基于Digits DetectNet的Jetson TX2目标检测尺寸测量集成咨询

Hey there! Let's break down how to integrate that OpenCV-based object size measurement into your detectnet-camera.cpp workflow on Jetson TX2. I've tackled similar integrations before, so here's a practical, step-by-step plan that should work for you:

First, let's align on the end-to-end flow we need to build:

Detect objects with your custom DetectNet model → Extract bounding box coordinates → Calculate real-world dimensions using the OpenCV reference-object method → Save results to a file

Step 1: Prep Your Reference Object & Calibrate Scale #

The OpenCV method relies on a known-size reference object in the same plane as your target objects. Do this first:

• Pick a reference object (e.g., a 2cm-wide coin, a 10cm ruler) that will appear in your camera's field of view.
• Capture a test frame, run your DetectNet model on it, and note the pixel width/height of the reference object (from its bounding box: right - left for width, bottom - top for height).
• Calculate your scale factor:

  // Example values - replace with your actual measurements
  const float REFERENCE_REAL_WIDTH = 2.0;  // Real width of reference (cm)
  const float REFERENCE_PIXEL_WIDTH = 48.0; // Pixel width of reference in test frame
  float scale = REFERENCE_REAL_WIDTH / REFERENCE_PIXEL_WIDTH; // cm per pixel
  

  Pro tip: If your scene is dynamic, add the reference object to your DetectNet class list so the model can auto-detect it and recalculate scale on the fly.

Step 2: Modify detectnet-camera.cpp for Integration #

You don't need to convert the CUDA image to an OpenCV Mat (saves GPU-CPU data transfer overhead!)—we only need the bounding box coordinates from DetectNet. Here's how to update the code:

2.1 Add Required Headers #

At the top of the file, include these for file handling and time stamping:

#include <fstream>
#include <chrono>
#include <string>

2.2 Insert Size Calculation & File Saving Logic #

Find the loop where DetectNet processes detections (look for detectNet::Detection* detections = net.Detect()). Add this code inside the loop that iterates over detections:

// Loop through each detected object
for (int n = 0; n < numDetections; n++)
{
    detectNet::Detection* det = detections + n;

    // Extract bounding box pixel dimensions
    int obj_pixel_width = det->Right - det->Left;
    int obj_pixel_height = det->Bottom - det->Top;

    // Calculate real-world dimensions using pre-calibrated scale
    float obj_real_width = obj_pixel_width * scale;
    float obj_real_height = obj_pixel_height * scale;

    // Optional: Print results to console for debugging
    printf("Detected %s (confidence: %.2f) | Real Size: W=%.2fcm, H=%.2fcm\n",
           det->Class, det->Confidence, obj_real_width, obj_real_height);

    // Save results to a CSV file (append mode)
    std::ofstream out_file("object_dimensions.csv", std::ios_base::app);
    if (out_file.is_open())
    {
        // Get current timestamp for tracking
        auto now = std::chrono::system_clock::now();
        std::time_t now_time = std::chrono::system_clock::to_time_t(now);

        // Write data in CSV format (adjust columns as needed)
        out_file << std::ctime(&now_time) << ","
                 << det->Class << ","
                 << det->Confidence << ","
                 << obj_real_width << ","
                 << obj_real_height << ","
                 << det->Left << "," << det->Top << "," << det->Right << "," << det->Bottom << "\n";
        
        out_file.close();
    }
}

Step 3: Update Build Configuration #

To link OpenCV (even though we're not using image processing, we might need its core headers), modify the CMakeLists.txt for the detectnet-camera project:

1. Add this line to find OpenCV:

   find_package(OpenCV REQUIRED)
   

2. Update the target link libraries to include OpenCV:

   target_link_libraries(detectnet-camera ${OpenCV_LIBS})
   

3. Recompile the project using the jetson-inference build system:

   cd /path/to/jetson-inference/build
   make -j$(nproc)
   

Key Notes for Accuracy & Performance #

• Same Plane Requirement: Ensure your target objects and reference object are on the same flat surface—otherwise perspective distortion will skew your size calculations.
• Dynamic Scale Adjustment: If your camera moves or the scene changes, add logic to re-detect the reference object and update the scale variable in real time.
• File I/O Optimization: To avoid slowing down real-time detection, consider buffering multiple results and writing to the file in batches instead of opening/closing it for every detection.
• Camera Calibration: For higher accuracy, calibrate your camera to correct lens distortion (use OpenCV's camera calibration tools) before calculating scale.

内容的提问来源于stack exchange，提问作者MadLord