Merge pull request opencv#10979 from dkurt:unite_dnn_samples

Author: alalek

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

@@ -13,50 +13,53 @@ We will demonstrate results of this example on the following picture.
 Source Code
 -----------
 
-We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/caffe_googlenet.cpp).
+We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
 
-@include dnn/caffe_googlenet.cpp
+@include dnn/classification.cpp
 
 Explanation
 -----------
 
 -# Firstly, download GoogLeNet model files:
-   [bvlc_googlenet.prototxt  ](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/bvlc_googlenet.prototxt) and
+   [bvlc_googlenet.prototxt  ](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/bvlc_googlenet.prototxt) and
    [bvlc_googlenet.caffemodel](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel)
 
    Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
-   [synset_words.txt](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/synset_words.txt).
+   [classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/classification_classes_ILSVRC2012.txt).
 
    Put these files into working dir of this program example.
 
 -# Read and initialize network using path to .prototxt and .caffemodel files
-   @snippet dnn/caffe_googlenet.cpp Read and initialize network
+   @snippet dnn/classification.cpp Read and initialize network
 
--# Check that network was read successfully
-   @snippet dnn/caffe_googlenet.cpp Check that network was read successfully
+   You can skip an argument `framework` if one of the files `model` or `config` has an
+   extension `.caffemodel` or `.prototxt`.
+   This way function cv::dnn::readNet can automatically detects a model's format.
 
 -# Read input image and convert to the blob, acceptable by GoogleNet
-   @snippet dnn/caffe_googlenet.cpp Prepare blob
-   We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction using cv::dnn::blobFromImage constructor.
+   @snippet dnn/classification.cpp Open a video file or an image file or a camera stream
 
--# Pass the blob to the network
-   @snippet dnn/caffe_googlenet.cpp Set input blob
-   In bvlc_googlenet.prototxt the network input blob named as "data", therefore this blob labeled as ".data" in opencv_dnn API.
+   cv::VideoCapture can load both images and videos.
+
+   @snippet dnn/classification.cpp Create a 4D blob from a frame
+   We convert the image to a 4-dimensional blob (so-called batch) with `1x3x224x224` shape
+   after applying necessary pre-processing like resizing and mean subtraction
+   `(-104, -117, -123)` for each blue, green and red channels correspondingly using cv::dnn::blobFromImage function.
 
-   Other blobs labeled as "name_of_layer.name_of_layer_output".
+-# Pass the blob to the network
+   @snippet dnn/classification.cpp Set input blob
 
 -# Make forward pass
-   @snippet dnn/caffe_googlenet.cpp Make forward pass
-   During the forward pass output of each network layer is computed, but in this example we need output from "prob" layer only.
+   @snippet dnn/classification.cpp Make forward pass
+   During the forward pass output of each network layer is computed, but in this example we need output from the last layer only.
 
 -# Determine the best class
-   @snippet dnn/caffe_googlenet.cpp Gather output
-   We put the output of "prob" layer, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the `prob` blob.
-   And find the index of element with maximal value in this one. This index correspond to the class of the image.
-
--# Print results
-   @snippet dnn/caffe_googlenet.cpp Print results
-   For our image we get:
-> Best class: #812 'space shuttle'
->
-> Probability: 99.6378%
+   @snippet dnn/classification.cpp Get a class with a highest score
+   We put the output of network, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the `prob` blob.
+   And find the index of element with maximal value in this one. This index corresponds to the class of the image.
+
+-# Run an example from command line
+   @code
+   ./example_dnn_classification --model=bvlc_googlenet.caffemodel --config=bvlc_googlenet.prototxt --width=224 --height=224 --classes=classification_classes_ILSVRC2012.txt --input=space_shuttle.jpg --mean="104 117 123"
+   @endcode
+   For our image we get prediction of class `space shuttle` with more than 99% sureness.

doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

@@ -74,46 +74,7 @@ When you build OpenCV add the following configuration flags:
 
 - `HALIDE_ROOT_DIR` - path to Halide build directory
 
-## Sample
-
-@include dnn/squeezenet_halide.cpp
-
-## Explanation
-Download Caffe model from SqueezeNet repository: [train_val.prototxt](https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/train_val.prototxt) and [squeezenet_v1.1.caffemodel](https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel).
-
-Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
-[synset_words.txt](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/synset_words.txt).
-
-Put these files into working dir of this program example.
-
--# Read and initialize network using path to .prototxt and .caffemodel files
-@snippet dnn/squeezenet_halide.cpp Read and initialize network
-
--# Check that network was read successfully
-@snippet dnn/squeezenet_halide.cpp Check that network was read successfully
-
--# Read input image and convert to the 4-dimensional blob, acceptable by SqueezeNet v1.1
-@snippet dnn/squeezenet_halide.cpp Prepare blob
-
--# Pass the blob to the network
-@snippet dnn/squeezenet_halide.cpp Set input blob
-
--# Enable Halide backend for layers where it is implemented
-@snippet dnn/squeezenet_halide.cpp Enable Halide backend
-
--# Make forward pass
-@snippet dnn/squeezenet_halide.cpp Make forward pass
-Remember that the first forward pass after initialization require quite more
-time that the next ones. It's because of runtime compilation of Halide pipelines
-at the first invocation.
-
--# Determine the best class
-@snippet dnn/squeezenet_halide.cpp Determine the best class
-
--# Print results
-@snippet dnn/squeezenet_halide.cpp Print results
-For our image we get:
-
-> Best class: #812 'space shuttle'
->
-> Probability: 97.9812%
+## Set Halide as a preferable backend
+@code
+net.setPreferableBackend(DNN_BACKEND_HALIDE);
+@endcode

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -18,40 +18,26 @@ VIDEO DEMO:
 Source Code
 -----------
 
-The latest version of sample source code can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/yolo_object_detection.cpp).
+Use a universal sample for object detection models written
+[in C++](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp) and
+[in Python](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.py) languages
 
-@include dnn/yolo_object_detection.cpp
-
-How to compile in command line with pkg-config
-----------------------------------------------
-
-@code{.bash}
-
-# g++ `pkg-config --cflags opencv` `pkg-config --libs opencv` yolo_object_detection.cpp -o yolo_object_detection
-
-@endcode
+Usage examples
+--------------
 
 Execute in webcam:
 
 @code{.bash}
 
-$ yolo_object_detection -camera_device=0  -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights   -class_names=[PATH-TO-DARKNET]/data/coco.names
-
-@endcode
-
-Execute with image:
-
-@code{.bash}
-
-$ yolo_object_detection -source=[PATH-IMAGE]  -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights   -class_names=[PATH-TO-DARKNET]/data/coco.names
+$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392
 
 @endcode
 
-Execute in video file:
+Execute with image or video file:
 
 @code{.bash}
 
-$ yolo_object_detection -source=[PATH-TO-VIDEO] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights   -class_names=[PATH-TO-DARKNET]/data/coco.names
+$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392 --input=[PATH-TO-IMAGE-OR-VIDEO-FILE]
 
 @endcode
 

modules/core/include/opencv2/core.hpp

@@ -3159,7 +3159,7 @@ class CV_EXPORTS_W Algorithm
 
 struct Param {
     enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7,
-           UNSIGNED_INT=8, UINT64=9, UCHAR=11 };
+           UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 };
 };
 
 
@@ -3252,6 +3252,14 @@ template<> struct ParamType<uchar>
     enum { type = Param::UCHAR };
 };
 
+template<> struct ParamType<Scalar>
+{
+    typedef const Scalar& const_param_type;
+    typedef Scalar member_type;
+
+    enum { type = Param::SCALAR };
+};
+
 //! @} core_basic
 
 } //namespace cv

modules/core/src/command_line_parser.cpp

@@ -104,6 +104,12 @@ static void from_str(const String& str, int type, void* dst)
         ss >> *(double*)dst;
     else if( type == Param::STRING )
         *(String*)dst = str;
+    else if( type == Param::SCALAR)
+    {
+        Scalar& scalar = *(Scalar*)dst;
+        for (int i = 0; i < 4 && !ss.eof(); ++i)
+            ss >> scalar[i];
+    }
     else
         CV_Error(Error::StsBadArg, "unknown/unsupported parameter type");
 

modules/core/test/test_utils.cpp

@@ -261,4 +261,26 @@ TEST(AutoBuffer, allocate_test)
     EXPECT_EQ(6u, abuf.size());
 }
 
+TEST(CommandLineParser, testScalar)
+{
+    static const char * const keys3 =
+            "{ s0 | 3 4 5 | default scalar }"
+            "{ s1 |       | single value scalar }"
+            "{ s2 |       | two values scalar (default with zeros) }"
+            "{ s3 |       | three values scalar }"
+            "{ s4 |       | four values scalar }"
+            "{ s5 |       | five values scalar }";
+
+    const char* argv[] = {"<bin>", "--s1=1.1", "--s3=1.1 2.2 3",
+                          "--s4=-4.2 1 0 3", "--s5=5 -4 3 2 1"};
+    const int argc = 5;
+    CommandLineParser parser(argc, argv, keys3);
+    EXPECT_EQ(parser.get<Scalar>("s0"), Scalar(3, 4, 5));
+    EXPECT_EQ(parser.get<Scalar>("s1"), Scalar(1.1));
+    EXPECT_EQ(parser.get<Scalar>("s2"), Scalar(0));
+    EXPECT_EQ(parser.get<Scalar>("s3"), Scalar(1.1, 2.2, 3));
+    EXPECT_EQ(parser.get<Scalar>("s4"), Scalar(-4.2, 1, 0, 3));
+    EXPECT_EQ(parser.get<Scalar>("s5"), Scalar(5, -4, 3, 2));
+}
+
 }} // namespace

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -153,7 +153,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         */
 
         int inputNameToIndex(String inputName);
-        int outputNameToIndex(String outputName);
+        int outputNameToIndex(const String& outputName);
     };
 
     /** @brief Classical recurrent layer

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -222,7 +222,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         /** @brief Returns index of output blob in output array.
          *  @see inputNameToIndex()
          */
-        virtual int outputNameToIndex(String outputName);
+        CV_WRAP virtual int outputNameToIndex(const String& outputName);
 
         /**
          * @brief Ask layer if it support specific backend for doing computations.
@@ -683,6 +683,29 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      */
      CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
 
+     /**
+      * @brief Read deep learning network represented in one of the supported formats.
+      * @param[in] model Binary file contains trained weights. The following file
+      *                  extensions are expected for models from different frameworks:
+      *                  * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/)
+      *                  * `*.pb` (TensorFlow, https://www.tensorflow.org/)
+      *                  * `*.t7` | `*.net` (Torch, http://torch.ch/)
+      *                  * `*.weights` (Darknet, https://pjreddie.com/darknet/)
+      * @param[in] config Text file contains network configuration. It could be a
+      *                   file with the following extensions:
+      *                  * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
+      *                  * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
+      *                  * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
+      * @param[in] framework Explicit framework name tag to determine a format.
+      * @returns Net object.
+      *
+      * This function automatically detects an origin framework of trained model
+      * and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow,
+      * @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config
+      * arguments does not matter.
+      */
+     CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
+
     /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
      *  @warning This function has the same limitations as readNetFromTorch().
      */

modules/dnn/src/dnn.cpp

@@ -399,7 +399,7 @@ struct DataLayer : public Layer
     void forward(std::vector<Mat*>&, std::vector<Mat>&, std::vector<Mat> &) {}
     void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) {}
 
-    int outputNameToIndex(String tgtName)
+    int outputNameToIndex(const String& tgtName)
     {
         int idx = (int)(std::find(outNames.begin(), outNames.end(), tgtName) - outNames.begin());
         return (idx < (int)outNames.size()) ? idx : -1;
@@ -2521,7 +2521,7 @@ int Layer::inputNameToIndex(String)
     return -1;
 }
 
-int Layer::outputNameToIndex(String)
+int Layer::outputNameToIndex(const String&)
 {
     return -1;
 }
@@ -2813,5 +2813,43 @@ BackendWrapper::BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape&
 
 BackendWrapper::~BackendWrapper() {}
 
+Net readNet(const String& _model, const String& _config, const String& _framework)
+{
+    String framework = _framework.toLowerCase();
+    String model = _model;
+    String config = _config;
+    const std::string modelExt = model.substr(model.rfind('.') + 1);
+    const std::string configExt = config.substr(config.rfind('.') + 1);
+    if (framework == "caffe" || modelExt == "caffemodel" || configExt == "caffemodel" ||
+                                modelExt == "prototxt" || configExt == "prototxt")
+    {
+        if (modelExt == "prototxt" || configExt == "caffemodel")
+            std::swap(model, config);
+        return readNetFromCaffe(config, model);
+    }
+    if (framework == "tensorflow" || modelExt == "pb" || configExt == "pb" ||
+                                     modelExt == "pbtxt" || configExt == "pbtxt")
+    {
+        if (modelExt == "pbtxt" || configExt == "pb")
+            std::swap(model, config);
+        return readNetFromTensorflow(model, config);
+    }
+    if (framework == "torch" || modelExt == "t7" || modelExt == "net" ||
+                                configExt == "t7" || configExt == "net")
+    {
+        return readNetFromTorch(model.empty() ? config : model);
+    }
+    if (framework == "darknet" || modelExt == "weights" || configExt == "weights" ||
+                                  modelExt == "cfg" || configExt == "cfg")
+    {
+        if (modelExt == "cfg" || configExt == "weights")
+            std::swap(model, config);
+        return readNetFromDarknet(config, model);
+    }
+    CV_Error(Error::StsError, "Cannot determine an origin framework of files: " +
+                              model + (config.empty() ? "" : ", " + config));
+    return Net();
+}
+
 CV__DNN_EXPERIMENTAL_NS_END
 }} // namespace

modules/dnn/src/layers/recurrent_layers.cpp

@@ -355,7 +355,7 @@ int LSTMLayer::inputNameToIndex(String inputName)
     return -1;
 }
 
-int LSTMLayer::outputNameToIndex(String outputName)
+int LSTMLayer::outputNameToIndex(const String& outputName)
 {
     if (outputName.toLowerCase() == "h")
         return 0;