Refactor CV-SDK and add deprecation banner

.github/workflows/example-checks.yml

@@ -15,11 +15,17 @@ jobs:
         run: |
           # Find directories that do not start with dot
           acap_list="$(find . -maxdepth 1 -type d -printf '%f\n' |grep -v '^[.]' | sort)"
-          # Find lines starting with '* ['
-          # The grep removes anything but '[*]', then remove entries with space
-          # or capital letter and finally removes the brackets.
-          endline="$(sed -n '/## Docker Hub images/=' README.md)"
-          readme_examples="$(sed -n "1,${endline}!d ; /* \[/p" README.md | grep -oe '\[.*\]' | grep -v '[A-Z ]' | grep -oe '[0-9a-z-]*')"
+
+          # Find example application links in README.md
+          # The regular expression '^\* \[[0-9a-z-]*\]' matches lines starting with '* ['
+          # followed by alphanumeric characters and hyphens, and a closing ']'
+          example_links="$(grep -o '^\* \[[0-9a-z-]*\]' README.md)"
+
+          # Remove '* [' prefix and trailing ']' from example links
+          # The sed command uses two substitutions:
+          # 1. 's/^\* \[//' removes the '* [' prefix from the start of the line
+          # 2. 's/\]$//' removes the trailing ']' from the end of the line
+          readme_examples="$(echo "$example_links" | sed 's/^\* \[//;s/\]$//')"
 
           # Control that all examples have an entry in README
           common=$(printf "${acap_list}\n${readme_examples}" | sort | uniq -d)

.github/workflows/hello-world-python.yml

@@ -25,4 +25,5 @@ jobs:
           imagetag: ${{ env.EXREPO }}_${{ env.EXNAME }}:1.0
         run: |
           cd $EXNAME
+          docker run --rm --privileged multiarch/qemu-user-static --credential yes --persistent yes
           docker build --no-cache --tag $imagetag .

.github/workflows/object-detector-python.yml

@@ -31,5 +31,6 @@ jobs:
           modeltag: ${{ env.EXREPO }}_${{ env.EXNAME }}-${{ matrix.chip }}-model:1.0
         run: |
           cd $EXNAME
+          docker run --rm --privileged multiarch/qemu-user-static --credential yes --persistent yes
           docker build --no-cache --build-arg CHIP=${{ matrix.chip }} --tag $imagetag .
           docker build --file Dockerfile.model --tag $modeltag  .

.github/workflows/opencv-qr-decoder-python.yml

@@ -25,4 +25,5 @@ jobs:
           imagetag: ${{ env.EXREPO }}_${{ env.EXNAME }}:1.0
         run: |
           cd $EXNAME
+          docker run --rm --privileged multiarch/qemu-user-static --credential yes --persistent yes
           docker build --no-cache --tag $imagetag .

.github/workflows/parameter-api-python.yml

@@ -25,4 +25,5 @@ jobs:
           imagetag: ${{ env.EXREPO }}_${{ env.EXNAME }}:1.0
         run: |
           cd $EXNAME
+          docker run --rm --privileged multiarch/qemu-user-static --credential yes --persistent yes
           docker build --no-cache --tag $imagetag .

README.md

@@ -4,6 +4,23 @@
 
 [![CI](https://github.com/AxisCommunications/acap-computer-vision-sdk-examples/actions/workflows/ci.yml/badge.svg)](https://github.com/AxisCommunications/acap-computer-vision-sdk-examples/actions/workflows/ci.yml)
 
+> [!IMPORTANT]
+> The ACAP Computer Vision solution is deprecated from AXIS OS 12.0.
+>
+> - The deprecation of the ACAP Computer Vision solution only affects container applications.
+>   It will still be possible to build and run analytics, computer vision and machine learning applications on Axis devices using
+>   ACAP Native SDK. Examples can be found in the
+>   [ACAP Native SDK examples](https://github.com/AxisCommunications/acap-native-sdk-examples) repository.
+>   For more context about this transition, please visit the [Enhanced ACAP SDK news article](https://www.axis.com/developer-community/news/enhanced-acap-sdk).
+> - `armv7hf` is no longer supported after AXIS OS 11.11.
+> - `aarch64` will be supported until the end of the AXIS OS 12 LTS cycle.
+>   After this period, the components of the Computer Vision solution will no longer
+>   receive updates or support.
+> - From AXIS OS 12.0, the artifact ACAP Computer Vision SDK in form of a
+>   container image is removed and utility libraries and scripts have been
+>   moved to [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime).
+>   This means that the ACAP Computer Vision SDK is no longer needed to run container applications.
+
 ## Mission
 
 Our mission is to provide an excellent development experience by enabling developers
@@ -32,15 +49,22 @@ developers analytics experience. All examples are using Docker framework and has
 README file in its directory which shows overview, example directory structure and
 step-by-step instructions on how to run applications on the camera.
 
-### Requirements
+## Requirements
 
 ### Supported architectures
 
 The examples support the following architectures:
 
 * aarch64
 
-### Example applications for video analytics
+### Required components
+
+The example applications make use of two special components:
+
+* [Docker ACAP](https://github.com/AxisCommunications/docker-acap) needs to be installed and running on the Axis device. It's required to deploy and run the example application.
+* [ACAP runtime](https://github.com/AxisCommunications/acap-runtime) is used in a few examples to get access to [APIs](https://axiscommunications.github.io/acap-documentation/docs/api/computer-vision-sdk-apis.html). ACAP Runtime is a container image and the example applications will pull it from Docker Hub when starting.
+
+## Example applications
 
 Below is a list of examples available in the repository:
 
@@ -60,12 +84,6 @@ Below is a list of examples available in the repository:
 * [web-server](./web-server/)
   * A C++ example which runs a Monkey web server on the camera.
 
-### Docker Hub images
-
-The examples are based on the [ACAP Computer Vision SDK](https://github.com/AxisCommunications/acap-computer-vision-sdk).
-This SDK is an image which contains APIs and tooling to build computer vision apps for running on camera, with support for Python.
-Additionally, there is the [ACAP Native SDK](https://github.com/AxisCommunications/acap-native-sdk), which is more geared towards building ACAPs that uses AXIS-developed APIs directly, and primarily does so using C/C++.
-
 ## How to work with Github repository
 
 You can help to make this repo a better one using the following commands.

hello-world-python/Dockerfile

@@ -1,17 +1,15 @@
 # syntax=docker/dockerfile:1
 
-ARG REPO=axisecp
-ARG SDK_VERSION=1.15
-ARG UBUNTU_VERSION=22.04
-
-
-FROM ${REPO}/acap-computer-vision-sdk:${SDK_VERSION}-aarch64 AS cv-sdk
+ARG UBUNTU_VERSION=24.04
 
 FROM arm64v8/ubuntu:${UBUNTU_VERSION}
 
-# Get the Python package from the CV SDK
-COPY --from=cv-sdk /axis/python /
+RUN DEBIAN_FRONTEND=noninteractive \
+    apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /opt/app
 
-WORKDIR /app
-COPY app/* /app
+COPY app/* .
 CMD ["python3", "simply_hello.py"]

hello-world-python/README.md

@@ -2,9 +2,9 @@
 
 # A minimal Python application
 
-This example demonstrates how to create a simple Python application using the ACAP Computer Vision SDK and run it on an edge device.
+This example demonstrates how to create a simple containerized Python application and run it on an edge device.
 
-Going from zero to a Python application running on an AXIS device is quite easy. First, the application script is written, as in the hello-world script in [app/simply_hello.py](app/simply_hello.py). Next, the [Dockerfile](Dockerfile) which builds the application image is constructed. This needs to pull in packages from the ACAP Computer Vision SDK, as is done using the `COPY` commands. Finally, the application needs to be built and uploaded, as is specified below.
+Going from zero to a Python application running on an Axis device is quite easy. First, the application script is written, as in the hello-world script in [app/simply_hello.py](app/simply_hello.py). Next, the [Dockerfile](Dockerfile) which builds the application image is constructed. The application needs to be built and uploaded, as is specified below.
 
 ## Example structure
 

minimal-ml-inference/Dockerfile

@@ -1,24 +1,40 @@
 # syntax=docker/dockerfile:1
 
+ARG RUNTIME_VERSION=2.0.0
 ARG REPO=axisecp
-ARG SDK_VERSION=1.15
-ARG UBUNTU_VERSION=22.04
+ARG IMAGE=acap-runtime
+ARG UBUNTU_VERSION=24.04
 
-# Specify which ACAP Computer Vision SDK to use
-FROM ${REPO}/acap-computer-vision-sdk:${SDK_VERSION}-aarch64 AS cv-sdk
+FROM ${REPO}/${IMAGE}:${RUNTIME_VERSION}-protofiles AS proto-image
+FROM arm64v8/ubuntu:${UBUNTU_VERSION} AS runtime-image
 
-# Define the runtime image
-FROM arm64v8/ubuntu:${UBUNTU_VERSION}
+RUN DEBIAN_FRONTEND=noninteractive \
+    apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    python3-pip \
+    python3-venv \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
 
-# Get packages from the CV SDK
-COPY --from=cv-sdk /axis/python /
-COPY --from=cv-sdk /axis/python-tfserving /
-COPY --from=cv-sdk /axis/python-vdoproto /
-COPY --from=cv-sdk /axis/python-numpy /
-COPY --from=cv-sdk /axis/opencv /
-COPY --from=cv-sdk /axis/openblas /
+# Copy list of grpc dependencies
+COPY --from=proto-image /build/requirements.txt .
+
+# Create a virtual environment for installations using pip
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+RUN pip install --no-cache-dir -r requirements.txt \
+    numpy \
+    opencv-python
+
+WORKDIR /opt/app
+
+# Install prebuilt TensorFlow and VDO proto files
+COPY --from=proto-image /build/tf/proto_utils .
+COPY --from=proto-image /build/vdo/proto_utils .
 
 # Copy the application script to the container
-WORKDIR /app
-COPY simple_inference.py .
+COPY app/* .
+
 CMD ["python3", "simple_inference.py"]

minimal-ml-inference/Dockerfile.model

@@ -4,7 +4,6 @@ ARG UBUNTU_VERSION=22.04
 
 FROM arm64v8/alpine as model-image
 
-
 # Get SSD Mobilenet V2
 ADD https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite models/
 ADD https://github.com/google-coral/edgetpu/raw/master/test_data/coco_labels.txt models/

minimal-ml-inference/README.md

@@ -2,7 +2,7 @@
 
 # A minimal, machine learning inference application
 
-This example demonstrates how to create a simple Python application that performs inference on the camera image. This is done using the ACAP Computer Vision SDK.
+This example demonstrates how to create a simple Python application that performs inference on the camera image.
 
 ## Overview
 
@@ -14,7 +14,7 @@ Lastly, there is a third container that holds the deep learning model, which is
 
 ```text
 model
-├── ssdlite-mobilenet-v2 - tflite model
+├── ssdlite-mobilenet-v2 - TFLite model
 └── objects.txt - list of object labels
 ```
 

minimal-ml-inference/config/env.aarch64.artpec8

@@ -1,3 +1,3 @@
 MODEL_PATH=/models/ssd_mobilenet_v2_coco_quant_postprocess.tflite
-INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:1.3.1-aarch64-containerized
-INFERENCE_CHIP=12
+INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:2.0.0-aarch64-containerized
+INFERENCE_CHIP=12

minimal-ml-inference/config/env.aarch64.cpu

@@ -1,3 +1,3 @@
 MODEL_PATH=/models/ssd_mobilenet_v2_coco_quant_postprocess.tflite
-INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:1.3.1-aarch64-containerized
-INFERENCE_CHIP=2
+INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:2.0.0-aarch64-containerized
+INFERENCE_CHIP=2

object-detector-python/Dockerfile

@@ -1,20 +1,40 @@
 # syntax=docker/dockerfile:1
 
+ARG RUNTIME_VERSION=2.0.0
 ARG REPO=axisecp
-ARG SDK_VERSION=1.15
-ARG UBUNTU_VERSION=22.04
+ARG IMAGE=acap-runtime
+ARG UBUNTU_VERSION=24.04
 
+FROM ${REPO}/${IMAGE}:${RUNTIME_VERSION}-protofiles AS proto-image
+FROM arm64v8/ubuntu:${UBUNTU_VERSION} AS runtime-image
 
-FROM ${REPO}/acap-computer-vision-sdk:${SDK_VERSION}-aarch64 AS cv-sdk
-FROM arm64v8/ubuntu:${UBUNTU_VERSION}
+RUN DEBIAN_FRONTEND=noninteractive \
+    apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    python3-pip \
+    python3-venv \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
 
-COPY --from=cv-sdk /axis/python /
-COPY --from=cv-sdk /axis/python-numpy /
-COPY --from=cv-sdk /axis/python-tfserving /
-COPY --from=cv-sdk /axis/python-vdoproto /
-COPY --from=cv-sdk /axis/opencv /
-COPY --from=cv-sdk /axis/openblas /
+# Copy list of grpc dependencies
+COPY --from=proto-image /build/requirements.txt .
+
+# Create a virtual environment for installations using pip
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+RUN pip install --no-cache-dir -r requirements.txt \
+    numpy \
+    opencv-python
+
+WORKDIR /opt/app
+
+# Install prebuilt TensorFlow and VDO proto files
+COPY --from=proto-image /build/tf/proto_utils .
+COPY --from=proto-image /build/vdo/proto_utils .
+
+# Copy the application script to the container
+COPY app/* .
 
-WORKDIR /app
-COPY app/* /app/
 CMD ["python3", "detector.py"]

object-detector-python/config/env.aarch64.artpec8

@@ -1,3 +1,3 @@
 MODEL_PATH=/models/ssd_mobilenet_v2_coco_quant_postprocess.tflite
-INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:1.3.1-aarch64-containerized
+INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:2.0.0-aarch64-containerized
 INFERENCE_CHIP=12

object-detector-python/config/env.aarch64.cpu

@@ -1,3 +1,3 @@
 MODEL_PATH=/models/ssd_mobilenet_v2_coco_quant_postprocess.tflite
-INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:1.3.1-aarch64-containerized
+INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:2.0.0-aarch64-containerized
 INFERENCE_CHIP=2

opencv-qr-decoder-python/Dockerfile

@@ -1,19 +1,39 @@
 # syntax=docker/dockerfile:1
 
+ARG RUNTIME_VERSION=2.0.0
 ARG REPO=axisecp
-ARG SDK_VERSION=1.15
-ARG UBUNTU_VERSION=22.04
+ARG IMAGE=acap-runtime
+ARG UBUNTU_VERSION=24.04
 
+FROM ${REPO}/${IMAGE}:${RUNTIME_VERSION}-protofiles AS proto-image
+FROM arm64v8/ubuntu:${UBUNTU_VERSION} AS runtime-image
 
-FROM ${REPO}/acap-computer-vision-sdk:${SDK_VERSION}-aarch64-runtime AS cv-sdk
-FROM arm64v8/ubuntu:${UBUNTU_VERSION}
-COPY --from=cv-sdk /axis/opencv /
-COPY --from=cv-sdk /axis/openblas /
-COPY --from=cv-sdk /axis/python-tfserving /
-COPY --from=cv-sdk /axis/python-vdoproto /
-COPY --from=cv-sdk /axis/python /
-COPY --from=cv-sdk /axis/python-numpy /
+RUN DEBIAN_FRONTEND=noninteractive \
+    apt-get update && apt-get install -y --no-install-recommends \
+    python3 \
+    python3-pip \
+    python3-venv \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy list of grpc dependencies
+COPY --from=proto-image /build/requirements.txt .
+
+# Create a virtual environment for installations using pip
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+RUN pip install --no-cache-dir -r requirements.txt \
+    numpy \
+    opencv-python
+
+WORKDIR /opt/app
+
+# Install prebuilt VDO proto files
+COPY --from=proto-image /build/vdo/proto_utils .
+
+# Copy the application script to the container
+COPY app/* .
 
-WORKDIR /app
-COPY app/qr.py .
 CMD ["python3", "qr.py"]

opencv-qr-decoder-python/README.md

@@ -8,7 +8,6 @@ It composes two different container images into an application that performs an
 The first container contains the actual program built in this example. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, that is used to capture images from the camera.
 
 NumPy is used to preprocess the images and OpenCV to detect and decode any QR codes within the image.
-Additionally, Python, NumPy and OpenBLAS are also copied from the Computer Vision SDK.
 
 ## Application layout
 

opencv-qr-decoder-python/docker-compose.yml

@@ -13,7 +13,7 @@ services:
     volumes:
       - inference-server:/tmp
   inference-server:
-    image: axisecp/acap-runtime:1.3.1-aarch64-containerized
+    image: axisecp/acap-runtime:2.0.0-aarch64-containerized
     logging:
       driver: "json-file"
       options: