vehicle-attributes-recognition-barrier

vehicle-attributes-recognition-barrier

Input

(Image from https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/vehicle-attributes-recognition-barrier-0042/assets/vehicle-attributes-recognition-barrier-0042-1.png)

Shape: (1, 72, 72, 3) BGR channel order

Car pose should be front facing cars.

Output

Estimating vehicle type and color

### Estimating vehicle type and color ###
- Type: car
- Color: black

Color list

COLOR_LIST = (
    'white', 'gray', 'yellow', 'red', 'green', 'blue', 'black'
)

Type list

TYPE_LIST = (
    'car', 'van', 'truck', 'bus'
)

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 vehicle-attributes-recognition-barrier.py 

If you want to specify the input image, put the image path after the --input option.

$ python3 vehicle-attributes-recognition-barrier.py --input IMAGE_PATH

If you want to perform object detection in preprocessing, use the --detection option.

$ python3 vehicle-attributes-recognition-barrier.py --input IMAGE_PATH --detection

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.
You can use --savepath option to specify the output file to save.

$ python3 vehicle-attributes-recognition-barrier.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH

Reference

• OpenVINO - Open Model Zoo repository - vehicle-attributes-recognition-barrier-0042
• OpenVINO - vehicle-attributes-recognition-barrier-0042

Framework

OpenVINO

Model Format

ONNX opset = 11

Netron

vehicle-attributes-recognition-barrier-0042.onnx.prototxt