Installing OpenCV on the Raspberry Pi?

The Raspberry Pi is a popular single-board computer that is often used for image processing and computer vision projects. By installing the OpenCV (Open Source Computer Vision Library) on your Raspberry Pi, you can access OpenCV’s extensive suite of algorithms and functions to build exciting CV and image processing applications.

Installing OpenCV on the Raspberry Pi?

Steps to Install OpenCV on Raspberry Pi

Installing OpenCV on your Raspberry Pi is a straightforward process that involves just a few steps:

  1. Update the Raspberry Pi OS and dependencies:

sudo apt update

sudo apt upgrade

sudo apt install build-essential cmake pkg-config libjpeg-dev libtiff5-dev libjasper-dev libpng-dev libwebp-dev libopenexr-dev libgdal-dev libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev yasm libopencore-amrnb-dev libopencore-amrwb-dev libv4l-dev libxine2-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev python3-dev python3-numpy

  1. Download the OpenCV source code:

cd ~

wget -O

wget -O

  1. Unzip the source code:



  1. Build OpenCV with contrib modules: This may take some time.

cd ~/opencv-4.x

mkdir build

cd build



-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-4.x/modules \








  1. Compile:

make -j4

  1. Install OpenCV:

sudo make install

sudo ldconfig

That’s it! OpenCV should now be successfully installed on your Raspberry Pi.

Optimizing OpenCV Performance on the Pi

There are a few settings you can tweak to improve OpenCV performance on the resource-constrained Raspberry Pi:

  • Overclock your Pi: Moderately overclock your Pi’s CPU and GPU for extra performance. But monitor temperatures!
  • Set VideoCore memory: Run sudo raspi-config and set 256MB or more video memory for the GPU in the advanced settings.
  • Use multithreading: OpenCV functions like cv::parallel_for_ utilize multicore CPUs well.
  • Reduce image size/resolution: Scale down images and video frames before processing them with OpenCV.
  • Use hardware accelerators: Enable NEON, VFPU, or MMAL support in OpenCV for huge performance gains.
  • Pick algorithms carefully: Some OpenCV algorithms are slower. Profile operations to identify bottlenecks.

With some optimizations, you can deploy fairly complex OpenCV pipelines on a Raspberry Pi! The small form factor and power efficiency makes it great for embedded applications.

Building Computer Vision Applications with OpenCV on Pi

Here are some example projects you can try building with OpenCV on Raspberry Pi:

Image Processing

  • Image filters (blurring, sharpening, edge detection etc)
  • Histogram equalization and color space conversions
  • Image transformations like rotation, translation and cropping
  • Stitching multiple images into a panorama

Object Detection

  • Detect common objects like cars, people or faces
  • Counting objects in an image
  • Measuring object sizes and distances

Motion Tracking

  • Tracking moving objects in a live video
  • Extracting motion trajectories
  • Speed estimation of moving objects

Augmented Reality

  • Adding virtual 3D objects on camera preview
  • Blending synthetic graphics into a real scene

The possibilities are endless! OpenCV has over 2500 optimized algorithms, from simple image filters to advanced deep learning models. It is the go-to library for computer vision applications.

So install it on your Raspberry Pi and start building futuristic CV projects today!

Key Takeaways

  • OpenCV provides over 2500 computer vision and image processing algorithms
  • Installing it on a Raspberry Pi enables portable computer vision applications
  • Moderate overclocking, GPU memory allocation and using hardware accelerators can optimize performance
  • Interesting projects like image filters, object detection, motion tracking, augmented reality apps etc. can be built
  • OpenCV and Raspberry Pi together make an inexpensive platform for CV experiments


Installing OpenCV on Raspberry Pi opens up a world of possibilities for computer vision application development. With OpenCV’s vast toolbox of battle-tested algorithms and the Pi’s portable form factor, you can build creative CV projects – anywhere, anytime!

So configure your RPi for optimal OpenCV performance, pick an exciting application idea, and start coding today! Embedded computer vision has never been more accessible. The compact Raspberry Pi running OpenCV puts futuristic AI capabilities right in the palm of your hands.


Q: What are the minimum system requirements to install OpenCV on Raspberry Pi?
A: A Raspberry Pi 2 or newer model with 1GB RAM is sufficient. For smooth video processing, Raspberry Pi 3 B+ or newer is recommended.

Q: How can I optimize OpenCV performance on the Raspberry Pi?
A: Overclocking, assigning more GPU memory, using hardware acceleration modules, multithreading and reducing input image sizes can help optimize OpenCV pipelines.

Q: What are some skills required to build CV apps with OpenCV on Raspberry Pi?
A: Basic knowledge of Linux, Python/C++ programming, and understanding of fundamental computer vision techniques is required. Linux and Python skill are most important.

Q: Can I install OpenCV 4.x on older Pi models?
A: Yes, OpenCV 4.x should work on most newer Pi models like Pi 3 B+ or Pi 4. You may have to tune some build configurations for older 32-bit platforms.

Q: What are some practical applications of OpenCV on Raspberry Pi?
A: You can build security cameras, smart doorbells, object trackers, augmented reality devices, image recognition gadgets, automotive assistive devices and much more.

Q: Is there an easier alternative to compiling OpenCV from source on Pi?
A: Yes, you can install OpenCV from the Python package manager PIP which hosts prebuilt wheels for OpenCV. Just run: pip install OpenCV-contrib-python

Q: Can I use OpenCV on Raspberry Pi with other languages like JavaScript or Java?
A: Yes, you can call OpenCV functions from node.js, Java and other languages with wrappers available. But Python is the most common language used.

Q: What hardware add-ons can I use with OpenCV on Raspberry Pi?
A: You can interface various cameras, lidar/depth sensors, thermal cameras, pan-tilt modules, motors for tracking and more over USB or GPIO.

Q: What are some other libraries that work well with OpenCV for computer vision?
A: Open3D, CUDA (for GPU acceleration), OpenCV contrib modules, Tensorflow, Tesseract OCR, Python scientific stack etc. work very well with OpenCV.

Q: Can I run advanced deep learning models on OpenCV with Raspberry Pi?
A: Basic CNNs can run but complex models are not feasible. You may use model optimization techniques or OpenCV’s DNN module with hardware acceleration enabled. For complex AI use a powerful desktop GPU.

Q: How do I debug crashes or errors with OpenCV programs on the Pi?
A: Use gdb or logging statements to debug. Validate all input parameters. Enable cores and memory dumps in system settings to diagnose crashes. Update OpenCV if needed.

Q: Where can I find Raspberry Pi and OpenCV projects for inspiration?
Great resources are the official Raspberry Pi projects page, PyImageSearch blog, OpenCV documentation samples, GitHub computer vision projects and medium articles on OpenCV.

Q: Should I learn OpenCV or OpenCV contrib modules first?
Focus on learning core OpenCV functionality like image processing, feature detection, calibration etc first. Then explore more complex contrib algorithms like machine learning, 3d vision etc.

Q: Is the Raspberry Pi powerful enough for commercial computer vision products?
For most basic vision tasks yes, but for commercial grade robustness, accuracy and throughput you still need more powerful hardware like edge AI accelerators, GPU systems etc. Depends on your product specs.

Q: How do I get started with building OpenCV apps on Raspberry Pi step-by-step?
Start with official RPi and OpenCV installation guides, then run some sample codes. Understand them first. Next try simple apps like image filters, ball tracking etc. Finally build more complex projects incrementally with reference architectures.

Q: Should I use a Raspberry Pi camera module or a USB webcam for computer vision projects?
A: The official Pi camera module is optimized to work well with the hardware encoder pipelines. USB webcams can be used too but may have more latency or compression artifacts.

Q: Can I use multiple Raspberry Pi cameras or webcams with OpenCV at once?
A: Yes, with a USB hub you can connect multiple Pi or USB cameras and process them concurrently using OpenCV’s Video Capture API and multiple threads.

Q: Is a Raspberry Pi alone enough for real-time object detection at decent frame rates?
A: For very lightweight models yes. But for accurate multi-object detection you will need extra hardware like Google Coral TPUs or Intel Neural Compute Sticks to offload the compute intensive DNN tasks.

Q: What are some tips for running OpenCV programs automatically on Raspberry Pi boot?
A: Add your program to rc.local, create a systemd service unit file or crontab entry to launch apps automatically when RPi boots up.

Q: How do I capture both video and audio from a webcam using OpenCV on Raspberry Pi?
A: Use the Video Capture API with CAP_DSHOW backend, set Camera Parameters and enable audio stream to redirect both video and audio to OpenCV.

Q: My real-time OpenCV video pipeline is too slow. How do I speed things up?
A: Reduce resolution, use multithreading, offload to GPU, optimize algorithm, lower FPS, compress frames & use hardware accelerators like Google Coral, Intel Movidius etc.

Q: How can I connect Arduino or other hardware sensors to the OpenCV application on my Raspberry Pi?
A: Communicate sensor data from Arduino to RPi over Serial, I2C or SPI. Then access the data from OpenCV Python/C++ application running on Pi.

Q: Is there an easy way to send real-time OpenCV video frames across network?
A: Yes! Stream video frames as JPEG images over Flask server or use solutions like gstreamer over RTP/RTSP for low latency h.264 video streaming.

Q: How do I create a machine learning model to use with OpenCV on Raspberry Pi?
A: Train model on desktop GPUs, optimize it for mobile, convert to ONNX format and run on RPi using OpenCV’s DNN module with hardware acceleration for optimal performance.

Q: What’s the best way for on-device model training with OpenCV and RPi?
A: Export small fully trains base model, then train top layers only for your data on RPi using transfer learning. Finetune with OpenCV’s ML modules.

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