All processing in graph demo

demos/smart_classroom_demo_gapi/CMakeLists.txt

@@ -5,11 +5,12 @@
 file (GLOB_RECURSE SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file (GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
 
-ie_add_sample(NAME smart_classroom_demo
+ie_add_sample(NAME smart_classroom_demo_gapi
               SOURCES ${SOURCES}
               HEADERS ${HEADERS}
               INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include"
               DEPENDENCIES monitors
-              OPENCV_DEPENDENCIES highgui)
+              OPENCV_DEPENDENCIES highgui
+              OPENCV_DEPENDENCIES gapi)
 
-target_link_libraries(smart_classroom_demo PRIVATE ngraph::ngraph)
+target_link_libraries(smart_classroom_demo_gapi PRIVATE ngraph::ngraph)

demos/smart_classroom_demo_gapi/README.md

@@ -1,4 +1,4 @@
-# Smart Classroom C++ Demo
+# G-API. Smart Classroom C++ Demo
 
 The demo shows an example of joint usage of several neural networks to detect student actions (sitting, standing, raising hand for the `person-detection-action-recognition-0005` model and sitting, writing, raising hand, standing, turned around, lie on the desk for the `person-detection-action-recognition-0006` model) and recognize people by faces in the classroom environment. The demo uses Async API for action and face detection networks. It allows to parallelize execution of face recognition and detection: while face recognition is running on one accelerator, face and action detection could be performed on another. You can use a set of the following pre-trained models with the demo:
 
@@ -28,12 +28,12 @@ To recognize faces on a frame, the demo needs a gallery of reference images. Eac
 
 Running the application with the `-h` option yields the following usage message:
 ```
-./smart_classroom_demo -h
+./smart_classroom_demo_gapi -h
 InferenceEngine:
     API version ............ <version>
     Build .................. <number>
 
-smart_classroom_demo [OPTION]
+smart_classroom_demo_gapi [OPTION]
 Options:
 
     -h                             Print a usage message.
@@ -44,16 +44,12 @@ Options:
     -m_fd '<path>'                 Required. Path to the Face Detection model (.xml) file.
     -m_lm '<path>'                 Required. Path to the Facial Landmarks Regression Retail model (.xml) file.
     -m_reid '<path>'               Required. Path to the Face Reidentification Retail model (.xml) file.
-    -l '<absolute_path>'           Optional. For CPU custom layers, if any. Absolute path to a shared library with the kernels implementation.
-          Or
-    -c '<absolute_path>'           Optional. For GPU custom kernels, if any. Absolute path to an .xml file with the kernels description.
-    -d_act '<device>'              Optional. Specify the target device for Person/Action Detection Retail (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device.
-    -d_fd '<device>'               Optional. Specify the target device for Face Detection Retail (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device.
-    -d_lm '<device>'               Optional. Specify the target device for Landmarks Regression Retail (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device.
-    -d_reid '<device>'             Optional. Specify the target device for Face Reidentification Retail (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device.
+    -d_act '<device>'              Optional. Specify the target device for Person/Action Detection Retail (the list of available devices is shown below). Default value is CPU.
+    -d_fd '<device>'               Optional. Specify the target device for Face Detection Retail (the list of available devices is shown below). Default value is CPU.
+    -d_lm '<device>'               Optional. Specify the target device for Landmarks Regression Retail (the list of available devices is shown below). Default value is CPU.
+    -d_reid '<device>'             Optional. Specify the target device for Face Reidentification Retail (the list of available devices is shown below). Default value is CPU.
     -out_v  '<path>'               Optional. File to write output video with visualization to.
     -greedy_reid_matching          Optional. Use faster greedy matching algorithm in face reid.
-    -pc                            Optional. Enables per-layer performance statistics.
     -r                             Optional. Output Inference results as raw values.
     -ad                            Optional. Output file name to save per-person action statistics in.
     -t_ad                          Optional. Probability threshold for person/action detection.
@@ -89,15 +85,15 @@ To run the demo, you can use public or pre-trained models. To download the pre-t
 
 Example of a valid command line to run the application with pre-trained models for recognizing students actions:
 ```sh
-./smart_classroom_demo -m_act <path_to_model>/person-detection-action-recognition-0005.xml \
+./_smart_classroom_demo_gapi -m_act <path_to_model>/person-detection-action-recognition-0005.xml \
                        -m_fd <path_to_model>/face-detection-adas-0001.xml \
                        -i <path_to_video>
 ```
 > **NOTE**: To recognize actions of students, use `person-detection-action-recognition-0005` model for 3 basic actions and `person-detection-action-recognition-0006` model for 6 actions.
 
 Example of a valid command line to run the application for recognizing first raised-hand students:
 ```sh
-./smart_classroom_demo -m_act <path_to_model>/person-detection-raisinghand-recognition-0001.xml \
+./smart_classroom_demo_gapi -m_act <path_to_model>/person-detection-raisinghand-recognition-0001.xml \
                        -a_top <number of first raised-hand students> \
                        -i <path_to_video>
 ```

demos/smart_classroom_demo_gapi/include/action_detector.hpp

@@ -1,4 +1,4 @@
-// Copyright (C) 2018-2019 Intel Corporation
+// Copyright (C) 2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
@@ -10,8 +10,6 @@
 
 #include <opencv2/core/core.hpp>
 
-#include "cnn.hpp"
-
 /**
 * @brief Class for detection with action info
 */
@@ -50,32 +48,12 @@ struct SSDHead {
     SSDHead(int step, const std::vector<cv::Size2f>& anchors) : step(step), anchors(anchors) {}
 };
 using SSDHeads = std::vector<SSDHead>;
-
 /**
 * @brief Config for the Action Detection model
 */
-struct ActionDetectorConfig : public CnnConfig {
-    explicit ActionDetectorConfig(const std::string& path_to_model)
-        : CnnConfig(path_to_model) {}
-
-    /** @brief Name of output blob with location info */
-    std::string old_loc_blob_name{"mbox_loc1/out/conv/flat"};
-    /** @brief Name of output blob with detection confidence info */
-    std::string old_det_conf_blob_name{"mbox_main_conf/out/conv/flat/softmax/flat"};
-    /** @brief Prefix of name of output blob with action confidence info */
-    std::string old_action_conf_blob_name_prefix{"out/anchor"};
-    /** @brief Name of output blob with priorbox info */
-    std::string old_priorbox_blob_name{"mbox/priorbox"};
-
-    /** @brief Name of output blob with location info */
-    std::string new_loc_blob_name{"ActionNet/out_detection_loc"};
-    /** @brief Name of output blob with detection confidence info */
-    std::string new_det_conf_blob_name{"ActionNet/out_detection_conf"};
-    /** @brief Prefix of name of output blob with action confidence info */
-    std::string new_action_conf_blob_name_prefix{"ActionNet/action_heads/out_head_"};
-    /** @brief Suffix of name of output blob with action confidence info */
-    std::string new_action_conf_blob_name_suffix{"_anchor_"};
-
+struct ActionDetectorConfig {
+    /** @brief Person detection action recognition 0006 network enable flag */
+    bool net_with_six_actions = false;
     /** @brief Scale parameter for Soft-NMS algorithm */
     float nms_sigma = 0.6f;
     /** @brief Threshold for detected objects */
@@ -96,8 +74,10 @@ struct ActionDetectorConfig : public CnnConfig {
     std::vector<int> new_anchors{1, 4};
     /** @brief Number of actions to detect */
     size_t num_action_classes = 3;
-    /** @brief Async execution flag */
-    bool is_async = true;
+    /** @brief Input image size height*/
+    int input_height = 0;
+    /** @brief Input image size width*/
+    int input_width = 0;
     /** @brief  SSD bbox encoding variances */
     float variances[4]{0.1f, 0.1f, 0.2f, 0.2f};
     SSDHeads new_det_heads{{8,  {{26.17863728f, 58.670372f}}},
@@ -107,26 +87,13 @@ struct ActionDetectorConfig : public CnnConfig {
                                  {93.5070856f, 201.107692f}}}};
 };
 
-
-class ActionDetection : public AsyncDetection<DetectedAction>, public BaseCnnDetection {
+class ActionDetection  {
 public:
     explicit ActionDetection(const ActionDetectorConfig& config);
 
-    void submitRequest() override;
-    void enqueue(const cv::Mat &frame) override;
-    void wait() override { BaseCnnDetection::wait(); }
-    void printPerformanceCounts(const std::string &fullDeviceName) override {
-        BaseCnnDetection::printPerformanceCounts(fullDeviceName);
-    }
-    DetectedActions fetchResults() override;
-
+    DetectedActions fetchResults(const std::vector<cv::Mat> &ssd_results, const cv::Mat &in_frame);
 private:
     ActionDetectorConfig config_;
-    InferenceEngine::ExecutableNetwork net_;
-    std::string input_name_;
-    InferenceEngine::BlobMap outputs_;
-
-    int enqueued_frames_ = 0;
     float width_ = 0;
     float height_ = 0;
     bool new_network_ = false;
@@ -204,7 +171,7 @@ class ActionDetection : public AsyncDetection<DetectedAction>, public BaseCnnDet
     * @brief Carry out Soft Non-Maximum Suppression algorithm under detected actions
     *
     * @param detections Detected actions
-    * @param sigma Scale parameter
+    * @param sigma Scale paramter
     * @param top_k Number of top-score bboxes
     * @param min_det_conf Minimum detection confidence
     * @param out_indices Out indices of valid detections
@@ -215,3 +182,7 @@ class ActionDetection : public AsyncDetection<DetectedAction>, public BaseCnnDet
                                const float min_det_conf,
                                std::vector<int>* out_indices) const;
 };
+
+struct ActionDetectionKernelInput {
+    std::shared_ptr<ActionDetection> ptr;
+};

demos/smart_classroom_demo_gapi/include/actions.hpp

@@ -1,4 +1,4 @@
-// Copyright (C) 2018-2019 Intel Corporation
+// Copyright (C) 2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //