Get started quickly with 3D Time-of-light applications
The remainder of the sample application identifies classification results that exceed a preset threshold and generates for those a label and bounding box displaying the captured image data, the label identified by the inference model, and its distance from the camera (Figure 8). Figure 8: Using a few lines of Python code and the OpenCV library, the DNN sample application in Analog Devices’ 3D ToF SDK distribution captures depth images, classifies them, and displays the identified object's label and distance. (Image source: Analog Devices)
As Analog Devices’ DNN sample application demonstrates, developers can use 3D ToF depth maps in combination with machine learning methods to create more sophisticated application features. Although applications that require low latency responses will more likely build these features with C/C++, the basic steps remain the same. Using 3D ToF data and high performance inference models, industrial robotic systems can more safely synchronize their movements with other equipment
or even with humans in "cobot" environments where humans and robots work cooperatively in close proximity. With different inference models, another application can use a high-resolution 3D ToF camera to classify fine movements for a gesture interface. In automotive applications, this same approach can help improve the accuracy of advanced driver- assistance systems (ADAS), taking full advantage of the high temporal and spatial resolution available with 3D ToF systems.
12
Powered by FlippingBook