Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

imgproc: add IntelligentScissors

Browse Source
pull/19194/head
Alexander Alekhin 4 years ago
parent 84676fefe3
commit 3eea3dd46b
14 changed files with 1637 additions and 7 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 127
      doc/js_tutorials/js_assets/js_intelligent_scissors.html
  2. 14
      doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown
  3. 4
      doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown
  4. 7
      doc/opencv.bib
  5. 19
      modules/imgproc/include/opencv2/imgproc.hpp
  6. 141
      modules/imgproc/include/opencv2/imgproc/segmentation.hpp
  7. 772
      modules/imgproc/src/intelligent_scissors.cpp
  8. 467
      modules/imgproc/test/test_intelligent_scissors.cpp
  9. 2
      modules/js/src/core_bindings.cpp
  10. 23
      modules/js/test/test_imgproc.js
  11. 17
      platforms/js/opencv_js.config.py
  12. 13
      samples/cpp/CMakeLists.txt
  13. 35
      samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp
  14. 3
      samples/samples_utils.cmake

127
doc/js_tutorials/js_assets/js_intelligent_scissors.html
Unescape View File

@ -0,0 +1,127 @@
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Intelligent Scissors Example</title>
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<h2>Intelligent Scissors Example</h2>
<p>
Click <b>Start</b> button to launch the code below.<br>
Then click on image to pick source point. After that you can hover mouse pointer over canvas to specify target point candidate.<br>
You can change the code in the &lt;textarea&gt; to investigate more. You can choose another image (need to "Stop" first).
</p>
<div>
<div class="control"><button id="tryIt" disabled>Start</button> <button id="stopIt" disabled>Stop</button></div>
<textarea class="code" rows="20" cols="100" id="codeEditor" spellcheck="false">
</textarea>
<p class="err" id="errorMessage"></p>
</div>
<div id="inputParams">
<div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
<canvas id="canvasInput"></canvas>
</div>
<div id="result" style="display:none">
<canvas id="canvasOutput"></canvas>
</div>
<script src="utils.js" type="text/javascript"></script>
<script id="codeSnippet" type="text/code-snippet">
let src = cv.imread('canvasInput');
//cv.resize(src, src, new cv.Size(1024, 1024));
cv.imshow('canvasOutput', src);
let tool = new cv.segmentation_IntelligentScissorsMB();
tool.setEdgeFeatureCannyParameters(32, 100);
tool.setGradientMagnitudeMaxLimit(200);
tool.applyImage(src);
let hasMap = false;
let canvas = document.getElementById('canvasOutput');
canvas.addEventListener('click', e => {
let startX = e.offsetX, startY = e.offsetY; console.log(startX, startY);
if (startX < src.cols && startY < src.rows)
{
console.time('buildMap');
tool.buildMap(new cv.Point(startX, startY));
console.timeEnd('buildMap');
hasMap = true;
}
});
canvas.addEventListener('mousemove', e => {
let x = e.offsetX, y = e.offsetY; //console.log(x, y);
let dst = src.clone();
if (hasMap && x >= 0 && x < src.cols && y >= 0 && y < src.rows)
{
let contour = new cv.Mat();
tool.getContour(new cv.Point(x, y), contour);
let contours = new cv.MatVector();
contours.push_back(contour);
let color = new cv.Scalar(0, 255, 0, 255); // RGBA
cv.polylines(dst, contours, false, color, 1, cv.LINE_8);
contours.delete(); contour.delete();
}
cv.imshow('canvasOutput', dst);
dst.delete();
});
canvas.addEventListener('dispose', e => {
src.delete();
tool.delete();
});
</script>
<script type="text/javascript">
let utils = new Utils('errorMessage');
utils.loadCode('codeSnippet', 'codeEditor');
utils.loadImageToCanvas('lena.jpg', 'canvasInput');
utils.addFileInputHandler('fileInput', 'canvasInput');
let disposeEvent = new Event('dispose');
let tryIt = document.getElementById('tryIt');
let stopIt = document.getElementById('stopIt');
tryIt.addEventListener('click', () => {
let e_input = document.getElementById('inputParams');
e_input.style.display = 'none';
let e_result = document.getElementById("result")
e_result.style.display = '';
var e = document.getElementById("canvasOutput");
var e_new = e.cloneNode(true);
e.parentNode.replaceChild(e_new, e); // reset event handlers
stopIt.removeAttribute('disabled');
tryIt.setAttribute('disabled', '');
utils.executeCode('codeEditor');
});
stopIt.addEventListener('click', () => {
let e_input = document.getElementById('inputParams');
e_input.style.display = '';
let e_result = document.getElementById("result")
e_result.style.display = 'none';
var e = document.getElementById("canvasOutput");
e.dispatchEvent(disposeEvent);
var e_new = e.cloneNode(true);
e.parentNode.replaceChild(e_new, e); // reset event handlers
tryIt.removeAttribute('disabled');
stopIt.setAttribute('disabled', '');
});
utils.loadOpenCv(() => {
tryIt.removeAttribute('disabled');
});
</script>
</body>
</html>

14
doc/js_tutorials/js_imgproc/js_intelligent_scissors/js_intelligent_scissors.markdown
Unescape View File

@ -0,0 +1,14 @@
Intelligent Scissors Demo {#tutorial_js_intelligent_scissors}
=========================
Goal
----
- Here you can check how to use IntelligentScissors tool for image segmentation task.
- Available methods and parameters: @ref cv::segmentation::IntelligentScissorsMB
\htmlonly
<iframe src="../../js_intelligent_scissors.html" width="100%"
onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
</iframe>
\endhtmlonly

4
doc/js_tutorials/js_imgproc/js_table_of_contents_imgproc.markdown
Unescape View File

@ -77,3 +77,7 @@ Image Processing {#tutorial_js_table_of_contents_imgproc}
- @subpage tutorial_js_imgproc_camera
Learn image processing for video capture.
- @subpage tutorial_js_intelligent_scissors
Learn how to use IntelligentScissors tool for image segmentation task.

7
doc/opencv.bib
Unescape View File

@ -768,6 +768,13 @@
pages = {432--441},
publisher = {Springer}
}
@INPROCEEDINGS{Mortensen95intelligentscissors,
author = {Eric N. Mortensen and William A. Barrett},
title = {Intelligent Scissors for Image Composition},
booktitle = {In Computer Graphics, SIGGRAPH Proceedings},
year = {1995},
pages = {191--198}
}
@inproceedings{Muja2009,
author = {Muja, Marius and Lowe, David G},
title = {Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration},

19
modules/imgproc/include/opencv2/imgproc.hpp
Unescape View File

@ -185,6 +185,7 @@ location of points on the plane, building special graphs (such as NNG,RNG), and
@defgroup imgproc_motion Motion Analysis and Object Tracking
@defgroup imgproc_feature Feature Detection
@defgroup imgproc_object Object Detection
@defgroup imgproc_segmentation Image Segmentation
@defgroup imgproc_c C API
@defgroup imgproc_hal Hardware Acceleration Layer
@{
@ -3227,6 +3228,9 @@ CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature
//! @} imgproc_hist
//! @addtogroup imgproc_segmentation
//! @{
/** @example samples/cpp/watershed.cpp
An example using the watershed algorithm
*/
@ -3254,11 +3258,11 @@ function.
size as image .
@sa findContours
@ingroup imgproc_misc
*/
CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
//! @} imgproc_segmentation
//! @addtogroup imgproc_filter
//! @{
@ -3304,7 +3308,7 @@ CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
//! @}
//! @addtogroup imgproc_misc
//! @addtogroup imgproc_segmentation
//! @{
/** @example samples/cpp/grabcut.cpp
@ -3334,6 +3338,11 @@ CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
InputOutputArray bgdModel, InputOutputArray fgdModel,
int iterCount, int mode = GC_EVAL );
//! @} imgproc_segmentation
//! @addtogroup imgproc_misc
//! @{
/** @example samples/cpp/distrans.cpp
An example on using the distance transform
*/
@ -4876,4 +4885,8 @@ Point LineIterator::pos() const
} // cv
#include "./imgproc/segmentation.hpp"
#endif

141
modules/imgproc/include/opencv2/imgproc/segmentation.hpp
Unescape View File

@ -0,0 +1,141 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_IMGPROC_SEGMENTATION_HPP
#define OPENCV_IMGPROC_SEGMENTATION_HPP
#include "opencv2/imgproc.hpp"
namespace cv {
namespace segmentation {
//! @addtogroup imgproc_segmentation
//! @{
/** @brief Intelligent Scissors image segmentation
*
* This class is used to find the path (contour) between two points
* which can be used for image segmentation.
*
* Usage example:
* @snippet snippets/imgproc_segmentation.cpp usage_example_intelligent_scissors
*
* Reference: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.3811&rep=rep1&type=pdf">"Intelligent Scissors for Image Composition"</a>
* algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University
* @cite Mortensen95intelligentscissors
*/
class CV_EXPORTS_W_SIMPLE IntelligentScissorsMB
{
public:
CV_WRAP
IntelligentScissorsMB();
/** @brief Specify weights of feature functions
*
* Consider keeping weights normalized (sum of weights equals to 1.0)
* Discrete dynamic programming (DP) goal is minimization of costs between pixels.
*
* @param weight_non_edge Specify cost of non-edge pixels (default: 0.43f)
* @param weight_gradient_direction Specify cost of gradient direction function (default: 0.43f)
* @param weight_gradient_magnitude Specify cost of gradient magnitude function (default: 0.14f)
*/
CV_WRAP
IntelligentScissorsMB& setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude);
/** @brief Specify gradient magnitude max value threshold
*
* Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article).
* Otherwize pixels with `gradient magnitude >= threshold` have zero cost.
*
* @note Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).
*
* @param gradient_magnitude_threshold_max Specify gradient magnitude max value threshold (default: 0, disabled)
*/
CV_WRAP
IntelligentScissorsMB& setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max = 0.0f);
/** @brief Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters
*
* This feature extractor is used by default according to article.
*
* Implementation has additional filtering for regions with low-amplitude noise.
* This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).
*
* @note Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).
*
* @note Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().
*
* @param gradient_magnitude_min_value Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)
*/
CV_WRAP
IntelligentScissorsMB& setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value = 0.0f);
/** @brief Switch edge feature extractor to use Canny edge detector
*
* @note "Laplacian Zero-Crossing" feature extractor is used by default (following to original article)
*
* @sa Canny
*/
CV_WRAP
IntelligentScissorsMB& setEdgeFeatureCannyParameters(
double threshold1, double threshold2,
int apertureSize = 3, bool L2gradient = false
);
/** @brief Specify input image and extract image features
*
* @param image input image. Type is #CV_8UC1 / #CV_8UC3
*/
CV_WRAP
IntelligentScissorsMB& applyImage(InputArray image);
/** @brief Specify custom features of imput image
*
* Customized advanced variant of applyImage() call.
*
* @param non_edge Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are `{0, 1}`.
* @param gradient_direction Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: `x^2 + y^2 == 1`
* @param gradient_magnitude Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range `[0, 1]`.
* @param image **Optional parameter**. Must be specified if subset of features is specified (non-specified features are calculated internally)
*/
CV_WRAP
IntelligentScissorsMB& applyImageFeatures(
InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude,
InputArray image = noArray()
);
/** @brief Prepares a map of optimal paths for the given source point on the image
*
* @note applyImage() / applyImageFeatures() must be called before this call
*
* @param sourcePt The source point used to find the paths
*/
CV_WRAP void buildMap(const Point& sourcePt);
/** @brief Extracts optimal contour for the given target point on the image
*
* @note buildMap() must be called before this call
*
* @param targetPt The target point
* @param[out] contour The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with `std::vector<Point>`)
* @param backward Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point)
*/
CV_WRAP void getContour(const Point& targetPt, OutputArray contour, bool backward = false) const;
#ifndef CV_DOXYGEN
struct Impl;
inline Impl* getImpl() const { return impl.get(); }
protected:
std::shared_ptr<Impl> impl;
#endif
};
//! @}
} // namespace segmentation
} // namespace cv
#endif // OPENCV_IMGPROC_SEGMENTATION_HPP

772
modules/imgproc/src/intelligent_scissors.cpp
Unescape View File

@ -0,0 +1,772 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "precomp.hpp"
//#include "opencv2/imgproc/segmentation.hpp"
#include <opencv2/core/utils/logger.hpp>
#include <queue> // std::priority_queue
namespace cv {
namespace segmentation {
namespace {
// 0 1 2
// 3 x 4
// 5 6 7
static const int neighbors[8][2] = {
{ -1, -1 },
{ 0, -1 },
{ 1, -1 },
{ -1, 0 },
{ 1, 0 },
{ -1, 1 },
{ 0, 1 },
{ 1, 1 },
};
// encoded reverse direction
static const int neighbors_encode[8] = {
7+1, 6+1, 5+1,
4+1, 3+1,
2+1, 1+1, 0+1
};
#define ACOS_TABLE_SIZE 64
// acos_table[x + ACOS_TABLE_SIZE] = acos(x / ACOS_TABLE_SIZE) / CV_PI (see local_cost)
// x = [ -ACOS_TABLE_SIZE .. ACOS_TABLE_SIZE ]
float* getAcosTable()
{
constexpr int N = ACOS_TABLE_SIZE;
static bool initialized = false;
static float acos_table[2*N + 1] = { 0 };
if (!initialized)
{
const float CV_PI_inv = static_cast<float>(1.0 / CV_PI);
for (int i = -N; i <= N; i++)
{
acos_table[i + N] = acosf(i / (float)N) * CV_PI_inv;
}
initialized = true;
}
return acos_table;
}
} // namespace anon
struct IntelligentScissorsMB::Impl
{
// proposed weights from the article (sum = 1.0)
float weight_non_edge = 0.43f;
float weight_gradient_direction = 0.43f;
float weight_gradient_magnitude = 0.14f;
enum EdgeFeatureMode {
FEATURE_ZERO_CROSSING = 0,
FEATURE_CANNY
};
EdgeFeatureMode edge_mode = FEATURE_ZERO_CROSSING;
// FEATURE_ZERO_CROSSING
float edge_gradient_magnitude_min_value = 0.0f;
// FEATURE_CANNY
double edge_canny_threshold1 = 10;
double edge_canny_threshold2 = 100;
int edge_canny_apertureSize = 3;
bool edge_canny_L2gradient = false;
float gradient_magnitude_threshold_max = 0.0f; // disabled thresholding
int sobelKernelSize = 3; // 1 or 3
int laplacianKernelSize = 3; // 1 or 3
// image features
Mat_<Point2f> gradient_direction; //< I: normalized laplacian x/y components
Mat_<float> gradient_magnitude; //< Fg: gradient cost function
Mat_<uchar> non_edge_feature; //< Fz: zero-crossing function
float weight_non_edge_compute = 0.0f;
// encoded paths map (produced by `buildMap()`)
Mat_<uchar> optimalPathsMap;
void resetFeatures_()
{
CV_TRACE_FUNCTION();
gradient_direction.release();
gradient_magnitude.release();
non_edge_feature.release();
weight_non_edge_compute = weight_non_edge;
optimalPathsMap.release();
}
Size src_size;
Mat image_;
Mat grayscale_;
void initImage_(InputArray image)
{
CV_TRACE_FUNCTION();
if (!image_.empty())
return;
CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, "");
src_size = image.size();
image_ = image.getMat();
}
void initGrayscale_(InputArray image)
{
CV_TRACE_FUNCTION();
if (!grayscale_.empty())
return;
CV_Assert(!image.empty());
CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, "");
src_size = image.size();
if (image.channels() > 1)
cvtColor(image, grayscale_, COLOR_BGR2GRAY);
else
grayscale_ = image.getMat();
}
Mat Ix_, Iy_;
void initImageDerives_(InputArray image)
{
CV_TRACE_FUNCTION();
if (!Ix_.empty())
return;
initGrayscale_(image);
Sobel(grayscale_, Ix_, CV_32FC1, 1, 0, sobelKernelSize);
Sobel(grayscale_, Iy_, CV_32FC1, 0, 1, sobelKernelSize);
}
Mat image_magnitude_;
void initImageMagnitude_(InputArray image)
{
CV_TRACE_FUNCTION();
if (!image_magnitude_.empty())
return;
initImageDerives_(image);
magnitude(Ix_, Iy_, image_magnitude_);
}
void cleanupFeaturesTemporaryArrays_()
{
CV_TRACE_FUNCTION();
image_.release();
grayscale_.release();
Ix_.release();
Iy_.release();
image_magnitude_.release();
}
Impl()
{
// nothing
CV_TRACE_FUNCTION();
}
void setWeights(float weight_non_edge_, float weight_gradient_direction_, float weight_gradient_magnitude_)
{
CV_TRACE_FUNCTION();
CV_CheckGE(weight_non_edge_, 0.0f, "");
CV_CheckGE(weight_gradient_direction_, 0.0f, "");
CV_CheckGE(weight_gradient_magnitude_, 0.0f, "");
CV_CheckGE(weight_non_edge_ + weight_gradient_direction_ + weight_gradient_magnitude_, FLT_EPSILON, "Sum of weights must be greater than zero");
weight_non_edge = weight_non_edge_;
weight_gradient_direction = weight_gradient_direction_;
weight_gradient_magnitude = weight_gradient_magnitude_;
resetFeatures_();
}
void setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max_)
{
CV_TRACE_FUNCTION();
CV_CheckGE(gradient_magnitude_threshold_max_, 0.0f, "");
gradient_magnitude_threshold_max = gradient_magnitude_threshold_max_;
resetFeatures_();
}
void setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value_)
{
CV_TRACE_FUNCTION();
CV_CheckGE(gradient_magnitude_min_value_, 0.0f, "");
edge_mode = FEATURE_ZERO_CROSSING;
edge_gradient_magnitude_min_value = gradient_magnitude_min_value_;
resetFeatures_();
}
void setEdgeFeatureCannyParameters(
double threshold1, double threshold2,
int apertureSize = 3, bool L2gradient = false
)
{
CV_TRACE_FUNCTION();
CV_CheckGE(threshold1, 0.0, "");
CV_CheckGE(threshold2, 0.0, "");
edge_mode = FEATURE_CANNY;
edge_canny_threshold1 = threshold1;
edge_canny_threshold2 = threshold2;
edge_canny_apertureSize = apertureSize;
edge_canny_L2gradient = L2gradient;
resetFeatures_();
}
void applyImageFeatures(
InputArray non_edge, InputArray gradient_direction_, InputArray gradient_magnitude_,
InputArray image
)
{
CV_TRACE_FUNCTION();
resetFeatures_();
cleanupFeaturesTemporaryArrays_();
src_size = Size(0, 0);
if (!non_edge.empty())
src_size = non_edge.size();
if (!gradient_direction_.empty())
{
Size gradient_direction_size = gradient_direction_.size();
if (!src_size.empty())
CV_CheckEQ(src_size, gradient_direction_size, "");
else
src_size = gradient_direction_size;
}
if (!gradient_magnitude_.empty())
{
Size gradient_magnitude_size = gradient_magnitude_.size();
if (!src_size.empty())
CV_CheckEQ(src_size, gradient_magnitude_size, "");
else
src_size = gradient_magnitude_size;
}
if (!image.empty())
{
Size image_size = image.size();
if (!src_size.empty())
CV_CheckEQ(src_size, image_size, "");
else
src_size = image_size;
}
// src_size must be filled
CV_Assert(!src_size.empty());
if (!non_edge.empty())
{
CV_CheckTypeEQ(non_edge.type(), CV_8UC1, "");
non_edge_feature = non_edge.getMat();
}
else
{
if (weight_non_edge == 0.0f)
{
non_edge_feature.create(src_size);
non_edge_feature.setTo(0);
}
else
{
if (image.empty())
CV_Error(Error::StsBadArg, "Non-edge feature parameter is missing. Input image parameter is required to extract this feature");
extractEdgeFeature_(image);
}
}
if (!gradient_direction_.empty())
{
CV_CheckTypeEQ(gradient_direction_.type(), CV_32FC2, "");
gradient_direction = gradient_direction_.getMat();
}
else
{
if (weight_gradient_direction == 0.0f)
{
gradient_direction.create(src_size);
gradient_direction.setTo(Scalar::all(0));
}
else
{
if (image.empty())
CV_Error(Error::StsBadArg, "Gradient direction feature parameter is missing. Input image parameter is required to extract this feature");
extractGradientDirection_(image);
}
}
if (!gradient_magnitude_.empty())
{
CV_CheckTypeEQ(gradient_magnitude_.type(), CV_32FC1, "");
gradient_magnitude = gradient_magnitude_.getMat();
}
else
{
if (weight_gradient_magnitude == 0.0f)
{
gradient_magnitude.create(src_size);
gradient_magnitude.setTo(Scalar::all(0));
}
else
{
if (image.empty())
CV_Error(Error::StsBadArg, "Gradient magnitude feature parameter is missing. Input image parameter is required to extract this feature");
extractGradientMagnitude_(image);
}
}
cleanupFeaturesTemporaryArrays_();
}
void extractEdgeFeature_(InputArray image)
{
CV_TRACE_FUNCTION();
if (edge_mode == FEATURE_CANNY)
{
CV_LOG_DEBUG(NULL, "Canny(" << edge_canny_threshold1 << ", " << edge_canny_threshold2 << ")");
Mat img_canny;
Canny(image, img_canny, edge_canny_threshold1, edge_canny_threshold2, edge_canny_apertureSize, edge_canny_L2gradient);
#if 0
threshold(img_canny, non_edge_feature, 254, 1, THRESH_BINARY_INV);
#else
// Canny result values are 0 or 255
bitwise_not(img_canny, non_edge_feature);
weight_non_edge_compute = weight_non_edge * (1.0f / 255.0f);
#endif
}
else // if (edge_mode == FEATURE_ZERO_CROSSING)
{
initGrayscale_(image);
Mat_<short> laplacian;
Laplacian(grayscale_, laplacian, CV_16S, laplacianKernelSize);
Mat_<uchar> zero_crossing(src_size, 1);
const size_t zstep = zero_crossing.step[0];
for (int y = 0; y < src_size.height - 1; y++)
{
const short* row0 = laplacian.ptr<short>(y);
const short* row1 = laplacian.ptr<short>(y + 1);
uchar* zrow0 = zero_crossing.ptr<uchar>(y);
//uchar* zrow1 = zero_crossing.ptr<uchar>(y + 1);
for (int x = 0; x < src_size.width - 1; x++)
{
const int v = row0[x];
const int neg_v = -v;
// - * 1
// 2 3 4
const int v1 = row0[x + 1];
const int v2 = (x > 0) ? row1[x - 1] : v;
const int v3 = row1[x + 0];
const int v4 = row1[x + 1];
if (v < 0)
{
if (v1 > 0)
{
zrow0[x + ((v1 < neg_v) ? 1 : 0)] = 0;
}
if (v2 > 0)
{
zrow0[x + ((v2 < neg_v) ? (zstep - 1) : 0)] = 0;
}
if (v3 > 0)
{
zrow0[x + ((v3 < neg_v) ? (zstep + 0) : 0)] = 0;
}
if (v4 > 0)
{
zrow0[x + ((v4 < neg_v) ? (zstep + 1) : 0)] = 0;
}
}
else
{
if (v1 < 0)
{
zrow0[x + ((v1 > neg_v) ? 1 : 0)] = 0;
}
if (v2 < 0)
{
zrow0[x + ((v2 > neg_v) ? (zstep - 1) : 0)] = 0;
}
if (v3 < 0)
{
zrow0[x + ((v3 > neg_v) ? (zstep + 0) : 0)] = 0;
}
if (v4 < 0)
{
zrow0[x + ((v4 > neg_v) ? (zstep + 1) : 0)] = 0;
}
}
}
}
if (edge_gradient_magnitude_min_value > 0)
{
initImageMagnitude_(image);
Mat mask = image_magnitude_ < edge_gradient_magnitude_min_value;
zero_crossing.setTo(1, mask); // reset low-amplitude noise
}
non_edge_feature = zero_crossing;
}
}
void extractGradientDirection_(InputArray image)
{
CV_TRACE_FUNCTION();
initImageMagnitude_(image); // calls internally: initImageDerives_(image);
gradient_direction.create(src_size);
for (int y = 0; y < src_size.height; y++)
{
const float* magnutude_row = image_magnitude_.ptr<float>(y);
const float* Ix_row = Ix_.ptr<float>(y);
const float* Iy_row = Iy_.ptr<float>(y);
Point2f* gradient_direction_row = gradient_direction.ptr<Point2f>(y);
for (int x = 0; x < src_size.width; x++)
{
const float m = magnutude_row[x];
if (m > FLT_EPSILON)
{
float m_inv = 1.0f / m;
gradient_direction_row[x] = Point2f(Ix_row[x] * m_inv, Iy_row[x] * m_inv);
}
else
{
gradient_direction_row[x] = Point2f(0, 0);
}
}
}
}
void extractGradientMagnitude_(InputArray image)
{
CV_TRACE_FUNCTION();
initImageMagnitude_(image); // calls internally: initImageDerives_(image);
Mat m;
double max_m = 0;
if (gradient_magnitude_threshold_max > 0)
{
threshold(image_magnitude_, m, gradient_magnitude_threshold_max, 0, THRESH_TRUNC);
max_m = gradient_magnitude_threshold_max;
}
else
{
m = image_magnitude_;
minMaxLoc(m, 0, &max_m);
}
if (max_m <= FLT_EPSILON)
{
CV_LOG_INFO(NULL, "IntelligentScissorsMB: input image gradient is almost zero")
gradient_magnitude.create(src_size);
gradient_magnitude.setTo(0);
}
else
{
m.convertTo(gradient_magnitude, CV_32F, -1.0 / max_m, 1.0); // normalize and inverse to range 0..1
}
}
void applyImage(InputArray image)
{
CV_TRACE_FUNCTION();
CV_CheckType(image.type(), image.type() == CV_8UC1 || image.type() == CV_8UC3 || image.type() == CV_8UC4, "");
resetFeatures_();
cleanupFeaturesTemporaryArrays_();
extractEdgeFeature_(image);
extractGradientDirection_(image);
extractGradientMagnitude_(image);
cleanupFeaturesTemporaryArrays_();
}
// details: see section 3.1 of the article
const float* acos_table = getAcosTable();
float local_cost(const Point& p, const Point& q) const
{
const bool isDiag = (p.x != q.x) && (p.y != q.y);
float fG = gradient_magnitude.at<float>(q);
const Point2f diff((float)(q.x - p.x), (float)(q.y - p.y));
const Point2f Ip = gradient_direction(p);
const Point2f Iq = gradient_direction(q);
const Point2f Dp(Ip.y, -Ip.x); // D(p) - 90 degrees clockwise
const Point2f Dq(Iq.y, -Iq.x); // D(q) - 90 degrees clockwise
float dp = Dp.dot(diff); // dp(p, q)
float dq = Dq.dot(diff); // dq(p, q)
if (dp < 0)
{
dp = -dp; // ensure dp >= 0
dq = -dq;
}
const float sqrt2_inv = 0.7071067811865475f; // 1.0 / sqrt(2)
if (isDiag)
{
dp *= sqrt2_inv; // normalize length of (q - p)
dq *= sqrt2_inv; // normalize length of (q - p)
}
else
{
fG *= sqrt2_inv;
}
#if 1
int dp_i = cvFloor(dp * ACOS_TABLE_SIZE); // dp is in range 0..1
dp_i = std::min(ACOS_TABLE_SIZE, std::max(0, dp_i));
int dq_i = cvFloor(dq * ACOS_TABLE_SIZE); // dq is in range -1..1
dq_i = std::min(ACOS_TABLE_SIZE, std::max(-ACOS_TABLE_SIZE, dq_i));
const float fD = acos_table[dp_i + ACOS_TABLE_SIZE] + acos_table[dq_i + ACOS_TABLE_SIZE];
#else
const float CV_PI_inv = static_cast<float>(1.0 / CV_PI);
const float fD = (acosf(dp) + acosf(dq)) * CV_PI_inv; // TODO optimize acos calls (through tables)
#endif
float cost =
weight_non_edge_compute * non_edge_feature.at<uchar>(q) +
weight_gradient_direction * fD +
weight_gradient_magnitude * fG;
return cost;
}
struct Pix
{
Point pt;
float cost; // NOTE: do not remove cost from here through replacing by cost(pt) map access
inline bool operator > (const Pix &b) const
{
return cost > b.cost;
}
};
void buildMap(const Point& start_point)
{
CV_TRACE_FUNCTION();
CV_Assert(!src_size.empty());
CV_Assert(!gradient_magnitude.empty() && "Features are missing. applyImage() must be called first");
CV_CheckGE(weight_non_edge + weight_gradient_direction + weight_gradient_magnitude, FLT_EPSILON, "");
#if 0 // debug
Rect wholeImage(0, 0, src_size.width, src_size.height);
Rect roi = Rect(start_point.x - 5, start_point.y - 5, 11, 11) & wholeImage;
std::cout << roi << std::endl;
std::cout << gradient_magnitude(roi) << std::endl;
std::cout << gradient_direction(roi) << std::endl;
std::cout << non_edge_feature(roi) << std::endl;
#endif
optimalPathsMap.release();
optimalPathsMap.create(src_size);
optimalPathsMap.setTo(0); // optimalPathsMap(start_point) = 0;
//
// Section 3.2
// Live-Wire 2-D DP graph search.
//
Mat_<float> cost_map(src_size, FLT_MAX); // g(q)
Mat_<uchar> processed(src_size, (uchar)0); // e(q)
// Note: std::vector is faster than std::deque
// TODO check std::set
std::priority_queue< Pix, std::vector<Pix>, std::greater<Pix> > L;
cost_map(start_point) = 0;
L.emplace(Pix{ start_point, 0/*cost*/ });
while (!L.empty())
{
Pix pix = L.top(); L.pop();
Point q = pix.pt; // 'q' from the article
if (processed(q))
continue; // already processed (with lower cost, see note below)
processed(q) = 1;
#if 1
const float cost_q = pix.cost;
#else
const float cost_q = cost_map(q);
CV_Assert(cost_q == pix.cost);
#endif
for (int n = 0; n < 8; n++) // scan neighbours
{
Point r(q.x + neighbors[n][0], q.y + neighbors[n][1]); // 'r' from the article
if (r.x < 0 || r.x >= src_size.width || r.y < 0 || r.y >= src_size.height)
continue; // out of range
#if !defined(__EMSCRIPTEN__) // slower in JS
float& cost_r = cost_map(r);
if (cost_r < cost_q)
continue; // already processed
#else
if (processed(r))
continue; // already processed
float& cost_r = cost_map(r);
CV_DbgCheckLE(cost_q, cost_r, "INTERNAL ERROR: sorted queue is corrupted");
#endif
float cost = cost_q + local_cost(q, r); // TODO(opt): compute partially until cost < cost_r
if (cost < cost_r)
{
#if 0 // avoid compiler warning
if (cost_r != FLT_MAX)
{
// In article the point 'r' is removed from the queue L
// to be re-inserted again with sorting against new optimized cost.
// We can do nothing, because "new point" will be placed before in the sorted queue.
// Old point will be skipped through "if (processed(q))" check above after processing of new optimal candidate.
//
// This approach leads to some performance impact, however it is much smaller than element removal from the sorted queue.
// So, do nothing.
}
#endif
cost_r = cost;
L.emplace(Pix{ r, cost });
optimalPathsMap(r) = (uchar)neighbors_encode[n];
}
}
}
}
void getContour(const Point& target, OutputArray contour_, bool backward)
{
CV_TRACE_FUNCTION();
CV_Assert(!optimalPathsMap.empty() && "buildMap() must be called before getContour()");
const int cols = optimalPathsMap.cols;
const int rows = optimalPathsMap.rows;
std::vector<Point> result; result.reserve(512);
size_t loop_check = 4096;
Point pt = target;
for (size_t i = 0; i < (size_t)rows * cols; i++) // don't hang on invalid maps
{
CV_CheckLT(pt.x, cols, "");
CV_CheckLT(pt.y, rows, "");
result.push_back(pt);
int direction = (int)optimalPathsMap(pt);
if (direction == 0)
break; // stop, start point is reached
CV_CheckLT(direction, 9, "Map is invalid");
Point next(pt.x + neighbors[direction - 1][0], pt.y + neighbors[direction - 1][1]);
pt = next;
if (result.size() == loop_check) // optional sanity check of invalid maps with loops (don't eat huge amount of memory)
{
loop_check *= 4; // next limit for loop check
for (const auto& pt_check : result)
{
CV_CheckNE(pt_check, pt, "Map is invalid. Contour loop is detected");
}
}
}
if (backward)
{
_InputArray(result).copyTo(contour_);
}
else
{
const int N = (int)result.size();
const int sz[1] = { N };
contour_.create(1, sz, CV_32SC2);
Mat_<Point> contour = contour_.getMat();
for (int i = 0; i < N; i++)
{
contour.at<Point>(i) = result[N - (i + 1)];
}
}
}
};
IntelligentScissorsMB::IntelligentScissorsMB()
: impl(std::make_shared<Impl>())
{
// nothing
}
IntelligentScissorsMB& IntelligentScissorsMB::setWeights(float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude)
{
CV_DbgAssert(impl);
impl->setWeights(weight_non_edge, weight_gradient_direction, weight_gradient_magnitude);
return *this;
}
IntelligentScissorsMB& IntelligentScissorsMB::setGradientMagnitudeMaxLimit(float gradient_magnitude_threshold_max)
{
CV_DbgAssert(impl);
impl->setGradientMagnitudeMaxLimit(gradient_magnitude_threshold_max);
return *this;
}
IntelligentScissorsMB& IntelligentScissorsMB::setEdgeFeatureZeroCrossingParameters(float gradient_magnitude_min_value)
{
CV_DbgAssert(impl);
impl->setEdgeFeatureZeroCrossingParameters(gradient_magnitude_min_value);
return *this;
}
IntelligentScissorsMB& IntelligentScissorsMB::setEdgeFeatureCannyParameters(
double threshold1, double threshold2,
int apertureSize, bool L2gradient
)
{
CV_DbgAssert(impl);
impl->setEdgeFeatureCannyParameters(threshold1, threshold2, apertureSize, L2gradient);
return *this;
}
IntelligentScissorsMB& IntelligentScissorsMB::applyImage(InputArray image)
{
CV_DbgAssert(impl);
impl->applyImage(image);
return *this;
}
IntelligentScissorsMB& IntelligentScissorsMB::applyImageFeatures(
InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude,
InputArray image
)
{
CV_DbgAssert(impl);
impl->applyImageFeatures(non_edge, gradient_direction, gradient_magnitude, image);
return *this;
}
void IntelligentScissorsMB::buildMap(const Point& pt)
{
CV_DbgAssert(impl);
impl->buildMap(pt);
}
void IntelligentScissorsMB::getContour(const Point& target, OutputArray contour, bool backward) const
{
CV_DbgAssert(impl);
impl->getContour(target, contour, backward);
}
}} // namespace

467
modules/imgproc/test/test_intelligent_scissors.cpp
Unescape View File

@ -0,0 +1,467 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
//#include "opencv2/imgproc/segmentation.hpp"
namespace opencv_test { namespace {
Mat getTestImageGray()
{
static Mat m;
if (m.empty())
{
m = imread(findDataFile("shared/lena.png"), IMREAD_GRAYSCALE);
}
return m.clone();
}
Mat getTestImageColor()
{
static Mat m;
if (m.empty())
{
m = imread(findDataFile("shared/lena.png"), IMREAD_COLOR);
}
return m.clone();
}
Mat getTestImage1()
{
static Mat m;
if (m.empty())
{
m.create(Size(200, 100), CV_8UC1);
m.setTo(Scalar::all(128));
Rect roi(50, 30, 100, 40);
m(roi).setTo(Scalar::all(0));
#if 0
imshow("image", m);
waitKey();
#endif
}
return m.clone();
}
Mat getTestImage2()
{
static Mat m;
if (m.empty())
{
m.create(Size(200, 100), CV_8UC1);
m.setTo(Scalar::all(128));
Rect roi(40, 30, 100, 40);
m(roi).setTo(Scalar::all(255));
#if 0
imshow("image", m);
waitKey();
#endif
}
return m.clone();
}
Mat getTestImage3()
{
static Mat m;
if (m.empty())
{
m.create(Size(200, 100), CV_8UC1);
m.setTo(Scalar::all(128));
Scalar color(0,0,0,0);
line(m, Point(30, 50), Point(50, 50), color, 1);
line(m, Point(50, 50), Point(80, 30), color, 1);
line(m, Point(150, 50), Point(80, 30), color, 1);
line(m, Point(150, 50), Point(180, 50), color, 1);
line(m, Point(80, 10), Point(80, 90), Scalar::all(200), 1);
line(m, Point(100, 10), Point(100, 90), Scalar::all(200), 1);
line(m, Point(120, 10), Point(120, 90), Scalar::all(200), 1);
#if 0
imshow("image", m);
waitKey();
#endif
}
return m.clone();
}
Mat getTestImage4()
{
static Mat m;
if (m.empty())
{
m.create(Size(200, 100), CV_8UC1);
for (int y = 0; y < m.rows; y++)
{
for (int x = 0; x < m.cols; x++)
{
float dx = (float)(x - 100);
float dy = (float)(y - 100);
float d = sqrtf(dx * dx + dy * dy);
m.at<uchar>(y, x) = saturate_cast<uchar>(100 + 100 * sin(d / 10 * CV_PI));
}
}
#if 0
imshow("image", m);
waitKey();
#endif
}
return m.clone();
}
Mat getTestImage5()
{
static Mat m;
if (m.empty())
{
m.create(Size(200, 100), CV_8UC1);
for (int y = 0; y < m.rows; y++)
{
for (int x = 0; x < m.cols; x++)
{
float dx = (float)(x - 100);
float dy = (float)(y - 100);
float d = sqrtf(dx * dx + dy * dy);
m.at<uchar>(y, x) = saturate_cast<uchar>(x / 2 + 100 * sin(d / 10 * CV_PI));
}
}
#if 0
imshow("image", m);
waitKey();
#endif
}
return m.clone();
}
void show(const Mat& img, const std::vector<Point> pts)
{
if (cvtest::debugLevel >= 10)
{
Mat dst = img.clone();
std::vector< std::vector<Point> > contours;
contours.push_back(pts);
polylines(dst, contours, false, Scalar::all(255));
imshow("dst", dst);
waitKey();
}
}
TEST(Imgproc_IntelligentScissorsMB, rect)
{
segmentation::IntelligentScissorsMB tool;
tool.applyImage(getTestImage1());
Point source_point(50, 30);
tool.buildMap(source_point);
Point target_point(100, 30);
std::vector<Point> pts;
tool.getContour(target_point, pts);
tool.applyImage(getTestImage2());
tool.buildMap(source_point);
std::vector<Point> pts2;
tool.getContour(target_point, pts2, true/*backward*/);
EXPECT_EQ(pts.size(), pts2.size());
}
TEST(Imgproc_IntelligentScissorsMB, lines)
{
segmentation::IntelligentScissorsMB tool;
Mat image = getTestImage3();
tool.applyImage(image);
Point source_point(30, 50);
tool.buildMap(source_point);
Point target_point(150, 50);
std::vector<Point> pts;
tool.getContour(target_point, pts);
EXPECT_EQ((size_t)121, pts.size());
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, circles)
{
segmentation::IntelligentScissorsMB tool;
tool.setGradientMagnitudeMaxLimit(10);
Mat image = getTestImage4();
tool.applyImage(image);
Point source_point(50, 50);
tool.buildMap(source_point);
Point target_point(150, 50);
std::vector<Point> pts;
tool.getContour(target_point, pts);
EXPECT_EQ((size_t)101, pts.size());
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, circles_gradient)
{
segmentation::IntelligentScissorsMB tool;
Mat image = getTestImage5();
tool.applyImage(image);
Point source_point(50, 50);
tool.buildMap(source_point);
Point target_point(150, 50);
std::vector<Point> pts;
tool.getContour(target_point, pts);
EXPECT_EQ((size_t)101, pts.size());
show(image, pts);
}
#define PTS_SIZE_EPS 2
TEST(Imgproc_IntelligentScissorsMB, grayscale)
{
segmentation::IntelligentScissorsMB tool;
Mat image = getTestImageGray();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 206;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_1_0_0_zerro_crossing_with_limit)
{
segmentation::IntelligentScissorsMB tool;
tool.setEdgeFeatureZeroCrossingParameters(64);
tool.setWeights(1.0f, 0.0f, 0.0f);
Mat image = getTestImageGray();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 207;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_1_0_0_canny)
{
segmentation::IntelligentScissorsMB tool;
tool.setEdgeFeatureCannyParameters(50, 100);
tool.setWeights(1.0f, 0.0f, 0.0f);
Mat image = getTestImageGray();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 201;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_0_1_0)
{
segmentation::IntelligentScissorsMB tool;
tool.setWeights(0.0f, 1.0f, 0.0f);
Mat image = getTestImageGray();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 166;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, check_features_grayscale_0_0_1)
{
segmentation::IntelligentScissorsMB tool;
tool.setWeights(0.0f, 0.0f, 1.0f);
Mat image = getTestImageGray();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 197;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, color)
{
segmentation::IntelligentScissorsMB tool;
Mat image = getTestImageColor();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 205;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, color_canny)
{
segmentation::IntelligentScissorsMB tool;
tool.setEdgeFeatureCannyParameters(32, 100);
Mat image = getTestImageColor();
tool.applyImage(image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 200;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, color_custom_features_invalid)
{
segmentation::IntelligentScissorsMB tool;
ASSERT_ANY_THROW(tool.applyImageFeatures(noArray(), noArray(), noArray()));
}
TEST(Imgproc_IntelligentScissorsMB, color_custom_features_edge)
{
segmentation::IntelligentScissorsMB tool;
Mat image = getTestImageColor();
Mat canny_edges;
Canny(image, canny_edges, 32, 100, 5);
Mat binary_edge_feature;
cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV);
tool.applyImageFeatures(binary_edge_feature, noArray(), noArray(), image);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 201;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, color_custom_features_all)
{
segmentation::IntelligentScissorsMB tool;
tool.setWeights(0.9f, 0.0f, 0.1f);
Mat image = getTestImageColor();
Mat canny_edges;
Canny(image, canny_edges, 50, 100, 5);
Mat binary_edge_feature; // 0, 1 values
cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV);
Mat_<Point2f> gradient_direction(image.size(), Point2f(0, 0)); // normalized
Mat_<float> gradient_magnitude(image.size(), 0); // cost function
tool.applyImageFeatures(binary_edge_feature, gradient_direction, gradient_magnitude);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 201;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
TEST(Imgproc_IntelligentScissorsMB, color_custom_features_edge_magnitude)
{
segmentation::IntelligentScissorsMB tool;
tool.setWeights(0.9f, 0.0f, 0.1f);
Mat image = getTestImageColor();
Mat canny_edges;
Canny(image, canny_edges, 50, 100, 5);
Mat binary_edge_feature; // 0, 1 values
cv::threshold(canny_edges, binary_edge_feature, 254, 1, THRESH_BINARY_INV);
Mat_<float> gradient_magnitude(image.size(), 0); // cost function
tool.applyImageFeatures(binary_edge_feature, noArray(), gradient_magnitude);
Point source_point(275, 63);
tool.buildMap(source_point);
Point target_point(413, 155);
std::vector<Point> pts;
tool.getContour(target_point, pts);
size_t gold = 201;
EXPECT_GE(pts.size(), gold - PTS_SIZE_EPS);
EXPECT_LE(pts.size(), gold + PTS_SIZE_EPS);
show(image, pts);
}
}} // namespace

2
modules/js/src/core_bindings.cpp
Unescape View File

@ -87,6 +87,8 @@ namespace hal {
using namespace emscripten;
using namespace cv;
using namespace cv::segmentation; // FIXIT
#ifdef HAVE_OPENCV_DNN
using namespace cv::dnn;
#endif

23
modules/js/test/test_imgproc.js
Unescape View File

@ -977,3 +977,26 @@ QUnit.test('warpPolar', function(assert) {
96, 83, 64, 45, 32
]);
});
QUnit.test('IntelligentScissorsMB', function(assert) {
const lines = new cv.Mat(50, 100, cv.CV_8U, new cv.Scalar(0));
lines.row(10).setTo(new cv.Scalar(255));
assert.ok(lines instanceof cv.Mat);
let tool = new cv.segmentation_IntelligentScissorsMB();
tool.applyImage(lines);
assert.ok(lines instanceof cv.Mat);
lines.delete();
tool.buildMap(new cv.Point(10, 10));
let contour = new cv.Mat();
tool.getContour(new cv.Point(50, 10), contour);
assert.equal(contour.type(), cv.CV_32SC2);
assert.ok(contour.total() == 41, contour.total());
tool.getContour(new cv.Point(80, 10), contour);
assert.equal(contour.type(), cv.CV_32SC2);
assert.ok(contour.total() == 71, contour.total());
});

17
platforms/js/opencv_js.config.py
Unescape View File

@ -18,8 +18,21 @@ imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP
'matchShapes', 'matchTemplate','medianBlur', 'minAreaRect', 'minEnclosingCircle', 'moments', 'morphologyEx', \
'pointPolygonTest', 'putText','pyrDown','pyrUp','rectangle','remap', 'resize','sepFilter2D','threshold', \
'undistort','warpAffine','warpPerspective','warpPolar','watershed', \
'fillPoly', 'fillConvexPoly'],
'CLAHE': ['apply', 'collectGarbage', 'getClipLimit', 'getTilesGridSize', 'setClipLimit', 'setTilesGridSize']}
'fillPoly', 'fillConvexPoly', 'polylines',
],
'CLAHE': ['apply', 'collectGarbage', 'getClipLimit', 'getTilesGridSize', 'setClipLimit', 'setTilesGridSize'],
'segmentation_IntelligentScissorsMB': [
'IntelligentScissorsMB',
'setWeights',
'setGradientMagnitudeMaxLimit',
'setEdgeFeatureZeroCrossingParameters',
'setEdgeFeatureCannyParameters',
'applyImage',
'applyImageFeatures',
'buildMap',
'getContour'
],
}
objdetect = {'': ['groupRectangles'],
'HOGDescriptor': ['load', 'HOGDescriptor', 'getDefaultPeopleDetector', 'getDaimlerPeopleDetector', 'setSVMDetector', 'detectMultiScale'],

13
samples/cpp/CMakeLists.txt
Unescape View File

@ -23,6 +23,9 @@ if(NOT BUILD_EXAMPLES OR NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
set(DEPS_example_snippet_imgproc_segmentation opencv_core opencv_imgproc)
set(DEPS_example_cpp_intelligent_scissors opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui)
project(cpp_samples)
ocv_include_modules_recurse(${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
file(GLOB_RECURSE cpp_samples RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.cpp)
@ -32,11 +35,17 @@ endif()
ocv_list_filterout(cpp_samples "real_time_pose_estimation/")
foreach(sample_filename ${cpp_samples})
set(package "cpp")
if(sample_filename MATCHES "tutorial_code")
if(sample_filename MATCHES "tutorial_code/snippet")
set(package "snippet")
elseif(sample_filename MATCHES "tutorial_code")
set(package "tutorial")
endif()
ocv_define_sample(tgt ${sample_filename} ${package})
ocv_target_link_libraries(${tgt} PRIVATE ${OPENCV_LINKER_LIBS} ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
set(deps ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
if(DEFINED DEPS_${tgt})
set(deps ${DEPS_${tgt}})
endif()
ocv_target_link_libraries(${tgt} PRIVATE ${OPENCV_LINKER_LIBS} ${deps})
if(sample_filename MATCHES "/gpu/" AND HAVE_opencv_cudaarithm AND HAVE_opencv_cuda_filters)
ocv_target_link_libraries(${tgt} PRIVATE opencv_cudaarithm opencv_cudafilters)
endif()

35
samples/cpp/tutorial_code/snippets/imgproc_segmentation.cpp
Unescape View File

@ -0,0 +1,35 @@
#include "opencv2/imgproc.hpp"
#include "opencv2/imgproc/segmentation.hpp"
using namespace cv;
static
void usage_example_intelligent_scissors()
{
Mat image(Size(1920, 1080), CV_8UC3, Scalar::all(128));
//! [usage_example_intelligent_scissors]
segmentation::IntelligentScissorsMB tool;
tool.setEdgeFeatureCannyParameters(16, 100) // using Canny() as edge feature extractor
.setGradientMagnitudeMaxLimit(200);
// calculate image features
tool.applyImage(image);
// calculate map for specified source point
Point source_point(200, 100);
tool.buildMap(source_point);
// fast fetching of contours
// for specified target point and the pre-calculated map (stored internally)
Point target_point(400, 300);
std::vector<Point> pts;
tool.getContour(target_point, pts);
//! [usage_example_intelligent_scissors]
}
int main()
{
usage_example_intelligent_scissors();
return 0;
}

3
samples/samples_utils.cmake
Unescape View File

@ -4,6 +4,9 @@
function(ocv_define_sample out_target source sub)
get_filename_component(name "${source}" NAME_WE)
set(the_target "example_${sub}_${name}")
if(OPENCV_DUMP_EXAMPLE_TARGET)
message(STATUS "Example: ${the_target} (${source})")
endif()
add_executable(${the_target} "${source}")
if(TARGET Threads::Threads AND NOT OPENCV_EXAMPLES_DISABLE_THREADS)
target_link_libraries(${the_target} PRIVATE Threads::Threads)

Write Preview
Loading…
Cancel
Save