demos: fix compilation warnings

demos/common/cpp/models/include/models/detection_model.h

@@ -45,7 +45,7 @@ class DetectionModel : public ImageModel {
     float confidenceThreshold;
     std::vector<std::string> labels;
 
-    std::string getLabelName(int labelID) {
-        return (size_t)labelID < labels.size() ? labels[labelID] : std::string("Label #") + std::to_string(labelID);
+    std::string getLabelName(size_t labelID) {
+        return labelID < labels.size() ? labels[labelID] : std::string("Label #") + std::to_string(labelID);
     }
 };

demos/common/cpp/models/include/models/detection_model_yolox.h

@@ -47,7 +47,7 @@ class ModelYoloX: public DetectionModel {
     void prepareInputsOutputs(std::shared_ptr<ov::Model>& model) override;
     void setStridesGrids();
 
-    double boxIOUThreshold;
+    float boxIOUThreshold;
     std::vector<std::pair<size_t, size_t>> grids;
     std::vector<size_t> expandedStrides;
     static const size_t numberOfClasses = 80;

demos/common/cpp/models/include/models/results.h

@@ -87,7 +87,7 @@ struct ClassificationResult : public ResultBase {
 };
 
 struct DetectedObject : public cv::Rect2f {
-    unsigned int labelID;
+    size_t labelID;
     std::string label;
     float confidence;
 };

demos/common/cpp/models/src/detection_model_yolov3_onnx.cpp

@@ -117,11 +117,7 @@ std::shared_ptr<InternalModelData> ModelYoloV3ONNX::preprocess(const InputData&
 
 namespace {
 float getScore(const ov::Tensor& scoresTensor, size_t classInd, size_t boxInd) {
-    const float* scoresPtr = scoresTensor.data<float>();
-    const auto shape = scoresTensor.get_shape();
-    int N = shape[2];
-
-    return scoresPtr[classInd * N + boxInd];
+    return scoresTensor.data<float>()[classInd * scoresTensor.get_shape()[2] + boxInd];
 }
 }
 
@@ -144,7 +140,7 @@ std::unique_ptr<ResultBase> ModelYoloV3ONNX::postprocess(InferenceResult& infRes
     // Generate detection results
     DetectionResult* result = new DetectionResult(infResult.frameId, infResult.metaData);
     size_t numberOfBoxes = indicesShape.size() == 3 ? indicesShape[1] : indicesShape[0];
-    int indicesStride = indicesShape.size() == 3 ? indicesShape[2] : indicesShape[1];
+    size_t indicesStride = indicesShape.size() == 3 ? indicesShape[2] : indicesShape[1];
 
     for (size_t i = 0; i < numberOfBoxes; ++i) {
         int batchInd = indicesData[i * indicesStride];

demos/common/cpp/models/src/detection_model_yolox.cpp

@@ -112,8 +112,8 @@ void ModelYoloX::setStridesGrids() {
 std::shared_ptr<InternalModelData> ModelYoloX::preprocess(const InputData& inputData,
                                                           ov::InferRequest& request) {
     const auto& origImg = inputData.asRef<ImageInputData>().inputImage;
-    double scale = std::min(static_cast<double>(netInputWidth) / origImg.cols,
-                            static_cast<double>(netInputHeight) / origImg.rows);
+    float scale = std::min(static_cast<float>(netInputWidth) / origImg.cols,
+                           static_cast<float>(netInputHeight) / origImg.rows);
 
     cv::Mat resizedImage = resizeImageExt(origImg, netInputWidth, netInputHeight, resizeMode,
                                           interpolationMode, nullptr, cv::Scalar(114, 114, 114));

demos/interactive_face_detection_demo/cpp/detectors.hpp

@@ -32,7 +32,7 @@ struct FaceDetection : BaseDetection {
     std::string output;
     std::string labels_output;
     double detectionThreshold;
-    int objectSize;
+    size_t objectSize;
     float width;
     float height;
     size_t model_input_width;

demos/text_detection_demo/cpp/include/cnn.hpp

@@ -32,7 +32,7 @@ class Cnn {
     ov::Core& m_core;
     cv::Size m_new_input_resolution;
     bool use_auto_resize;
-    int m_channels;
+    size_t m_channels;
     cv::Size m_input_size;
     std::string m_input_name;
     std::vector<std::string> m_output_names;

demos/text_detection_demo/cpp/src/cnn.cpp

@@ -53,7 +53,7 @@ Cnn::Cnn(
     m_model->reshape({ {m_input_name, input_shape} });
 
     m_channels = input_shape[ov::layout::channels_idx(input_layout)];
-    m_input_size = cv::Size(input_shape[ov::layout::width_idx(input_layout)], input_shape[ov::layout::height_idx(input_layout)]);
+    m_input_size = cv::Size(int(input_shape[ov::layout::width_idx(input_layout)]), int(input_shape[ov::layout::height_idx(input_layout)]));
 
     // Collect output names
     ov::OutputVector outputs = m_model->outputs();