* Upgrade presets for OpenCV 4.9.0, Leptonica 1.84.0, OpenCL 3.0.15,…

… Triton Inference Server 2.41.0

.github/workflows/tritonserver.yml

@@ -19,6 +19,6 @@ env:
 jobs:
   linux-x86_64:
     runs-on: ubuntu-20.04
-    container: nvcr.io/nvidia/tritonserver:23.09-py3
+    container: nvcr.io/nvidia/tritonserver:23.12-py3
     steps:
       - uses: bytedeco/javacpp-presets/.github/actions/deploy-ubuntu@actions

CHANGELOG.md

@@ -5,7 +5,7 @@
  * Refactor and improve presets for PyTorch ([pull #1360](https://github.com/bytedeco/javacpp-presets/pull/1360))
  * Include `mkl_lapack.h` header file in presets for MKL ([issue #1388](https://github.com/bytedeco/javacpp-presets/issues/1388))
  * Map new higher-level C++ API of Triton Inference Server ([pull #1361](https://github.com/bytedeco/javacpp-presets/pull/1361))
- * Upgrade presets for OpenCV 4.8.1, FFmpeg 6.1, HDF5 1.14.3, MKL 2024.0, DNNL 3.3.3, OpenBLAS 0.3.25, ARPACK-NG 3.9.1, CPython 3.12.1, NumPy 1.26.2, SciPy 1.11.4, LLVM 17.0.6, Leptonica 1.83.1, Tesseract 5.3.3, CUDA 12.3.1, cuDNN 8.9.7, NCCL 2.19.3, PyTorch 2.1.2 ([pull #1426](https://github.com/bytedeco/javacpp-presets/pull/1426)), TensorFlow Lite 2.15.0, Triton Inference Server 2.38.0, DepthAI 2.24.0, ONNX 1.15.0, ONNX Runtime 1.16.3, TVM 0.14.0, and their dependencies
+ * Upgrade presets for OpenCV 4.9.0, FFmpeg 6.1, HDF5 1.14.3, MKL 2024.0, DNNL 3.3.3, OpenBLAS 0.3.25, ARPACK-NG 3.9.1, CPython 3.12.1, NumPy 1.26.2, SciPy 1.11.4, LLVM 17.0.6, Leptonica 1.84.0, Tesseract 5.3.3, CUDA 12.3.1, cuDNN 8.9.7, NCCL 2.19.3, OpenCL 3.0.15, PyTorch 2.1.2 ([pull #1426](https://github.com/bytedeco/javacpp-presets/pull/1426)), TensorFlow Lite 2.15.0, Triton Inference Server 2.41.0, DepthAI 2.24.0, ONNX 1.15.0, ONNX Runtime 1.16.3, TVM 0.14.0, and their dependencies
 
 ### June 6, 2023 version 1.5.9
  * Virtualize `nvinfer1::IGpuAllocator` from TensorRT to allow customization ([pull #1367](https://github.com/bytedeco/javacpp-presets/pull/1367))

README.md

@@ -179,7 +179,7 @@ The JavaCPP Presets depend on Maven, a powerful build system for Java, so before
 
 Each child module in turn relies by default on the included [`cppbuild.sh` scripts](#the-cppbuildsh-scripts), explained below, to install its corresponding native libraries in the `cppbuild` subdirectory. To use native libraries already installed somewhere else on the system, other installation directories than `cppbuild` can also be specified either in the `pom.xml` files or in the `.java` configuration files. The following versions are supported:
 
- * OpenCV 4.8.x  https://opencv.org/releases/
+ * OpenCV 4.9.x  https://opencv.org/releases/
  * FFmpeg 6.1.x  http://ffmpeg.org/download.html
  * FlyCapture 2.13.x  https://www.flir.com/products/flycapture-sdk
  * Spinnaker 3.0.x https://www.flir.com/products/spinnaker-sdk
@@ -212,7 +212,7 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * libffi 3.4.x  https://github.com/libffi/libffi
  * libpostal 1.1  https://github.com/openvenues/libpostal
  * LibRaw 0.21.x  https://www.libraw.org/download
- * Leptonica 1.83.x  http://www.leptonica.org/download.html
+ * Leptonica 1.84.x  http://www.leptonica.org/download.html
  * Tesseract 5.3.x  https://github.com/tesseract-ocr/tesseract
  * Caffe 1.0  https://github.com/BVLC/caffe
  * OpenPose 1.7.0  https://github.com/CMU-Perceptual-Computing-Lab/openpose
@@ -228,7 +228,7 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * TensorFlow 1.15.x  https://github.com/tensorflow/tensorflow
  * TensorFlow Lite 2.15.x  https://github.com/tensorflow/tensorflow
  * TensorRT 8.6.x  https://developer.nvidia.com/tensorrt
- * Triton Inference Server 2.38.x  https://developer.nvidia.com/nvidia-triton-inference-server
+ * Triton Inference Server 2.41.x  https://developer.nvidia.com/nvidia-triton-inference-server
  * The Arcade Learning Environment 0.8.x  https://github.com/mgbellemare/Arcade-Learning-Environment
  * DepthAI 2.24.x  https://github.com/luxonis/depthai-core
  * ONNX 1.15.x  https://github.com/onnx/onnx

depthai/README.md

@@ -51,7 +51,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>opencv-platform</artifactId>
-            <version>4.8.1-1.5.10-SNAPSHOT</version>
+            <version>4.9.0-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

depthai/platform/pom.xml

@@ -23,7 +23,7 @@
     <dependency>
       <groupId>org.bytedeco</groupId>
       <artifactId>opencv-platform</artifactId>
-      <version>4.8.1-${project.parent.version}</version>
+      <version>4.9.0-${project.parent.version}</version>
     </dependency>
      <dependency>
        <groupId>org.bytedeco</groupId>

depthai/pom.xml

@@ -18,7 +18,7 @@
     <dependency>
       <groupId>org.bytedeco</groupId>
       <artifactId>opencv</artifactId>
-      <version>4.8.1-${project.parent.version}</version>
+      <version>4.9.0-${project.parent.version}</version>
       <optional>true</optional>
     </dependency>
     <dependency>
@@ -47,7 +47,7 @@
           <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>opencv</artifactId>
-            <version>4.8.1-${project.parent.version}</version>
+            <version>4.9.0-${project.parent.version}</version>
           </dependency>
           <dependency>
             <groupId>org.bytedeco</groupId>
@@ -58,7 +58,7 @@
           <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>opencv</artifactId>
-            <version>4.8.1-${project.parent.version}</version>
+            <version>4.9.0-${project.parent.version}</version>
             <classifier>${javacpp.platform}</classifier>
           </dependency>
         </dependencies>

depthai/samples/pom.xml

@@ -17,7 +17,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>opencv-platform</artifactId>
-            <version>4.8.1-1.5.10-SNAPSHOT</version>
+            <version>4.9.0-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

leptonica/README.md

@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * Leptonica 1.83.1  http://www.leptonica.org/
+ * Leptonica 1.84.0  http://www.leptonica.org/
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -46,7 +46,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>leptonica-platform</artifactId>
-            <version>1.83.1-1.5.10-SNAPSHOT</version>
+            <version>1.84.0-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

leptonica/cppbuild.sh

@@ -10,16 +10,16 @@ fi
 NASM_VERSION=2.14
 ZLIB=zlib-1.3
 GIFLIB=giflib-5.2.1
-LIBJPEG=libjpeg-turbo-2.1.5.1
+LIBJPEG=libjpeg-turbo-3.0.1
 LIBPNG=libpng-1.6.40 # warning: libpng16 doesn't work on CentOS 6 for some reason
 LIBTIFF=tiff-4.6.0
 LIBWEBP=libwebp-1.3.2
 OPENJPEG_VERSION=2.5.0
-LEPTONICA_VERSION=1.83.1
+LEPTONICA_VERSION=1.84.0
 download https://download.videolan.org/contrib/nasm/nasm-$NASM_VERSION.tar.gz nasm-$NASM_VERSION.tar.gz
 download http://zlib.net/$ZLIB.tar.gz $ZLIB.tar.gz
 download http://downloads.sourceforge.net/project/giflib/$GIFLIB.tar.gz $GIFLIB.tar.gz
-download http://downloads.sourceforge.net/project/libjpeg-turbo/2.1.5.1/$LIBJPEG.tar.gz $LIBJPEG.tar.gz
+download http://downloads.sourceforge.net/project/libjpeg-turbo/3.0.1/$LIBJPEG.tar.gz $LIBJPEG.tar.gz
 download https://sourceforge.net/projects/libpng/files/libpng16/1.6.40/$LIBPNG.tar.gz $LIBPNG.tar.gz
 download http://download.osgeo.org/libtiff/$LIBTIFF.tar.gz $LIBTIFF.tar.gz
 download http://downloads.webmproject.org/releases/webp/$LIBWEBP.tar.gz $LIBWEBP.tar.gz
@@ -53,6 +53,7 @@ sedinplace 's/VERSION   ${VERSION_PLAIN}/VERSION   6/g' leptonica-$LEPTONICA_VER
 sedinplace 's/leptonica-${VERSION_PLAIN}/leptonica-6/g' leptonica-$LEPTONICA_VERSION/src/CMakeLists.txt
 sedinplace 's/FATAL_ERROR/WARNING/g' leptonica-$LEPTONICA_VERSION/CMakeLists.txt
 sedinplace 's/${WEBP_LIBRARY}/${WEBP_LIBRARY} ${CMAKE_INSTALL_PREFIX}\/lib\/libsharpyuv.a/g' leptonica-$LEPTONICA_VERSION/CMakeLists.txt
+sedinplace 's/${TIFF_LIBRARIES}/${TIFF_LIBRARIES} ${CMAKE_INSTALL_PREFIX}\/lib\/libsharpyuv.a ${CMAKE_INSTALL_PREFIX}\/lib\/libjpeg.a/g' leptonica-$LEPTONICA_VERSION/src/CMakeLists.txt
 
 cd nasm-$NASM_VERSION
 # fix for build with GCC 8.x

leptonica/platform/pom.xml

@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>leptonica-platform</artifactId>
-  <version>1.83.1-${project.parent.version}</version>
+  <version>1.84.0-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for Leptonica</name>
 
   <properties>

leptonica/pom.xml

@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>leptonica</artifactId>
-  <version>1.83.1-${project.parent.version}</version>
+  <version>1.84.0-${project.parent.version}</version>
   <name>JavaCPP Presets for Leptonica</name>
 
   <dependencies>

leptonica/samples/pom.xml

@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>leptonica-platform</artifactId>
-            <version>1.83.1-1.5.10-SNAPSHOT</version>
+            <version>1.84.0-1.5.10-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

leptonica/src/gen/java/org/bytedeco/leptonica/L_REGPARAMS.java

@@ -125,7 +125,7 @@ public class L_REGPARAMS extends Pointer {
     /** generate, compare or display                     */
     public native @Cast("l_int32") int mode(); public native L_REGPARAMS mode(int setter);
     /** index into saved files for this test; 0-based    */
-    public native @Cast("l_int32") int index(); public native L_REGPARAMS index(int setter);
+    public native @Cast("l_atomic") int index(); public native L_REGPARAMS index(int setter);
     /** overall result of the test                       */
     public native @Cast("l_int32") int success(); public native L_REGPARAMS success(int setter);
     /** 1 if in display mode; 0 otherwise                */