Complete Arducam Camera Guide for Raspberry Pi: Setup, Configuration & Projects

Introduction

Arducam has revolutionized camera solutions for Raspberry Pi and embedded systems, offering everything from basic camera modules to advanced multi-camera setups and high-resolution sensors.

Whether you're building a security camera, time-lapse system, or machine learning application, this guide covers everything you need to get started with Arducam on Raspberry Pi.

Arducam Camera Options

Browse our complete Arducam camera collection for all available models.

Hardware Setup

Connecting the Camera

1. Power off your Raspberry Pi completely
2. Locate the CSI camera port (between HDMI and audio jack on Pi 4)
3. Gently pull up the plastic clip on the connector
4. Insert the ribbon cable with blue side facing the Ethernet port
5. Push the plastic clip back down to secure the cable
6. Power on the Raspberry Pi

Software Configuration

Enable Camera Interface

On Raspberry Pi OS, enable the camera using raspi-config:

sudo raspi-config
# Navigate to: Interface Options → Camera → Enable
# Reboot when prompted

Install Required Packages

# Update system
sudo apt update && sudo apt upgrade -y

# Install libcamera (modern camera stack)
sudo apt install -y libcamera-apps

# Install Python libraries for OpenCV
sudo apt install -y python3-opencv python3-picamera2

# Verify camera detection
libcamera-hello --list-cameras

Capturing Images and Video

Basic Commands (libcamera)

# Preview (5 second display)
libcamera-hello -t 5000

# Capture still image
libcamera-still -o test.jpg

# Capture with custom resolution
libcamera-still -o hires.jpg --width 4056 --height 3040

# Record video (10 seconds)
libcamera-vid -t 10000 -o video.h264

# Time-lapse (capture every 5 seconds)
libcamera-still -t 60000 --timelapse 5000 -o image%04d.webp

Python with Picamera2

from picamera2 import Picamera2
import time

# Initialize camera
picam2 = Picamera2()

# Configure for still capture
config = picam2.create_still_configuration()
picam2.configure(config)

# Start and capture
picam2.start()
time.sleep(2) # Allow auto-exposure to settle
picam2.capture_file("photo.webp")
picam2.stop()

print("Image captured: photo.webp")

Computer Vision Projects

Real-time Object Detection with OpenCV

import cv2
from picamera2 import Picamera2

# Initialize camera
picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(
 main={"size": (640, 480)}
))
picam2.start()

# Load face detection model
face_cascade = cv2.CascadeClassifier(
 cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)

while True:
 # Capture frame
 frame = picam2.capture_array()
 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
 # Detect faces
 faces = face_cascade.detectMultiScale(gray, 1.1, 4)
 
 # Draw rectangles
 for (x, y, w, h) in faces:
 cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
 cv2.imshow('Face Detection', frame)
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break

cv2.destroyAllWindows()
picam2.stop()

Multi-Camera Setup

Troubleshooting

Project Ideas