Textile inspection video: Sample video that is used to simulate a textile inspection camera stream. The video includes 5 types of common textile defects, some of which are not easily noticeable to the human eye.
Textile Defect Classifier UDF: Ingest the frames from the Video Ingestion pipeline and performs inference on each frame to classify it as defective or not. The rate at which each frame is classified is also recorded and displayed on the output Visualizer window of the
Edge Insights for Industrial
software stack. The UDF utilizes the Inference Engine module from the
Intel® Distribution of OpenVINO™ toolkit
to optimize the inference on Intel hardware.
Configuration files: VideoIngestion and VideoAnalytics configuration files to add the textile defect classifier UDF to the
Edge Insights for Industrial
pipeline.
Intel hardware-optimized models: Deep learning classification models to detect textile defects optimized using the
Intel® Distribution of OpenVINO™ toolkit
; IR files generated by the Model Optimizer.

Change working directory to the Textile Defect Classifier package and create an environment variable

$EII_ HOME

to point to the

IEdgeInsights

install path for ease of reference:

cd $WORKDIR/edge_insights_industrial/Edge_Insights_for_Industrial_<version>/textile_defect_classifier/textile_defect_classifier/

EII_HOME=$WORKDIR/edge_insights_industrial/Edge_Insights_for_Industrial_<version>/IEdgeInsights 

$WORKDIR

in this tutorial refers to the host system path where the

Edge Insights for Industrial

package was downloaded and unzipped.

<version>

indicates the downloaded version of

Edge Insights for Industrial

.

Copy the Textile Defect Classifier UDF file to the

Edge Insights for Industrial

UDFs directory:

cp -r textile/ $EII_HOME/common/video/udfs/python/.

Modify the VideoIngestion (VI) and VideoAnalytics (VA) configuration files to add Textile Defect Classifier to the IEdgeInsights pipeline.

Make a copy of the original VI & VA

config.json

file:

mv $EII_HOME/VideoIngestion/config.json $EII_HOME/VideoIngestion/config.json_original

mv $EII_HOME/VideoAnalytics/config.json $EII_HOME/VideoAnalytics/config.json_original

Copy the new VI and VA

config.json

to execute the textile classification application.

Optionally, edit the

va_config.json

to use the inference hardware of choice [CPU/GPU/Myriad/HDDL]. Default is CPU.

Note that the deep learning model used in the reference implementation is a deep model, and hence will take a few minutes for the initial loading into an accelerator (GPU/Myriad/HDDL) memory.

cp vi_config.json $EII_HOME/VideoIngestion/config.json
cp va_config.json $EII_HOME/VideoAnalytics/config.json

Copy the textile inspection video sample to simulate camera stream input:

cp textile.mp4 $EII_HOME/VideoIngestion/test_videos/.

Add Tensorflow to VideoAnalytics

requirements.txt

. This Python module is being used for pre-processing the input image for the Textile Defect Classifier DL model.

echo -e "\ntensorflow==1.15.0" >> $EII_HOME/VideoAnalytics/va_requirements.txt

Different applications may have other requirements that can be added to the VideoAnalytics or VideoIngestion

requirements.txt

file.

Follow the

Edge Insights for Industrial

instructions to provision and launch the containers:

# Configure the IEdgeInsights pipeline to use VideoIngestion, VideoAnalytics, Visualizer containers
cd $EII_HOME/build/
python3 eis_builder.py -f video-streaming.yml

# Provision
cd $EII_HOME/build/provision
sudo ./provision_eis.sh ../docker-compose.yml

# Build
cd $EII_HOME/build/
sudo sg docker -c 'docker-compose up --build -d'

# Enable visualizer display
xhost +

cd $EII_HOME/build
sudo sg docker -c 'docker-compose down'