Merge pull request #90 from intel/v0.6.1_PR

Release v0.6.1 beta

.gitignore

@@ -7,5 +7,5 @@ docker/Dockerfile.env
 docker/final.env
 models
 tests/results/**/*
-samples/lva_ai_extension/tests/results/**/*
+samples/ava_ai_extension/tests/results/**/*
 samples/edgex_bridge/edgex/**/*

README.md

@@ -156,6 +156,8 @@ The file path is specified in the `destination` section of the REST request and
 ### Queued, Running and Completed
 The vaclient `run` command starts the pipeline. The underlying REST request returns a `pipeline instance` which is used to query the state of the pipeline.
 All being well it will go into `QUEUED` then `RUNNING` state. We can interrogate the pipeline status by using the vaclient `start` command that kicks off the pipeline like `run` and then exits displaying the `pipeline instance` which is used by the `status` command to view pipeline state.
+> **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
 <snip>

docker/build.sh

@@ -10,7 +10,8 @@ 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"
+BASE_IMAGE_GSTREAMER="openvino/ubuntu20_data_runtime:2021.4.1"
+
 BASE_IMAGE=${BASE_IMAGE:-""}
 BASE_BUILD_CONTEXT=
 BASE_BUILD_DOCKERFILE=
@@ -35,7 +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=${OPEN_MODEL_ZOO_VERSION:-"2021.4"}
+OPEN_MODEL_ZOO_VERSION=${OPEN_MODEL_ZOO_VERSION:-"2021.4.1"}
 FORCE_MODEL_DOWNLOAD=
 
 DEFAULT_GSTREAMER_BASE_BUILD_TAG="video-analytics-serving-gstreamer-base"
@@ -240,9 +241,9 @@ get_options() {
 
     if [ -z "$BASE_IMAGE" ]; then
         if [ $FRAMEWORK = 'ffmpeg' ]; then
-            BASE_IMAGE=$BASE_IMAGE_FFMPEG
+            BASE_IMAGE=${CACHE_PREFIX}$BASE_IMAGE_FFMPEG
         else
-            BASE_IMAGE=$BASE_IMAGE_GSTREAMER
+            BASE_IMAGE=${CACHE_PREFIX}$BASE_IMAGE_GSTREAMER
         fi
     fi
 
@@ -258,10 +259,14 @@ get_options() {
         echo " "
         echo "----------------------------"
         echo "Running Model Downloader..."
-        echo "OMZ Tools Image: $OPEN_MODEL_ZOO_TOOLS_IMAGE"
+        echo "OMZ Tools Image: ${CACHE_PREFIX}$OPEN_MODEL_ZOO_TOOLS_IMAGE"
         echo "OMZ Version: $OPEN_MODEL_ZOO_VERSION"
         echo "----------------------------"
-        $SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $MODELS --output $SOURCE_DIR $FORCE_MODEL_DOWNLOAD --open-model-zoo-image $OPEN_MODEL_ZOO_TOOLS_IMAGE --open-model-zoo-version $OPEN_MODEL_ZOO_VERSION $DRY_RUN
+        $SOURCE_DIR/tools/model_downloader/model_downloader.sh --model-list $MODELS \
+          --output $SOURCE_DIR $FORCE_MODEL_DOWNLOAD \
+          --open-model-zoo-image ${CACHE_PREFIX}$OPEN_MODEL_ZOO_TOOLS_IMAGE \
+          --open-model-zoo-version $OPEN_MODEL_ZOO_VERSION \
+          $DRY_RUN
     elif [ -d "$MODELS" ]; then
         if [ ! -d "$SOURCE_DIR/models" ]; then
             $RUN_PREFIX mkdir $SOURCE_DIR/models
@@ -380,16 +385,15 @@ if [ "$BASE" == "BUILD" ]; then
     show_base_options
 
     launch "$RUN_PREFIX docker build "$BASE_BUILD_CONTEXT" -f "$BASE_BUILD_DOCKERFILE" $BASE_BUILD_OPTIONS $BASE_BUILD_ARGS -t $BASE_BUILD_TAG"
-
     BASE_IMAGE=$BASE_BUILD_TAG
 else
     # Ensure image is latest from Docker Hub
-    launch "$RUN_PREFIX docker pull ${CACHE_PREFIX}$BASE_IMAGE"
+    launch "$RUN_PREFIX docker pull $BASE_IMAGE"
 fi
 
 # BUILD IMAGE
 
-BUILD_ARGS+=" --build-arg BASE=${CACHE_PREFIX}$BASE_IMAGE "
+BUILD_ARGS+=" --build-arg BASE=$BASE_IMAGE "
 BUILD_ARGS+=" --build-arg FRAMEWORK=$FRAMEWORK "
 if [ -n "$MODELS" ]; then
     BUILD_ARGS+="--build-arg MODELS_PATH=$MODELS_PATH "
@@ -429,8 +433,8 @@ if [ ! -z "$ENVIRONMENT_FILE_LIST" ]; then
     cat $ENVIRONMENT_FILE_LIST | grep -E '=' | 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\n" >> $DOCKERFILE_DIR/Dockerfile.env   
-fi  
+    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
+fi
 
 show_image_options
 

docker/run.sh

@@ -86,9 +86,9 @@ enable_hardware_access() {
         USER_GROUPS+="--group-add $(stat -c '%g' /dev/dri/render*) "
     fi
 
-    # NCS2
+    # Intel(R) NCS2
     if [ -d /dev/bus/usb ]; then
-        echo "Found /dev/bus/usb - enabling for NCS2"
+        echo "Found /dev/bus/usb - enabling for Intel(R) NCS2"
         DEVICE_CGROUP_RULE=--device-cgroup-rule=\'c\ 189:*\ rmw\'
         VOLUME_MOUNT+="-v /dev/bus/usb:/dev/bus/usb "
     fi

docs/building_video_analytics_serving.md

@@ -26,7 +26,7 @@ can be customized to meet an application's requirements.
 
 | Command | Media Analytics Base Image | Image Name | Description |
 | ---     | ---        | --- | ----        |
-| `./docker/build.sh`| **ubuntu20_data_runtime:2021.4** docker [image](https://hub.docker.com/r/openvino/ubuntu20_data_runtime) |`video-analytics-serving-gstreamer` | DL Streamer based microservice with default pipeline definitions and deep learning models. |
+| `./docker/build.sh`| **ubuntu20_data_runtime:2021.4.1** docker [image](https://hub.docker.com/r/openvino/ubuntu20_data_runtime) |`video-analytics-serving-gstreamer` | DL Streamer based microservice with default pipeline definitions and deep learning models. |
 | `./docker/build.sh --framework ffmpeg --open-model-zoo...`| **xeone3-ubuntu1804-analytics-ffmpeg:20.10** docker [image](https://hub.docker.com/r/openvisualcloud/xeon-ubuntu1804-analytics-ffmpeg) |`video-analytics-serving-ffmpeg`| FFmpeg Video Analytics based microservice with default pipeline definitions and deep learning models. |
 ### Building with OpenVINO, Ubuntu 20.04 and DL Streamer Support
 **Example:**
@@ -70,7 +70,7 @@ All validation is done in docker environment. Host built (aka "bare metal") conf
 
 | **Base Image** | **Framework** | **Openvino Version** | **Link** | **Default** |
 |---------------------|---------------|---------------|------------------------|-------------|
-| OpenVINO 2021.4 ubuntu20_data_runtime | GStreamer | 2021.4 | [Docker Hub](https://hub.docker.com/r/openvino/ubuntu20_data_runtime) | Y |
+| OpenVINO 2021.4.1 ubuntu20_data_runtime | GStreamer | 2021.4.1 | [Docker Hub](https://hub.docker.com/r/openvino/ubuntu20_data_runtime) | Y |
 | Open Visual Cloud 20.10 xeone3-ubuntu1804-analytics-ffmpeg | FFmpeg | 2021.1 | [Docker Hub](https://hub.docker.com/r/openvisualcloud/xeone3-ubuntu1804-analytics-ffmpeg) | Y |
 
 ---

docs/changing_object_detection_models.md

@@ -52,7 +52,6 @@ Use [vaclient](/vaclient/README.md) to list the models. Check that `object_detec
 ```
 $ ./vaclient/vaclient.sh list-models
  - emotion_recognition/1
- - object_detection/1
  - object_detection/person_vehicle_bike
  - object_classification/vehicle_attributes
  - audio_detection/environment
@@ -129,13 +128,12 @@ Expected output (abbreviated):
 
 ```
 [ SUCCESS ] Generated IR version 10 model.
-[ SUCCESS ] XML file: /tmp/tmp8mq6f1ti/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml
-[ SUCCESS ] BIN file: /tmp/tmp8mq6f1ti/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.bin
-[ SUCCESS ] Total execution time: 5.75 seconds.
-[ SUCCESS ] Memory consumed: 533 MB.
-It's been a while, check for a new version of Intel(R) Distribution of OpenVINO(TM) toolkit here https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/choose-download.html?cid=other&source=Prod&campid=ww_2020_bu_IOTG_OpenVINO-2021-1&content=upg_pro&medium=organic_uid_agjj or on the GitHub*
+[ SUCCESS ] XML file: /tmp/tmps4pxnu7y/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.xml
+[ SUCCESS ] BIN file: /tmp/tmps4pxnu7y/public/yolo-v2-tiny-tf/FP32/yolo-v2-tiny-tf.bin
+[ SUCCESS ] Total execution time: 9.70 seconds.
+[ SUCCESS ] Memory consumed: 584 MB.
 
-Downloaded yolo-v2-tiny-tf model-proc file from gst-video-analytics repo
+Copied model_proc to: /output/models/object_detection/yolo-v2-tiny-tf/yolo-v2-tiny-tf.json
 ```
 
 The model will now be in `models` folder in the root of the project:
@@ -195,7 +193,7 @@ Edited pipeline template:
 
 ```
 "template": ["uridecodebin name=source",
-            " ! gvadetect model={models[person_vehicle_bike_detection][yolo-v2-tiny-tf][network]} name=detection",
+            " ! gvadetect model={models[object_detection][yolo-v2-tiny-tf][network]} name=detection",
             " ! gvametaconvert name=metaconvert ! gvametapublish name=destination",
             " ! appsink name=appsink"
             ]
@@ -215,7 +213,6 @@ The `list-models` command now shows 9 models, including `object_detection/yolo-v
 ```bash
 $ ./vaclient/vaclient.sh list-models
  - emotion_recognition/1
- - object_detection/1
  - object_detection/yolo-v2-tiny-tf
  - object_detection/person_vehicle_bike
  - object_classification/vehicle_attributes
@@ -275,7 +272,6 @@ Once started you can verify that the new model has been loaded.
 ```bash
 $ ./vaclient/vaclient.sh list-models
  - emotion_recognition/1
- - object_detection/1
  - object_detection/yolo-v2-tiny-tf
  - object_detection/person_vehicle_bike
  - object_classification/vehicle_attributes