Create Object Detection using AI in .Net MAUI

ThankYou Tuhin...

Part - 2

See the workflow diagram below:

1. Input: Image or video frame.

2. Object Detection: Detect objects using YOLO or SSD.

3. Height/Width Measurement: Calculate dimensions using bounding box and reference object.

4. Depth Estimation: Use a depth estimation model or stereo vision.

5. Output: Display object dimensions and depth.

Building a project to detect objects and measure their height, width, and depth using AI is an exciting challenge. 

1. Understand the Problem

You want to:

• Detect objects in an image or video.

• Measure the object's height, width, and depth.

• Use AI and computer vision techniques.

2. Tools and Libraries

Here are some tools and libraries you can use:

• Emgu CV: A .NET wrapper for OpenCV, great for computer vision tasks.

• OpenCV: A powerful computer vision library.

• YOLO (You Only Look Once): A popular object detection model.

• TensorFlow/PyTorch: For training custom AI models (if needed).

• Python or C#: Python is easier for beginners, but since you're using Emgu CV, you might prefer C#.

Step 1: Set Up Your Environment

1. Install Emgu CV:

   • Use NuGet Package Manager in Visual Studio to install Emgu CV.

   • Command: Install-Package Emgu.CV

2. Install Python (Optional):

   • If you prefer Python, install Python and libraries like OpenCV, NumPy, and TensorFlow/PyTorch.

   • Use pip install opencv-python numpy tensorflow in the terminal.

Step 2: Object Detection

1. Pre-trained Models:

   • Use a pre-trained object detection model like YOLO or SSD (Single Shot MultiBox Detector).

   • Download YOLO weights and configuration files from the official YOLO website or OpenCV's DNN module.

2. Implement Object Detection:

   • Load the model using Emgu CV or OpenCV.

   • Pass an image or video frame to the model to detect objects.

   • Draw bounding boxes around detected objects.

   Example Code (C# with Emgu CV):

   using Emgu.CV;
   using Emgu.CV.Dnn;
   using Emgu.CV.Structure;
   
   // Load YOLO model
   var net = DnnInvoke.ReadNetFromDarknet("yolov3.cfg", "yolov3.weights");
   var layerNames = net.GetUnconnectedOutLayersNames();
   
   // Load image
   var image = new Mat("input.jpg");
   
   // Create blob from image
   var blob = DnnInvoke.BlobFromImage(image, 1 / 255.0, new Size(416, 416), new MCvScalar(0, 0, 0), true, false);
   
   // Set input and forward pass
   net.SetInput(blob);
   var detections = net.Forward(layerNames);
   
   // Process detections
   foreach (var detection in detections)
   {
       // Extract bounding box, class ID, and confidence
       // Draw bounding box on the image
   }
   
   // Display the result
   CvInvoke.Imshow("Object Detection", image);
   CvInvoke.WaitKey(0);

Step 3: Measure Height and Width

1. Bounding Box Dimensions:

   • The bounding box from object detection gives you the object's height and width in pixels.

   • Convert pixel measurements to real-world units (e.g., centimeters) using a reference object of known size.

   Example:

   • Place a reference object (e.g., a credit card) in the image.

   • Measure its pixel width and use it to calculate the scale (e.g., 1 cm = X pixels).

Step 4: Measure Depth

1. Depth Estimation:

   • Depth requires additional information, such as stereo vision or a depth sensor (e.g., Intel RealSense, Kinect).

   • If you're using a single camera, you can estimate depth using perspective projection or machine learning models trained for depth estimation.

   Example:

   • Use a pre-trained depth estimation model like MiDaS.

   • Integrate the model with your object detection pipeline.

Step 5: Integrate Everything

1. Combine object detection, height/width measurement, and depth estimation into a single pipeline.

2. Display the results (e.g., bounding box, dimensions, and depth) on the image or video.

4. Example Workflow

1. Input: Image or video frame.

2. Object Detection: Detect objects using YOLO or SSD.

3. Height/Width Measurement: Calculate dimensions using bounding box and reference object.

4. Depth Estimation: Use a depth estimation model or stereo vision.

5. Output: Display object dimensions and depth.

Object-Detection-Measurement/
+-- Input/                  # Folder for input images/videos
+-- Output/                 # Folder for processed images/videos
+-- Models/                 # Folder for YOLO/depth estimation models
+-- Program.cs              # Main C# code
+-- Emgu.CV.dll             # Emgu CV library
+-- yolov3.cfg              # YOLO configuration file