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optimize thinning implementation #3750

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optimize thinning implementation #3750

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fc18abe
use norm_l1 to replace absdiff
ansleliu May 31, 2024
e881f95
remove marker form thinningIteration and remove "if" from forEach
ansleliu May 31, 2024
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117 changes: 28 additions & 89 deletions modules/ximgproc/src/thinning.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -92,94 +92,35 @@ static uint8_t lut_guo_iter1[] = {
1, 1, 1, 1};

// Applies a thinning iteration to a binary image
static void thinningIteration(Mat img, int iter, int thinningType){
Mat marker = Mat::zeros(img.size(), CV_8UC1);
static void thinningIteration(Mat &img, Mat &marker, const uint8_t* const lut) {
int rows = img.rows;
int cols = img.cols;
marker.col(0).setTo(1);
marker.col(cols - 1).setTo(1);
marker.row(0).setTo(1);
marker.row(rows - 1).setTo(1);

if(thinningType == THINNING_ZHANGSUEN){
marker.forEach<uchar>([=](uchar& value, const int postion[]) {
int i = postion[0];
int j = postion[1];
if (i == 0 || j == 0 || i == rows - 1 || j == cols - 1)
return;

auto ptr = img.ptr(i, j); // p1

// p9 p2 p3
// p8 p1 p4
// p7 p6 p5
uchar p2 = ptr[-cols];
uchar p3 = ptr[-cols + 1];
uchar p4 = ptr[1];
uchar p5 = ptr[cols + 1];
uchar p6 = ptr[cols];
uchar p7 = ptr[cols - 1];
uchar p8 = ptr[-1];
uchar p9 = ptr[-cols - 1];

int neighbors = p9 | (p2 << 1) | (p3 << 2) | (p4 << 3) | (p5 << 4) | (p6 << 5) | (p7 << 6) | (p8 << 7);

if (iter == 0)
value = lut_zhang_iter0[neighbors];
else
value = lut_zhang_iter1[neighbors];

//int A = (p2 == 0 && p3 == 1) + (p3 == 0 && p4 == 1) +
// (p4 == 0 && p5 == 1) + (p5 == 0 && p6 == 1) +
// (p6 == 0 && p7 == 1) + (p7 == 0 && p8 == 1) +
// (p8 == 0 && p9 == 1) + (p9 == 0 && p2 == 1);
//int B = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
//int m1 = iter == 0 ? (p2 * p4 * p6) : (p2 * p4 * p8);
//int m2 = iter == 0 ? (p4 * p6 * p8) : (p2 * p6 * p8);
//if (A == 1 && (B >= 2 && B <= 6) && m1 == 0 && m2 == 0) value = 0;
// else value = 1;
});
}
if(thinningType == THINNING_GUOHALL){
marker.forEach<uchar>([=](uchar& value, const int postion[]) {
int i = postion[0];
int j = postion[1];
if (i == 0 || j == 0 || i == rows - 1 || j == cols - 1)
return;

auto ptr = img.ptr(i, j); // p1

// p9 p2 p3
// p8 p1 p4
// p7 p6 p5
uchar p2 = ptr[-cols];
uchar p3 = ptr[-cols + 1];
uchar p4 = ptr[1];
uchar p5 = ptr[cols + 1];
uchar p6 = ptr[cols];
uchar p7 = ptr[cols - 1];
uchar p8 = ptr[-1];
uchar p9 = ptr[-cols - 1];

int neighbors = p9 | (p2 << 1) | (p3 << 2) | (p4 << 3) | (p5 << 4) | (p6 << 5) | (p7 << 6) | (p8 << 7);

if (iter == 0)
value = lut_guo_iter0[neighbors];
else
value = lut_guo_iter1[neighbors];

//int C = ((!p2) & (p3 | p4)) + ((!p4) & (p5 | p6)) +
// ((!p6) & (p7 | p8)) + ((!p8) & (p9 | p2));
//int N1 = (p9 | p2) + (p3 | p4) + (p5 | p6) + (p7 | p8);
//int N2 = (p2 | p3) + (p4 | p5) + (p6 | p7) + (p8 | p9);
//int N = N1 < N2 ? N1 : N2;
//int m = iter == 0 ? ((p6 | p7 | (!p9)) & p8) : ((p2 | p3 | (!p5)) & p4);
//if ((C == 1) && ((N >= 2) && ((N <= 3)) & (m == 0))) value = 0;
// else value = 1;
});
}
marker.forEach<uchar>([=](uchar& value, const int postion[]) {
int i = postion[0];
int j = postion[1];
if (i == 0 || j == 0 || i == rows - 1 || j == cols - 1) { return; }

auto ptr = img.ptr(i, j); // p1
uchar p2 = ptr[-cols];
uchar p3 = ptr[-cols + 1];
uchar p4 = ptr[1];
uchar p5 = ptr[cols + 1];
uchar p6 = ptr[cols];
uchar p7 = ptr[cols - 1];
uchar p8 = ptr[-1];
uchar p9 = ptr[-cols - 1];

int neighbors = p9 | (p2 << 1) | (p3 << 2) | (p4 << 3) | (p5 << 4) | (p6 << 5) | (p7 << 6) | (p8 << 7);
value = lut[neighbors];
});

img &= marker;
marker.setTo(0);
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}

// Apply the thinning procedure to a given image
Expand All @@ -188,21 +129,19 @@ void thinning(InputArray input, OutputArray output, int thinningType){
CV_CheckTypeEQ(processed.type(), CV_8UC1, "");
// Enforce the range of the input image to be in between 0 - 255
processed /= 255;

Mat prev = processed.clone();
Mat diff;

Mat marker = Mat::zeros(processed.size(), CV_8UC1);
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const auto lutIter0 = (thinningType == THINNING_GUOHALL) ? lut_guo_iter0 : lut_zhang_iter0;
const auto lutIter1 = (thinningType == THINNING_GUOHALL) ? lut_guo_iter1 : lut_zhang_iter1;
do {
thinningIteration(processed, 0, thinningType);
thinningIteration(processed, 1, thinningType);
absdiff(processed, prev, diff);
if (!hasNonZero(diff)) break;
thinningIteration(processed, marker, lutIter0);
thinningIteration(processed, marker, lutIter1);
const auto res = cv::norm(processed, prev, cv::NORM_L1);
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if (res <= 0) { break; }
processed.copyTo(prev);
}
while (true);
} while (true);

processed *= 255;

output.assign(processed);
}

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