Merge pull request #99 from intel/v0.7.0_PR

.gitignore

@@ -1,11 +1,10 @@
 __pycache__/
+.cache/
 .vscode/
 .bash_history
 *.generated
 docker/openvino_base_environment.txt
 docker/Dockerfile.env
 docker/final.env
 models
-tests/results/**/*
-samples/ava_ai_extension/tests/results/**/*
 samples/edgex_bridge/edgex/**/*

README.md

@@ -116,7 +116,9 @@ VA Serving includes a sample client [vaclient](./vaclient/README.md) that can co
 Before running a pipeline, we need to know what pipelines are available. We do this using vaclient's `list-pipeline` command.
 In new shell run the following command:
 ```bash
-$ ./vaclient/vaclient.sh list-pipelines
+./vaclient/vaclient.sh list-pipelines
+ ```
+ ```
  - object_detection/person_vehicle_bike
  - object_classification/vehicle_attributes
  - audio_detection/environment
@@ -125,12 +127,15 @@ $ ./vaclient/vaclient.sh list-pipelines
 > **Note:** The pipelines you will see may differ slightly
 
 Pipelines are displayed as a name/version tuple. The name reflects the action and version supplies more details of that action. Let's go with `object_detection/person_vehicle_bike`. Now we need to choose a media source. We recommend the [IoT Devkit sample videos](https://github.com/intel-iot-devkit/sample-videos) to get started. As the pipeline version indicates support for detecting people, person-bicycle-car-detection.mp4 would be a good choice.
+> **Note:** Make sure to include `raw=true` parameter in the Github URL as shown in our examples. Failure to do so will result in a pipeline execution error.
 
 vaclient offers a `run` command that takes two additional arguments the `pipeline` and the `uri` for the media source. The `run` command displays inference results until either the media is exhausted or `CTRL+C` is pressed.
 
 Inference result bounding boxes are displayed in the format `label (confidence) [top left width height] {meta-data}` provided applicable data is present. At the end of the pipeline run, the average fps is shown.
 ```
-$ ./vaclient/vaclient.sh run object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+./vaclient/vaclient.sh run object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+```
+```
 Timestamp 48583333333
 - vehicle (0.95) [0.00, 0.12, 0.15, 0.36]
 Timestamp 48666666666
@@ -159,55 +164,76 @@ All being well it will go into `QUEUED` then `RUNNING` state. We can interrogate
 > **NOTE:** The pipeline instance value depends on the number of pipelines started while the server is running so may differ from the value shown in the following examples.
 
 ```
-$ ./vaclient/vaclient.sh start object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+./vaclient/vaclient.sh start object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+```
+```
 <snip>
-Starting pipeline...
-Pipeline running: object_detection/person_vehicle_bike, instance = 2
+Starting pipeline object_detection/person_vehicle_bike, instance = 2
 ```
 You will need both the pipeline tuple and `instance` id for the status command. This command will display pipeline state:
 ```
-$ ./vaclient/vaclient.sh status object_detection/person_vehicle_bike 2
+./vaclient/vaclient.sh status object_detection/person_vehicle_bike 2
+```
+```
 <snip>
 RUNNING
 ```
 Then wait for a minute or so and try again. Pipeline will be completed.
 ```
-$ ./vaclient/vaclient.sh status object_detection/person_vehicle_bike 2
+./vaclient/vaclient.sh status object_detection/person_vehicle_bike 2
+```
+```
 <snip>
 COMPLETED
 ```
 ### Aborted
 If a pipeline is stopped, rather than allowed to complete, it goes into the ABORTED state.
 Start the pipeline again, this time we'll stop it.
 ```
-$ ./vaclient/vaclient.sh start object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+./vaclient/vaclient.sh start object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true
+```
+```
 <snip>
-Starting pipeline...
-Pipeline running: object_detection/person_vehicle_bike, instance = 3
-$ ./vaclient/vaclient.sh status object_detection/person_vehicle_bike 3
+Starting pipeline object_detection/person_vehicle_bike, instance = 3
+```
+```
+./vaclient/vaclient.sh status object_detection/person_vehicle_bike 3
+```
+```
 <snip>
 RUNNING
-$ ./vaclient/vaclient.sh stop object_detection/person_vehicle_bike 3
+```
+```
+./vaclient/vaclient.sh stop object_detection/person_vehicle_bike 3
+```
+```
 <snip>
 Stopping Pipeline...
 Pipeline stopped
 avg_fps: 24.33
-$ ./vaclient/vaclient.sh status object_detection/person_vehicle_bike 3
+```
+```
+./vaclient/vaclient.sh status object_detection/person_vehicle_bike 3
+```
+```
 <snip>
 ABORTED
 ```
 ### Error
 The error state covers a number of outcomes such as the request could not be satisfied, a pipeline dependency was missing or an initialization problem. We can create an error condition by supplying a valid but unreachable uri.
 ```
-$ ./vaclient/vaclient.sh start object_detection/person_vehicle_bike http://bad-uri
+./vaclient/vaclient.sh start object_detection/person_vehicle_bike http://bad-uri
+```
+```
 <snip>
-Starting pipeline...
-Pipeline running: object_detection/person_vehicle_bike, instance = 4
+Starting pipeline object_detection/person_vehicle_bike, instance = 4
 ```
 Note that VA Serving does not report an error at this stage as it goes into `QUEUED` state before it realizes that the source is not providing media.
 Checking on state a few seconds later will show the error.
 ```
-$ ./vaclient/vaclient.sh status object_detection/person_vehicle_bike 4
+./vaclient/vaclient.sh status object_detection/person_vehicle_bike 4
+```
+```
 <snip>
 ERROR
 ```
@@ -217,16 +243,15 @@ RTSP allows you to connect to a server and display a video stream. VA Serving in
 
 First start VA Serving with RTSP enabled. By default, the RTSP stream will use port 8554.
 ```
-$ docker/run.sh --enable-rtsp -v /tmp:/tmp
+docker/run.sh --enable-rtsp -v /tmp:/tmp
 ```
 Then start a pipeline specifying the RTSP server endpoint path `vaserving`. In this case the RTSP endpoint would be `rtsp://localhost:8554/vaserving`
 ```
-$ ./vaclient/vaclient.sh start object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true --rtsp-path vaserving
+./vaclient/vaclient.sh run object_detection/person_vehicle_bike https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4?raw=true --rtsp-path vaserving
 ```
 If you see the error
 ```
-Starting pipeline...
-Pipeline running: object_detection/person_vehicle_bike, instance = 1
+Starting pipeline object_detection/person_vehicle_bike, instance = 1
 Error in pipeline, please check vaserving log messages
 ```
 You probably forgot to enable RTSP in the server.
@@ -240,9 +265,11 @@ Now start `vlc` and from the `Media` menu select `Open Network Stream`. For URL
 ## Change Pipeline and Source Media
 With vaclient it is easy to customize service requests. Here will use a vehicle classification pipeline `object_classification/vehicle_attributes` with the Iot Devkit video `car-detection.mp4`. Note how vaclient now displays classification metadata including type and color of vehicle.
 ```
- $ ./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true
-Starting pipeline...
-Pipeline running: object_classification/vehicle_attributes, instance = 1
+./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true
+```
+```
+Starting pipeline object_classification/vehicle_attributes, instance = 1
+Pipeline running
 <snip>
 Timestamp 18080000000
 - vehicle (1.00) [0.41, 0.00, 0.57, 0.33] {'color': 'red', 'type': 'car'}
@@ -271,9 +298,10 @@ If you look at video you can see that there are some errors in classification -
 Inference accelerator devices can be easily selected using the device parameter. Here we run the car classification pipeline again,
 but this time use the integrated GPU for detection inference by setting the `detection-device` parameter.
 ```
-$ ./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true --parameter detection-device GPU --parameter detection-model-instance-id person_vehicle_bike_detection_gpu
-Starting pipeline...
-Pipeline running: object_classification/vehicle_attributes, instance = 2
+./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true --parameter detection-device GPU --parameter detection-model-instance-id person_vehicle_bike_detection_gpu
+```
+```
+Starting pipeline object_classification/vehicle_attributes, instance = 2
 ```
 > **Note:** The GPU inference plug-in dynamically builds OpenCL kernels when it is first loaded resulting in a ~30s delay before inference results are produced.
 
@@ -286,7 +314,9 @@ As the previous example has shown, the vaclient application works by converting
 The `--show-request` option displays the REST verb, uri and body in the request.
 Let's repeat the previous GPU inference example, adding RTSP output and show the underlying request.
 ```
-$ ./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true --parameter detection-device GPU --rtsp-path vaserving --show-request
+./vaclient/vaclient.sh run object_classification/vehicle_attributes https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true --parameter detection-device GPU --rtsp-path vaserving --show-request
+```
+```
 <snip>
 POST http://localhost:8080/pipelines/object_classification/vehicle_attributes
 Body:{'source': {'uri': 'https://github.com/intel-iot-devkit/sample-videos/blob/master/car-detection.mp4?raw=true', 'type': 'uri'}, 'destination': {'metadata': {'type': 'file', 'path': '/tmp/results.jsonl', 'format': 'json-lines'}, 'frame': {'type': 'rtsp', 'path': 'vaserving'}}, 'parameters': {'detection-device': 'GPU'}}
@@ -332,11 +362,11 @@ They are easier to understand when the json is pretty-printed
 
 The `--show-request` output can be easily converted int a curl command.
 ```
-$ curl <URI> -X <VERB> -H "Content-Type: application/json' -d <BODY>
+curl <URI> -X <VERB> -H "Content-Type: application/json' -d <BODY>
 ```
 So the above request would be as below. Note the pipeline instance `1` returned by the request.
 ```bash
-$ curl localhost:8080/pipelines/object_classification/vehicle_attributes -X POST -H \
+curl localhost:8080/pipelines/object_classification/vehicle_attributes -X POST -H \
 'Content-Type: application/json' -d \
 '{
   "source": {
@@ -358,6 +388,8 @@ $ curl localhost:8080/pipelines/object_classification/vehicle_attributes -X POST
     "detection-device": "GPU"
   }
 }'
+```
+```
 1
 ```
 # Changing Pipeline Model

docker/Dockerfile

@@ -45,6 +45,15 @@ RUN if [[ ${VA_SERVING_BASE} == *"openvino/ubuntu20_data_runtime:2021.2" ]]; the
     rm -rf /var/lib/apt/lists/* ;\
     fi
 
+# Install boost library required for HDDL plugin
+RUN if [[ ${VA_SERVING_BASE} == *"openvino/ubuntu20_data_runtime"* ]]; then \
+    DEBIAN_FRONTEND=noninteractive apt-get update && \
+    apt-get install -y -q --no-install-recommends \
+    libboost-program-options1.71.0 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/* ;\
+    fi
+
 RUN DEBIAN_FRONTEND=noninteractive apt-get update && \
     apt-get upgrade -y -q && \
     apt-get dist-upgrade -y -q && \
@@ -70,6 +79,9 @@ COPY ./vaserving /home/video-analytics-serving/vaserving
 COPY ./vaclient /home/video-analytics-serving/vaclient
 COPY --chown=vaserving ./tools /home/video-analytics-serving/tools
 
+# Copy GVA Python extensions
+COPY ./extensions /home/video-analytics-serving/extensions
+
 # Media Analytics Framework set via environment variable
 ENV FRAMEWORK=${FRAMEWORK}
 WORKDIR /home/video-analytics-serving
@@ -107,9 +119,6 @@ ONBUILD ARG PIPELINES_PATH
 ONBUILD ENV PIPELINES_PATH=${PIPELINES_PATH}
 ONBUILD COPY ${PIPELINES_PATH} /home/video-analytics-serving/pipelines
 
-# Copy GVA Python extensions
-ONBUILD COPY ./extensions /home/video-analytics-serving/extensions
-
 # Stage that is used is controlled via PIPELINES_COMMAND build argument
 FROM ${PIPELINES_COMMAND} as video-analytics-serving-with-models-and-pipelines
 ########################################################

docker/build.sh

@@ -10,7 +10,7 @@ DOCKERFILE_DIR=$(dirname "$(readlink -f "$0")")
 SOURCE_DIR=$(dirname "$DOCKERFILE_DIR")
 
 BASE_IMAGE_FFMPEG="openvisualcloud/xeone3-ubuntu1804-analytics-ffmpeg:20.10"
-BASE_IMAGE_GSTREAMER="openvino/ubuntu20_data_runtime:2021.4.1"
+BASE_IMAGE_GSTREAMER="openvino/ubuntu20_data_runtime:2021.4.2"
 
 BASE_IMAGE=${BASE_IMAGE:-""}
 BASE_BUILD_CONTEXT=
@@ -36,7 +36,7 @@ BASE_BUILD_OPTIONS="--network=host "
 
 SUPPORTED_IMAGES=($BASE_IMAGE_GSTREAMER $BASE_IMAGE_FFMPEG)
 OPEN_MODEL_ZOO_TOOLS_IMAGE=${OPEN_MODEL_ZOO_TOOLS_IMAGE:-"openvino/ubuntu20_data_dev"}
-OPEN_MODEL_ZOO_VERSION=${OPEN_MODEL_ZOO_VERSION:-"2021.4.1"}
+OPEN_MODEL_ZOO_VERSION=${OPEN_MODEL_ZOO_VERSION:-"2021.4.2"}
 FORCE_MODEL_DOWNLOAD=
 
 DEFAULT_GSTREAMER_BASE_BUILD_TAG="video-analytics-serving-gstreamer-base"
@@ -419,7 +419,7 @@ cp -f $DOCKERFILE_DIR/Dockerfile $DOCKERFILE_DIR/Dockerfile.env
 ENVIRONMENT_FILE_LIST=
 
 if [[ "$BASE_IMAGE" == *"openvino/"* ]]; then
-    $RUN_PREFIX docker run -t --rm $DOCKER_RUN_ENVIRONMENT --entrypoint /bin/bash -e HOSTNAME=BASE $BASE_IMAGE "-i" "-c" "env" > $DOCKERFILE_DIR/openvino_base_environment.txt
+    $RUN_PREFIX docker run -t --rm --entrypoint /bin/bash -e HOSTNAME=BASE $BASE_IMAGE "-i" "-c" "env" > $DOCKERFILE_DIR/openvino_base_environment.txt
     ENVIRONMENT_FILE_LIST+="$DOCKERFILE_DIR/openvino_base_environment.txt "
 fi
 
@@ -430,7 +430,7 @@ for ENVIRONMENT_FILE in ${ENVIRONMENT_FILES[@]}; do
 done
 
 if [ ! -z "$ENVIRONMENT_FILE_LIST" ]; then
-    cat $ENVIRONMENT_FILE_LIST | grep -E '=' | tr '\n' ' ' | tr '\r' ' ' > $DOCKERFILE_DIR/final.env
+    cat $ENVIRONMENT_FILE_LIST | grep -E '=' | sed -e 's/,\s\+/,/g' | tr '\n' ' ' | tr '\r' ' ' > $DOCKERFILE_DIR/final.env
     echo "  HOME=/home/video-analytics-serving " >> $DOCKERFILE_DIR/final.env
     echo "ENV " | cat - $DOCKERFILE_DIR/final.env | tr -d '\n' >> $DOCKERFILE_DIR/Dockerfile.env
     printf "\nENV PYTHONPATH=\$PYTHONPATH:/home/video-analytics-serving\nENV GST_PLUGIN_PATH=\$GST_PLUGIN_PATH:/usr/lib/x86_64-linux-gnu/gstreamer-1.0/" >> $DOCKERFILE_DIR/Dockerfile.env

docker/run.sh

@@ -83,7 +83,11 @@ enable_hardware_access() {
     if ls /dev/dri/render* 1> /dev/null 2>&1; then
         echo "Found /dev/dri/render entry - enabling for GPU"
         DEVICES+='--device /dev/dri '
-        USER_GROUPS+="--group-add $(stat -c '%g' /dev/dri/render*) "
+        RENDER_GROUPS=$(stat -c '%g' /dev/dri/render*)
+        for group in $RENDER_GROUPS
+        do
+            USER_GROUPS+="--group-add $group "
+        done
     fi
 
     # Intel(R) NCS2
@@ -94,10 +98,21 @@ enable_hardware_access() {
     fi
 
     # HDDL
-    if [ -e /dev/ion ]; then
-        echo "Found /dev/ion - enabling for HDDL-R"
-        DEVICES+="--device /dev/ion "
-        VOLUME_MOUNT+="-v /var/tmp:/var/tmp "
+    if compgen -G /dev/myriad* > /dev/null ; then
+        echo "Found /dev/myriad devices - enabling for HDDL-R"
+        VOLUME_MOUNT+="-v /var/tmp:/var/tmp -v /dev/shm:/dev/shm "
+    fi
+
+    # Webcam
+    for device in $(ls /dev | grep video); do
+        echo "Found /dev/$device - enabling webcam"
+        DEVICES+="--device /dev/$device "
+    done
+
+    # Microphone
+    if [ -e /dev/snd ]; then
+        echo "Found /dev/snd - enabling microphone"
+        DEVICES+="--device /dev/snd "
     fi
 }
 
@@ -298,16 +313,19 @@ if [ "${MODE}" == "DEV" ]; then
         PIPELINES=$SOURCE_DIR/pipelines/$FRAMEWORK
     fi
     PRIVILEGED="--privileged "
+elif [ ! -z "$ENTRYPOINT" ]; then
+    MODE=CUSTOM_ENTRYPOINT
 elif [ "${MODE}" == "SERVICE" ]; then
     if [ -z "$PORTS" ]; then
         PORTS+="-p 8080:8080 "
     fi
-    enable_hardware_access
 else
     echo "Invalid Mode"
     show_help
 fi
 
+enable_hardware_access
+
 if [ ! -z "$ENABLE_RTSP" ]; then
     ENVIRONMENT+="-e ENABLE_RTSP=true -e RTSP_PORT=$RTSP_PORT "
     PORTS+="-p $RTSP_PORT:$RTSP_PORT "