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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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pull/19314/head
Alexander Alekhin 4 years ago
parent 941a9792f7 fbcc532de5
commit d3bc563c6e
10 changed files with 472 additions and 1633 deletions
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  1. 4
      CMakeLists.txt
  2. 3
      README.md
  3. 3
      doc/js_tutorials/js_assets/js_template_matching_matchTemplate.html
  4. 2
      doc/js_tutorials/js_setup/js_usage/js_usage.markdown
  5. 17
      doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
  6. 2
      doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
  7. 1578
      modules/core/include/opencv2/core/hal/intrin_wasm.hpp
  8. 478
      modules/dnn/src/tensorflow/tf_importer.cpp
  9. 16
      modules/imgproc/include/opencv2/imgproc.hpp
  10. 2
      samples/python/tutorial_code/video/background_subtraction/bg_sub.py

4
CMakeLists.txt
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@ -1047,7 +1047,9 @@ endif()
if(CMAKE_GENERATOR MATCHES Xcode)
status(" Xcode:" ${XCODE_VERSION})
endif()
if(NOT CMAKE_GENERATOR MATCHES "Xcode|Visual Studio")
if(CMAKE_GENERATOR MATCHES "Xcode|Visual Studio|Multi-Config")
status(" Configuration:" ${CMAKE_CONFIGURATION_TYPES})
else()
status(" Configuration:" ${CMAKE_BUILD_TYPE})
endif()

3
README.md
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@ -5,7 +5,8 @@
* Homepage: <https://opencv.org>
* Courses: <https://opencv.org/courses>
* Docs: <https://docs.opencv.org/master/>
* Q&A forum: <http://answers.opencv.org>
* Q&A forum: <https://forum.opencv.org>
* previous forum (read only): <http://answers.opencv.org>
* Issue tracking: <https://github.com/opencv/opencv/issues>
* Additional OpenCV functionality: <https://github.com/opencv/opencv_contrib>

3
doc/js_tutorials/js_assets/js_template_matching_matchTemplate.html
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@ -74,7 +74,8 @@ let utils = new Utils('errorMessage');
utils.loadCode('codeSnippet', 'codeEditor');
utils.loadImageToCanvas('lena.jpg', 'imageCanvasInput');
utils.loadImageToCanvas('lenaFace.png', 'templateCanvasInput');
utils.addFileInputHandler('fileInput', 'canvasInput');
utils.addFileInputHandler('fileInput', 'imageCanvasInput');
utils.addFileInputHandler('templateFileInput', 'templateCanvasInput');
let tryIt = document.getElementById('tryIt');
tryIt.addEventListener('click', () => {

2
doc/js_tutorials/js_setup/js_usage/js_usage.markdown
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@ -82,7 +82,7 @@ In this tutorial, we just show a cv.Mat on screen. To show a cv.Mat, you need a
You can use cv.imshow to show cv.Mat on the canvas.
@code{.js}
cv.imshow(mat, "outputCanvas");
cv.imshow("outputCanvas", mat);
@endcode
Putting all of the steps together, the final index.html is shown below.

17
doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
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@ -20,10 +20,10 @@ scale invariant.
![image](images/sift_scale_invariant.jpg)
So, in 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
In 2004, **D.Lowe**, University of British Columbia, came up with a new algorithm, Scale
Invariant Feature Transform (SIFT) in his paper, **Distinctive Image Features from Scale-Invariant
Keypoints**, which extract keypoints and compute its descriptors. *(This paper is easy to understand
and considered to be best material available on SIFT. So this explanation is just a short summary of
and considered to be best material available on SIFT. This explanation is just a short summary of
this paper)*.
There are mainly four steps involved in SIFT algorithm. We will see them one-by-one.
@ -102,16 +102,17 @@ reasons. In that case, ratio of closest-distance to second-closest distance is t
greater than 0.8, they are rejected. It eliminates around 90% of false matches while discards only
5% correct matches, as per the paper.
So this is a summary of SIFT algorithm. For more details and understanding, reading the original
paper is highly recommended. Remember one thing, this algorithm is patented. So this algorithm is
included in [the opencv contrib repo](https://github.com/opencv/opencv_contrib)
This is a summary of SIFT algorithm. For more details and understanding, reading the original
paper is highly recommended.
SIFT in OpenCV
--------------
So now let's see SIFT functionalities available in OpenCV. Let's start with keypoint detection and
draw them. First we have to construct a SIFT object. We can pass different parameters to it which
are optional and they are well explained in docs.
Now let's see SIFT functionalities available in OpenCV. Note that these were previously only
available in [the opencv contrib repo](https://github.com/opencv/opencv_contrib), but the patent
expired in the year 2020. So they are now included in the main repo. Let's start with keypoint
detection and draw them. First we have to construct a SIFT object. We can pass different
parameters to it which are optional and they are well explained in docs.
@code{.py}
import numpy as np
import cv2 as cv

2
doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
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@ -224,7 +224,7 @@ First you apply the transform:
- *theta*: The resolution of the parameter \f$\theta\f$ in radians. We use **1 degree**
(CV_PI/180)
- *threshold*: The minimum number of intersections to "*detect*" a line
- *minLinLength*: The minimum number of points that can form a line. Lines with less than
- *minLineLength*: The minimum number of points that can form a line. Lines with less than
this number of points are disregarded.
- *maxLineGap*: The maximum gap between two points to be considered in the same line.

1578
modules/core/include/opencv2/core/hal/intrin_wasm.hpp
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478
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -11,6 +11,11 @@ Implementation of Tensorflow models parser
#include "../precomp.hpp"
#include <opencv2/core/utils/logger.defines.hpp>
#undef CV_LOG_STRIP_LEVEL
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1
#include <opencv2/core/utils/logger.hpp>
#ifdef HAVE_PROTOBUF
#include "tf_io.hpp"
@ -93,7 +98,7 @@ void blobShapeFromTensor(const tensorflow::TensorProto &tensor, MatShape& shape)
shape[i] = (int)_shape.dim(i).size();
}
else
shape.resize(1, 1); // Scalar.
shape.resize(1, 1); // Scalar. // FIXIT: should be empty
}
else
{
@ -258,7 +263,7 @@ const tensorflow::AttrValue& getLayerAttr(const tensorflow::NodeDef &layer, cons
return layer.attr().at(name);
}
static int getDataLayout(const tensorflow::NodeDef& layer)
static DataLayout getDataLayout(const tensorflow::NodeDef& layer)
{
if (hasLayerAttr(layer, "data_format"))
{
@ -280,10 +285,13 @@ static inline std::string getNodeName(const std::string& tensorName)
return tensorName.substr(0, tensorName.rfind(':'));
}
static inline int getDataLayout(const std::string& layerName,
const std::map<String, int>& data_layouts)
static inline
DataLayout getDataLayout(
const std::string& layerName,
const std::map<String, DataLayout>& data_layouts
)
{
std::map<String, int>::const_iterator it = data_layouts.find(getNodeName(layerName));
std::map<String, DataLayout>::const_iterator it = data_layouts.find(getNodeName(layerName));
return it != data_layouts.end() ? it->second : DATA_LAYOUT_UNKNOWN;
}
@ -439,15 +447,20 @@ void ExcludeLayer(tensorflow::GraphDef& net, const int layer_index, const int in
net.mutable_node()->DeleteSubrange(layer_index, 1);
}
class TFImporter {
class TFImporter
{
public:
TFImporter(const char *model, const char *config = NULL);
TFImporter(const char *dataModel, size_t lenModel,
TFImporter(Net& net, const char *model, const char *config = NULL);
TFImporter(Net& net, const char *dataModel, size_t lenModel,
const char *dataConfig = NULL, size_t lenConfig = 0);
protected:
Net& dstNet;
void populateNet();
void populateNet(Net dstNet);
void parseNode(const tensorflow::NodeDef& layer);
DataLayout predictOutputDataLayout(const tensorflow::NodeDef& layer);
private:
void kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob);
void connect(const std::map<String, int>& layers_name_id_map, Net& network, const Pin& outPin,
@ -467,23 +480,53 @@ private:
std::vector<String> netInputsNames;
std::vector<MatShape> netInputShapes;
std::set<String> layers_to_ignore;
std::map<String, DataLayout> data_layouts;
// find all Const layers for params
std::map<String, int> value_id;
// A map with constant blobs which are shared between multiple layers.
std::map<String, Mat> sharedWeights;
std::map<String, int> layer_id;
};
TFImporter::TFImporter(const char *model, const char *config)
TFImporter::TFImporter(Net& net, const char *model, const char *config)
: dstNet(net)
{
if (model && model[0])
{
CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow model from file: " << model);
ReadTFNetParamsFromBinaryFileOrDie(model, &netBin);
}
if (config && config[0])
{
CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow config from file: " << config);
ReadTFNetParamsFromTextFileOrDie(config, &netTxt);
}
populateNet();
}
TFImporter::TFImporter(const char *dataModel, size_t lenModel,
const char *dataConfig, size_t lenConfig)
TFImporter::TFImporter(
Net& net,
const char *dataModel, size_t lenModel,
const char *dataConfig, size_t lenConfig
)
: dstNet(net)
{
if (dataModel != NULL && lenModel > 0)
{
CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow model from memory (" << lenModel << " bytes)");
ReadTFNetParamsFromBinaryBufferOrDie(dataModel, lenModel, &netBin);
}
if (dataConfig != NULL && lenConfig > 0)
{
CV_LOG_DEBUG(NULL, "DNN/TF: processing TensorFlow config from memory (" << lenConfig << " bytes)");
ReadTFNetParamsFromTextBufferOrDie(dataConfig, lenConfig, &netTxt);
}
populateNet();
}
void TFImporter::kernelFromTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
@ -612,84 +655,98 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe
static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& const_layers,
std::set<String>& layers_to_ignore)
{
CV_LOG_DEBUG(NULL, "DNN/TF: addConstNodes(): handling " << net.node_size() << " nodes...");
for (int li = 0; li < net.node_size(); li++)
{
const tensorflow::NodeDef &layer = net.node(li);
String name = layer.name();
String type = layer.op();
if (type == "Dequantize")
//CV_LOG_DEBUG(NULL, "DNN/TF: layer_id=" << li << " - '" << name << "' @ " << type);
try
{
// Example of Dequantize node:
// name: "conv2d_1/bias"
// op: "Dequantize"
// input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8)
// input: "conv2d_1/bias_quantized_min"
// input: "conv2d_1/bias_quantized_max"
// attr { key: "T" value { type: DT_QUINT8 } } (quantized type)
// attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique)
CV_Assert(layer.input_size() == 3);
for (int i = 0; i < 3; ++i)
CV_Assert(const_layers.find(layer.input(i)) != const_layers.end());
CV_Assert(hasLayerAttr(layer, "mode") &&
getLayerAttr(layer, "mode").s() == "MIN_FIRST");
int tensorId = const_layers[layer.input(0)];
int minId = const_layers[layer.input(1)];
int maxId = const_layers[layer.input(2)];
tensorflow::TensorProto* tensor = net.mutable_node(tensorId)
->mutable_attr()->at("value")
.mutable_tensor();
CV_Assert(tensor->dtype() == tensorflow::DT_QUINT8);
Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
CV_Assert_N(qMin.total() == 1, qMin.type() == CV_32FC1,
qMax.total() == 1, qMax.type() == CV_32FC1);
Mat content = getTensorContent(*tensor);
float minVal = qMin.at<float>(0);
float rangeScale = (qMax.at<float>(0) - minVal) / 255;
CV_Assert(rangeScale >= 0);
content.convertTo(content, CV_32FC1, rangeScale,
rangeScale * cvRound(minVal / rangeScale));
tensor->set_dtype(tensorflow::DT_FLOAT);
tensor->set_tensor_content(content.data, content.total() * content.elemSize1());
net.mutable_node(tensorId)->set_name(name);
CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second);
if (type == "Dequantize")
{
// Example of Dequantize node:
// name: "conv2d_1/bias"
// op: "Dequantize"
// input: "conv2d_1/bias_quantized_const" (tensor of dtype DT_QUINT8)
// input: "conv2d_1/bias_quantized_min"
// input: "conv2d_1/bias_quantized_max"
// attr { key: "T" value { type: DT_QUINT8 } } (quantized type)
// attr { key: "mode" value { s: "MIN_FIRST" } } (quantization technique)
CV_CheckEQ(layer.input_size(), 3, "Dequantize: 3 inputs is supported only");
for (int i = 0; i < 3; ++i)
CV_Assert(const_layers.find(layer.input(i)) != const_layers.end());
CV_Assert(hasLayerAttr(layer, "mode") &&
getLayerAttr(layer, "mode").s() == "MIN_FIRST");
int tensorId = const_layers[layer.input(0)];
int minId = const_layers[layer.input(1)];
int maxId = const_layers[layer.input(2)];
tensorflow::TensorProto* tensor = net.mutable_node(tensorId)
->mutable_attr()->at("value")
.mutable_tensor();
CV_CheckEQ((int)tensor->dtype(), (int)tensorflow::DT_QUINT8, "");
Mat qMin = getTensorContent(net.node(minId).attr().at("value").tensor());
Mat qMax = getTensorContent(net.node(maxId).attr().at("value").tensor());
CV_CheckEQ(qMin.total(), (size_t)1, "");
CV_CheckTypeEQ(qMin.type(), CV_32FC1, "");
CV_CheckEQ(qMax.total(), (size_t)1, "");
CV_CheckTypeEQ(qMax.type(), CV_32FC1, "");
Mat content = getTensorContent(*tensor);
float minVal = qMin.at<float>(0);
float rangeScale = (qMax.at<float>(0) - minVal) / 255;
CV_Assert(rangeScale >= 0);
content.convertTo(content, CV_32FC1, rangeScale,
rangeScale * cvRound(minVal / rangeScale));
tensor->set_dtype(tensorflow::DT_FLOAT);
tensor->set_tensor_content(content.data, content.total() * content.elemSize1());
net.mutable_node(tensorId)->set_name(name);
CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second);
layers_to_ignore.insert(name);
continue;
}
else if (type != "Const")
continue; // only Const parameters are supported
if (layer.attr().find("value") != layer.attr().end())
{
CV_Assert(const_layers.insert(std::make_pair(name, li)).second);
}
layers_to_ignore.insert(name);
continue;
}
else if (type != "Const")
continue; // only Const parameters are supported
if (layer.attr().find("value") != layer.attr().end())
catch (const std::exception& e)
{
CV_Assert(const_layers.insert(std::make_pair(name, li)).second);
CV_LOG_ERROR(NULL, "DNN/TF: Can't handle node='" << name << "'. Exception: " << e.what());
throw;
}
layers_to_ignore.insert(name);
}
CV_LOG_DEBUG(NULL, "DNN/TF: layers_to_ignore.size() = " << layers_to_ignore.size());
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static int predictOutputDataLayout(const tensorflow::GraphDef& net,
const tensorflow::NodeDef& layer,
const std::map<String, int>& data_layouts)
DataLayout TFImporter::predictOutputDataLayout(const tensorflow::NodeDef& layer)
{
int layout = getDataLayout(layer);
DataLayout layout = getDataLayout(layer);
if (layout != DATA_LAYOUT_UNKNOWN)
{
CV_LOG_DEBUG(NULL, "DNN/TF: predictOutputDataLayout(" << layer.name() << " @ " << layer.op() << ") => " << (int)layout << " (from attrs)");
return layout;
}
// Determine layout by layer's inputs
std::map<String, int>::const_iterator it;
for (int i = 0, n = layer.input_size(); i < n; ++i)
{
it = data_layouts.find(getNodeName(layer.input(i)));
std::map<String, DataLayout>::const_iterator it = data_layouts.find(getNodeName(layer.input(i)));
if (it != data_layouts.end())
{
if (layout != DATA_LAYOUT_UNKNOWN)
@ -703,71 +760,72 @@ static int predictOutputDataLayout(const tensorflow::GraphDef& net,
}
if (layout != DATA_LAYOUT_UNKNOWN)
{
CV_LOG_DEBUG(NULL, "DNN/TF: predictOutputDataLayout(" << layer.name() << " @ " << layer.op() << ") => " << (int)layout << " (from inputs)");
return layout;
}
// Determine layout by layer's consumers recursively.
it = data_layouts.find(layer.name());
std::map<String, DataLayout>::const_iterator it = data_layouts.find(layer.name());
CV_Assert(it != data_layouts.end());
return it->second;
}
void TFImporter::populateNet(Net dstNet)
void TFImporter::populateNet()
{
if (!netTxt.ByteSize())
removePhaseSwitches(netBin);
CV_Assert(netBin.ByteSize() || netTxt.ByteSize());
RemoveIdentityOps(netBin);
RemoveIdentityOps(netTxt);
CV_LOG_INFO(NULL, "DNN/TF: parsing model"
<< (netBin.has_versions() ? cv::format(" produced by TF v%d (min_consumer=%d)", (int)netBin.versions().producer(), (int)netBin.versions().min_consumer()) : cv::String(" (N/A version info)"))
<< ". Number of nodes = " << netBin.node_size()
);
if (!netTxt.ByteSize())
if (netTxt.ByteSize())
{
simplifySubgraphs(netBin);
sortByExecutionOrder(netBin);
CV_LOG_INFO(NULL, "DNN/TF: parsing config"
<< (netTxt.has_versions() ? cv::format(" produced by TF v%d (min_consumer=%d)", (int)netTxt.versions().producer(), (int)netTxt.versions().min_consumer()) : cv::String(" (N/A version info)"))
<< ". Number of nodes = " << netTxt.node_size()
);
RemoveIdentityOps(netBin);
CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(model) => " << netBin.node_size() << " nodes");
RemoveIdentityOps(netTxt);
CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(config) => " << netTxt.node_size() << " nodes");
sortByExecutionOrder(netTxt);
CV_LOG_DEBUG(NULL, "DNN/TF: sortByExecutionOrder(config) => " << netTxt.node_size() << " nodes");
}
else
{
sortByExecutionOrder(netTxt);
}
removePhaseSwitches(netBin);
CV_LOG_DEBUG(NULL, "DNN/TF: removePhaseSwitches(model) => " << netBin.node_size() << " nodes");
std::set<String> layers_to_ignore;
RemoveIdentityOps(netBin);
CV_LOG_DEBUG(NULL, "DNN/TF: RemoveIdentityOps(model) => " << netBin.node_size() << " nodes");
simplifySubgraphs(netBin);
CV_LOG_DEBUG(NULL, "DNN/TF: simplifySubgraphs(model) => " << netBin.node_size() << " nodes");
sortByExecutionOrder(netBin);
CV_LOG_DEBUG(NULL, "DNN/TF: sortByExecutionOrder(model) => " << netBin.node_size() << " nodes");
}
tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin;
int layersSize = net.node_size();
std::map<String, int> data_layouts;
// Pre-fill data layouts where they are set explicitly.
// Assuming that nodes are in topological order
for (int i = net.node_size() - 1; i >= 0; --i)
for (int i = layersSize - 1; i >= 0; --i)
{
const tensorflow::NodeDef& layer = net.node(i);
std::string name = layer.name();
int layout = getDataLayout(layer);
std::map<String, int>::iterator it = data_layouts.find(name);
if (it != data_layouts.end())
{
if (layout != DATA_LAYOUT_UNKNOWN)
{
if (it->second == DATA_LAYOUT_UNKNOWN)
it->second = layout;
else if (it->second != layout)
{
it->second = DATA_LAYOUT_UNKNOWN;
layout = DATA_LAYOUT_UNKNOWN;
}
}
else
layout = it->second;
}
else
data_layouts[name] = layout;
CV_LOG_DEBUG(NULL, "DNN/TF: node(" << i << " - '" << name << "') propagating layout...");
// Specify input layers to have the same data layout.
for (int j = 0; j < layer.input_size(); ++j)
try
{
name = getNodeName(layer.input(j));
it = data_layouts.find(name);
DataLayout layout = getDataLayout(layer);
std::map<String, DataLayout>::iterator it = data_layouts.find(name);
if (it != data_layouts.end())
{
if (layout != DATA_LAYOUT_UNKNOWN)
@ -775,38 +833,94 @@ void TFImporter::populateNet(Net dstNet)
if (it->second == DATA_LAYOUT_UNKNOWN)
it->second = layout;
else if (it->second != layout)
{
it->second = DATA_LAYOUT_UNKNOWN;
layout = DATA_LAYOUT_UNKNOWN;
}
}
else
layout = it->second;
}
else
data_layouts[name] = layout;
// Specify input layers to have the same data layout.
for (int j = 0; j < layer.input_size(); ++j)
{
name = getNodeName(layer.input(j));
it = data_layouts.find(name);
if (it != data_layouts.end())
{
if (layout != DATA_LAYOUT_UNKNOWN)
{
if (it->second == DATA_LAYOUT_UNKNOWN)
it->second = layout;
else if (it->second != layout)
it->second = DATA_LAYOUT_UNKNOWN;
}
}
else
data_layouts[name] = layout;
}
}
catch (const std::exception& e)
{
CV_LOG_ERROR(NULL, "DNN/TF: Can't propagate layout for node='" << name << "'. Exception: " << e.what());
throw;
}
}
// find all Const layers for params
std::map<String, int> value_id;
// A map with constant blobs which are shared between multiple layers.
std::map<String, Mat> sharedWeights;
addConstNodes(netBin, value_id, layers_to_ignore);
addConstNodes(netTxt, value_id, layers_to_ignore);
std::map<String, int> layer_id;
for (int li = 0; li < layersSize; li++)
{
tensorflow::NodeDef layer = net.node(li);
String name = layer.name();
String type = layer.op();
const tensorflow::NodeDef& layer = net.node(li);
const std::string name = layer.name();
const std::string type = layer.op();
const int ninputs = layer.input_size();
CV_LOG_DEBUG(NULL, "DNN/TF: (" << li << "/" << layersSize << ") Parse layer " << name << " @ " << type << " with " << ninputs << " inputs");
parseNode(layer);
}
for (size_t i = 0; i < netInputsNames.size(); i++)
{
CV_LOG_DEBUG(NULL, "DNN/TF: Model input: " << i << " - '" << netInputsNames[i] << "'");
CV_Assert(!netInputsNames[i].empty());
}
dstNet.setInputsNames(netInputsNames);
CV_LOG_DEBUG(NULL, "DNN/TF: ===================== Import completed =====================");
}
void TFImporter::parseNode(const tensorflow::NodeDef& layer_)
{
tensorflow::NodeDef layer = layer_;
tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin;
/*const*/ std::string name = layer.name();
/*const*/ std::string type = layer.op();
/*const*/ int num_inputs = layer.input_size();
try
{
LayerParams layerParams;
if(layers_to_ignore.find(name) != layers_to_ignore.end())
continue;
if (layers_to_ignore.find(name) != layers_to_ignore.end())
{
CV_LOG_DEBUG(NULL, "DNN/TF: ignored");
return;
}
int predictedLayout = predictOutputDataLayout(net, layer, data_layouts);
DataLayout predictedLayout = predictOutputDataLayout(layer);
data_layouts[name] = predictedLayout;
if (type == "Conv2D" || type == "SpaceToBatchND" || type == "DepthwiseConv2dNative" || type == "Pad" || type == "MirrorPad" || type == "Conv3D")
{
CV_CheckGT(num_inputs, 0, "");
// The first node of dilated convolution subgraph.
// Extract input node, dilation rate and paddings.
std::string input = layer.input(0);
@ -824,7 +938,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "input"
// input: "SpaceToBatchND/block_shape"
// input: "SpaceToBatchND/paddings"
CV_Assert(layer.input_size() == 3);
CV_CheckEQ(num_inputs, 3, "");
DictValue dilation = parseDims(getConstBlob(layer, value_id, 1));
CV_Assert(dilation.size() == 2);
@ -839,10 +953,14 @@ void TFImporter::populateNet(Net dstNet)
layerParams.set("pad_w", paddings.at<float>(2));
CV_Assert(next_layers.size() == 1);
layer = net.node(next_layers[0].second);
layers_to_ignore.insert(next_layers[0].first);
// FIXIT don't override, rewrite this code
layer = net.node(next_layers[0].second);
name = layer.name();
type = layer.op();
num_inputs = layer.input_size();
CV_LOG_DEBUG(NULL, "DNN/TF: switched to layer " << name << " @ " << type << ") with " << num_inputs << " inputs");
}
else if (type == "Pad" || type == "MirrorPad")
{
@ -876,7 +994,7 @@ void TFImporter::populateNet(Net dstNet)
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(input), id, 0);
continue;
return;
}
else
{
@ -886,10 +1004,14 @@ void TFImporter::populateNet(Net dstNet)
layerParams.set("pad_h", paddings.at<int32_t>(4));
layerParams.set("pad_w", paddings.at<int32_t>(6));
layer = net.node(next_layers[0].second);
layers_to_ignore.insert(next_layers[0].first);
// FIXIT don't override, rewrite this code
layer = net.node(next_layers[0].second);
name = layer.name();
type = layer.op();
num_inputs = layer.input_size();
CV_LOG_DEBUG(NULL, "DNN/TF: switched to layer " << name << " @ " << type << ") with " << num_inputs << " inputs");
}
}
@ -1011,13 +1133,14 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "BiasAdd" || type == "Add" || type == "AddV2" || type == "Sub" || type=="AddN")
{
CV_CheckGT(num_inputs, 0, "");
bool haveConst = false;
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
for(int ii = 0; !haveConst && ii < num_inputs; ++ii)
{
Pin input = parsePin(layer.input(ii));
haveConst = value_id.find(input.name) != value_id.end();
}
CV_Assert(!haveConst || layer.input_size() == 2);
CV_Assert(!haveConst || num_inputs == 2);
if (haveConst)
{
@ -1054,7 +1177,7 @@ void TFImporter::populateNet(Net dstNet)
int id = dstNet.addLayer(name, "Eltwise", layerParams);
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)
for (int ii = 0; ii < num_inputs; ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
@ -1065,7 +1188,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "MatMul")
{
CV_Assert(layer.input_size() == 2);
CV_CheckEQ(num_inputs, 2, "");
// For the object detection networks, TensorFlow Object Detection API
// predicts deltas for bounding boxes in yxYX (ymin, xmin, ymax, xmax)
@ -1077,7 +1200,7 @@ void TFImporter::populateNet(Net dstNet)
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
StrIntVector next_layers = getNextLayers(net, name, "BiasAdd");
StrIntVector next_layers = getNextLayers(net, name, "BiasAdd"); // FIXIT Use layers fusion instead
if (next_layers.empty())
{
next_layers = getNextLayers(net, name, "Add");
@ -1135,8 +1258,9 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "Reshape")
{
CV_CheckGT(num_inputs, 0, "");
Pin inpId = parsePin(layer.input(0));
int inpLayout = getDataLayout(layer.input(0), data_layouts);
DataLayout inpLayout = getDataLayout(layer.input(0), data_layouts);
// There are two possible implementations: reshape an input using
// predefined sizes or use a second input blob as a source of new shape.
if (value_id.find(layer.input(1)) != value_id.end())
@ -1185,6 +1309,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "Flatten" || type == "Squeeze")
{
CV_CheckGT(num_inputs, 0, "");
Pin inpId = parsePin(layer.input(0));
int inpLayout = getDataLayout(layer.input(0), data_layouts);
if (type == "Squeeze")
@ -1231,6 +1356,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "Transpose")
{
CV_CheckGT(num_inputs, 0, "");
Mat perm = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(perm.type() == CV_32SC1);
int* permData = (int*)perm.data;
@ -1304,6 +1430,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "LRN")
{
CV_CheckGT(num_inputs, 0, "");
if(hasLayerAttr(layer, "alpha")) {
layerParams.set("alpha", getLayerAttr(layer, "alpha").f());
}
@ -1322,11 +1449,12 @@ void TFImporter::populateNet(Net dstNet)
int id = dstNet.addLayer(name, "LRN", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
}
else if (type == "Concat" || type == "ConcatV2")
{
int axisId = (type == "Concat" ? 0 : layer.input_size() - 1);
CV_CheckGT(num_inputs, 0, "");
int axisId = (type == "Concat" ? 0 : num_inputs - 1);
int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0);
if (getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
@ -1337,7 +1465,7 @@ void TFImporter::populateNet(Net dstNet)
// input(0) or input(n-1) is concat_dim
int from = (type == "Concat" ? 1 : 0);
int to = (type == "Concat" ? layer.input_size() : layer.input_size() - 1);
int to = (type == "Concat" ? num_inputs : num_inputs - 1);
for (int ii = from; ii < to; ii++)
{
@ -1370,6 +1498,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "MaxPool" || type == "MaxPool3D")
{
CV_CheckGT(num_inputs, 0, "");
layerParams.set("pool", "max");
setKSize(layerParams, layer);
@ -1381,10 +1510,11 @@ void TFImporter::populateNet(Net dstNet)
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
}
else if (type == "AvgPool" || type == "AvgPool3D")
{
CV_CheckGT(num_inputs, 0, "");
layerParams.set("pool", "ave");
layerParams.set("ave_pool_padded_area", false);
setKSize(layerParams, layer);
@ -1394,11 +1524,11 @@ void TFImporter::populateNet(Net dstNet)
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
}
else if (type == "MaxPoolGrad")
{
CV_Assert(layer.input_size() == 3);
CV_CheckEQ(num_inputs, 3, "");
layerParams.set("pool_k_h", 0);
layerParams.set("pool_k_w", 0);
@ -1457,7 +1587,7 @@ void TFImporter::populateNet(Net dstNet)
// TODO: slicing input may be Const op
// TODO: slicing kernels for convolutions - in current implementation it is impossible
// TODO: add parsing num of slices parameter
CV_Assert(layer.input_size() == 2);
CV_CheckEQ(num_inputs, 2, "");
// num_split
// 1st blob is dims tensor
int axis = getConstBlob(layer, value_id, 0).int_val().Get(0);
@ -1480,7 +1610,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "input_node"
// input: "Slice/begin"
// input: "Slice/size"
CV_Assert(layer.input_size() == 3);
CV_CheckEQ(num_inputs, 3, "");
Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
Mat sizes = getTensorContent(getConstBlob(layer, value_id, 2));
CV_Assert_N(!begins.empty(), !sizes.empty());
@ -1505,7 +1635,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "StridedSlice")
{
CV_Assert(layer.input_size() == 4);
CV_CheckEQ(num_inputs, 4, "");
Mat begins = getTensorContent(getConstBlob(layer, value_id, 1));
Mat ends = getTensorContent(getConstBlob(layer, value_id, 2));
Mat strides = getTensorContent(getConstBlob(layer, value_id, 3));
@ -1544,8 +1674,9 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "Mul" || type == "RealDiv")
{
CV_CheckGT(num_inputs, 0, "");
int constId = -1;
for(int ii = 0; ii < layer.input_size(); ++ii)
for(int ii = 0; ii < num_inputs; ++ii)
{
Pin input = parsePin(layer.input(ii));
if (value_id.find(input.name) != value_id.end())
@ -1554,12 +1685,12 @@ void TFImporter::populateNet(Net dstNet)
break;
}
}
CV_Assert((constId != -1) || (layer.input_size() == 2));
CV_Assert((constId != -1) || (num_inputs == 2));
if (constId != -1)
{
// Multiplication by constant.
CV_Assert(layer.input_size() == 2);
CV_CheckEQ(num_inputs, 2, "");
Mat scaleMat = getTensorContent(getConstBlob(layer, value_id));
CV_Assert(scaleMat.type() == CV_32FC1);
if (type == "RealDiv")
@ -1643,7 +1774,7 @@ void TFImporter::populateNet(Net dstNet)
// Check if all the inputs have the same shape.
bool equalInpShapes = true;
MatShape outShape0;
for (int ii = 0; ii < layer.input_size() && !netInputShapes.empty(); ii++)
for (int ii = 0; ii < num_inputs && !netInputShapes.empty(); ii++)
{
Pin pin = parsePin(layer.input(ii));
int inpId = layer_id.find(pin.name)->second;
@ -1681,7 +1812,7 @@ void TFImporter::populateNet(Net dstNet)
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)
for (int ii = 0; ii < num_inputs; ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
@ -1698,9 +1829,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "BatchNorm/beta"
// input: "BatchNorm/moving_mean"
// input: "BatchNorm/moving_variance"
if (layer.input_size() != 5)
CV_Error(Error::StsNotImplemented,
"Expected gamma, beta, mean and std");
CV_CheckEQ(num_inputs, 5, "Expected gamma, beta, mean and std");
Pin inpId = parsePin(layer.input(0));
bool isTraining = hasLayerAttr(layer, "is_training") && getLayerAttr(layer, "is_training").b();
@ -1768,9 +1897,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "conv2d_transpose/output_shape"
// input: "weights"
// input: "input"
if (layer.input_size() != 3)
CV_Error(Error::StsNotImplemented,
"Expected output shape, weights and input nodes");
CV_CheckEQ(num_inputs, 3, "Expected output shape, weights and input nodes");
layerParams.set("bias_term", false);
layerParams.blobs.resize(1);
@ -1845,8 +1972,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "lstm_block_wrapper/w_f_diag"
// input: "lstm_block_wrapper/w_o_diag"
// input: "lstm_block_wrapper/bias"
if (layer.input_size() != 9)
CV_Error(Error::StsNotImplemented, "Unexpected number of input nodes");
CV_CheckEQ(num_inputs, 9, "Unexpected number of input nodes");
if (hasLayerAttr(layer, "forget_bias"))
layerParams.set("forget_bias", getLayerAttr(layer, "forget_bias").f());
@ -1912,6 +2038,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "ResizeNearestNeighbor" || type == "ResizeBilinear" || type == "FusedResizeAndPadConv2D")
{
CV_CheckGT(num_inputs, 0, "");
std::string convWeights = "";
if (type == "FusedResizeAndPadConv2D")
{
@ -1919,30 +2046,32 @@ void TFImporter::populateNet(Net dstNet)
// input: "decoder/ResizeBilinear/size"
// input: "decoder/decoder_conv0/Conv2D_dummy_paddings"
// input: "decoder/decoder_conv0/weights"
CV_CheckEQ(layer.input_size(), 4, "Number of input for FusedResizeAndPadConv2D");
CV_CheckEQ(num_inputs, 4, "Number of input for FusedResizeAndPadConv2D");
Mat paddings = getTensorContent(getConstBlob(layer, value_id, 2));
CV_CheckEQ(countNonZero(paddings), 0, "Unsupported mode");
convWeights = layer.input(3);
layer.mutable_input()->DeleteSubrange(2, 2);
layer.mutable_input()->DeleteSubrange(2, 2); // FIXIT do NOT modify input model
num_inputs = layer.input_size();
name = name + "/resize";
if (hasLayerAttr(layer, "resize_align_corners"))
{
// FIXIT do NOT modify input model
layer.mutable_attr()->insert(
::google::protobuf::MapPair<std::string, tensorflow::AttrValue>("align_corners",
getLayerAttr(layer, "resize_align_corners")));
}
}
if (layer.input_size() == 2)
if (num_inputs == 2)
{
Mat outSize = getTensorContent(getConstBlob(layer, value_id, 1));
CV_CheckTypeEQ(outSize.type(), CV_32SC1, ""); CV_CheckEQ(outSize.total(), (size_t)2, "");
layerParams.set("height", outSize.at<int>(0, 0));
layerParams.set("width", outSize.at<int>(0, 1));
}
else if (layer.input_size() == 3)
else if (num_inputs == 3)
{
Mat factorHeight = getTensorContent(getConstBlob(layer, value_id, 1));
Mat factorWidth = getTensorContent(getConstBlob(layer, value_id, 2));
@ -1952,7 +2081,7 @@ void TFImporter::populateNet(Net dstNet)
layerParams.set("zoom_factor_y", factorHeight.at<float>(0));
}
else
CV_Assert(layer.input_size() == 2 || layer.input_size() == 3);
CV_Check(num_inputs, num_inputs == 2 || num_inputs == 3, "");
if (type == "ResizeNearestNeighbor")
layerParams.set("interpolation", "nearest");
@ -1973,12 +2102,12 @@ void TFImporter::populateNet(Net dstNet)
// Step back to add convolution
if (type == "FusedResizeAndPadConv2D")
{
tensorflow::NodeDef* conv = net.mutable_node(li);
conv->clear_input();
conv->add_input(name);
conv->add_input(convWeights);
conv->set_op("Conv2D");
li -= 1;
tensorflow::NodeDef conv = layer_;
conv.clear_input();
conv.add_input(name);
conv.add_input(convWeights);
conv.set_op("Conv2D");
parseNode(conv);
}
}
else if (type == "L2Normalize")
@ -1986,7 +2115,7 @@ void TFImporter::populateNet(Net dstNet)
// op: "L2Normalize"
// input: "input"
// input: "reduction_indices" (axis)
CV_Assert(layer.input_size() == 2);
CV_CheckEQ(num_inputs, 2, "");
Mat reductionIndices = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(reductionIndices.type() == CV_32SC1);
@ -2011,6 +2140,7 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "PriorBox")
{
CV_CheckEQ(num_inputs, 2, "");
if (hasLayerAttr(layer, "min_size"))
layerParams.set("min_size", getLayerAttr(layer, "min_size").i());
if (hasLayerAttr(layer, "max_size"))
@ -2043,12 +2173,13 @@ void TFImporter::populateNet(Net dstNet)
}
else if (type == "Softmax")
{
CV_CheckGT(num_inputs, 0, "");
if (hasLayerAttr(layer, "axis"))
layerParams.set("axis", getLayerAttr(layer, "axis").i());
int id = dstNet.addLayer(name, "Softmax", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
}
else if (type == "CropAndResize")
{
@ -2056,7 +2187,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "input"
// input: "boxes"
// input: "sizes"
CV_Assert(layer.input_size() == 3);
CV_CheckEQ(num_inputs, 3, "");
Mat cropSize = getTensorContent(getConstBlob(layer, value_id, 2));
CV_CheckTypeEQ(cropSize.type(), CV_32SC1, ""); CV_CheckEQ(cropSize.total(), (size_t)2, "");
@ -2084,6 +2215,7 @@ void TFImporter::populateNet(Net dstNet)
// determine out shape: NxCxHxW --Slice--> 1xCxHxW
// out_shape = 1xCxHxW if keepDims else (1xCxHxW --Flatten--> CxHxW)
// global pool: NxCxHxW --Flatten--> Nx(C*H*W) --Reshape--> 1x1xNx(C*H*W) --Pooling--> 1x1x1x(C*H*W) --Reshape--> out_shape
CV_CheckGT(num_inputs, 0, "");
Mat indices = getTensorContent(getConstBlob(layer, value_id, 1));
CV_Assert(indices.type() == CV_32SC1);
@ -2218,6 +2350,7 @@ void TFImporter::populateNet(Net dstNet)
// Example: given a list with "N" tensors of shape (C, H, W):
// if axis == 0 then the output tensor will have the shape (N, C, H, W),
// if axis == 1 then the output tensor will have the shape (C, N, H, W).
CV_CheckGT(num_inputs, 0, "");
CV_Assert(hasLayerAttr(layer, "axis"));
int dim = (int)getLayerAttr(layer, "axis").i();
if (dim != 0)
@ -2225,7 +2358,7 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(hasLayerAttr(layer, "N"));
int num = (int)getLayerAttr(layer, "N").i();
CV_Assert(layer.input_size() == num);
CV_CheckEQ(num_inputs, num, "");
std::string base_name = name + "/reshape_";
std::vector<int> reshape_ids;
for (int i = 0; i < num; i++) {
@ -2256,7 +2389,7 @@ void TFImporter::populateNet(Net dstNet)
// input: "input"
// input: "mix"
// input: "max"
CV_Assert(layer.input_size() == 3);
CV_CheckEQ(num_inputs, 3, "");
Mat minValue = getTensorContent(getConstBlob(layer, value_id, 1));
Mat maxValue = getTensorContent(getConstBlob(layer, value_id, 2));
@ -2275,6 +2408,7 @@ void TFImporter::populateNet(Net dstNet)
type == "Relu" || type == "Elu" ||
type == "Identity" || type == "Relu6")
{
CV_CheckGT(num_inputs, 0, "");
std::string dnnType = type;
if (type == "Abs") dnnType = "AbsVal";
else if (type == "Tanh") dnnType = "TanH";
@ -2284,7 +2418,7 @@ void TFImporter::populateNet(Net dstNet)
int id = dstNet.addLayer(name, dnnType, layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, num_inputs);
}
else
{
@ -2308,7 +2442,7 @@ void TFImporter::populateNet(Net dstNet)
// All the Const input nodes are added to layer's blobs.
std::vector<std::string> inputsNames;
for (int i = 0; i < layer.input_size(); ++i)
for (int i = 0; i < num_inputs; ++i)
{
// Check if input is a Const node.
if (value_id.find(layer.input(i)) != value_id.end())
@ -2328,7 +2462,11 @@ void TFImporter::populateNet(Net dstNet)
}
}
}
dstNet.setInputsNames(netInputsNames);
catch (const std::exception& e)
{
CV_LOG_ERROR(NULL, "DNN/TF: Can't parse layer for node='" << name << "'. Exception: " << e.what());
throw;
}
}
} // namespace
@ -2337,18 +2475,16 @@ void TFImporter::populateNet(Net dstNet)
Net readNetFromTensorflow(const String &model, const String &config)
{
TFImporter importer(model.c_str(), config.c_str());
Net net;
importer.populateNet(net);
TFImporter importer(net, model.c_str(), config.c_str());
return net;
}
Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
const char* bufferConfig, size_t lenConfig)
{
TFImporter importer(bufferModel, lenModel, bufferConfig, lenConfig);
Net net;
importer.populateNet(net);
TFImporter importer(net, bufferModel, lenModel, bufferConfig, lenConfig);
return net;
}

16
modules/imgproc/include/opencv2/imgproc.hpp
Unescape View File

@ -587,7 +587,7 @@ enum ColorConversionCodes {
COLOR_YCrCb2BGR = 38,
COLOR_YCrCb2RGB = 39,
COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
COLOR_RGB2HSV = 41,
COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
@ -595,27 +595,27 @@ enum ColorConversionCodes {
COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
COLOR_RGB2Luv = 51,
COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..180 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
COLOR_RGB2HLS = 53,
COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
COLOR_HSV2BGR = 54, //!< backward conversions HSV to RGB/BGR with H range 0..180 if 8 bit image
COLOR_HSV2RGB = 55,
COLOR_Lab2BGR = 56,
COLOR_Lab2RGB = 57,
COLOR_Luv2BGR = 58,
COLOR_Luv2RGB = 59,
COLOR_HLS2BGR = 60,
COLOR_HLS2BGR = 60, //!< backward conversions HLS to RGB/BGR with H range 0..180 if 8 bit image
COLOR_HLS2RGB = 61,
COLOR_BGR2HSV_FULL = 66,
COLOR_BGR2HSV_FULL = 66, //!< convert RGB/BGR to HSV (hue saturation value) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hsv "color conversions"
COLOR_RGB2HSV_FULL = 67,
COLOR_BGR2HLS_FULL = 68,
COLOR_BGR2HLS_FULL = 68, //!< convert RGB/BGR to HLS (hue lightness saturation) with H range 0..255 if 8 bit image, @ref color_convert_rgb_hls "color conversions"
COLOR_RGB2HLS_FULL = 69,
COLOR_HSV2BGR_FULL = 70,
COLOR_HSV2BGR_FULL = 70, //!< backward conversions HSV to RGB/BGR with H range 0..255 if 8 bit image
COLOR_HSV2RGB_FULL = 71,
COLOR_HLS2BGR_FULL = 72,
COLOR_HLS2BGR_FULL = 72, //!< backward conversions HLS to RGB/BGR with H range 0..255 if 8 bit image
COLOR_HLS2RGB_FULL = 73,
COLOR_LBGR2Lab = 74,

2
samples/python/tutorial_code/video/background_subtraction/bg_sub.py
Unescape View File

@ -18,7 +18,7 @@ else:
## [capture]
capture = cv.VideoCapture(cv.samples.findFileOrKeep(args.input))
if not capture.isOpened:
if not capture.isOpened():
print('Unable to open: ' + args.input)
exit(0)
## [capture]

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