AI on the Edge LESSON 35: Running Multiple Pi Cameras and USB Cameras on the Pi 5
Hello everybody! Paul McWhorter here from toptechboy.com, welcoming you back to another thrilling session of our AI on the Edge series. Today, we are taking our hardware vision capabilities to the next level. If you have ever wondered how to scale up your Raspberry Pi 5 setup beyond just a single lens, this lesson is for you. We are going to write a Python script that pulls live feeds from multiple Raspberry Pi cameras and multiple USB webcams simultaneously, displaying all four streams cleanly in real time with an on-screen frames-per-second (FPS) tracker.
Codex Knowledge: Multi-Camera Architecture on the Pi 5
When working with edge hardware like the Raspberry Pi 5, managing multiple high-bandwidth video streams requires understanding how the underlying libraries interact with the Linux kernel and system memory. In this lesson, we leverage two distinct hardware interfaces:
- Picamera2 API: We initialize two separate native Pi camera instances using
Picamera2(0)andPicamera2(1). By explicitly configuring the preview size to 640×360, setting the format toRGB888, locking the frame rate to 60 FPS, and callingalign()before starting, we ensure the hardware pipelines are optimized for low-latency streaming without frame-drop bottlenecks. - OpenCV VideoCapture: For our USB webcams, we use OpenCV’s
cv2.VideoCapture()mapped to specific device indices (in our setup, indices 16 and 18). We explicitly set the frame width, height, and target frame rate properties to keep the data flow synchronized with our Pi camera streams. - Window Layout Management: Using OpenCV highgui functions like
cv2.namedWindow,cv2.moveWindow, andcv2.resizeWindow, we programmatically arrange all four camera feeds into a neat 2×2 grid on your desktop workspace, preventing windows from stacking blindly on top of each other.
General Knowledge: The Evolution of Multi-Stream Machine Vision
In industrial automation, robotics, and advanced edge AI deployments, relying on a single camera angle is rarely enough. Multi-camera systems are the gold standard for comprehensive spatial awareness, 3D depth estimation, object tracking across wide fields of view, and panoramic monitoring. Historically, running multiple high-resolution video streams required bulky, power-hungry desktop workstations equipped with expensive capture cards. Today, single-board computers like the Raspberry Pi 5—combined with optimized kernel drivers and efficient software wrappers like Picamera2—allow engineers and creators to build robust, multi-sensor vision arrays right at the edge at a fraction of the cost and power consumption.
Python Source Code
Here is the complete, production-ready script for Lesson 35. Make sure your cameras are securely connected and properly indexed before running the program.
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import cv2 import time from picamera2 import Picamera2 W = 640 H = 360 tStart = time.time() fps = 0 RES = (W, H) # Initialize Pi Camera 1 piCam1 = Picamera2(0) piCam1.preview_configuration.main.size = RES piCam1.preview_configuration.main.format = "RGB888" piCam1.preview_configuration.controls.FrameRate = 60 piCam1.preview_configuration.align() piCam1.configure("preview") piCam1.start() # Initialize Pi Camera 2 piCam2 = Picamera2(1) piCam2.preview_configuration.main.size = RES piCam2.preview_configuration.main.format = "RGB888" piCam2.preview_configuration.controls.FrameRate = 60 piCam2.preview_configuration.align() piCam2.configure("preview") piCam2.start() # Initialize USB Camera 1 usbCam1 = cv2.VideoCapture(16) usbCam1.set(cv2.CAP_PROP_FRAME_WIDTH, W) usbCam1.set(cv2.CAP_PROP_FRAME_HEIGHT, H) usbCam1.set(cv2.CAP_PROP_FPS, 30) # Initialize USB Camera 2 usbCam2 = cv2.VideoCapture(18) usbCam2.set(cv2.CAP_PROP_FRAME_WIDTH, W) usbCam2.set(cv2.CAP_PROP_FRAME_HEIGHT, H) usbCam2.set(cv2.CAP_PROP_FPS, 30) textLowerLeft = (int(W * 0.01), int(H * 0.05)) fontFace = cv2.FONT_HERSHEY_SIMPLEX fontThickness = int(W / 425) fontScale = H * 0.0015 fontColor = (0, 0, 255) # Setup Window Grid Layout cv2.namedWindow('Camera1', cv2.WINDOW_GUI_NORMAL) cv2.moveWindow('Camera1', 0, 65) cv2.resizeWindow('Camera1', W, H) cv2.namedWindow('Camera2', cv2.WINDOW_GUI_NORMAL) cv2.moveWindow('Camera2', W, 65) cv2.resizeWindow('Camera2', W, H) cv2.namedWindow('Camera3', cv2.WINDOW_GUI_NORMAL) cv2.moveWindow('Camera3', 0, 65 + H + 20) cv2.resizeWindow('Camera3', W, H) cv2.namedWindow('Camera4', cv2.WINDOW_GUI_NORMAL) cv2.moveWindow('Camera4', W, 65 + H + 20) cv2.resizeWindow('Camera4', W, H) while True: deltaT = time.time() - tStart tStart = time.time() fps = fps * 0.95 + (1 / deltaT) * 0.05 frame1 = piCam1.capture_array() # frame1 = cv2.flip(frame1, -1) frame2 = piCam2.capture_array() frame2 = cv2.flip(frame2, -1) _ , frame3 = usbCam1.read() _ , frame4 = usbCam2.read() myText = "FPS: " + str(round(fps, 1)) cv2.putText(frame1, myText, textLowerLeft, fontFace, fontScale, fontColor, fontThickness) cv2.imshow("Camera1", frame1) cv2.imshow("Camera2", frame2) cv2.imshow("Camera3", frame3) cv2.imshow("Camera4", frame4) if cv2.waitKey(1) == ord('q'): break cv2.destroyAllWindows() print('Program Terminated') |
Conclusion
There you have it! You are now successfully driving a multi-camera computer vision array right off your Raspberry Pi 5. Play around with the window positioning, check your device indices if your USB cameras don’t immediately pop up, and get ready because in our next lesson we will start piping these multi-source frames directly into our neural network inference models. Keep tinkering, stay curious, and I will see you in the next lesson!