Retail Use Cases

There are various use cases available, click one each hyperlink to find out more details:

dlstreamer gst
grpc python
gst c-api
demos grpc OVMS
grpc go

dlstreamer gst use case:

build dlstreamer gst Docker image

to build run:
```
make build-gst
```

run dlstreamer gst use cases

run one gst dlstreamer pipeline and use the default object detection only (yolov5s.sh)
```
make run-gst
```
run three gst dlstreamer pipelines and use the default object detection only (yolov5s.sh)
```
PIPELINE_COUNT=3 make run-gst
```
run two gst dlstreamer pipelines and use the object detection with classification (yolov5s_effnetb0.sh)
```
PIPELINE_SCRIPT=yolov5s_effnetb0.sh PIPELINE_COUNT=2 make run-gst
```
shutdown Docker containers
```
make down-gst
```
clean up the output results
```
make clean-results
```

grpc_python use case:

build grpc_python Docker image

to build run:
```
make build-grpc_python
```

run grpc_python use cases

run one grpc_python pipeline and use the default model (instance-segmentation-security-1040)
```
make run-grpc_python
```
show the supported MODEL_NAME for grpc_python
```
make list-grpc-python-model-names
```

run three grpc_python pipelines and use yolov5s model

PIPELINE_COUNT=3 MODEL_NAME=yolov5s make run-grpc_python

shutdown Docker containers
```
make down-grpc_python
```
clean up the output results
```
make clean-results
```

gst_capi use cases:

There are three different pipelines for gst_capi use cases: capi_yolov5, capi_yolov5_ensemble, and capi_face_detection.

build all capi pipeline Docker images

to build all capis run:
```
make build-all-capis
```
to build one at a time run:
- for capi_yolov5
```
make build-capi_yolov5
```
- for capi_yolov5_ensemble
```
make build-capi_yolov5_ensemble
```
- for capi_face_detection
```
make build-capi_face_detection
```

run gst_capi use cases

run one capi_yolov5 pipeline
```
make run-capi_yolov5
```
shutdown capi_yolov5
```
make down-capi_yolov5
```
run one capi_yolov5_ensemble pipeline
```
make run-capi_yolov5_ensemble
```
shutdown capi_yolov5_ensemble
```
make down-capi_yolov5_ensemble
```
run one capi_face_detection pipeline
```
make run-capi_face_detection
```
shutdown capi_face_detection
```
make down-capi_face_detection
```

run two capi_yolov5_ensemble pipelines

PIPELINE_COUNT=2 make run-capi_yolov5_ensemble

shutdown Docker containers

make down-capi_yolov5
make down-capi_yolov5_ensemble
make down-capi_face_detection

clean up the output results
```
make clean-results
```

demos use case:

There are three different pipelines for demos use case: classification, instance_segmentation, and object_detection.

build all demos Docker image

to build run:
```
make build-demos
```

run demos use case

run one classification pipeline
```
make run-demo-classification
```
run one instance segmentation pipeline
```
make run-demo-instance-segmentation
```

run two object detection pipelines

PIPELINE_COUNT=2 make run-demo-object-detection

shutdown classification pipeline Docker container
```
make down-demo-classification
```
shutdown all Docker containers
```
make down-demos-all
```
clean up the output results
```
make clean-results
```

grpc_go use case:

build grpc_go Docker image

to build run:
```
make build-grpc-go
```

run grpc_go use cases

run one grpc_go pipeline
```
make run-grpc-go
```
run two grpc_go pipelines
```
PIPELINE_COUNT=2 make run-grpc-go
```
shutdown Docker containers
```
make down-grpc-go
```
clean up the output results
```
make clean-results
```

Disclaimer

GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html?gi-language=c. You are solely responsible for determining if your use of Gstreamer requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of Gstreamer.

Certain third-party software or hardware identified in this document only may be used upon securing a license directly from the third-party software or hardware owner. The identification of non-Intel software, tools, or services in this document does not constitute a sponsorship, endorsement, or warranty by Intel.

Datasets & Models Disclaimer:

To the extent that any data, datasets or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality. By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license.

Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets or models.

intel-retail / retail-use-cases

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