clang-format configuration provided in the latest commit of PR boostorg#596. Formatting is applied to test file and example file as well. I have made no changes whatsoever after using the clang-format.

harshitpant1 · 2021-06-23T18:05:19Z

As you might have noticed at some places (especially in the test file) reformatting should have been done after using clang-format, but I had a feeling to keep things as they are after they have been automatically formatted.

Let me know about your preferences on this.

Note that the second last commit has proper manual formatting.

mloskot · 2021-06-25T08:08:38Z

It is fine to try format any new code using the .clang-format that we are discussing as part of #596

harshitpant1 added 2 commits April 3, 2021 10:20

Add distance transform functionality

5e25b48

Fix b2 jamfile error

2cedb4a

harshitpant1 added 7 commits April 4, 2021 00:36

Improve test coverage

8c12da2

Improve formatting; Correct name in copyright info

05a1875

Improve Metafunctions

91fadc5

Use constexpr wherever preferred; Replace static const bool by inheriting std::true_type or std::false_type in metafunctions.

Move all metafunctions to detail namespace

f12a145

Moves 2 metafunctions: check_mask_size and check_distance_type to detail namespace and prefixes them with 'dt' (distance_transform) to avoid naming conflicts.

Add some more explanatory and reference comments

0bfb69d

improve test file formatting

76c6410

Use clang-format for formatting

bd907af

Uses the clang-format configuration provided in the latest commit of PR boostorg#596. Formatting is applied to test file and example file as well. I have made no changes whatsoever after using the clang-format.

mloskot added the cat/feature New feature or functionality label Jun 25, 2021

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Add distance transform functionality#594

Add distance transform functionality#594
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harshitpant1:feature/distance_transform

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