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

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This commit is contained in:
Alexander Alekhin
2018-05-10 18:19:56 +03:00
parent 000a13b6a3 9e3b6a228a
commit 51e543050c
20 changed files with 515 additions and 248 deletions
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samples/cpp/tutorial_code/dnn/custom_layers.cpp
+10 -10
View File
@@ -16,18 +16,18 @@ public:
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int requiredOutputs,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &internals) const;
std::vector<std::vector<int> > &internals) const CV_OVERRIDE;
//! [MyLayer::getMemoryShapes]
//! [MyLayer::forward]
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals);
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE;
//! [MyLayer::forward]
//! [MyLayer::finalize]
virtual void finalize(const std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs);
virtual void finalize(const std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs) CV_OVERRIDE;
//! [MyLayer::finalize]
virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals);
virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE;
};
//! [A custom layer interface]
@@ -49,7 +49,7 @@ public:
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int requiredOutputs,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &internals) const
std::vector<std::vector<int> > &internals) const CV_OVERRIDE
{
CV_UNUSED(requiredOutputs); CV_UNUSED(internals);
std::vector<int> outShape(4);
@@ -62,7 +62,7 @@ public:
}
// Implementation of this custom layer is based on https://github.com/cdmh/deeplab-public/blob/master/src/caffe/layers/interp_layer.cpp
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals)
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE
{
CV_UNUSED(internals);
cv::Mat& inp = *inputs[0];
@@ -105,7 +105,7 @@ public:
}
}
virtual void forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) {}
virtual void forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) CV_OVERRIDE {}
private:
int outWidth, outHeight;
@@ -132,7 +132,7 @@ public:
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int requiredOutputs,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &internals) const
std::vector<std::vector<int> > &internals) const CV_OVERRIDE
{
CV_UNUSED(requiredOutputs); CV_UNUSED(internals);
std::vector<int> outShape(4);
@@ -146,7 +146,7 @@ public:
// This implementation is based on a reference implementation from
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals)
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE
{
CV_UNUSED(internals);
cv::Mat& inp = *inputs[0];
@@ -187,7 +187,7 @@ public:
}
}
virtual void forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) {}
virtual void forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) CV_OVERRIDE {}
private:
static inline int offset(const cv::MatSize& size, int c, int x, int y, int b)
samples/dnn/object_detection.cpp
+5 -4
View File
@@ -7,12 +7,13 @@
const char* keys =
"{ help h | | Print help message. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ model m | | Path to a binary file of model contains trained weights. "
"It could be a file with extensions .caffemodel (Caffe), "
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet).}"
"{ config c | | Path to a text file of model contains network configuration. "
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet).}"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes | | Optional path to a text file with names of classes to label detected objects. }"
"{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
@@ -91,7 +92,7 @@ int main(int argc, char** argv)
if (parser.has("input"))
cap.open(parser.get<String>("input"));
else
cap.open(0);
cap.open(parser.get<int>("device"));
// Process frames.
Mat frame, blob;
samples/dnn/openpose.cpp
+4 -4
View File
@@ -61,12 +61,16 @@ int main(int argc, char **argv)
"{ p proto | | (required) model configuration, e.g. hand/pose.prototxt }"
"{ m model | | (required) model weights, e.g. hand/pose_iter_102000.caffemodel }"
"{ i image | | (required) path to image file (containing a single person, or hand) }"
"{ width | 368 | Preprocess input image by resizing to a specific width. }"
"{ height | 368 | Preprocess input image by resizing to a specific height. }"
"{ t threshold | 0.1 | threshold or confidence value for the heatmap }"
);
String modelTxt = parser.get<string>("proto");
String modelBin = parser.get<string>("model");
String imageFile = parser.get<String>("image");
int W_in = parser.get<int>("width");
int H_in = parser.get<int>("height");
float thresh = parser.get<float>("threshold");
if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty())
{
@@ -75,10 +79,6 @@ int main(int argc, char **argv)
return 0;
}
// fixed input size for the pretrained network
int W_in = 368;
int H_in = 368;
// read the network model
Net net = readNetFromCaffe(modelTxt, modelBin);
samples/dnn/segmentation.cpp
+5 -4
View File
@@ -7,12 +7,13 @@
const char* keys =
"{ help h | | Print help message. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ model m | | Path to a binary file of model contains trained weights. "
"It could be a file with extensions .caffemodel (Caffe), "
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet). }"
"{ config c | | Path to a text file of model contains network configuration. "
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet). }"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes | | Optional path to a text file with names of classes. }"
"{ colors | | Optional path to a text file with colors for an every class. "
@@ -111,7 +112,7 @@ int main(int argc, char** argv)
if (parser.has("input"))
cap.open(parser.get<String>("input"));
else
cap.open(0);
cap.open(parser.get<int>("device"));
//! [Open a video file or an image file or a camera stream]
// Process frames.