# Face Detection

**Repository Path**: ybli_code/Face-Detection

## Basic Information

- **Project Name**: Face Detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Face Detection

Run the application with the `-h` option to see the usage message:
```
python3 Face_Detection.py -h
```
The command yields the following usage message:
```
usage: Face_Detection.py [-h] -m MODEL -i INPUT
                                          [-l CPU_EXTENSION] [-d DEVICE] 
                                          [--labels LABELS] [-pt PROB_THRESHOLD]

Options:
  -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 video file or image. 'cam' for
                        capturing video stream from camera
  -l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
                        Optional. Required for CPU custom layers. Absolute
                        path to a shared library with the kernels
                        implementations.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU,
                        GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
                        look for a suitable plugin for device specified.
                        Default value is CPU
  --labels LABELS       Optional. Path to labels mapping file
  -pt PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD
                        Optional. Probability threshold for detections
                        filtering
  --no_show             Optional. Don't show output
```

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 Face_Detection.py -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/ssd.xml -d GPU
```

## Demo 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
* **OpenCV time**: frame decoding + time to render the bounding boxes, labels, and displaying 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.