画像のセグメント化 Python* デモ#

usage: segmentation_demo.py [-h] -m MODEL -i INPUT 
                            [-at {segmentation,salient_object_detection}] 
                            [--adapter {openvino,ovms}] [-d DEVICE] [-c COLORS] 
                            [-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] 
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.
    -at {segmentation, salient_object_detection}, --architecture_type {segmentation, salient_object_detection} 
                          Required. Specify model's architecture type.
    -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. 
    --adapter {openvino,ovms} 
                          Optional. Specify the model adapter. Default is 
                          openvino.
    -d DEVICE, --device DEVICE 
                          Optional. Specify the target device to infer on; CPU or 
                          GPU is acceptable. The demo 
                          will look for a suitable plugin for device specified.
                          Default value is CPU.

Common model options: 
    -c COLORS, --colors COLORS 
                          Optional. Path to a text file containing colors for 
                          classes.
    --labels LABELS       Optional. Labels mapping file. 

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.
    --only_masks 
                          Optional. Display only masks. Could be switched by TAB key.

Debug options: 
    -r, --raw_output_message 
                          Optional. Output inference results as mask histogram.