This demo showcases Object Detection with YOLO* V3 and Async API.
To learn more about Async API features, please refer to Object Detection for SSD Demo, Async API Performance Showcase.
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 provides resulting bounding boxes, labels, and other information. You can copy and paste this code without pulling Open Model Zoo demos helpers into your application
- Demonstration of the Async API in action. For this, the demo features two modes toggled by the Tab key:
- Old-style "Sync" way, where the frame captured with OpenCV executes back-to-back with the Detection
- Truly "Async" way, where the detection is performed on a current frame, while OpenCV captures the next frame
How It Works
On the start-up, the application reads command-line parameters and loads a network to the Inference Engine. Upon getting a frame from the OpenCV VideoCapture, it performs inference and displays the results.
NOTE: By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with
--reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
Running the application with the
-h option yields the following usage message:
python3 object_detection_demo_yolov3_async.py -h
The command yields the following usage message:
usage: object_detection_demo_yolov3_async.py [-h] -m MODEL -i INPUT
[-l CPU_EXTENSION] [-d DEVICE]
[-iout IOU_THRESHOLD] [-r]
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-i INPUT, --input INPUT
Required. Path to an image/video file. (Specify 'cam'
to work with camera)
-l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
Optional. Required for CPU custom layers. Absolute
path to a shared library with the kernels
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The sample
will look for a suitable plugin for device specified.
Default value is CPU
--labels LABELS Optional. Labels mapping file
-t PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD
Optional. Probability threshold for detections
-iout IOU_THRESHOLD, --iou_threshold IOU_THRESHOLD
Optional. Intersection over union threshold for
overlapping detections filtering
Optional. Output inference results raw values showing
-nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS
Optional. Number of infer requests
-nstreams NUM_STREAMS, --num_streams NUM_STREAMS
Optional. Number of streams to use for inference on
the CPU or/and GPU in throughput mode (for HETERO and
MULTI device cases use format
<device1>:<nstreams1>,<device2>:<nstreams2> or just
-nthreads NUMBER_THREADS, --number_threads NUMBER_THREADS
Optional. Number of threads to use for inference on
CPU (including HETERO cases)
Optional. Iterate over input infinitely
-no_show, --no_show Optional. Don't show output
-u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
Optional. List of monitors to show initially.
--keep_aspect_ratio Optional. Keeps aspect ratio on resize.
The number of InferRequests is specified by -nireq flag. An increase of this number usually leads to an increase of performance, since in this case several InferRequests can be processed simultaneously if the device supports parallelization. However, a large number of InferRequests increases the latency because each frame still has to wait before being sent for inference.
For higher FPS, it is recommended that you set -nireq to slightly exceed the -nstreams value, summed across all devices used.
NOTE: This demo is based on the callback functionality from the Inference Engine Python API. The selected approach makes the execution in multi-device mode optimal by preventing wait delays caused by the differences in device performance. However, the internal organization of the callback mechanism in Python API leads to FPS decrease. Please, keep it in mind and use the C++ version of this demo for performance-critical cases.
Running the application with the empty list of options yields the usage message given above and an error message. You can use the following command to do inference on GPU with a pre-trained object detection model:
python3 object_detection_demo_yolov3_async.py -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/yolo_v3.xml -d GPU
To run the demo, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO Model Downloader or from 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.
The only GUI knob is to use Tab to switch between the synchronized execution and the true Async mode.
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:
- OpenCV time: frame decoding + time to render the bounding boxes, labels, and to display the results.
- Detection time: inference time for the object detection network. It is reported in the Sync mode only.
- Wallclock time, which is combined application-level performance.