Smartlab Python* Demo

This is the demo application with smartlab object detection and smartlab action recognition algorithms. This demo takes multi-view video inputs to identify objects and actions, then evaluates scores for teacher’s reference. The UI is shown as:


The left picture and right picture show top view and side view on the test bench respectively. For object detection part, blue bounding boxes are shown. Below these pictures, progress bar is shown for action types, and the colors of actions correspond to the action names above. Scoring part is below the entire UI and there are 8 score points. [1] means student can get 1 point while [0] means student loses the point. [-] means under evaluation.


Architecture of smart science lab contains object detection, action recognition and scoring evaluator. The following pre-trained models are delivered with the product:

  • smartlab-object-detection-0001 + smartlab-object-detection-0002 + smartlab-object-detection-0003 + smartlab-object-detection-0004, which are models to detect 10 objects including: balance, weights, tweezers, box, battery, tray, ruler, rider, scale, hand.

Action recognition include two options:

  • mode multiview: smartlab-action-recognition-0001-encoder-top + smartlab-action-recognition-0001-encoder-side + smartlab-action-recognition-0001-decoder, identifying 3 action types.

  • mode mstcn: smartlab-sequence-modelling-0001 + smartlab-sequence-modelling-0002, identifying 14 action types.

How It works

The demo pipeline consists of several steps:

  • Decode read frames from the two input videos

  • Detector detect objects (balance, weights, tweezers, box, battery, tray, ruler, rider scale, hand)

  • Segmentor segment and classify video frames based on action type of the frame

  • Evaluator give scores of the current state

  • Display display the whole UI

Preparing to Run

Example input video:

The list of models supported by the demo is in <omz_dir>/demos/smartlab_demo/python/models.lst file. This file can be used as a parameter for Model Downloader to download. An example of using the Model Downloader:

omz_downloader --list models.lst

Supported Models

  • smartlab-object-detection-0001

  • smartlab-object-detection-0002

  • smartlab-object-detection-0003

  • smartlab-object-detection-0004

  • mode mtcnn

    • smartlab-sequence-modelling-0001

    • smartlab-sequence-modelling-0002

  • mode multiview

    • smartlab-action-recognition-0001-encoder-top

    • smartlab-action-recognition-0001-encoder-side

    • smartlab-action-recognition-0001-decoder


Refer to the tables Intel’s Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the demo with -h shows this help message:

usage: [-h] [-d DEVICE] -tv TOPVIEW -sv SIDEVIEW -m_ta M_TOPALL -m_tm M_TOPMOVE -m_sa M_SIDEALL -m_sm M_SIDEMOVE [--mode MODE] [-m_en M_ENCODER] [-m_en_t M_ENCODER_TOP] [-m_en_s M_ENCODER_SIDE] -m_de M_DECODER [--no_show]

  -h, --help            Show this help message and exit.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target to infer on CPU or GPU.
  -tv TOPVIEW, --topview TOPVIEW
                        Required. Topview stream to be processed. The input must be a single image, a folder of images, video file or camera id.
  -sv SIDEVIEW, --sideview SIDEVIEW
                        Required. SideView to be processed. The input must be a single image, a folder of images, video file or camera id.
  -m_ta M_TOPALL, --m_topall M_TOPALL
                        Required. Path to topview all class model.
  -m_tm M_TOPMOVE, --m_topmove M_TOPMOVE
                        Required. Path to topview moving class model.
  -m_sa M_SIDEALL, --m_sideall M_SIDEALL
                        Required. Path to sidetview all class model.
  -m_sm M_SIDEMOVE, --m_sidemove M_SIDEMOVE
                        Required. Path to sidetview moving class model.
  --mode MODE           Optional. Action recognition mode: multiview or mstcn
  -m_en M_ENCODER, --m_encoder M_ENCODER
                        Required for mstcn mode. Path to encoder model.
  -m_en_t M_ENCODER_TOP, --m_encoder_top M_ENCODER_TOP
                        Required for multiview mode. Path to encoder model for top view.
  -m_en_s M_ENCODER_SIDE, --m_encoder_side M_ENCODER_SIDE
                        Required for multiview mode. Path to encoder model for side view.
  -m_de M_DECODER, --m_decoder M_DECODER
                        Required. Path to decoder model.
  --no_show             Optional. Don't show output.

For example, run the demo with multiview mode:

    -tv stream_1_top.mp4
    -sv stream_1_left.mp4
    -m_ta "./intel/smartlab-object-detection-0001/FP32/smartlab-object-detection-0001.xml"
    -m_tm "./intel/smartlab-object-detection-0002/FP32/smartlab-object-detection-0002.xml"
    -m_sa "./intel/smartlab-object-detection-0003/FP32/smartlab-object-detection-0003.xml"
    -m_sm "./intel/smartlab-object-detection-0004/FP32/smartlab-object-detection-0004.xml"
    -m_en_t "./intel/smartlab-action-recognition-0001/smartlab-action-recognition-0001-encoder-top/FP32/smartlab-action-recognition-0001-encoder-top.xml"
    -m_en_s "./intel/smartlab-action-recognition-0001/smartlab-action-recognition-0001-encoder-side/FP32/smartlab-action-recognition-0001-encoder-side.xml"
    -m_de "./intel/smartlab-action-recognition-0001/smartlab-action-recognition-0001-decoder/FP32/smartlab-action-recognition-0001-decoder.xml"

run the demo with mstcn mode:

    -tv stream_1_top.mp4
    -sv stream_1_left.mp4
    -m_ta "./intel/smartlab-object-detection-0001/FP32/smartlab-object-detection-0001.xml"
    -m_tm "./intel/smartlab-object-detection-0002/FP32/smartlab-object-detection-0002.xml"
    -m_sa "./intel/smartlab-object-detection-0003/FP32/smartlab-object-detection-0003.xml"
    -m_sm "./intel/smartlab-object-detection-0004/FP32/smartlab-object-detection-0004.xml"
    --mode mstcn
    -m_en "./intel/sequence_modelling/FP32/smartlab-sequence-modelling-0001.xml"
    -m_de "./intel/sequence_modelling/FP32/smartlab-sequence-modelling-0002.xml"

Demo Output

The application uses OpenCV to display the online results.