mirror of
https://github.com/opencv/opencv.git
synced 2025-08-06 06:26:29 +08:00
Merge remote-tracking branch 'upstream/3.4' into merge-3.4
This commit is contained in:
Alexander Alekhin
commit
c9fc27b337
@ -40,6 +40,7 @@ if(DOXYGEN_FOUND)
|
||||
foreach(m ${OPENCV_MODULES_MAIN} ${OPENCV_MODULES_EXTRA})
|
||||
list(FIND blacklist ${m} _pos)
|
||||
if(${_pos} EQUAL -1)
|
||||
list(APPEND CMAKE_DOXYGEN_ENABLED_SECTIONS "HAVE_opencv_${m}")
|
||||
# include folder
|
||||
set(header_dir "${OPENCV_MODULE_opencv_${m}_LOCATION}/include")
|
||||
if(EXISTS "${header_dir}")
|
||||
|
||||
@ -41,8 +41,7 @@ you need 256 values to show the above histogram. But consider, what if you need
|
||||
of pixels for all pixel values separately, but number of pixels in a interval of pixel values? say
|
||||
for example, you need to find the number of pixels lying between 0 to 15, then 16 to 31, ..., 240 to 255.
|
||||
You will need only 16 values to represent the histogram. And that is what is shown in example
|
||||
given in [OpenCV Tutorials on
|
||||
histograms](http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.html#histogram-calculation).
|
||||
given in @ref tutorial_histogram_calculation "OpenCV Tutorials on histograms".
|
||||
|
||||
So what you do is simply split the whole histogram to 16 sub-parts and value of each sub-part is the
|
||||
sum of all pixel count in it. This each sub-part is called "BIN". In first case, number of bins
|
||||
|
||||
@ -15,18 +15,18 @@ Installing OpenCV from prebuilt binaries
|
||||
|
||||
-# Below Python packages are to be downloaded and installed to their default locations.
|
||||
|
||||
-# [Python-2.7.x](http://www.python.org/ftp/python/2.7.13/python-2.7.13.msi).
|
||||
-# Python 3.x (3.4+) or Python 2.7.x from [here](https://www.python.org/downloads/).
|
||||
|
||||
-# [Numpy](https://sourceforge.net/projects/numpy/files/NumPy/1.10.2/numpy-1.10.2-win32-superpack-python2.7.exe/download).
|
||||
-# Numpy package (for example, using `pip install numpy` command).
|
||||
|
||||
-# [Matplotlib](https://sourceforge.net/projects/matplotlib/files/matplotlib/matplotlib-1.5.0/windows/matplotlib-1.5.0.win32-py2.7.exe/download) (*Matplotlib is optional, but recommended since we use it a lot in our tutorials*).
|
||||
-# Matplotlib (`pip install matplotlib`) (*Matplotlib is optional, but recommended since we use it a lot in our tutorials*).
|
||||
|
||||
-# Install all packages into their default locations. Python will be installed to `C:/Python27/`.
|
||||
-# Install all packages into their default locations. Python will be installed to `C:/Python27/` in case of Python 2.7.
|
||||
|
||||
-# After installation, open Python IDLE. Enter **import numpy** and make sure Numpy is working fine.
|
||||
|
||||
-# Download latest OpenCV release from [sourceforge
|
||||
site](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.6/OpenCV-2.4.6.0.exe/download)
|
||||
-# Download latest OpenCV release from [GitHub](https://github.com/opencv/opencv/releases) or
|
||||
[SourceForge site](https://sourceforge.net/projects/opencvlibrary/files/)
|
||||
and double-click to extract it.
|
||||
|
||||
-# Goto **opencv/build/python/2.7** folder.
|
||||
@ -49,16 +49,13 @@ Building OpenCV from source
|
||||
|
||||
-# [Visual Studio 2012](http://go.microsoft.com/?linkid=9816768)
|
||||
|
||||
-# [CMake](http://www.cmake.org/files/v2.8/cmake-2.8.11.2-win32-x86.exe)
|
||||
-# [CMake](https://cmake.org/download/)
|
||||
|
||||
-# Download and install necessary Python packages to their default locations
|
||||
|
||||
-# [Python 2.7.x](http://python.org/ftp/python/2.7.5/python-2.7.5.msi)
|
||||
-# Python
|
||||
|
||||
-# [Numpy](http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/numpy-1.7.1-win32-superpack-python2.7.exe/download)
|
||||
|
||||
-# [Matplotlib](https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.0/matplotlib-1.3.0.win32-py2.7.exe)
|
||||
(*Matplotlib is optional, but recommended since we use it a lot in our tutorials.*)
|
||||
-# Numpy
|
||||
|
||||
@note In this case, we are using 32-bit binaries of Python packages. But if you want to use
|
||||
OpenCV for x64, 64-bit binaries of Python packages are to be installed. Problem is that, there
|
||||
|
||||
@ -30,4 +30,9 @@ If you want to change unit use -u option (mm inches, px, m)
|
||||
|
||||
If you want to change page size use -w and -h options
|
||||
|
||||
If you want to create a ChArUco board read tutorial Detection of ChArUco Corners in opencv_contrib tutorial(https://docs.opencv.org/3.4/df/d4a/tutorial_charuco_detection.html)
|
||||
@cond HAVE_opencv_aruco
|
||||
If you want to create a ChArUco board read @ref tutorial_charuco_detection "tutorial Detection of ChArUco Corners" in opencv_contrib tutorial.
|
||||
@endcond
|
||||
@cond !HAVE_opencv_aruco
|
||||
If you want to create a ChArUco board read tutorial Detection of ChArUco Corners in opencv_contrib tutorial.
|
||||
@endcond
|
||||
|
||||
@ -97,9 +97,7 @@ I1 = gI1; // Download, gI1.download(I1) will work too
|
||||
@endcode
|
||||
Once you have your data up in the GPU memory you may call GPU enabled functions of OpenCV. Most of
|
||||
the functions keep the same name just as on the CPU, with the difference that they only accept
|
||||
*GpuMat* inputs. A full list of these you will find in the documentation: [online
|
||||
here](http://docs.opencv.org/modules/gpu/doc/gpu.html) or the OpenCV reference manual that comes
|
||||
with the source code.
|
||||
*GpuMat* inputs.
|
||||
|
||||
Another thing to keep in mind is that not for all channel numbers you can make efficient algorithms
|
||||
on the GPU. Generally, I found that the input images for the GPU images need to be either one or
|
||||
|
||||
@ -71,7 +71,7 @@ The structure of package contents looks as follows:
|
||||
|
||||
- `doc` folder contains various OpenCV documentation in PDF format. It's also available online at
|
||||
<http://docs.opencv.org>.
|
||||
@note The most recent docs (nightly build) are at <http://docs.opencv.org/2.4>. Generally, it's more
|
||||
@note The most recent docs (nightly build) are at <http://docs.opencv.org/master>. Generally, it's more
|
||||
up-to-date, but can refer to not-yet-released functionality.
|
||||
@todo I'm not sure that this is the best place to talk about OpenCV Manager
|
||||
|
||||
@ -85,10 +85,6 @@ applications developers:
|
||||
- Automatic updates and bug fixes;
|
||||
- Trusted OpenCV library source. All packages with OpenCV are published on Google Play;
|
||||
|
||||
For additional information on OpenCV Manager see the:
|
||||
|
||||
- [Slides](https://docs.google.com/a/itseez.com/presentation/d/1EO_1kijgBg_BsjNp2ymk-aarg-0K279_1VZRcPplSuk/present#slide=id.p)
|
||||
- [Reference Manual](http://docs.opencv.org/android/refman.html)
|
||||
|
||||
Manual OpenCV4Android SDK setup
|
||||
-------------------------------
|
||||
@ -96,8 +92,8 @@ Manual OpenCV4Android SDK setup
|
||||
### Get the OpenCV4Android SDK
|
||||
|
||||
-# Go to the [OpenCV download page on
|
||||
SourceForge](http://sourceforge.net/projects/opencvlibrary/files/opencv-android/) and download
|
||||
the latest available version. Currently it's [OpenCV-2.4.9-android-sdk.zip](http://sourceforge.net/projects/opencvlibrary/files/opencv-android/2.4.9/OpenCV-2.4.9-android-sdk.zip/download).
|
||||
SourceForge](http://sourceforge.net/projects/opencvlibrary/files/) and download
|
||||
the latest available version. This tutorial is based on this package: [OpenCV-2.4.9-android-sdk.zip](http://sourceforge.net/projects/opencvlibrary/files/opencv-android/2.4.9/OpenCV-2.4.9-android-sdk.zip/download).
|
||||
-# Create a new folder for Android with OpenCV development. For this tutorial we have unpacked
|
||||
OpenCV SDK to the `C:\Work\OpenCV4Android\` directory.
|
||||
|
||||
|
||||
@ -27,8 +27,8 @@ lein run
|
||||
Preamble
|
||||
--------
|
||||
|
||||
For detailed instruction on installing OpenCV with desktop Java support refer to the [corresponding
|
||||
tutorial](http://docs.opencv.org/2.4.4-beta/doc/tutorials/introduction/desktop_java/java_dev_intro.html).
|
||||
For detailed instruction on installing OpenCV with desktop Java support refer to the @ref tutorial_java_dev_intro "corresponding
|
||||
tutorial".
|
||||
|
||||
If you are in hurry, here is a minimum quick start guide to install OpenCV on Mac OS X:
|
||||
|
||||
@ -302,7 +302,7 @@ Then you can start interacting with OpenCV by just referencing the fully qualifi
|
||||
classes.
|
||||
|
||||
@note
|
||||
[Here](http://docs.opencv.org/java/) you can find the full OpenCV Java API.
|
||||
[Here](https://docs.opencv.org/master/javadoc/index.html) you can find the full OpenCV Java API.
|
||||
|
||||
@code{.clojure}
|
||||
user=> (org.opencv.core.Point. 0 0)
|
||||
@ -387,8 +387,7 @@ user=> (javadoc Rect)
|
||||
@endcode
|
||||
### Mimic the OpenCV Java Tutorial Sample in the REPL
|
||||
|
||||
Let's now try to port to Clojure the [opencv java tutorial
|
||||
sample](http://docs.opencv.org/2.4.4-beta/doc/tutorials/introduction/desktop_java/java_dev_intro.html).
|
||||
Let's now try to port to Clojure the @ref tutorial_java_dev_intro "OpenCV Java tutorial sample".
|
||||
Instead of writing it in a source file we're going to evaluate it at the REPL.
|
||||
|
||||
Following is the original Java source code of the cited sample.
|
||||
|
||||
@ -94,8 +94,6 @@ the weight vector \f$\beta\f$ and the bias \f$\beta_{0}\f$ of the optimal hyperp
|
||||
Source Code
|
||||
-----------
|
||||
|
||||
@note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html#introductiontosvms)
|
||||
|
||||
@add_toggle_cpp
|
||||
- **Downloadable code**: Click
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp)
|
||||
|
||||
@ -89,9 +89,6 @@ Source Code
|
||||
You may also find the source code in `samples/cpp/tutorial_code/ml/non_linear_svms` folder of the OpenCV source library or
|
||||
[download it from here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp).
|
||||
|
||||
@note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code
|
||||
using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/non_linear_svms/non_linear_svms.html#nonlinearsvms)
|
||||
|
||||
@add_toggle_cpp
|
||||
- **Downloadable code**: Click
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp)
|
||||
|
||||
@ -10,6 +10,7 @@
|
||||
#ifdef HAVE_PROTOBUF
|
||||
|
||||
#include "tf_graph_simplifier.hpp"
|
||||
#include <queue>
|
||||
|
||||
namespace cv { namespace dnn {
|
||||
CV__DNN_INLINE_NS_BEGIN
|
||||
@ -883,7 +884,6 @@ void sortByExecutionOrder(tensorflow::GraphDef& net)
|
||||
nodesToAdd.pop_back();
|
||||
|
||||
permIds.push_back(nodeToAdd);
|
||||
// std::cout << net.node(nodeToAdd).name() << '\n';
|
||||
|
||||
for (int i = 0; i < edges[nodeToAdd].size(); ++i)
|
||||
{
|
||||
@ -902,6 +902,85 @@ void sortByExecutionOrder(tensorflow::GraphDef& net)
|
||||
permute(net.mutable_node(), permIds);
|
||||
}
|
||||
|
||||
// Remove training switches (Switch and Merge nodes and corresponding subgraphs).
|
||||
void removePhaseSwitches(tensorflow::GraphDef& net)
|
||||
{
|
||||
std::vector<int> nodesToRemove;
|
||||
std::map<std::string, int> nodesMap;
|
||||
std::map<std::string, int>::iterator nodesMapIt;
|
||||
std::queue<int> mergeOpSubgraphNodes;
|
||||
for (int i = 0; i < net.node_size(); ++i)
|
||||
{
|
||||
const tensorflow::NodeDef& node = net.node(i);
|
||||
nodesMap.insert(std::make_pair(node.name(), i));
|
||||
if (node.op() == "Switch" || node.op() == "Merge")
|
||||
{
|
||||
CV_Assert(node.input_size() > 0);
|
||||
// Replace consumers' inputs.
|
||||
for (int j = 0; j < net.node_size(); ++j)
|
||||
{
|
||||
tensorflow::NodeDef* consumer = net.mutable_node(j);
|
||||
for (int k = 0; k < consumer->input_size(); ++k)
|
||||
{
|
||||
std::string inpName = consumer->input(k);
|
||||
inpName = inpName.substr(0, inpName.rfind(':'));
|
||||
if (inpName == node.name())
|
||||
{
|
||||
consumer->set_input(k, node.input(0));
|
||||
}
|
||||
}
|
||||
}
|
||||
nodesToRemove.push_back(i);
|
||||
if (node.op() == "Merge")
|
||||
mergeOpSubgraphNodes.push(i);
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<int> numConsumers(net.node_size(), 0);
|
||||
for (int i = 0; i < net.node_size(); ++i)
|
||||
{
|
||||
const tensorflow::NodeDef& node = net.node(i);
|
||||
for (int j = 0; j < node.input_size(); ++j)
|
||||
{
|
||||
std::string inpName = node.input(j);
|
||||
inpName = inpName.substr(1 + (int)inpName.find('^'), inpName.rfind(':'));
|
||||
nodesMapIt = nodesMap.find(inpName);
|
||||
CV_Assert(nodesMapIt != nodesMap.end());
|
||||
numConsumers[nodesMapIt->second] += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Remove subgraphs of unused nodes which are terminated by Merge nodes.
|
||||
while (!mergeOpSubgraphNodes.empty())
|
||||
{
|
||||
const tensorflow::NodeDef& node = net.node(mergeOpSubgraphNodes.front());
|
||||
mergeOpSubgraphNodes.pop();
|
||||
for (int i = 0; i < node.input_size(); ++i)
|
||||
{
|
||||
std::string inpName = node.input(i);
|
||||
inpName = inpName.substr(1 + (int)inpName.find('^'), inpName.rfind(':'));
|
||||
nodesMapIt = nodesMap.find(inpName);
|
||||
CV_Assert(nodesMapIt != nodesMap.end());
|
||||
|
||||
int inpNodeId = nodesMapIt->second;
|
||||
if (numConsumers[inpNodeId] == 1)
|
||||
{
|
||||
mergeOpSubgraphNodes.push(inpNodeId);
|
||||
nodesToRemove.push_back(inpNodeId);
|
||||
}
|
||||
else if (numConsumers[inpNodeId] > 0)
|
||||
numConsumers[inpNodeId] -= 1;
|
||||
}
|
||||
}
|
||||
std::sort(nodesToRemove.begin(), nodesToRemove.end());
|
||||
for (int i = nodesToRemove.size() - 1; i >= 0; --i)
|
||||
{
|
||||
if (nodesToRemove[i] < net.node_size()) // Ids might be repeated.
|
||||
net.mutable_node()->DeleteSubrange(nodesToRemove[i], 1);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
CV__DNN_INLINE_NS_END
|
||||
}} // namespace dnn, namespace cv
|
||||
|
||||
|
||||
@ -27,6 +27,8 @@ void releaseTensor(tensorflow::TensorProto* tensor);
|
||||
|
||||
void sortByExecutionOrder(tensorflow::GraphDef& net);
|
||||
|
||||
void removePhaseSwitches(tensorflow::GraphDef& net);
|
||||
|
||||
CV__DNN_INLINE_NS_END
|
||||
}} // namespace dnn, namespace cv
|
||||
|
||||
|
||||
@ -657,6 +657,9 @@ static int predictOutputDataLayout(const tensorflow::GraphDef& net,
|
||||
|
||||
void TFImporter::populateNet(Net dstNet)
|
||||
{
|
||||
if (!netTxt.ByteSize())
|
||||
removePhaseSwitches(netBin);
|
||||
|
||||
RemoveIdentityOps(netBin);
|
||||
RemoveIdentityOps(netTxt);
|
||||
|
||||
|
||||
@ -185,6 +185,16 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
|
||||
runTensorFlowNet("mvn_batch_norm_1x1");
|
||||
}
|
||||
|
||||
TEST_P(Test_TensorFlow_layers, slim_batch_norm)
|
||||
{
|
||||
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
|
||||
throw SkipTestException("Test is disabled for DLIE");
|
||||
// Output values range: [-40.0597, 207.827]
|
||||
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.041 : default_l1;
|
||||
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.33 : default_lInf;
|
||||
runTensorFlowNet("slim_batch_norm", false, l1, lInf);
|
||||
}
|
||||
|
||||
TEST_P(Test_TensorFlow_layers, pooling)
|
||||
{
|
||||
runTensorFlowNet("max_pool_even");
|
||||
|
||||
@ -401,6 +401,10 @@ bool TiffDecoder::readData( Mat& img )
|
||||
(!is_tiled && tile_height0 == std::numeric_limits<uint32>::max()) )
|
||||
tile_height0 = m_height;
|
||||
|
||||
CV_Assert((int)tile_width0 > 0 && (int)tile_width0 < std::numeric_limits<int>::max());
|
||||
CV_Assert((int)tile_height0 > 0 && (int)tile_height0 < std::numeric_limits<int>::max());
|
||||
CV_Assert(((uint64_t)tile_width0 * tile_height0 * ncn * (bpp / bitsPerByte) < (CV_BIG_UINT(1) << 30)) && "TIFF tile size is too large: >= 1Gb");
|
||||
|
||||
if (dst_bpp == 8)
|
||||
{
|
||||
// we will use TIFFReadRGBA* functions, so allocate temporary buffer for 32bit RGBA
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
// This sample is based on "Camera calibration With OpenCV" tutorial:
|
||||
// http://docs.opencv.org/doc/tutorials/calib3d/camera_calibration/camera_calibration.html
|
||||
// https://docs.opencv.org/3.4/d4/d94/tutorial_camera_calibration.html
|
||||
//
|
||||
// It uses standard OpenCV asymmetric circles grid pattern 11x4:
|
||||
// https://github.com/opencv/opencv/blob/2.4/doc/acircles_pattern.png.
|
||||
// https://github.com/opencv/opencv/blob/3.4/doc/acircles_pattern.png
|
||||
// The results are the camera matrix and 5 distortion coefficients.
|
||||
//
|
||||
// Tap on highlighted pattern to capture pattern corners for calibration.
|
||||
|
||||
@ -24,11 +24,11 @@ int main(int argc, char *argv[])
|
||||
vector<String> typeAlgoMatch;
|
||||
vector<String> fileName;
|
||||
// This descriptor are going to be detect and compute
|
||||
typeDesc.push_back("AKAZE-DESCRIPTOR_KAZE_UPRIGHT"); // see http://docs.opencv.org/trunk/d8/d30/classcv_1_1AKAZE.html
|
||||
typeDesc.push_back("AKAZE"); // see http://docs.opencv.org/trunk/d8/d30/classcv_1_1AKAZE.html
|
||||
typeDesc.push_back("ORB"); // see http://docs.opencv.org/trunk/de/dbf/classcv_1_1BRISK.html
|
||||
typeDesc.push_back("BRISK"); // see http://docs.opencv.org/trunk/db/d95/classcv_1_1ORB.html
|
||||
// This algorithm would be used to match descriptors see http://docs.opencv.org/trunk/db/d39/classcv_1_1DescriptorMatcher.html#ab5dc5036569ecc8d47565007fa518257
|
||||
typeDesc.push_back("AKAZE-DESCRIPTOR_KAZE_UPRIGHT"); // see https://docs.opencv.org/master/d8/d30/classcv_1_1AKAZE.html
|
||||
typeDesc.push_back("AKAZE"); // see http://docs.opencv.org/master/d8/d30/classcv_1_1AKAZE.html
|
||||
typeDesc.push_back("ORB"); // see http://docs.opencv.org/master/de/dbf/classcv_1_1BRISK.html
|
||||
typeDesc.push_back("BRISK"); // see http://docs.opencv.org/master/db/d95/classcv_1_1ORB.html
|
||||
// This algorithm would be used to match descriptors see http://docs.opencv.org/master/db/d39/classcv_1_1DescriptorMatcher.html#ab5dc5036569ecc8d47565007fa518257
|
||||
typeAlgoMatch.push_back("BruteForce");
|
||||
typeAlgoMatch.push_back("BruteForce-L1");
|
||||
typeAlgoMatch.push_back("BruteForce-Hamming");
|
||||
|
||||
Loading…
Reference in New Issue
Block a user