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Go to AWS and login.
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Go to Kinesis Video Streams and create a video stream.
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Go to EC2 and launch a t2.small instance.
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SSH into the EC2 instance.
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Execute the following commands in the EC2 instance:
sudo apt update git clone https://github.com/awslabs/amazon-kinesis-video-streams-producer-sdk-cpp.git mkdir -p amazon-kinesis-video-streams-producer-sdk-cpp/build cd amazon-kinesis-video-streams-producer-sdk-cpp/build sudo apt-get install libssl-dev libcurl4-openssl-dev liblog4cplus-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev gstreamer1.0-plugins-base-apps gstreamer1.0-plugins-bad gstreamer1.0-plugins-good gstreamer1.0-plugins-ugly gstreamer1.0-tools sudo apt install cmake sudo apt-get install g++ sudo apt-get install build-essential cmake .. -DBUILD_DEPENDENCIES=OFF -DBUILD_GSTREAMER_PLUGIN=ON make sudo make install cd .. export GST_PLUGIN_PATH=`pwd`/build export LD_LIBRARY_PATH=`pwd`/open-source/local/lib -
Download the video we will be using to test this project:
cd ~ wget https://raw.githubusercontent.com/computervisioneng/real-time-number-plate-recognition-anpr/main/sample_30fps_1440.mp4 -
Go to IAM and create a new user with AmazonKinesisVideoStreamsFullAccess permissions.
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Select the IAM user you created, go to Security credentials and create access keys.
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In the EC2 instance run the following command:
gst-launch-1.0 -v filesrc location="./sample_30fps_1440.mp4" ! qtdemux name=demux ! queue ! h264parse ! video/x-h264,stream-format=avc,alignment=au ! kvssink name=sink stream-name="stream-name" access-key="access-key" secret-key="secret-key" aws-region="region-name" streaming-type=offline demux. ! queue ! aacparse ! sink.
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Go to EC2 and launch a t2.xlarge instance with 30GB storage size.
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SSH into the EC2 instance.
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Execute the following commands in the EC2 instance:
sudo apt update sudo apt install python3-virtualenv virtualenv venv --python=python3 source venv/bin/activate git clone https://github.com/computervisioneng/amazon-kinesis-video-streams-consumer-library-for-python.git cd amazon-kinesis-video-streams-consumer-library-for-python git clone https://github.com/abewley/sort.git pip install -r requirements.txt pip install -r sort/requirements.txt pip install ultralytics sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6 -y sudo apt-get install python3-tk -
Go to IAM and create an access role for the EC2 instance with the following policies: AmazonKinesisVideoStreamsFullAccess, AmazonDynamoDBFullAccess, AmazonS3FullAccess and AmazonSQSFullAccess.
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Attach the IAM role to the EC2 instance.
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Download the object detector into the EC2 instance.
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Go to S3 and create an S3 bucket.
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Go to Dynamodb and create two tables.
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Go to SQS and create a FIFO queue.
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Go to Lambda and create a new Lambda function with the files: lambda_function.py and util.py.
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Go to IAM and create an access role for the Lambda function with the following policies: AmazonDynamoDBFullAccess, AmazonS3FullAccess, AmazonSQSFullAccess and TextractFullAccess.
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Attach the IAM role to the Lambda function.
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Edit Lambda function timeout to 1 minute.
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Go to the S3 bucket and create a new event notification to trigger the lambda function.
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In the EC2 instance, go to amazon-kinesis-video-streams-consumer-library-for-python/kvs_consumer_library_example_object_detection_and_tracking.py and edit the variable names.
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In the Lambda function, go to lambda_function.py and edit the variable names.
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Execute the following commands:
cd ~/amazon-kinesis-video-streams-consumer-library-for-python python kvs_consumer_library_example_object_detection_and_tracking.py
- Clone this repository https://github.com/computervisioneng/amazon-kinesis-video-streams-consumer-library-for-python.git
- Download the files main_plot.py and process_queue.py.
- Download the directory loading_frames.
- Go to IAM and create a new user with AmazonKinesisVideoStreamsFullAccess, AmazonDynamoDBFullAccess and AmazonSQSFullAccess permissions.
- Select the IAM user you created, go to Security credentials and create access keys.
- Go to main_plot.py, process_queue.py and amazon-kinesis-video-streams-consumer-library-for-python/kvs_consumer_library_example_visualization.py and edit the variables.
- Create a virtual environment and install requirements.
- Execute process_queue.py.
- Execute main_plot.py.
- Execute amazon-kinesis-video-streams-consumer-library-for-python/kvs_consumer_library_example_object_detection_and_tracking.py.