aazz44ss/OpenVINO_Object_Detection
Using OpenVINO to implement multi video decode and object detection inference with SSD model.
Object_Detection implement with OpenVINO 2019 R1
This demo showcases Mutiple Video Object Detection with SSD.
This demo haven't implement NIC part, the data is on the local side.
Other demo objectives are:
- Video as input support via OpenCV
- Visualization of the resulting bounding boxes and text labels (from the .labels file) or class number (if no file is provided)
- OpenCV is used to draw resulting bounding boxes, labels, so you can copy paste this code without
need to pull Inference Engine demos helpers to your app
Running
Running the application with the -h option yields the following usage message:
./object_detection_demo_ssd_async -h
InferenceEngine:
API version ............ <version>
Build .................. <number>
object_detection_demo_ssd_async [OPTION]
Options:
-h Print a usage message.
-i "<path>" Required. Path to a video file (specify "cam" to work with camera).
-m "<path>" Required. Path to an .xml file with a trained model.
-l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernel implementations.
Or
-c "<absolute_path>" Required for GPU custom kernels. Absolute path to the .xml file with the kernel descriptions.
-d "<device>" Optional. Specify the target device to infer on (CPU, GPU, FPGA, HDDL or MYRIAD). The demo will look for a suitable plugin for a specified device.
-pc Optional. Enables per-layer performance report.
-r Optional. Inference results as raw values.
-t Optional. Probability threshold for detections.
-auto_resize Optional. Enables resizable input with support of ROI crop & auto resize.
-stream Number of streams.Running the application with the empty list of options yields the usage message given above and an error message.
To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.
NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the [Model Optimizer tool]
You can use the following command to do inference on FPGA with a pre-trained object detection model:
./object_detection_demo_ssd_async -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/ssd.xml -d HETERO:FPGA,CPU -stream 5Demo Output
The demo uses OpenCV to display the resulting frame with detections (rendered as bounding boxes and labels, if provided).
In the default mode the demo reports
See Also
- [Using Inference Engine Samples]
- [Model Optimizer]
- [Model Downloader]