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fix: fix build for ros noetic #2

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4 changes: 2 additions & 2 deletions camera_model/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ find_package(catkin REQUIRED COMPONENTS
find_package(Boost REQUIRED COMPONENTS filesystem program_options system)
include_directories(${Boost_INCLUDE_DIRS})

find_package(OpenCV 3.4.5 REQUIRED)
find_package(OpenCV REQUIRED)

# set(EIGEN_INCLUDE_DIR "/usr/local/include/eigen3")
find_package(Ceres REQUIRED)
Expand All @@ -34,7 +34,7 @@ include_directories(
include_directories("include")


add_executable(Calibration
add_executable(Calibration
src/intrinsic_calib.cc
src/chessboard/Chessboard.cc
src/calib/CameraCalibration.cc
Expand Down
8 changes: 4 additions & 4 deletions camera_model/src/calib/CameraCalibration.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -232,7 +232,7 @@ CameraCalibration::drawResults(std::vector<cv::Mat>& images) const
cv::Mat& image = images.at(i);
if (image.channels() == 1)
{
cv::cvtColor(image, image, CV_GRAY2RGB);
cv::cvtColor(image, image, cv::COLOR_GRAY2RGB);
}

std::vector<cv::Point2f> estImagePoints;
Expand All @@ -250,12 +250,12 @@ CameraCalibration::drawResults(std::vector<cv::Mat>& images) const
cv::circle(image,
cv::Point(cvRound(pObs.x * drawMultiplier),
cvRound(pObs.y * drawMultiplier)),
5, green, 2, CV_AA, drawShiftBits);
5, green, 2, cv::LINE_AA, drawShiftBits);

cv::circle(image,
cv::Point(cvRound(pEst.x * drawMultiplier),
cvRound(pEst.y * drawMultiplier)),
5, red, 2, CV_AA, drawShiftBits);
5, red, 2, cv::LINE_AA, drawShiftBits);

float error = cv::norm(pObs - pEst);

Expand All @@ -272,7 +272,7 @@ CameraCalibration::drawResults(std::vector<cv::Mat>& images) const

cv::putText(image, oss.str(), cv::Point(10, image.rows - 10),
cv::FONT_HERSHEY_COMPLEX, 0.5, cv::Scalar(255, 255, 255),
1, CV_AA);
1, cv::LINE_AA);
}
}

Expand Down
44 changes: 22 additions & 22 deletions camera_model/src/chessboard/Chessboard.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -17,24 +17,24 @@ Chessboard::Chessboard(cv::Size boardSize, cv::Mat& image)
{
if (image.channels() == 1)
{
cv::cvtColor(image, mSketch, CV_GRAY2BGR);
cv::cvtColor(image, mSketch, cv::COLOR_GRAY2BGR);
image.copyTo(mImage);
}
else
{
image.copyTo(mSketch);
cv::cvtColor(image, mImage, CV_BGR2GRAY);
cv::cvtColor(image, mImage, cv::COLOR_BGR2GRAY);
}
}

void
Chessboard::findCorners(bool useOpenCV)
{
mCornersFound = findChessboardCorners(mImage, mBoardSize, mCorners,
CV_CALIB_CB_ADAPTIVE_THRESH +
CV_CALIB_CB_NORMALIZE_IMAGE +
CV_CALIB_CB_FILTER_QUADS +
CV_CALIB_CB_FAST_CHECK,
cv::CALIB_CB_ADAPTIVE_THRESH +
cv::CALIB_CB_NORMALIZE_IMAGE +
cv::CALIB_CB_FILTER_QUADS +
cv::CALIB_CB_FAST_CHECK,
useOpenCV);

if (mCornersFound)
Expand Down Expand Up @@ -141,24 +141,24 @@ Chessboard::findChessboardCornersImproved(const cv::Mat& image,
// Image histogram normalization and
// BGR to Grayscale image conversion (if applicable)
// MARTIN: Set to "false"
if (image.channels() != 1 || (flags & CV_CALIB_CB_NORMALIZE_IMAGE))
if (image.channels() != 1 || (flags & cv::CALIB_CB_NORMALIZE_IMAGE))
{
cv::Mat norm_img(image.rows, image.cols, CV_8UC1);

if (image.channels() != 1)
{
cv::cvtColor(image, norm_img, CV_BGR2GRAY);
cv::cvtColor(image, norm_img, cv::COLOR_BGR2GRAY);
img = norm_img;
}

if (flags & CV_CALIB_CB_NORMALIZE_IMAGE)
if (flags & cv::CALIB_CB_NORMALIZE_IMAGE)
{
cv::equalizeHist(image, norm_img);
img = norm_img;
}
}

if (flags & CV_CALIB_CB_FAST_CHECK)
if (flags & cv::CALIB_CB_FAST_CHECK)
{
if (!checkChessboard(img, patternSize))
{
Expand Down Expand Up @@ -189,13 +189,13 @@ Chessboard::findChessboardCornersImproved(const cv::Mat& image,
cv::Mat thresh_img;

// convert the input grayscale image to binary (black-n-white)
if (flags & CV_CALIB_CB_ADAPTIVE_THRESH)
if (flags & cv::CALIB_CB_ADAPTIVE_THRESH)
{
int blockSize = lround(prevSqrSize == 0 ?
std::min(img.cols,img.rows)*(k%2 == 0 ? 0.2 : 0.1): prevSqrSize*2)|1;

// convert to binary
cv::adaptiveThreshold(img, thresh_img, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, blockSize, (k/2)*5);
cv::adaptiveThreshold(img, thresh_img, 255, cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, blockSize, (k/2)*5);
}
else
{
Expand All @@ -204,16 +204,16 @@ Chessboard::findChessboardCornersImproved(const cv::Mat& image,
int thresh_level = lround(mean - 10);
thresh_level = std::max(thresh_level, 10);

cv::threshold(img, thresh_img, thresh_level, 255, CV_THRESH_BINARY);
cv::threshold(img, thresh_img, thresh_level, 255, cv::THRESH_BINARY);
}

// MARTIN's Code
// Use both a rectangular and a cross kernel. In this way, a more
// homogeneous dilation is performed, which is crucial for small,
// distorted checkers. Use the CROSS kernel first, since its action
// on the image is more subtle
cv::Mat kernel1 = cv::getStructuringElement(CV_SHAPE_CROSS, cv::Size(3,3), cv::Point(1,1));
cv::Mat kernel2 = cv::getStructuringElement(CV_SHAPE_RECT, cv::Size(3,3), cv::Point(1,1));
cv::Mat kernel1 = cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3,3), cv::Point(1,1));
cv::Mat kernel2 = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3,3), cv::Point(1,1));

if (dilations >= 1)
cv::dilate(thresh_img, thresh_img, kernel1);
Expand Down Expand Up @@ -317,7 +317,7 @@ Chessboard::findChessboardCornersImproved(const cv::Mat& image,
}

cv::cornerSubPix(image, corners, cv::Size(11, 11), cv::Size(-1,-1),
cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::MAX_ITER, 30, 0.1));

return true;
}
Expand Down Expand Up @@ -1172,7 +1172,7 @@ Chessboard::generateQuads(std::vector<ChessboardQuadPtr>& quads,
std::vector<cv::Vec4i> hierarchy;

// Initialize contour retrieving routine
cv::findContours(image, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
cv::findContours(image, contours, hierarchy, cv::RETR_CCOMP, cv::CHAIN_APPROX_SIMPLE);

std::vector< std::vector<cv::Point> > quadContours;

Expand Down Expand Up @@ -1238,7 +1238,7 @@ Chessboard::generateQuads(std::vector<ChessboardQuadPtr>& quads,
dp = pt[1] - pt[2];
double d4 = sqrt(dp.dot(dp));

if (!(flags & CV_CALIB_CB_FILTER_QUADS) ||
if (!(flags & cv::CALIB_CB_FILTER_QUADS) ||
(d3*4 > d4 && d4*4 > d3 && d3*d4 < area*1.5 && area > minSize &&
d1 >= 0.15 * p && d2 >= 0.15 * p))
{
Expand Down Expand Up @@ -1583,20 +1583,20 @@ Chessboard::checkChessboard(const cv::Mat& image, cv::Size patternSize) const
bool result = false;
for (float threshLevel = blackLevel; threshLevel < whiteLevel && !result; threshLevel += 20.0f)
{
cv::threshold(white, thresh, threshLevel + blackWhiteGap, 255, CV_THRESH_BINARY);
cv::threshold(white, thresh, threshLevel + blackWhiteGap, 255, cv::THRESH_BINARY);

std::vector< std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;

// Initialize contour retrieving routine
cv::findContours(thresh, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
cv::findContours(thresh, contours, hierarchy, cv::RETR_CCOMP, cv::CHAIN_APPROX_SIMPLE);

std::vector<std::pair<float, int> > quads;
getQuadrangleHypotheses(contours, quads, 1);

cv::threshold(black, thresh, threshLevel, 255, CV_THRESH_BINARY_INV);
cv::threshold(black, thresh, threshLevel, 255, cv::THRESH_BINARY_INV);

cv::findContours(thresh, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
cv::findContours(thresh, contours, hierarchy, cv::RETR_CCOMP, cv::CHAIN_APPROX_SIMPLE);
getQuadrangleHypotheses(contours, quads, 0);

const size_t min_quads_count = patternSize.width * patternSize.height / 2;
Expand Down
2 changes: 1 addition & 1 deletion camera_model/src/intrinsic_calib.cc
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Expand Up @@ -237,7 +237,7 @@ int main(int argc, char** argv)
{
cv::putText(cbImages.at(i), cbImageFilenames.at(i), cv::Point(10,20),
cv::FONT_HERSHEY_COMPLEX, 0.5, cv::Scalar(255, 255, 255),
1, CV_AA);
1, cv::LINE_AA);
cv::imshow("Image", cbImages.at(i));
cv::waitKey(0);
}
Expand Down
4 changes: 2 additions & 2 deletions feature_tracker/src/feature_tracker_node.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ void img_callback(const sensor_msgs::ImageConstPtr &img_msg)
{
// 一些常规的reset操作
ROS_WARN("image discontinue! reset the feature tracker!");
first_image_flag = true;
first_image_flag = true;
last_image_time = 0;
pub_count = 1;
std_msgs::Bool restart_flag;
Expand Down Expand Up @@ -184,7 +184,7 @@ void img_callback(const sensor_msgs::ImageConstPtr &img_msg)
for (int i = 0; i < NUM_OF_CAM; i++)
{
cv::Mat tmp_img = stereo_img.rowRange(i * ROW, (i + 1) * ROW);
cv::cvtColor(show_img, tmp_img, CV_GRAY2RGB);
cv::cvtColor(show_img, tmp_img, cv::COLOR_GRAY2RGB);

for (unsigned int j = 0; j < trackerData[i].cur_pts.size(); j++)
{
Expand Down
54 changes: 26 additions & 28 deletions pose_graph/src/ThirdParty/DVision/BRIEF.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@
* File: BRIEF.cpp
* Author: Dorian Galvez
* Date: September 2010
* Description: implementation of BRIEF (Binary Robust Independent
* Elementary Features) descriptor by
* Description: implementation of BRIEF (Binary Robust Independent
* Elementary Features) descriptor by
* Michael Calonder, Vincent Lepetitand Pascal Fua
* + close binary tests (by Dorian Galvez-Lopez)
* License: see the LICENSE.txt file
Expand Down Expand Up @@ -36,49 +36,49 @@ BRIEF::~BRIEF()

// ---------------------------------------------------------------------------

void BRIEF::compute(const cv::Mat &image,
void BRIEF::compute(const cv::Mat &image,
const std::vector<cv::KeyPoint> &points,
vector<bitset> &descriptors,
bool treat_image) const
{
const float sigma = 2.f;
const cv::Size ksize(9, 9);

cv::Mat im;
if(treat_image)
{
cv::Mat aux;
if(image.depth() == 3)
{
cv::cvtColor(image, aux, CV_RGB2GRAY);
cv::cvtColor(image, aux, cv::COLOR_RGB2GRAY);
}
else
{
aux = image;
}

cv::GaussianBlur(aux, im, ksize, sigma, sigma);

}
else
{
im = image;
}

assert(im.type() == CV_8UC1);
assert(im.isContinuous());

// use im now
const int W = im.cols;
const int H = im.rows;

descriptors.resize(points.size());
std::vector<bitset>::iterator dit;

std::vector<cv::KeyPoint>::const_iterator kit;

int x1, y1, x2, y2;

dit = descriptors.begin();
for(kit = points.begin(); kit != points.end(); ++kit, ++dit)
{
Expand All @@ -91,24 +91,24 @@ void BRIEF::compute(const cv::Mat &image,
y1 = (int)(kit->pt.y + m_y1[i]);
x2 = (int)(kit->pt.x + m_x2[i]);
y2 = (int)(kit->pt.y + m_y2[i]);
if(x1 >= 0 && x1 < W && y1 >= 0 && y1 < H

if(x1 >= 0 && x1 < W && y1 >= 0 && y1 < H
&& x2 >= 0 && x2 < W && y2 >= 0 && y2 < H)
{
if( im.ptr<unsigned char>(y1)[x1] < im.ptr<unsigned char>(y2)[x2] )
{
dit->set(i);
}
}
} // if (x,y)_1 and (x,y)_2 are in the image

} // for each (x,y)
} // for each keypoint
}

// ---------------------------------------------------------------------------

void BRIEF::generateTestPoints()
{
{
m_x1.resize(m_bit_length);
m_y1.resize(m_bit_length);
m_x2.resize(m_bit_length);
Expand All @@ -117,31 +117,31 @@ void BRIEF::generateTestPoints()
const float g_mean = 0.f;
const float g_sigma = 0.2f * (float)m_patch_size;
const float c_sigma = 0.08f * (float)m_patch_size;

float sigma2;
if(m_type == RANDOM)
sigma2 = g_sigma;
else
sigma2 = c_sigma;

const int max_v = m_patch_size / 2;

DUtils::Random::SeedRandOnce();

for(int i = 0; i < m_bit_length; ++i)
{
int x1, y1, x2, y2;

do
{
x1 = DUtils::Random::RandomGaussianValue(g_mean, g_sigma);
} while( x1 > max_v || x1 < -max_v);

do
{
y1 = DUtils::Random::RandomGaussianValue(g_mean, g_sigma);
} while( y1 > max_v || y1 < -max_v);

float meanx, meany;
if(m_type == RANDOM)
meanx = meany = g_mean;
Expand All @@ -150,17 +150,17 @@ void BRIEF::generateTestPoints()
meanx = x1;
meany = y1;
}

do
{
x2 = DUtils::Random::RandomGaussianValue(meanx, sigma2);
} while( x2 > max_v || x2 < -max_v);

do
{
y2 = DUtils::Random::RandomGaussianValue(meany, sigma2);
} while( y2 > max_v || y2 < -max_v);

m_x1[i] = x1;
m_y1[i] = y1;
m_x2[i] = x2;
Expand All @@ -170,5 +170,3 @@ void BRIEF::generateTestPoints()
}

// ----------------------------------------------------------------------------


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