1 files changed, 132 insertions, 0 deletions
diff --git a/runtimes/contrib/tflite_classify/src/tflite_classify.cc b/runtimes/contrib/tflite_classify/src/tflite_classify.cc
new file mode 100644
index 000000000..40c15f331
--- /dev/null
+++ b/runtimes/contrib/tflite_classify/src/tflite_classify.cc
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ImageClassifier.h"
+
+#include <iostream>
+
+#include <boost/filesystem.hpp>
+#include <opencv2/opencv.hpp>
+
+namespace fs = boost::filesystem;
+
+int main(const int argc, char **argv)
+{
+  const std::string MODEL_FILE = "tensorflow_inception_graph.tflite";
+  const std::string LABEL_FILE = "imagenet_comp_graph_label_strings.txt";
+
+  const std::string INPUT_NAME = "input";
+  const std::string OUTPUT_NAME = "output";
+  const int INPUT_SIZE = 224;
+  const int IMAGE_MEAN = 117;
+  const int IMAGE_STD = 1;
+  const int OUTPUT_SIZE = 1008;
+
+  const int FRAME_WIDTH = 640;
+  const int FRAME_HEIGHT = 480;
+
+  bool use_nnapi = false;
+  bool debug_mode = false;
+
+  if (std::getenv("USE_NNAPI") != nullptr)
+  {
+    use_nnapi = true;
+  }
+
+  if (std::getenv("DEBUG_MODE") != nullptr)
+  {
+    debug_mode = true;
+  }
+
+  std::cout << "USE_NNAPI : " << use_nnapi << std::endl;
+  std::cout << "DEBUG_MODE : " << debug_mode << std::endl;
+
+  std::cout << "Model : " << MODEL_FILE << std::endl;
+  std::cout << "Label : " << LABEL_FILE << std::endl;
+
+  if (!fs::exists(MODEL_FILE))
+  {
+    std::cerr << "model file not found: " << MODEL_FILE << std::endl;
+    exit(1);
+  }
+
+  if (!fs::exists(LABEL_FILE))
+  {
+    std::cerr << "label file not found: " << LABEL_FILE << std::endl;
+    exit(1);
+  }
+
+  // Create ImageClassifier
+  std::unique_ptr<ImageClassifier> classifier(
+      new ImageClassifier(MODEL_FILE, LABEL_FILE, INPUT_SIZE, IMAGE_MEAN, IMAGE_STD, INPUT_NAME,
+                          OUTPUT_NAME, use_nnapi));
+
+  // Cam setting
+  cv::VideoCapture cap(0);
+  cv::Mat frame;
+
+  // Initialize camera
+  cap.set(CV_CAP_PROP_FRAME_WIDTH, FRAME_WIDTH);
+  cap.set(CV_CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT);
+  cap.set(CV_CAP_PROP_FPS, 5);
+
+  std::vector<Recognition> results;
+  clock_t begin, end;
+  while (cap.isOpened())
+  {
+    // Get image data
+    if (!cap.read(frame))
+    {
+      std::cout << "Frame is null..." << std::endl;
+      break;
+    }
+
+    if (debug_mode)
+    {
+      begin = clock();
+    }
+    // Recognize image
+    results = classifier->recognizeImage(frame);
+    if (debug_mode)
+    {
+      end = clock();
+    }
+
+    // Show result data
+    std::cout << std::endl;
+    if (results.size() > 0)
+    {
+      for (int i = 0; i < results.size(); ++i)
+      {
+        std::cout << results[i].title << ": " << results[i].confidence << std::endl;
+      }
+    }
+    else
+    {
+      std::cout << "." << std::endl;
+    }
+    if (debug_mode)
+    {
+      std::cout << "Frame: " << FRAME_WIDTH << "x" << FRAME_HEIGHT << std::endl;
+      std::cout << "Crop: " << INPUT_SIZE << "x" << INPUT_SIZE << std::endl;
+      std::cout << "Inference time(ms): " << ((end - begin) / (CLOCKS_PER_SEC / 1000)) << std::endl;
+    }
+  }
+
+  cap.release();
+
+  return 0;
+}