Merge pull request #12599 from mshabunin:printf

apps/interactive-calibration/calibController.cpp

@@ -210,7 +210,7 @@ void calib::calibDataController::filterFrames()
                 worstElemIndex = i;
             }
         }
-        showOverlayMessage(cv::format("Frame %d is worst", worstElemIndex + 1));
+        showOverlayMessage(cv::format("Frame %zu is worst", worstElemIndex + 1));
 
         if(mCalibData->imagePoints.size()) {
             mCalibData->imagePoints.erase(mCalibData->imagePoints.begin() + worstElemIndex);

apps/interactive-calibration/frameProcessor.cpp

@@ -318,7 +318,7 @@ cv::Mat CalibProcessor::processFrame(const cv::Mat &frame)
             saveFrameData();
             bool isFrameBad = checkLastFrame();
             if (!isFrameBad) {
-                std::string displayMessage = cv::format("Frame # %d captured", std::max(mCalibData->imagePoints.size(),
+                std::string displayMessage = cv::format("Frame # %zu captured", std::max(mCalibData->imagePoints.size(),
                                                                                         mCalibData->allCharucoCorners.size()));
                 if(!showOverlayMessage(displayMessage))
                     showCaptureMessage(frame, displayMessage);

modules/core/include/opencv2/core/operations.hpp

@@ -51,6 +51,12 @@
 
 #include <cstdio>
 
+#if defined(__GNUC__) || defined(__clang__) // at least GCC 3.1+, clang 3.5+
+#  define CV_FORMAT_PRINTF(string_idx, first_to_check) __attribute__ ((format (printf, string_idx, first_to_check)))
+#else
+#  define CV_FORMAT_PRINTF(A, B)
+#endif
+
 //! @cond IGNORED
 
 namespace cv
@@ -386,8 +392,19 @@ template<typename _Tp> static inline _Tp randu()
 The function acts like sprintf but forms and returns an STL string. It can be used to form an error
 message in the Exception constructor.
 @param fmt printf-compatible formatting specifiers.
+
+**Note**:
+|Type|Specifier|
+|-|-|
+|`const char*`|`%s`|
+|`char`|`%c`|
+|`float` / `double`|`%f`,`%g`|
+|`int`, `long`, `long long`|`%d`, `%ld`, ``%lld`|
+|`unsigned`, `unsigned long`, `unsigned long long`|`%u`, `%lu`, `%llu`|
+|`uint64` -> `uintmax_t`, `int64` -> `intmax_t`|`%ju`, `%jd`|
+|`size_t`|`%zu`|
  */
-CV_EXPORTS String format( const char* fmt, ... );
+CV_EXPORTS String format( const char* fmt, ... ) CV_FORMAT_PRINTF(1, 2);
 
 ///////////////////////////////// Formatted output of cv::Mat /////////////////////////////////
 

modules/core/src/channels.cpp

@@ -237,11 +237,11 @@ static bool ocl_mixChannels(InputArrayOfArrays _src, InputOutputArrayOfArrays _d
         dstargs[i] = dst[dst_idx];
         dstargs[i].offset += dst_cnidx * esz;
 
-        declsrc += format("DECLARE_INPUT_MAT(%d)", i);
-        decldst += format("DECLARE_OUTPUT_MAT(%d)", i);
-        indexdecl += format("DECLARE_INDEX(%d)", i);
-        declproc += format("PROCESS_ELEM(%d)", i);
-        declcn += format(" -D scn%d=%d -D dcn%d=%d", i, src[src_idx].channels(), i, dst[dst_idx].channels());
+        declsrc += format("DECLARE_INPUT_MAT(%zu)", i);
+        decldst += format("DECLARE_OUTPUT_MAT(%zu)", i);
+        indexdecl += format("DECLARE_INDEX(%zu)", i);
+        declproc += format("PROCESS_ELEM(%zu)", i);
+        declcn += format(" -D scn%zu=%d -D dcn%zu=%d", i, src[src_idx].channels(), i, dst[dst_idx].channels());
     }
 
     ocl::Kernel k("mixChannels", ocl::core::mixchannels_oclsrc,

modules/core/src/lda.cpp

@@ -184,7 +184,7 @@ Mat LDA::subspaceProject(InputArray _W, InputArray _mean, InputArray _src) {
     }
     // make sure mean is correct if not empty
     if(!mean.empty() && (mean.total() != (size_t) d)) {
-        String error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %d.", d, mean.total());
+        String error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %zu.", d, mean.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // create temporary matrices
@@ -222,7 +222,7 @@ Mat LDA::subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
     }
     // make sure mean is correct if not empty
     if(!mean.empty() && (mean.total() != (size_t) W.rows)) {
-        String error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.", W.cols, mean.total());
+        String error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %zu.", W.cols, mean.total());
         CV_Error(Error::StsBadArg, error_message);
     }
     // initialize temporary matrices
@@ -1076,7 +1076,7 @@ void LDA::lda(InputArrayOfArrays _src, InputArray _lbls) {
     }
     // throw error if less labels, than samples
     if (labels.size() != static_cast<size_t>(N)) {
-        String error_message = format("The number of samples must equal the number of labels. Given %d labels, %d samples. ", labels.size(), N);
+        String error_message = format("The number of samples must equal the number of labels. Given %zu labels, %d samples. ", labels.size(), N);
         CV_Error(Error::StsBadArg, error_message);
     }
     // warn if within-classes scatter matrix becomes singular

modules/core/src/matrix_operations.cpp

@@ -892,7 +892,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst,
             tileHeight = min(tileHeight, defDev.localMemSize() / buf_cols / CV_ELEM_SIZE(CV_MAKETYPE(wdepth, cn)) / maxItemInGroupCount);
         }
         char cvt[3][40];
-        cv::String build_opt = format("-D OP_REDUCE_PRE -D BUF_COLS=%d -D TILE_HEIGHT=%d -D %s -D dim=1"
+        cv::String build_opt = format("-D OP_REDUCE_PRE -D BUF_COLS=%d -D TILE_HEIGHT=%zu -D %s -D dim=1"
                                             " -D cn=%d -D ddepth=%d"
                                             " -D srcT=%s -D bufT=%s -D dstT=%s"
                                             " -D convertToWT=%s -D convertToBufT=%s -D convertToDT=%s%s",

modules/core/src/ocl.cpp

@@ -3104,9 +3104,9 @@ bool Kernel::Impl::run(int dims, size_t globalsize[], size_t localsize[],
     if (retval != CL_SUCCESS)
 #endif
     {
-        cv::String msg = cv::format("clEnqueueNDRangeKernel('%s', dims=%d, globalsize=%dx%dx%d, localsize=%s) sync=%s", name.c_str(), (int)dims,
+        cv::String msg = cv::format("clEnqueueNDRangeKernel('%s', dims=%d, globalsize=%zux%zux%zu, localsize=%s) sync=%s", name.c_str(), (int)dims,
                         globalsize[0], (dims > 1 ? globalsize[1] : 1), (dims > 2 ? globalsize[2] : 1),
-                        (localsize ? cv::format("%dx%dx%d", localsize[0], (dims > 1 ? localsize[1] : 1), (dims > 2 ? localsize[2] : 1)) : cv::String("NULL")).c_str(),
+                        (localsize ? cv::format("%zux%zux%zu", localsize[0], (dims > 1 ? localsize[1] : 1), (dims > 2 ? localsize[2] : 1)) : cv::String("NULL")).c_str(),
                         sync ? "true" : "false"
                         );
         if (retval != CL_SUCCESS)
@@ -3317,7 +3317,7 @@ struct ProgramSource::Impl
         default:
             CV_Error(Error::StsInternal, "Internal error");
         }
-        sourceHash_ = cv::format("%08llx", hash);
+        sourceHash_ = cv::format("%08jx", (uintmax_t)hash);
         isHashUpdated = true;
     }
 

modules/core/test/test_io.cpp

@@ -584,29 +584,29 @@ TEST(Core_InputOutput, FileStorageSpaces)
     const int valueCount = 5;
     std::string values[5] = { "", " ", " ", "  a", " some string" };
     for (size_t i = 0; i < valueCount; i++) {
-        EXPECT_NO_THROW(f << cv::format("key%d", i) << values[i]);
+        EXPECT_NO_THROW(f << cv::format("key%zu", i) << values[i]);
     }
     cv::FileStorage f2(f.releaseAndGetString(), cv::FileStorage::READ | cv::FileStorage::MEMORY);
     std::string valuesRead[valueCount];
     for (size_t i = 0; i < valueCount; i++) {
-        EXPECT_NO_THROW(f2[cv::format("key%d", i)] >> valuesRead[i]);
+        EXPECT_NO_THROW(f2[cv::format("key%zu", i)] >> valuesRead[i]);
         ASSERT_STREQ(values[i].c_str(), valuesRead[i].c_str());
     }
     std::string fileName = cv::tempfile(".xml");
     cv::FileStorage g1(fileName, cv::FileStorage::WRITE);
     for (size_t i = 0; i < 2; i++) {
-        EXPECT_NO_THROW(g1 << cv::format("key%d", i) << values[i]);
+        EXPECT_NO_THROW(g1 << cv::format("key%zu", i) << values[i]);
     }
     g1.release();
     cv::FileStorage g2(fileName, cv::FileStorage::APPEND);
     for (size_t i = 2; i < valueCount; i++) {
-        EXPECT_NO_THROW(g2 << cv::format("key%d", i) << values[i]);
+        EXPECT_NO_THROW(g2 << cv::format("key%zu", i) << values[i]);
     }
     g2.release();
     cv::FileStorage g3(fileName, cv::FileStorage::READ);
     std::string valuesReadAppend[valueCount];
     for (size_t i = 0; i < valueCount; i++) {
-        EXPECT_NO_THROW(g3[cv::format("key%d", i)] >> valuesReadAppend[i]);
+        EXPECT_NO_THROW(g3[cv::format("key%zu", i)] >> valuesReadAppend[i]);
         ASSERT_STREQ(values[i].c_str(), valuesReadAppend[i].c_str());
     }
     g3.release();

modules/dnn/src/dnn.cpp

@@ -2347,7 +2347,7 @@ struct Net::Impl
         LayerData &ld = layers[pin.lid];
         if ((size_t)pin.oid >= ld.outputBlobs.size())
         {
-            CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %d outputs, "
+            CV_Error(Error::StsOutOfRange, format("Layer \"%s\" produce only %zu outputs, "
                                            "the #%d was requested", ld.name.c_str(),
                                            ld.outputBlobs.size(), pin.oid));
         }

modules/dnn/src/layers/permute_layer.cpp

@@ -88,7 +88,7 @@ public:
             {
                 CV_Error(Error::StsBadArg,
                          format("Orders of dimensions in Permute layer parameter"
-                                "must be in [0...%d]", _numAxes - 1));
+                                "must be in [0...%zu]", _numAxes - 1));
             }
             if (std::find(_order.begin(), _order.end(), currentOrder) != _order.end())
             {

modules/dnn/src/torch/THDiskFile.cpp

@@ -69,7 +69,7 @@ static size_t fread__(void *ptr, size_t size, size_t nitems, FILE *stream)
     {                                                                   \
       dfself->file.hasError = 1; /* shouldn't we put hasError to 0 all the time ? */ \
       if(!dfself->file.isQuiet)                                         \
-        THError("read error: read %d blocks instead of %d", nread, n);  \
+        THError("read error: read %ld blocks instead of %ld", nread, n);\
     }                                                                   \
                                                                         \
     return nread;                                                       \
@@ -120,7 +120,7 @@ static void THDiskFile_seek(THFile *self, long position)
   {
     dfself->file.hasError = 1;
     if(!dfself->file.isQuiet)
-      THError("unable to seek at position %d", position);
+      THError("unable to seek at position %ld", position);
   }
 }
 
@@ -351,7 +351,7 @@ static long THDiskFile_readLong(THFile *self, int64 *data, long n)
   {
     dfself->file.hasError = 1; /* shouldn't we put hasError to 0 all the time ? */
     if(!dfself->file.isQuiet)
-      THError("read error: read %d blocks instead of %d", nread, n);
+      THError("read error: read %ld blocks instead of %ld", nread, n);
   }
 
   return nread;

modules/imgproc/src/featureselect.cpp

@@ -126,7 +126,7 @@ static bool ocl_goodFeaturesToTrack( InputArray _image, OutputArray _corners,
             return false;
 
         ocl::Kernel k2("maxEigenValTask", ocl::imgproc::gftt_oclsrc,
-                       format("-D OP_MAX_EIGEN_VAL -D WGS=%d -D WGS2_ALIGNED=%d -D groupnum=%d",
+                       format("-D OP_MAX_EIGEN_VAL -D WGS=%zu -D WGS2_ALIGNED=%d -D groupnum=%d",
                               wgs, wgs2_aligned, dbsize));
         if (k2.empty())
             return false;

modules/imgproc/src/histogram.cpp

@@ -1123,7 +1123,7 @@ static bool ocl_calcHist1(InputArray _src, OutputArray _hist, int ddepth = CV_32
     int kercn = dev.isAMD() && use16 ? 16 : std::min(4, ocl::predictOptimalVectorWidth(_src));
 
     ocl::Kernel k1("calculate_histogram", ocl::imgproc::histogram_oclsrc,
-                   format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d -D kercn=%d -D T=%s%s",
+                   format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%zu -D kercn=%d -D T=%s%s",
                           BINS, compunits, wgs, kercn,
                           kercn == 4 ? "int" : ocl::typeToStr(CV_8UC(kercn)),
                           _src.isContinuous() ? " -D HAVE_SRC_CONT" : ""));
@@ -3253,7 +3253,7 @@ static bool ocl_equalizeHist(InputArray _src, OutputArray _dst)
     int kercn = dev.isAMD() && use16 ? 16 : std::min(4, ocl::predictOptimalVectorWidth(_src));
 
     ocl::Kernel k1("calculate_histogram", ocl::imgproc::histogram_oclsrc,
-                   format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d -D kercn=%d -D T=%s%s",
+                   format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%zu -D kercn=%d -D T=%s%s",
                           BINS, compunits, wgs, kercn,
                           kercn == 4 ? "int" : ocl::typeToStr(CV_8UC(kercn)),
                           _src.isContinuous() ? " -D HAVE_SRC_CONT" : ""));

modules/objdetect/src/hog.cpp

@@ -1328,7 +1328,7 @@ static bool ocl_compute_hists(int nbins, int block_stride_x, int block_stride_y,
     if(is_cpu)
        opts = "-D CPU ";
     else
-        opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+        opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
     k.create("compute_hists_lut_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
     if(k.empty())
         return false;
@@ -1401,7 +1401,7 @@ static bool ocl_normalize_hists(int nbins, int block_stride_x, int block_stride_
         if(is_cpu)
            opts = "-D CPU ";
         else
-            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+            opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
         k.create("normalize_hists_36_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
         if(k.empty())
             return false;
@@ -1420,7 +1420,7 @@ static bool ocl_normalize_hists(int nbins, int block_stride_x, int block_stride_
         if(is_cpu)
            opts = "-D CPU ";
         else
-            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+            opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
         k.create("normalize_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
         if(k.empty())
             return false;
@@ -1870,7 +1870,7 @@ static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y
         if(is_cpu)
            opts = "-D CPU ";
         else
-            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+            opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
         k.create("classify_hists_180_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
         if(k.empty())
             return false;
@@ -1886,7 +1886,7 @@ static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y
         if(is_cpu)
            opts = "-D CPU ";
         else
-            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+            opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
         k.create("classify_hists_252_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
         if(k.empty())
             return false;
@@ -1902,7 +1902,7 @@ static bool ocl_classify_hists(int win_height, int win_width, int block_stride_y
         if(is_cpu)
            opts = "-D CPU ";
         else
-            opts = cv::format("-D WAVE_SIZE=%d", k.preferedWorkGroupSizeMultiple());
+            opts = cv::format("-D WAVE_SIZE=%zu", k.preferedWorkGroupSizeMultiple());
         k.create("classify_hists_kernel", ocl::objdetect::objdetect_hog_oclsrc, opts);
         if(k.empty())
             return false;

modules/photo/src/fast_nlmeans_denoising_opencl.hpp

@@ -95,12 +95,13 @@ static bool ocl_fastNlMeansDenoising(InputArray _src, OutputArray _dst, const fl
     int almostTemplateWindowSizeSqBinShift = -1;
 
     char buf[4][40];
+    const unsigned psz = (depth == CV_8U ? sizeof(uchar) : sizeof(ushort)) * (cn == 3 ? 4 : cn);
     String opts = format("-D OP_CALC_FASTNLMEANS -D TEMPLATE_SIZE=%d -D SEARCH_SIZE=%d"
                          " -D pixel_t=%s -D int_t=%s -D wlut_t=%s"
                          " -D weight_t=%s -D convert_weight_t=%s -D sum_t=%s -D convert_sum_t=%s"
                          " -D BLOCK_COLS=%d -D BLOCK_ROWS=%d"
                          " -D CTA_SIZE=%d -D TEMPLATE_SIZE2=%d -D SEARCH_SIZE2=%d"
-                         " -D convert_int_t=%s -D cn=%d -D psz=%d -D convert_pixel_t=%s%s",
+                         " -D convert_int_t=%s -D cn=%d -D psz=%u -D convert_pixel_t=%s%s",
                          templateWindowSize, searchWindowSize,
                          ocl::typeToStr(type), ocl::typeToStr(CV_32SC(cn)),
                          ocl::typeToStr(CV_32SC(hn)),
@@ -115,7 +116,7 @@ static bool ocl_fastNlMeansDenoising(InputArray _src, OutputArray _dst, const fl
                          BLOCK_COLS, BLOCK_ROWS,
                          ctaSize, templateWindowHalfWize, searchWindowHalfSize,
                          ocl::convertTypeStr(depth, CV_32S, cn, buf[2]), cn,
-                         (depth == CV_8U ? sizeof(uchar) : sizeof(ushort)) * (cn == 3 ? 4 : cn),
+                         psz,
                          ocl::convertTypeStr(CV_32S, depth, cn, buf[3]),
                          normType == NORM_L1 ? " -D ABS" : "");
 

modules/videoio/src/container_avi.cpp

@@ -22,7 +22,7 @@ inline D safe_int_cast(S val, const char * msg = 0)
     if (!in_range_r || !in_range_l)
     {
         if (!msg)
-            CV_Error_(Error::StsOutOfRange, ("Can not convert integer values (%s -> %s), value 0x%llx is out of range", typeid(S).name(), typeid(D).name(), val));
+            CV_Error_(Error::StsOutOfRange, ("Can not convert integer values (%s -> %s), value 0x%jx is out of range", typeid(S).name(), typeid(D).name(), (uintmax_t)val));
         else
             CV_Error(Error::StsOutOfRange, msg);
     }

samples/dnn/segmentation.cpp

@@ -186,7 +186,7 @@ void colorizeSegmentation(const Mat &score, Mat &segm)
     else if (chns != (int)colors.size())
     {
         CV_Error(Error::StsError, format("Number of output classes does not match "
-                                         "number of colors (%d != %d)", chns, colors.size()));
+                                         "number of colors (%d != %zu)", chns, colors.size()));
     }
 
     Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
@@ -231,7 +231,7 @@ void showLegend()
         if ((int)colors.size() != numClasses)
         {
             CV_Error(Error::StsError, format("Number of output classes does not match "
-                                             "number of labels (%d != %d)", colors.size(), classes.size()));
+                                             "number of labels (%zu != %zu)", colors.size(), classes.size()));
         }
         legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
         for (int i = 0; i < numClasses; i++)