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dnn: fix build warnings

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This commit is contained in:
Alexander Alekhin 2017-06-26 16:22:50 +03:00
parent ee54bafe6b
commit 623de337e8
6 changed files with 26 additions and 26 deletions
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8
modules/dnn/include/opencv2/dnn/shape_utils.hpp
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@ -60,7 +60,7 @@ inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
struct _Range : public cv::Range
{
_Range(const Range &r) : cv::Range(r) {}
_Range(int start, int size = 1) : cv::Range(start, start + size) {}
_Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {}
};
static inline Mat slice(const Mat &m, const _Range &r0)
@ -148,13 +148,13 @@ static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1)
static inline int total(const MatShape& shape, int start = -1, int end = -1)
{
if (start == -1) start = 0;
if (end == -1) end = shape.size();
if (end == -1) end = (int)shape.size();
if (shape.empty())
return 0;
int elems = 1;
CV_Assert(start < shape.size() && end <= shape.size() &&
CV_Assert(start < (int)shape.size() && end <= (int)shape.size() &&
start <= end);
for(int i = start; i < end; i++)
{
@ -187,7 +187,7 @@ inline int clamp(int ax, int dims)
inline int clamp(int ax, const MatShape& shape)
{
return clamp(ax, shape.size());
return clamp(ax, (int)shape.size());
}
}

4
samples/dnn/caffe_googlenet.cpp
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@ -50,7 +50,7 @@ using namespace cv::dnn;
using namespace std;
/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
@ -59,7 +59,7 @@ void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
*classId = classNumber.x;
}
std::vector<String> readClassNames(const char *filename = "synset_words.txt")
static std::vector<String> readClassNames(const char *filename = "synset_words.txt")
{
std::vector<String> classNames;

8
samples/dnn/fcn_semsegm.cpp
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@ -33,9 +33,9 @@ static vector<cv::Vec3b> readColors(const string &filename = "pascal-classes.txt
string name; ss >> name;
int temp;
cv::Vec3b color;
ss >> temp; color[0] = temp;
ss >> temp; color[1] = temp;
ss >> temp; color[2] = temp;
ss >> temp; color[0] = (uchar)temp;
ss >> temp; color[1] = (uchar)temp;
ss >> temp; color[2] = (uchar)temp;
colors.push_back(color);
}
}
@ -64,7 +64,7 @@ static void colorizeSegmentation(const Mat &score, const vector<cv::Vec3b> &colo
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = ch;
ptrMaxCl[col] = (uchar)ch;
}
}
}

4
samples/dnn/squeezenet_halide.cpp
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@ -19,7 +19,7 @@ using namespace cv::dnn;
#include <cstdlib>
/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
@ -28,7 +28,7 @@ void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
*classId = classNumber.x;
}
std::vector<std::string> readClassNames(const char *filename = "synset_words.txt")
static std::vector<std::string> readClassNames(const char *filename = "synset_words.txt")
{
std::vector<std::string> classNames;

16
samples/dnn/ssd_object_detection.cpp
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@ -13,22 +13,22 @@ using namespace std;
const size_t width = 300;
const size_t height = 300;
Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
static Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
{
Mat mean;
const int meanValues[3] = {104, 117, 123};
vector<Mat> meanChannels;
for(size_t i = 0; i < 3; i++)
for(int i = 0; i < 3; i++)
{
Mat channel(imageHeight, imageWidth, CV_32F, Scalar(meanValues[i]));
Mat channel((int)imageHeight, (int)imageWidth, CV_32F, Scalar(meanValues[i]));
meanChannels.push_back(channel);
}
cv::merge(meanChannels, mean);
return mean;
}
Mat preprocess(const Mat& frame)
static Mat preprocess(const Mat& frame)
{
Mat preprocessed;
frame.convertTo(preprocessed, CV_32F);
@ -124,7 +124,7 @@ int main(int argc, char** argv)
if(confidence > confidenceThreshold)
{
size_t objectClass = detectionMat.at<float>(i, 1);
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
@ -139,9 +139,9 @@ int main(int argc, char** argv)
<< " " << xRightTop
<< " " << yRightTop << std::endl;
Rect object(xLeftBottom, yLeftBottom,
xRightTop - xLeftBottom,
yRightTop - yLeftBottom);
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
rectangle(frame, object, Scalar(0, 255, 0));
}

12
samples/dnn/torch_enet.cpp
View File

@ -141,7 +141,7 @@ static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vecto
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = ch;
ptrMaxCl[col] = (uchar)ch;
}
}
}
@ -161,8 +161,8 @@ static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vecto
if (classNames.size() == colors.size())
{
int blockHeight = 30;
legend.create(blockHeight*classNames.size(), 200, CV_8UC3);
for(int i = 0; i < classNames.size(); i++)
legend.create(blockHeight*(int)classNames.size(), 200, CV_8UC3);
for(int i = 0; i < (int)classNames.size(); i++)
{
cv::Mat block = legend.rowRange(i*blockHeight, (i+1)*blockHeight);
block = colors[i];
@ -194,9 +194,9 @@ static vector<Vec3b> readColors(const String &filename, vector<String>& classNam
string name; ss >> name;
int temp;
cv::Vec3b color;
ss >> temp; color[0] = temp;
ss >> temp; color[1] = temp;
ss >> temp; color[2] = temp;
ss >> temp; color[0] = (uchar)temp;
ss >> temp; color[1] = (uchar)temp;
ss >> temp; color[2] = (uchar)temp;
classNames.push_back(name);
colors.push_back(color);
}