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New threaded RTSP and AI image recognition.
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#include "inference.h"
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#include <algorithm>
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#include <iostream>
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const std::vector<std::string> InferenceEngine::CLASS_NAMES = {
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"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
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"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
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"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
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"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
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"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
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"scissors", "teddy bear", "hair drier", "toothbrush"};
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InferenceEngine::InferenceEngine(const std::string &model_path)
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: env(ORT_LOGGING_LEVEL_WARNING, "ONNXRuntime"),
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session_options(),
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session(env, model_path.c_str(), session_options),
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input_shape{1, 3, 640, 640}
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{
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session_options.SetIntraOpNumThreads(1);
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session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_BASIC);
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}
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InferenceEngine::~InferenceEngine() {}
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/*
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* Function to preprocess the image
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*
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* @param image_path: path to the image
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* @param orig_width: original width of the image
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* @param orig_height: original height of the image
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*
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* @return: vector of floats representing the preprocessed image
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*/
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std::vector<float> InferenceEngine::preprocessImage(const cv::Mat &image)
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{
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if (image.empty())
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{
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throw std::runtime_error("Could not read the image");
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}
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cv::Mat resized_image;
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cv::resize(image, resized_image, cv::Size(input_shape[2], input_shape[3]));
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resized_image.convertTo(resized_image, CV_32F, 1.0 / 255);
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std::vector<cv::Mat> channels(3);
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cv::split(resized_image, channels);
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std::vector<float> input_tensor_values;
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for (int c = 0; c < 3; ++c)
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{
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input_tensor_values.insert(input_tensor_values.end(), (float *)channels[c].data, (float *)channels[c].data + input_shape[2] * input_shape[3]);
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}
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return input_tensor_values;
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}
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/*
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* Function to filter the detections based on the confidence threshold
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*
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* @param results: vector of floats representing the output tensor
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* @param confidence_threshold: minimum confidence threshold
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* @param img_width: width of the input image
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* @param img_height: height of the input image
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* @param orig_width: original width of the image
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* @param orig_height: original height of the image
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*
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* @return: vector of Detection objects
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*/
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std::vector<Detection> InferenceEngine::filterDetections(const std::vector<float> &results, float confidence_threshold, int img_width, int img_height, int orig_width, int orig_height)
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{
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std::vector<Detection> detections;
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const int num_detections = results.size() / 6;
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for (int i = 0; i < num_detections; ++i)
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{
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float left = results[i * 6 + 0];
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float top = results[i * 6 + 1];
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float right = results[i * 6 + 2];
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float bottom = results[i * 6 + 3];
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float confidence = results[i * 6 + 4];
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int class_id = results[i * 6 + 5];
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if (confidence >= confidence_threshold)
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{
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int x = static_cast<int>(left * orig_width / img_width);
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int y = static_cast<int>(top * orig_height / img_height);
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int width = static_cast<int>((right - left) * orig_width / img_width);
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int height = static_cast<int>((bottom - top) * orig_height / img_height);
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detections.push_back(
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{confidence,
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cv::Rect(x, y, width, height),
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class_id,
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CLASS_NAMES[class_id]});
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}
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}
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return detections;
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}
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/*
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* Function to run inference
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*
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* @param input_tensor_values: vector of floats representing the input tensor
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*
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* @return: vector of floats representing the output tensor
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*/
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std::vector<float> InferenceEngine::runInference(const std::vector<float> &input_tensor_values)
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{
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Ort::AllocatorWithDefaultOptions allocator;
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std::string input_name = getInputName();
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std::string output_name = getOutputName();
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const char *input_name_ptr = input_name.c_str();
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const char *output_name_ptr = output_name.c_str();
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Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
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Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, const_cast<float *>(input_tensor_values.data()), input_tensor_values.size(), input_shape.data(), input_shape.size());
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auto output_tensors = session.Run(Ort::RunOptions{nullptr}, &input_name_ptr, &input_tensor, 1, &output_name_ptr, 1);
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float *floatarr = output_tensors[0].GetTensorMutableData<float>();
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size_t output_tensor_size = output_tensors[0].GetTensorTypeAndShapeInfo().GetElementCount();
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return std::vector<float>(floatarr, floatarr + output_tensor_size);
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}
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/*
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* Function to draw the labels on the image
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*
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* @param image: input image
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* @param detections: vector of Detection objects
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*
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* @return: image with labels drawn
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*/
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cv::Mat InferenceEngine::draw_labels(const cv::Mat &image, const std::vector<Detection> &detections)
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{
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cv::Mat result = image.clone();
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for (const auto &detection : detections)
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{
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cv::rectangle(result, detection.bbox, cv::Scalar(0, 255, 0), 2);
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std::string label = detection.class_name + ": " + std::to_string(detection.confidence);
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int baseLine;
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cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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cv::rectangle(
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result,
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cv::Point(detection.bbox.x, detection.bbox.y - labelSize.height),
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cv::Point(detection.bbox.x + labelSize.width, detection.bbox.y + baseLine),
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cv::Scalar(255, 255, 255),
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cv::FILLED);
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cv::putText(
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result,
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label,
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cv::Point(
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detection.bbox.x,
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detection.bbox.y),
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cv::FONT_HERSHEY_SIMPLEX,
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0.5,
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cv::Scalar(0, 0, 0),
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1);
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}
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return result;
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}
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/*
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* Function to get the input name
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*
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* @return: name of the input tensor
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*/
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std::string InferenceEngine::getInputName()
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{
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Ort::AllocatorWithDefaultOptions allocator;
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Ort::AllocatedStringPtr name_allocator = session.GetInputNameAllocated(0, allocator);
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return std::string(name_allocator.get());
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}
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/*
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* Function to get the output name
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*
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* @return: name of the output tensor
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*/
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std::string InferenceEngine::getOutputName()
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{
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Ort::AllocatorWithDefaultOptions allocator;
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Ort::AllocatedStringPtr name_allocator = session.GetOutputNameAllocated(0, allocator);
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return std::string(name_allocator.get());
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}
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