DepthAI API Demo Program

This repo contains demo application, which can load different networks, create pipelines, record video, etc.

Documentation is available at https://docs.luxonis.com/.

Python modules (Dependencies)

DepthAI Demo requires numpy, opencv-python and depthai. To get the versions of these packages you need for the program, use pip: (Make sure pip is upgraded: python3 -m pip install -U pip)

python3 install_requirements.py

Examples

python3 depthai_demo.py - RGB & CNN inference example python3 depthai_demo.py -vid <path_to_video_or_yt_link> - CNN inference on video example python3 depthai_demo.py -cnn person-detection-retail-0013 - Run person-detection-retail-0013 model from resources/nn directory python3 depthai_demo.py -cnn tiny-yolo-v3 -sh 8 - Run tiny-yolo-v3 model from resources/nn directory and compile for 8 shaves

Usage

$ depthai_demo.py --help

usage: depthai_demo.py [-h] [-nd] [-cam {left,right,color}]
                       [-vid VIDEO] [-hq] [-dd] [-cnnp CNN_PATH]
                       [-cnn CNN_MODEL] [-sh SHAVES]
                       [-cnn-size CNN_INPUT_SIZE] [-rgbr {1080,2160,3040}]
                       [-rgbf RGB_FPS] [-dct DISPARITY_CONFIDENCE_THRESHOLD]
                       [-med {0,3,5,7}] [-lrc] [-scale SCALE] [-sbb]
                       [-sbb-sf SBB_SCALE_FACTOR] [-sync]
                       [-monor {400,720,800}] [-monof MONO_FPS]

optional arguments:
  -h, --help            show this help message and exit
  -nd, --no-debug       Prevent debug output
  -cam {left,right,color}, --camera {left,right,color}
                        Use one of DepthAI cameras for inference (conflicts
                        with -vid)
  -vid VIDEO, --video VIDEO
                        Path to video file to be used for inference (conflicts
                        with -cam)
  -hq, --high_quality   Low quality visualization - uses resized frames
  -dd, --disable_depth  Disable depth information
  -cnnp CNN_PATH, --cnn_path CNN_PATH
                        Path to cnn model directory to be run
  -cnn CNN_MODEL, --cnn_model CNN_MODEL
                        Cnn model to run on DepthAI
  -sh SHAVES, --shaves SHAVES
                        Name of the nn to be run from default depthai
                        repository
  -cnn-size CNN_INPUT_SIZE, --cnn_input_size CNN_INPUT_SIZE
                        Neural network input dimensions, in "WxH" format, e.g.
                        "544x320"
  -rgbr {1080,2160,3040}, --rgb_resolution {1080,2160,3040}
                        RGB cam res height: (1920x)1080, (3840x)2160 or
                        (4056x)3040. Default: 1080
  -rgbf RGB_FPS, --rgb_fps RGB_FPS
                        RGB cam fps: max 118.0 for H:1080, max 42.0 for
                        H:2160. Default: 30.0
  -dct DISPARITY_CONFIDENCE_THRESHOLD, --disparity_confidence_threshold DISPARITY_CONFIDENCE_THRESHOLD
                        Disparity confidence threshold, used for depth
                        measurement. Default: 200
  -med {0,3,5,7}, --stereo_median_size {0,3,5,7}
                        Disparity / depth median filter kernel size (N x N) .
                        0 = filtering disabled. Default: 7
  -lrc, --stereo_lr_check
                        Enable stereo 'Left-Right check' feature.
  -scale SCALE, --scale SCALE
                        Scale factor for the output window. Default: 1.0
  -sbb, --spatial_bounding_box
                        Display spatial bounding box (ROI) when displaying
                        spatial information. The Z coordinate get's calculated
                        from the ROI (average)
  -sbb-sf SBB_SCALE_FACTOR, --sbb_scale_factor SBB_SCALE_FACTOR
                        Spatial bounding box scale factor. Sometimes lower
                        scale factor can give better depth (Z) result.
                        Default: 0.3
  -sync, --sync         Enable NN/camera synchronization. If enabled, camera
                        source will be from the NN's passthrough attribute
  -monor {400,720,800}, --mono_resolution {400,720,800}
                        Mono cam res height: (1280x)720, (1280x)800 or
                        (640x)400. Default: 400
  -monof MONO_FPS, --mono_fps MONO_FPS
                        Mono cam fps: max 60.0 for H:720 or H:800, max 120.0
                        for H:400. Default: 30.0

Conversion of existing trained models into Intel Movidius binary format

OpenVINO toolkit contains components which allow conversion of existing supported trained Caffe and Tensorflow models into Intel Movidius binary format through the Intermediate Representation (IR) format.

Example of the conversion:

1. First the model_optimizer tool will convert the model to IR format:

   cd <path-to-openvino-folder>/deployment_tools/model_optimizer
   python3 mo.py --model_name ResNet50 --output_dir ResNet50_IR_FP16 --framework tf --data_type FP16 --input_model inference_graph.pb
   

   • The command will produce the following files in the ResNet50_IR_FP16 directory:
     • ResNet50.bin - weights file;
     • ResNet50.xml - execution graph for the network;
     • ResNet50.mapping - mapping between layers in original public/custom model and layers within IR.

2. The weights (.bin) and graph (.xml) files produced above (or from the Intel Model Zoo) will be required for building a blob file, with the help of the myriad_compile tool. When producing blobs, the following constraints must be applied:

   CMX-SLICES = 4 
   SHAVES = 4 
   INPUT-FORMATS = 8 
   OUTPUT-FORMATS = FP16/FP32 (host code for meta frame display should be updated accordingly)
   

   Example of command execution:

   <path-to-openvino-folder>/deployment_tools/inference_engine/lib/intel64/myriad_compile -m ./ResNet50.xml -o ResNet50.blob -ip U8 -VPU_NUMBER_OF_SHAVES 4 -VPU_NUMBER_OF_CMX_SLICES 4
   

Reporting issues

We are actively developing the DepthAI framework, and it's crucial for us to know what kind of problems you are facing.
If you run into a problem, please follow the steps below and email [email protected]:

1. Run log_system_information.sh and share the output from (log_system_information.txt).
2. Take a photo of a device you are using (or provide us a device model)
3. Describe the expected results;
4. Describe the actual running results (what you see after started your script with DepthAI)
5. How you are using the DepthAI python API (code snippet, for example)
6. Console output