分類 Python* デモ#

usage: classification_demo.py [-h] -m MODEL [--adapter {openvino,ovms}] 
                              -i INPUT [-d DEVICE] 
                              [--labels LABELS] 
                              [-topk {1,2,3,4,5,6,7,8,9,10}] 
                              [--layout LAYOUT] 
                              [-nireq NUM_INFER_REQUESTS] 
                              [-nstreams NUM_STREAMS] 
                              [-nthreads NUM_THREADS] 
                              [--loop] [-o OUTPUT] [-limit OUTPUT_LIMIT] 
                              [--no_show] [--output_resolution OUTPUT_RESOLUTION] 
                              [-u UTILIZATION_MONITORS] 
                              [--reverse_input_channels] 
                              [--mean_values MEAN_VALUES MEAN_VALUES MEAN_VALUES] 
                              [--scale_values SCALE_VALUES SCALE_VALUES SCALE_VALUES] 
                              [-r] 

Options: 
  -h, --help            Show this help message and exit.
  -m MODEL, --model MODEL 
                        Required.Path to an .xml file with a trained model or 
                        address of model inference service if using OVMS adapter.
  --adapter {openvino,ovms} 
                        Optional. Specify the model adapter.
                        Default is openvino.
  -i INPUT, --input INPUT 
                        Required.An input to process.The input must be a single image, 
                        a folder of images, video file or camera id. 
  -d DEVICE, --device DEVICE 
                        Optional.Specify a device to infer on (the list of available devices is shown below).Use '-d HETERO:<comma-separated_devices_list>' format to specify HETERO plugin.Use '-d MULTI:<comma-separated_devices_list>’ format to specify MULTI plugin.Default is CPU 

Common model options: 
  --labels LABELS 
                        Optional.Labels mapping file.
  -topk {1,2,3,4,5,6,7,8,9,10} 
                        Optional.Number of top results.                         Default value is 5. Must be from 1 to 10.
  --layout LAYOUT 
                        Optional. Model inputs layouts. Ex.NCHW or 
                        input0:NCHW,input1:NC in case of more than one input.

Inference options: 
  -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 <nstreams>).
  -nthreads NUM_THREADS, --num_threads NUM_THREADS 
                        Optional. Number of threads to use for inference on CPU (including HETERO cases). 

Input/output options: 
  --loop 
                        Optional.Enable reading the input in a loop.
  -o OUTPUT, --output OUTPUT 
                        Optional.Name of the output file(s) to save.Frames of odd width or height can be truncated.See https://github.com/opencv/opencv/pull/24086 
  -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT 
                        Optional.Number of frames to store in output.If 0 is set, all frames are stored.
  --no_show 
                        Optional.Don't show output.
  --output_resolution OUTPUT_RESOLUTION 
                        Optional.Specify the maximum output window resolution in (width x height) format.Example: 1280x720.Input frame size used by default.
  -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS 
                        Optional.List of monitors to show initially.

Input transform options: 
  --reverse_input_channels 
                        Optional.Switch the input channels order from BGR to RGB.
  --mean_values MEAN_VALUES MEAN_VALUES MEAN_VALUES 
                        Optional.Normalize input by subtracting the mean values per channel.Example: 255.0 255.0 255.0 
  --scale_values SCALE_VALUES SCALE_VALUES SCALE_VALUES 
                        Optional.Divide input by scale values per channel.Division is applied after mean values subtraction.Example: 255.0 255.0 255.0 

Debug options: 
  -r, --raw_output_message 
                        Optional. Output inference results raw values showing.